5028 lines
264 KiB
TeX
5028 lines
264 KiB
TeX
\documentclass{report}
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\usepackage[section]{placeins}
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\usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry}
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\usepackage{siunitx}
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\usepackage{multicol}
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\setlength{\columnsep}{1cm}
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\usepackage[acronym]{glossaries}
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\usepackage[T1]{fontenc}
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\usepackage{enumitem}
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\usepackage{titlesec}
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\usepackage{titlecaps}
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\usepackage{upgreek}
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\usepackage{graphicx}
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\usepackage{subcaption}
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\usepackage{nth}
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\usepackage{hyperref} % must be before cleveref
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\usepackage[capitalize]{cleveref}
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\usepackage[version=4]{mhchem}
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\usepackage{pgfgantt}
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\usepackage{setspace}
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\usepackage{listings}
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\usepackage{tocloft}
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\usepackage{epigraph}
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\hypersetup{
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colorlinks=true,
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linkcolor=black,
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filecolor=black,
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citecolor=black,
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urlcolor=black,
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}
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\newcommand{\dmspaper}{Dwarshuis et al. Functionalized microcarriers improve T
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cell manufacturing by facilitating migratory memory T cell production and
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increasing CD4/CD8 ratio.~2019.~biorxiv.~https://doi.org/10.1101/646760}
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\newcommand{\modelpaper}{Odeh-Couvertier et al. Predicting T Cell Quality During
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Manufacturing Through an Artificial Intelligence-based Integrative Multi-Omics
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Analytical Platform.~2019.~biorxiv.~https://doi.org/10.1101/2021.05.05.442854}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% my attempt to make MATLAB code look pretty
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\definecolor{dkgreen}{rgb}{0,0.6,0}
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\definecolor{gray}{rgb}{0.5,0.5,0.5}
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\definecolor{mauve}{rgb}{0.58,0,0.82}
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||
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\lstset{frame=tb,
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language=Matlab,
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aboveskip=3mm,
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belowskip=3mm,
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||
showstringspaces=false,
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||
columns=flexible,
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||
basicstyle={\small\ttfamily},
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||
numbers=none,
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||
numberstyle=\tiny\color{gray},
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keywordstyle=\color{blue},
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||
commentstyle=\color{dkgreen},
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||
stringstyle=\color{mauve},
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breaklines=true,
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breakatwhitespace=true,
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tabsize=3
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% benevolently force figures stay in their own subsection
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%
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% NOTE the placeins package only has a 'section' option which puts
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% floatbarriers after every \section call; this does the same for \subsection
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\makeatletter
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\AtBeginDocument{%
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\expandafter\renewcommand\expandafter\subsection\expandafter
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{\expandafter\@fb@secFB\subsection}%
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\newcommand\@fb@subsecFB{\FloatBarrier
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\gdef\@fb@afterHHook{\@fb@topbarrier \gdef\@fb@afterHHook{}}}%
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\g@addto@macro\@afterheading{\@fb@afterHHook}%
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\gdef\@fb@afterHHook{}%
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}
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\makeatother
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% ...also center them
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\makeatletter
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\g@addto@macro\@floatboxreset\centering
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\makeatother
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% header configuration
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%
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% NOTE glossary can't apparently be used in section header (even thought it
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% would be nice)
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\doublespacing{}
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\titleformat{\chapter}[block]{\filcenter\bfseries\Large}
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{\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase}
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\titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap}
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\titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap}
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\titleformat{\subsubsection}[runin]{\bfseries}{}{0pt}{\titlecap}
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\setlist[description]{font=$\bullet$~\textbf\normalfont}
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\renewcommand*{\contentsname}{TABLE OF CONTENTS}
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\renewcommand{\listfigurename}{LIST OF FIGURES}
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\renewcommand{\listtablename}{LIST OF TABLES}
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\renewcommand{\cfttoctitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftaftertoctitle}{\hspace*{\fill}}
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\renewcommand{\cftlottitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftafterlottitle}{\hspace*{\fill}}
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\renewcommand{\cftloftitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftafterloftitle}{\hspace*{\fill}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% acronyms for the lazy
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%
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% adding as many as possible has the added benefit of making the thesis longer
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% and making me sound more sophisticated
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% the many flavors of T cells
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\newcommand{\tcellacronym}[4]{
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\newacronym[shortplural={T\textsubscript{#2}#4
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cells}]{#1}{T\textsubscript{#2}#4}{#3 T cell}
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}
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\renewcommand{\glossarysection}[2][]{} % remove glossary title
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\makeglossaries
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\tcellacronym{tn}{n}{naive}{}
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\tcellacronym{tcm}{cm}{central memory}{}
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\tcellacronym{tscm}{scm}{stem-memory}{}
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\tcellacronym{tem}{em}{effector-memory}{}
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\tcellacronym{teff}{eff}{effector}{}
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\tcellacronym{treg}{reg}{regulatory}{}
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\tcellacronym{th}{h}{helper}{}
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\tcellacronym{tc}{c}{cytotoxic}{}
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\tcellacronym{th1}{h}{type 1 helper}{1}
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\tcellacronym{th2}{h}{type 2 helper}{2}
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\tcellacronym{th17}{h}{IL-17 helper}{17}
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\newacronym{bcaa}{BCAA}{branched-chain amino acid}
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\newacronym{til}{TIL}{tumor infiltrating lymphocyte}
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\newacronym{tcr}{TCR}{T cell receptor}
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\newacronym{act}{ACT}{adoptive cell therapies}
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\newacronym{qc}{QC}{quality control}
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\newacronym{car}{CAR}{chimeric antigen receptor}
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\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
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\newacronym{ecm}{ECM}{extracellular matrix}
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\newacronym{cqa}{CQA}{critical quality attribute}
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\newacronym{cpp}{CPP}{critical process parameter}
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\newacronym{dms}{DMS}{degradable microscaffold}
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\newacronym{doe}{DOE}{design of experiments}
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\newacronym{adoe}{ADOE}{adaptive design of experiments}
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||
\newacronym{gmp}{GMP}{Good Manufacturing Practices}
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\newacronym{cho}{CHO}{Chinese hamster ovary}
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\newacronym{all}{ALL}{acute lymphoblastic leukemia}
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\newacronym{cll}{CLL}{chronic lymphoblastic leukemia}
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\newacronym{pdms}{PDMS}{polydimethylsiloxane}
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\newacronym{dc}{DC}{dendritic cell}
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\newacronym{il}{IL}{interleukin}
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\newacronym{il2}{IL2}{interleukin 2}
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\newacronym{il15}{IL15}{interleukin 15}
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\newacronym{il15r}{IL15R}{interleukin 15 receptor}
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\newacronym{rhil2}{rhIL2}{recombinant human interleukin 2}
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\newacronym{apc}{APC}{antigen presenting cell}
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\newacronym{mhc}{MHC}{major histocompatibility complex}
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\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
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\newacronym{nmr}{NMR}{nuclear magnetic resonance}
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\newacronym{haba}{HABA}{4-hydroxyazobenene-2-carboxylic-acid}
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\newacronym{pbs}{PBS}{phosphate buffered saline}
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\newacronym{bca}{BCA}{bicinchoninic acid assay}
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\newacronym{bsa}{BSA}{bovine serum albumin}
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\newacronym{hsa}{HSA}{human serum albumin}
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\newacronym{stp}{STP}{streptavidin}
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\newacronym{stppe}{STP-PE}{streptavidin-phycoerythrin}
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\newacronym{snb}{SNB}{sulfo-nhs-biotin}
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\newacronym{cug}{CuG}{Cultispher G}
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\newacronym{cus}{CuS}{Cultispher S}
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\newacronym{pbmc}{PBMC}{peripheral blood mononuclear cells}
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\newacronym{macs}{MACS}{magnetic activated cell sorting}
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||
\newacronym{aopi}{AO/PI}{acridine orange/propidium iodide}
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||
\newacronym{igg}{IgG}{immunoglobulin G}
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||
\newacronym{pe}{PE}{phycoerythrin}
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\newacronym{fitc}{FITC}{Fluorescein}
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\newacronym{fitcbt}{FITC-BT}{Fluorescein-biotin}
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\newacronym{ptnl}{PTN-L}{Protein L}
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\newacronym{af647}{AF647}{Alexa Fluor 647}
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\newacronym{anova}{ANOVA}{analysis of variance}
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\newacronym{crispr}{CRISPR}{clustered regularly interspaced short
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palindromic repeats}
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\newacronym{mtt}{MTT}{3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide}
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\newacronym{bmi}{BMI}{body mass index}
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\newacronym{a2b1}{A2B1}{integrin $\upalpha$1$\upbeta$1}
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\newacronym{a2b2}{A2B2}{integrin $\upalpha$1$\upbeta$2}
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\newacronym{nsg}{NSG}{NOD scid gamma}
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\newacronym{colb}{COL-B}{collagenase B}
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\newacronym{cold}{COL-D}{collagenase D}
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\newacronym{tsne}{tSNE}{t-stochastic neighbor embedding}
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\newacronym{umap}{UMAP}{uniform manifold approximation and projection}
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\newacronym{anv}{AXV}{Annexin-V}
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\newacronym{pi}{PI}{propidium iodide}
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\newacronym{rt}{RT}{room temperature}
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\newacronym{cas37}{Cas3/7}{Caspase-3/7}
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\newacronym{bcl2}{BCL-2}{B cell lymphoma 2}
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||
\newacronym{tmb}{TMB}{3,3',5,5'-Tetramethylbenzidine}
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\newacronym{gvhd}{GVHD}{graft-vs-host disease}
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\newacronym{bcma}{BCMA}{B-cell maturation antigen}
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\newacronym{di}{DI}{deionized}
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\newacronym{moi}{MOI}{multiplicity of infection}
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\newacronym{ifng}{IFN$\upgamma$}{interferon-$\upgamma$}
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\newacronym{tnfa}{TNF$\upalpha$}{tumor necrosis factor-$\upalpha$}
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\newacronym{sql}{SQL}{structured query language}
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\newacronym{fcs}{FCS}{flow cytometry standard}
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||
\newacronym{ivis}{IVIS}{in vivo imaging system}
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\newacronym{iacuc}{IACUC}{institutional animal care and use committee}
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\newacronym{hbss}{HBSS}{Hank's buffered saline solution}
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\newacronym{leaf}{LEAF}{low endotoxin, azide-free}
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\newacronym{cytof}{CyTOF}{cytometry time-of-flight}
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\newacronym{spade}{SPADE}{spanning-tree progression analysis of
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density-normalized events}
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\newacronym{ml}{ML}{machine learning}
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\newacronym{rf}{RF}{random forest}
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\newacronym{sr}{SR}{symbolic regression}
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\newacronym{gbm}{GBM}{gradient boosted trees}
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\newacronym{cif}{CIF}{conditional inference forests}
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\newacronym{lasso}{LASSO}{least absolute shrinkage and selection operator}
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\newacronym{svm}{SVM}{support vector machines}
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\newacronym{plsr}{PLSR}{partial least squares regression}
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\newacronym{mse}{MSE}{mean squared error}
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\newacronym{loocv}{LOO-CV}{leave-one-out cross validation}
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||
\newacronym{hsqc}{HSQC}{heteronuclear single quantum coherence}
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\newacronym{hla}{HLA}{human leukocyte antigen}
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||
\newacronym{zfn}{ZFN}{zinc-finger nuclease}
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||
\newacronym{talen}{TALEN}{transcription activator-like effector nuclease}
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||
\newacronym{qbd}{QbD}{quality-by-design}
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\newacronym{aws}{AWS}{Amazon Web Services}
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||
\newacronym{qpcr}{qPCR}{quantitative polymerase chain reaction}
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||
\newacronym{cstr}{CSTR}{continuously stirred tank bioreactor}
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||
\newacronym{esc}{ESC}{embryonic stem cell}
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||
\newacronym{msc}{MSC}{mesenchymal stromal cells}
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||
\newacronym{scfv}{scFv}{single-chain fragment variable}
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||
\newacronym{hepes}{HEPES}{4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid}
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||
\newacronym{nhs}{NHS}{N-hydroxysulfosuccinimide}
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||
\newacronym{tocsy}{TOCSY}{total correlation spectroscopy}
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||
\newacronym{hplc}{HPLC}{high-performance liquid chromatography}
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||
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||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% SI units for uber nerds
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% NOTE the \SI macro is depreciated but the arch repo (!!!) hasn't been updated
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% with the latest package yet (texlive-science)
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\sisetup{per-mode=symbol,list-units=single}
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\DeclareSIUnit\IU{IU}
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\DeclareSIUnit\rpm{RPM}
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\DeclareSIUnit\carrier{carrier}
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\DeclareSIUnit\dms{DMS}
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\DeclareSIUnit\cell{cells}
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\DeclareSIUnit\ab{mAb}
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\DeclareSIUnit\normal{N}
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\DeclareSIUnit\molar{M}
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\DeclareSIUnit\mM{\milli\molar}
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\DeclareSIUnit\uM{\micro\molar}
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\DeclareSIUnit\gforce{\times{} g}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% commands for lazy farts like me
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% gatech format conformity
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\newcommand{\mytitle}{
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\Large{
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\textbf{
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Optimizing T Cell Manufacturing and Quality Using Functionalized
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Degradable Microscaffolds
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}
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||
}
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||
}
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\newcommand{\mycommitteemember}[3]{
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\begin{flushleft}
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\noindent
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#1 \\
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#2 \\
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\textit{#3}
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\end{flushleft}
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}
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% a BME's best friend
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\newcommand{\invivo}{\textit{in vivo}}
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\newcommand{\invitro}{\textit{in vitro}}
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\newcommand{\exvivo}{\textit{ex vivo}}
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\newcommand{\Invivo}{\textit{In vivo}}
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\newcommand{\Invitro}{\textit{In vitro}}
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\newcommand{\Exvivo}{\textit{Ex vivo}}
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||
|
||
% various CD-whatever crap
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||
\newcommand{\cd}[1]{CD{#1}}
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||
\newcommand{\anti}[1]{anti-{#1}}
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\newcommand{\antih}[1]{anti-human {#1}}
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\newcommand{\antim}[1]{anti-mouse {#1}}
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\newcommand{\acd}[1]{\anti{\cd{#1}}}
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\newcommand{\ahcd}[1]{\antih{\cd{#1}}}
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\newcommand{\amcd}[1]{\antim{\cd{#1}}}
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\newcommand{\pos}[1]{#1+}
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\newcommand{\cdp}[1]{\pos{\cd{#1}}}
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\newcommand{\pcd}[1]{\cdp{#1}~\si{\percent}}
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||
\newcommand{\cdn}[1]{\cd{#1}-}
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||
\newcommand{\ptmem}{\cdp{62L}\pos{CCR7}}
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||
\newcommand{\ptmemp}{\ptmem{}~\si{\percent}}
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||
\newcommand{\pth}{\cdp{4}}
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||
\newcommand{\pthp}{\pth{}~\si{\percent}}
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\newcommand{\ptk}{\cdp{8}}
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\newcommand{\ptmemh}{\pth\ptmem}
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||
\newcommand{\ptmemk}{\ptk\ptmem}
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||
\newcommand{\dpthp}{$\Updelta$\pthp{}}
|
||
\newcommand{\ptcar}{\gls{car}+}
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||
\newcommand{\ptcarp}{\ptcar~\si{\percent}}
|
||
|
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% so I don't need to worry about abbreviating all the different interleukins
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||
\newcommand{\il}[1]{\gls{il}-#1}
|
||
|
||
% DOE stuff I don't feel like typing ad-nauseam
|
||
\newcommand{\pilII}{\gls{il2} concentration}
|
||
\newcommand{\pdms}{\gls{dms} concentration}
|
||
\newcommand{\pmab}{functional \gls{mab} surface density}
|
||
\newcommand{\rmemh}{total \ptmemh{} cells}
|
||
\newcommand{\rmemk}{total \ptmemk{} cells}
|
||
\newcommand{\rratio}{CD4/CD8 ratio}
|
||
|
||
% vendor and product stuff I don't feel like typing
|
||
\newcommand{\catnum}[2]{(#1, #2)}
|
||
\newcommand{\product}[3]{#1 \catnum{#2}{#3}}
|
||
\newcommand{\thermo}{Thermo Fisher}
|
||
\newcommand{\gehc}{GE Healthcare}
|
||
\newcommand{\sigald}{Sigma Aldrich}
|
||
\newcommand{\miltenyi}{Miltenyi Biotech}
|
||
\newcommand{\bl}{Biolegend}
|
||
\newcommand{\bd}{Becton Dickenson}
|
||
|
||
% the obligatory misc category
|
||
\newcommand{\inlinecode}{\texttt}
|
||
\newcommand{\subcap}[2]{\subref{#1}) #2}
|
||
\newcommand{\sigkey}{Significance test key: *p<0.1; **p < 0.05; ***p<0.01}
|
||
|
||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||
% ditto for environments
|
||
|
||
\newenvironment{mytitlepage}{
|
||
\begin{singlespace}
|
||
\begin{center}
|
||
}
|
||
{
|
||
\end{center}
|
||
\end{singlespace}
|
||
}
|
||
|
||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||
% begin document (proceed with caution)
|
||
|
||
\begin{document}
|
||
|
||
\begin{titlepage}
|
||
\begin{mytitlepage}
|
||
\mytitle{}
|
||
|
||
\vfill
|
||
|
||
\Large{
|
||
A Dissertation \\
|
||
Presented to \\
|
||
The Academic Faculty \\
|
||
|
||
\vspace{1.5em}
|
||
|
||
by
|
||
|
||
\vspace{1.5em}
|
||
|
||
Nathan John Dwarshuis, B.S. \\
|
||
|
||
\vfill
|
||
|
||
In Partial Fulfillment \\
|
||
of the Requirements for the Degree \\
|
||
Doctor of Philosophy in Biomedical Engineering in the \\
|
||
Wallace H. Coulter Department of Biomedical Engineering
|
||
|
||
\vfill
|
||
|
||
Georgia Institute of Technology and Emory University \\
|
||
August 2021
|
||
|
||
\vfill
|
||
|
||
COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
|
||
}
|
||
\end{mytitlepage}
|
||
\end{titlepage}
|
||
|
||
\onecolumn \pagenumbering{roman}
|
||
\clearpage
|
||
\begin{mytitlepage}
|
||
\mytitle{}
|
||
\end{mytitlepage}
|
||
|
||
\vfill
|
||
|
||
\large{
|
||
\noindent
|
||
Committee Members
|
||
|
||
\begin{multicols}{2}
|
||
\begin{singlespace}
|
||
|
||
\mycommitteemember{Dr.\ Krishnendu\ Roy\ (Advisor)}
|
||
{Department of Biomedical Engineering}
|
||
{Georgia Institute of Technology and Emory University}
|
||
|
||
\vspace{1.5em}
|
||
|
||
\mycommitteemember{Dr.\ Madhav\ Dhodapkar}
|
||
{Department of Hematology and Medical Oncology}
|
||
{Emory University}
|
||
|
||
\vspace{1.5em}
|
||
|
||
\mycommitteemember{Dr.\ Melissa\ Kemp}
|
||
{Department of Biomedical Engineering}
|
||
{Georgia Institute of Technology and Emory University}
|
||
|
||
\columnbreak{}
|
||
\null{}
|
||
\vfill
|
||
|
||
\mycommitteemember{Dr.\ Wilbur\ Lam}
|
||
{Department of Biomedical Engineering}
|
||
{Georgia Institute of Technology and Emory University}
|
||
|
||
\vspace{1.5em}
|
||
|
||
\mycommitteemember{Dr.\ Sakis\ Mantalaris}
|
||
{Department of Biomedical Engineering}
|
||
{Georgia Institute of Technology and Emory University}
|
||
|
||
\end{singlespace}
|
||
\end{multicols}
|
||
|
||
\vspace{1.5em}
|
||
|
||
\hfill Date Approved:
|
||
}
|
||
|
||
\clearpage
|
||
|
||
\vspace*{\fill}
|
||
|
||
\begin{quote}
|
||
You cannot answer a question that you cannot ask, and you cannot ask a
|
||
question for which you have no words.
|
||
|
||
\hfill \textit{-- Judea Pearl}
|
||
\end{quote}
|
||
|
||
\vspace{1in}
|
||
\vspace*{\fill}
|
||
|
||
\clearpage
|
||
|
||
\chapter*{acknowledgements}
|
||
\addcontentsline{toc}{chapter}{Acknowledgments}
|
||
|
||
There are many people without which this work would not have been possible.
|
||
Firstly, I would like to thank my advisor, Krish Roy, for his mentoring and
|
||
guidance as well as his support in my exploring my own ideas throughout this
|
||
project. In addition to the Roy lab as a whole, I should specifically recognize
|
||
Dr's Ranjna Madan-Lala and Kyung-Ho Roh for writing the NSF EAGER grant that
|
||
initially funded this work and providing the foundation of ideas, Dr Hannah
|
||
Wilson Song for assisting me with much of the cell-based work, Dr Pallab Pradhan
|
||
for assisting with the animal studies, and Miguel Armenta Ochoa and Ritika Jain
|
||
for their assistance as well as their ideas for new directions of this work as
|
||
they continue it beyond my tenure. I would also like to thank the undergraduates
|
||
and high school students I had the pleasure of mentoring, Anokhi Patel, Kate
|
||
Richardson, Zahra Mousavi Karimi, Sambhav Jain, Lauren Bailey.
|
||
|
||
Beyond the Roy lab, I should thank the Cell Manufacturing Technologies (CMaT)
|
||
family at large, especially our collaborators Art Edison, Max Colonna, Wandaliz
|
||
Torres-Garcia, Valerie Odeh-Couvertier, Theresa Kotanchek, and Bruce Levine.
|
||
Additionally, I would like to thank the staff and faculty who fundamentally
|
||
supported this work, including Andrea Soyland, Punya Mardhanan, Carol Mills,
|
||
Carla Zachery, Adrienne Williams, Sommer Durham, Laxmi Krishnan, Andrew Shaw,
|
||
Aaron Lifland, and Paramita Chatterjee.
|
||
|
||
Finally, I would like to thank my friends and family for their unconditional
|
||
support throughout this process, as well as Arch Enemy and Megadeth
|
||
for obvious reasons :)
|
||
|
||
\clearpage
|
||
|
||
\tableofcontents
|
||
|
||
\clearpage
|
||
|
||
\listoffigures
|
||
\addcontentsline{toc}{chapter}{LIST OF FIGURES}
|
||
|
||
\clearpage
|
||
|
||
\listoftables
|
||
\addcontentsline{toc}{chapter}{LIST OF TABLES}
|
||
|
||
\clearpage
|
||
|
||
\chapter*{LIST OF SYMBOLS AND ABBREVIATIONS}
|
||
\addcontentsline{toc}{chapter}{LIST OF SYMBOLS AND ABBREVIATIONS}
|
||
|
||
\printglossary[type=\acronymtype]
|
||
|
||
\clearpage
|
||
\pagenumbering{arabic}
|
||
|
||
\clearpage
|
||
|
||
\chapter*{summary}
|
||
\addcontentsline{toc}{chapter}{SUMMARY}
|
||
|
||
\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
|
||
manufacturing large numbers of high quality cells remains challenging. Currently
|
||
approved T cell expansion technologies involve \acd{3} and \acd{28} \glspl{mab},
|
||
usually mounted on magnetic beads. This method fails to recapitulate many key
|
||
signals found \invivo{} and is also heavily licensed by a few companies,
|
||
limiting its long-term usefulness to manufactures and clinicians. Furthermore,
|
||
highly potent anti-tumor T cells are generally less-differentiated subtypes such
|
||
as \acrlongpl{tcm} and \acrlongpl{tscm}. Despite this understanding, little has
|
||
been done to optimize T cell expansion for generating these subtypes, including
|
||
measurement and feedback control strategies that are necessary for any modern
|
||
manufacturing process.
|
||
|
||
The goal of this dissertation was to develop a microcarrier-based \gls{dms} T
|
||
cell expansion system and determine biologically-meaningful \glspl{cqa} and
|
||
\glspl{cpp} that could be used to optimize for highly-potent T cells. In
|
||
\cref{aim1}, we develop and characterized the \gls{dms} system, including
|
||
quality control steps. We also demonstrate the feasiblity of expanding
|
||
high-quality T cells. In \cref{aim2a,aim2b}, we use \gls{doe} methodology to
|
||
optimize the \gls{dms} platform, and develop a computational pipeline to
|
||
identify and model the effect of measurable \glspl{cqa}, and \glspl{cpp} on the
|
||
final product. In \cref{aim3}, we demonstrate the effectiveness of the \gls{dms}
|
||
platform \invivo{}. This thesis lays the groundwork for a novel T cell expansion
|
||
method which can be utilized at scale for a clinical trial and beyond.
|
||
|
||
\clearpage
|
||
|
||
\chapter{INTRODUCTION}
|
||
|
||
\section*{overview}
|
||
|
||
T cell-based immunotherapies have received great interest from clinicians and
|
||
industry due to their potential to treat, and often cure, cancer and other
|
||
diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
|
||
received FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
|
||
two genetically-modified \gls{car} T cell therapies against B cell malignancies.
|
||
Despite these successes, \gls{car} T cell therapies are constrained by an
|
||
expensive and difficult-to-scale manufacturing process with little control on
|
||
cell quality and phenotype\cite{Roddie2019, Dwarshuis2017}. State-of-the-art T
|
||
cell manufacturing techniques focus on \acd{3} and \acd{28} activation and
|
||
expansion, typically presented on superparamagnetic, iron-based microbeads
|
||
(Invitrogen Dynabead, Miltenyi MACS beads), on nanobeads (Miltenyi TransACT), or
|
||
in soluble tetramers (Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016,
|
||
Piscopo2017, Bashour2015}. These strategies overlook many of the signaling
|
||
components present in the secondary lymphoid organs where T cells expand
|
||
\invivo{}. Typically, T cells are activated under close cell-cell contact, which
|
||
allows for efficient autocrine/paracrine signaling via growth-stimulating
|
||
cytokines such as \gls{il2}. Additionally, the lymphoid tissues are comprised of
|
||
\gls{ecm} components such as collagen and stromal cells, which provide signals
|
||
to upregulate proliferation, cytokine production, and pro-survival
|
||
pathways\cite{Gendron2003, Ohtani2008, Boisvert2007, Ben-Horin2004}.
|
||
|
||
A variety of solutions have been proposed to make the T cell expansion process
|
||
more physiological. Including feeder cell cultures\cite{Forget2014} and
|
||
biomaterials-based methods such as lipid-coated microrods or 3D scaffold
|
||
gels\cite{Cheung2018,Delalat2017,meyer15_immun,Lambert2017} that attempt to
|
||
recapitulate the cellular membrane, large interfacial contact area,
|
||
3D-structure, or soft surfaces T cells normally experience \invivo{}. While
|
||
these have been shown to activation and expand T cells, they either are not
|
||
scalable (in the case of feeder cells) or still lack many of the signals and
|
||
cues T cells experience as the expand. Additionally, none have been shown to
|
||
preferentially expand highly-potent T cell necessary for anti-cancer therapies.
|
||
Such high potency cells including subtypes with low differentiation state such
|
||
as \gls{tscm} and \gls{tcm} cells or CD4 cells, all of which have been shown to
|
||
be necessary for durable responses\cite{Xu2014, Fraietta2018, Gattinoni2011,
|
||
Gattinoni2012,Wang2018, Yang2017}. Methods to increase memory and CD4 T cells
|
||
in the final product are needed. Furthermore, \gls{qbd} principles such as
|
||
discovering and validating novel \glspl{cqa} and \glspl{cpp} in the space of T
|
||
cell manufacturing are required to reproducibly manufacture these subtypes and
|
||
ensure low-cost and safe products with maximal effectiveness in the clinic
|
||
|
||
This dissertation describes a novel \acrlong{dms}-based method derived from
|
||
porous microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} for
|
||
use in T cell expansion cultures. Microcarriers have historically been used
|
||
throughout the bioprocess industry for adherent cultures such as \gls{cho} cells
|
||
but not with suspension cells such as T cells\cite{Heathman2015, Sart2011}. The
|
||
microcarriers chosen to make the \gls{dms} in this work have a microporous
|
||
structure that allows T cells to grow inside and along the surface, providing
|
||
ample cell-cell contact for enhanced autocrine and paracrine signaling.
|
||
Furthermore, the 3D surface of the carriers provides a larger contact area for T
|
||
cells to interact with the \glspl{mab} relative to beads; this may better
|
||
emulate the large contact surface area that occurs between T cells and
|
||
\glspl{dc}.
|
||
|
||
\section*{hypothesis}
|
||
|
||
The hypothesis of this dissertation was that using \glspl{dms} created from
|
||
off-the-shelf microcarriers and coated with activating \glspl{mab} would lead to
|
||
higher quantity and quality T cells as compared to state-of-the-art bead-based
|
||
expansion. We also hypothesized that T cells have measurable biological
|
||
signatures that are predictive of downstream outcomes and phenotypes. The
|
||
objective of this dissertation was to develop this platform, test its
|
||
effectiveness both \invitro{} and \invivo{}, and develop computational pipelines
|
||
to discover novel \glspl{cpp} and \glspl{cqa} that can be translated to a
|
||
manufacturing environment and a clinical trial setting.
|
||
|
||
\section*{specific aims}
|
||
|
||
The specific aims of this dissertation are outlined in
|
||
\cref{fig:graphical_overview}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics[width=\textwidth]{../figures/overview.png}
|
||
|
||
\endgroup
|
||
\caption[Project Overview]{High-level overview.}
|
||
\label{fig:graphical_overview}
|
||
\end{figure*}
|
||
|
||
\subsection*{aim 1: develop and optimize a novel T cell expansion process that
|
||
mimics key components of the lymph nodes}
|
||
|
||
In this first aim, we demonstrated the process for manufacturing \glspl{dms},
|
||
including quality control steps that are necessary for translation of this
|
||
platform into a scalable manufacturing setting. We also demonstrate that the
|
||
\gls{dms} platform leads to higher overall expansion of T cells and higher
|
||
overall fractions of potent memory and CD4+ subtypes desired for T cell
|
||
therapies. Finally, we demonstrate \invitro{} that the \gls{dms} platform can be
|
||
used to generate functional \gls{car} T cells targeted toward CD19.
|
||
|
||
\subsection*{aim 2: develop methods to control and predict T cell quality}
|
||
|
||
For this second aim, we investigated methods to identify and control \glspl{cqa}
|
||
and \glspl{cpp} for manufacturing T cells using the \gls{dms} platform. This was
|
||
accomplished through two sub-aims:
|
||
|
||
\begin{itemize}
|
||
\item[A --] Develop computational methods to control and predict T cell
|
||
expansion and quality
|
||
\item[B --] Perturb \gls{dms} expansion to identify additional mechanistic
|
||
controls for expansion and quality
|
||
\end{itemize}
|
||
|
||
\subsection*{aim 3: confirm potency of T cells from novel T cell expansion
|
||
process using \invivo{} xenograft mouse model}
|
||
|
||
In this final aim, we demonstrate the effectiveness of \gls{dms}-expanded T
|
||
cells compared to state-of-the-art beads using \invivo{} mouse models for
|
||
\gls{all}.
|
||
|
||
\section*{outline}
|
||
|
||
In \cref{background}, we provide additional background on the current state of T
|
||
cell manufacturing and how the work in this dissertation moves the field
|
||
forward. In \cref{aim1,aim2a,aim2b,aim3} we present the work pertaining to Aims
|
||
1, 2a, 2b, and 3 respectively. Finally, in \cref{conclusions} we present our
|
||
conclusions as well as provide insights for how this work can be extended in the
|
||
future.
|
||
|
||
\chapter{BACKGROUND AND INNOVATION}\label{background}
|
||
|
||
\section{Background}
|
||
|
||
\subsection{Quality by Design in Cell Manufacturing}
|
||
|
||
The challenges for the cell manufacturing field are significant. Unlike other
|
||
industries which manufacture inanimate products such as automobiles and
|
||
semiconductors, the cell manufacturing industry needs to contend with the fact
|
||
that cells are living entities which can change with every process
|
||
manipulation\cite{Kirouac2008, Little2006, Pirnay2012, Rousseau2013}. This is
|
||
further compounded by the lack of standardization and limited regulation.
|
||
|
||
In order to overcome these barriers, adopting a systemic approach to cell
|
||
manufacturing using \acrlong{qbd} principles will be extremely
|
||
important\cite{Kirouac2008}. In \gls{qbd}, the objective is to reproducibly
|
||
manufacturing products which minimizes risk for downstream stakeholders (in this
|
||
case, the patient). Broadly, this entails determining \acrlongpl{cqa} and
|
||
\acrlongpl{cpp} and incorporating them into models which can explain and predict
|
||
the cell manufacturing process.
|
||
|
||
\Glspl{cqa} are measurable properties of the product that can be used to define
|
||
its functionality and hence quality. \glspl{cqa} are important for defining the
|
||
characteristics of a `good' product (release criteria) but also for ensuring
|
||
that a process is on track to making such a product (process control). In the
|
||
space of cell manufacturing, examples of \glspl{cqa} include markers on the
|
||
surface of cells and readouts from functional assays such as killing assays. In
|
||
general, these are poorly understood if they exist at all.
|
||
|
||
\glspl{cpp} are parameters which may be tuned and varied to control the outcome
|
||
of process and the quality of the final product. In cell manufacturing, these
|
||
are poorly understood. Examples in the cell manufacturing space include the type
|
||
of media used and the amount of \il{2} added. Once \glspl{cpp} are known, they
|
||
can be optimized to ensure that costs are minimized and potency of the cellular
|
||
product is maximized.
|
||
|
||
The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of
|
||
this work are discussed further in \cref{sec:background_doe} and
|
||
\cref{sec:background_quality}/\cref{sec:background_cqa} respectively.
|
||
|
||
\subsection{T Cells for Immunotherapies}
|
||
|
||
A variety of T cell therapies have been utilized with varying degrees of
|
||
success, and we describe a few of the most prominent below. We should note that
|
||
while this work focuses on the application of \gls{car} T cell therapies, in
|
||
theory the technology developed in this dissertation could theoretically apply
|
||
to any T cell-based therapy with little to no modification.
|
||
|
||
One of the first successful T cell-based immunotherapies against cancer is
|
||
\glspl{til}\cite{Rosenberg2015}. This method works by taking tumor specimens
|
||
from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{}, and
|
||
then administered back to the patient along with a high dose of
|
||
\il{2}\cite{Rosenberg1988}. In particular, \gls{til} therapy has shown robust
|
||
results in treating melanoma\cite{Rosenberg2011}, although \glspl{til} have been
|
||
found in other solid tumors such as gastointestinal, cervical, lung, and
|
||
ovarian\cite{Rosenberg2015, Wang2014, Foppen2015, Solinas2017, June2007,
|
||
Santoiemma2015}, and their presence is generally associate with favorable
|
||
outcomes\cite{Clark1989}. \glspl{til} are heterogeneous cell mixtures and
|
||
generally are comprised of CD3 T cells and $\upgamma\updelta$ T
|
||
cells\cite{Nishimura1999, Cordova2012}. To date, there are over 250 open
|
||
clinical trials using \glspl{til}.
|
||
|
||
Besides \glspl{til}, the other broad class of T cell immunotherapies that has
|
||
achieved great success in treating cancer in recent decades are gene-modified T
|
||
cells. Rather than expand T cells that are present natively (as is the case with
|
||
\gls{til} therapy), gene-modified T cell therapies entail extracting T cells
|
||
from either the cancer patient (autologous) or a healthy donor (allogeneic) and
|
||
reprogramming them genetically to target a tumor antigen (see
|
||
\cref{sec:background_source} for an overview of how T cells can be sourced).
|
||
This approach offers much more flexibility, as the degree of reprogramming is
|
||
only limited by the scale and possibilities of gene-editing technology, which
|
||
has rapidly accelerated in recent decades\cite{Rosenberg2015}.
|
||
|
||
T cells with transduced \glspl{tcr} were first designed to overcome the
|
||
limitations of \glspl{til}\cite{Rosenberg2015, Wang2014}. In this case, T cells
|
||
are transduced \exvivo{} with a lentiviral vector to express a \gls{tcr}
|
||
targeting a tumor antigen. T cells transduced with \glspl{tcr} have shown robust
|
||
results in melanoma patients\cite{Robbins2011}, synovial
|
||
sarcoma\cite{Morgan2006}, and others\cite{Ikeda2016}. To date, there are over
|
||
200 clinical trials using T cells with transduced \glspl{tcr}.
|
||
|
||
While transduced \glspl{tcr} offer some flexibility in retargeting T cells
|
||
toward relevant tumor antigens, they are still limited in that they can only
|
||
target antigens that are presented via \gls{mhc} complexes. \acrlong{car} T
|
||
cells overcome this limitation by using linking a \gls{tcr}-independent antigen
|
||
recognition domain with the stimulatory and costimulatory machinery of a T cell
|
||
\gls{car} T cells were first demonstrated in 1989, where the authors swapped the
|
||
antigen-recognition domains of a native \gls{tcr} with a that of a foreign
|
||
\gls{tcr}\cite{Gross1989}. Since then, this method has progressed to using an
|
||
\gls{scfv} with a CD3$\upzeta$ stimulatory domain along with the CD28, OX-40, or
|
||
4-1BB domains for costimulation. Since these can all be expressed with one
|
||
protein sequence, \gls{car} T cells are relatively simple to produce and require
|
||
only a single genetic transduction step (usually a lentiviral vector) to
|
||
reprogram a batch T cells \exvivo{} toward the desired antigen. \gls{car} T
|
||
cells have primarily found success in against CD19- and CD20-expressing tumors
|
||
such as \gls{all} and \gls{cll} (eg B-cell malignancies)\cite{Kalos2011,
|
||
Brentjens2011, Kochenderfer2010, Maude2014, Till2012, Till2008}.
|
||
|
||
Out of all the T cell therapies discussed thus far, \gls{car} T cells have
|
||
experienced the most commercial success and excitement. In 2017, Novartis and
|
||
Kite Pharma acquired FDA approval for \textit{Kymriah} and \textit{Yescarta}
|
||
respectively, both of which are \gls{car} T cell therapies against B-cell
|
||
malignancies. \gls{car} T cells are under further exploration for use in many
|
||
other tumors, including multiple myeloma, mesothelioma, pancreatic cancer,
|
||
glioblastoma, neuroblastoma, and prostate cancer, breast cancer, non-small-cell
|
||
lung cancer, and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To
|
||
date, there are almost 1000 clinical trials using \gls{car} T cells.
|
||
|
||
\subsection{Scaling T Cell Expansion}
|
||
|
||
In order to scale T cell therapies to meet clinical demands, automation and
|
||
bioreactors will be necessary. To this end, there are several choices that have
|
||
found success in the clinic.
|
||
|
||
The WAVE bioreactor (GE Healthcare) is the choice of expansion for many clinical
|
||
applications\cite{Brentjens2011, Hollyman2009, Brentjens2013}. It is part of a
|
||
broader class of bioreactors that consist of rocking platforms that agitate a
|
||
bag filled with media and cells. Importantly, it has built-in sensors for
|
||
measuring media flow rate, \ce{CO2}, \ce{O2}, pH, and nutrient consumption which
|
||
enables automation. Unfortunately, in some settings this is not considered
|
||
scalable as only one bag per bioreactor is allowed at once\cite{Roddie2019}. The
|
||
other disadvantage with the WAVE system is that it keeps cells far apart by
|
||
design, which could have negative impact on cross-talk, activation, and
|
||
growth\cite{Somerville2012}.
|
||
|
||
% BACKGROUND find clinical trials (if any) that use this
|
||
Alternatively, the CliniMACS Prodigy (Miltenyi) is an all-in-one system that is
|
||
a fully closed system that removes the need for expensive cleanrooms and
|
||
associated personnel\cite{Kaiser2015, Bunos2015}. It contains modules to perform
|
||
transduction, expansion, and washing. This setup also implies that fewer
|
||
mistakes and handling errors will be made, since many of the steps are internal
|
||
to the machine. Initial investigations have shown that it can yield T cells
|
||
doses required for clinical use\cite{Zhu2018}. At the time of writing, several
|
||
clinical trial are underway which use the CliniMACS, although mostly for
|
||
stem-cell based cell treatments.
|
||
|
||
Finally, another option that has been investigated for T cell expansion is the
|
||
Grex bioreactor (Wilson Wolf). This is effectively a tall tissue-culture plate
|
||
with a porous membrane at the bottom, which allows gas exchange to the active
|
||
cell culture at the bottom of the plate while permitting large volumes of media
|
||
to be loaded on top without suffocating the cells. While this is quite similar
|
||
to plates and flasks normally used for small-scale research, the important
|
||
difference is that its larger size requires fewer interactions and keeps the
|
||
cells at a higher nutrient concentration for longer periods of time. However, it
|
||
is still a an open system and requires manual (by default) interaction from an
|
||
operator to load, feed, and harvest the cell product. Grex bioreactors have been
|
||
using to grow \glspl{til}\cite{Jin2012} and virus-specific T
|
||
cells\cite{Gerdemann2011}.
|
||
|
||
Much work is still required in the space of bioreactor design for T cell
|
||
manufacturing, but novel T cell expansion technologies such as that described in
|
||
this work need to consider how it may be used at scale in such a system.
|
||
|
||
\subsection{Cell Sources in T Cell Manufacturing}\label{sec:background_source}
|
||
|
||
T cells for cell manufacturing can be obtained broadly via two paradigms:
|
||
autologous and allogeneic. The former involves obtaining T cells from a patient
|
||
and giving them back to the same patient after \exvivo{} expansion and genetic
|
||
modification. The latter involves taking T cells from a healthy donor, expanding
|
||
them and manipulating them as desired, storing them long term, and then giving
|
||
them to multiple patients. There are advantages and disadvantages to both, and
|
||
in some cases such as \gls{til} therapy, the only option is to use autologous
|
||
therapy.
|
||
|
||
Autologous T cells by default are much safer. By definition, they will have no
|
||
cross-reactivity with the patient and thus \gls{gvhd} is not a
|
||
concern\cite{Decker2012}. However, there are numerous disadvantages. Autologous
|
||
therapies are over 20 times more costly as the process needs to be repeated for
|
||
every patient\cite{Harrison2019}. Compounding this, many cell products are
|
||
manufactured at a centralized location, so patient T cells need to be shipped
|
||
twice on dry ice from the hospital and back. This adds days to the process,
|
||
which is critical for patients with fast moving diseases. Manufacturing could be
|
||
done on-site in a decentralized manner, but this requires more equipment and
|
||
personnel overall. Using cells from a diseased patient has many drawbacks in
|
||
itself. Cancer patients (especially those with chronic illnesses) often have
|
||
exhausted T cells which expand far less readily and are consequently less
|
||
potent\cite{Wherry2015, Ando2019, Zheng2017}. Additionally, they may have high
|
||
frequencies of \glspl{treg} which inhibitory\cite{Decker2012}. Removing these
|
||
cells as well as purifying \glspl{th1} may enhance the potency of the final
|
||
product\cite{Goldstein2012, Drela2004, Rankin2003, Luheshi2013, Grotz2015};
|
||
however, this would make the overall process more expensive as an additional
|
||
separation step would be required.
|
||
|
||
Allogeneic T cell therapies overcome nearly all of these disadvantages. Donor
|
||
\glspl{pbmc} are easy to obtain, they can be processed in centralized locations,
|
||
they can be stored easily under liquid nitrogen, and donors could be screened to
|
||
find those with optimal anti-tumor cells. The key is overcoming \gls{gvhd}.
|
||
Obviously this could be done the same way as done for transplants where patients
|
||
find a `match' for their \gls{hla} type, but this severally limits options. This
|
||
can be overcome by using advanced gene-editing tools which can both add and
|
||
delete genetic material (eg \glspl{zfn}, \glspl{talen}, or \gls{crispr}) to
|
||
remove the native \gls{tcr} which would prevent the donor T cells from attacking
|
||
host tissue\cite{Liu2019, Wiebking2020, Provasi2012, Berdien2014, Themeli2015}.
|
||
This obviously complicates the process, as additional edits besides the
|
||
insertion of the \gls{car} would be required, and these technologies are not yet
|
||
very efficient. To date there are about 10 open clinical trials utilizing
|
||
allogeneic T cell therapies edited with \gls{crispr} to reduce the likelihood of
|
||
\gls{gvhd}.
|
||
|
||
\subsection{Overview of T Cell Quality}\label{sec:background_quality}
|
||
|
||
T cells are highly heterogeneous and can exist in a variety of states and
|
||
subtypes, many of which can be measured (at least indirectly) though biomarkers
|
||
such as cell surface proteins. Identifying and understanding these biomarkers
|
||
are the basis for \glspl{cqa} which can be used to for process control, release
|
||
criteria, and initial cell source screening.
|
||
|
||
One of the most important dimensions of T cell quality is that of
|
||
differentiation. T cells begin their life in circulation (eg after they exit the
|
||
thymus) as \glspl{tn}. When they become activated in the secondary lymph node
|
||
organs, they differentiate from \gls{tn} to \glspl{tscm}, \glspl{tcm},
|
||
\glspl{tem}, and finally \glspl{teff}\cite{Gattinoni2012}. Subtypes earlier in
|
||
this process are generally called `memory' or `memory-like' cells (eg \gls{tscm}
|
||
and \gls{tcm}), and have been shown to have increased potency toward a variety
|
||
of tumors, presumably due to their higher capacity for self-renewal and
|
||
replication, enhanced migratory capacity, and/or increased engraftment
|
||
potential\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011, Turtle2009}.
|
||
The capacity for self-renewal is especially important for T cells therapies, as
|
||
evidenced by the fact that \gls{til} therapies with longer telomeres tend to
|
||
create more durable responses\cite{Donia2012}. Additionally, clonal diversity
|
||
decreases following the infusion of \gls{car} T cell therapies, which
|
||
demonstrates that only a few clones are self-renewing and therefore responsible
|
||
for the overall response\cite{Sheih2020}. Memory T cells can be quantified
|
||
easily using surface markers such as CD62L, CCR7, CD27, CD45RA, and CD45RO.
|
||
Furthermore, memory markers are inversely related to exhaustion markers which
|
||
are negatively associated with clinical outcomes\cite{Lee2013}. These cells in
|
||
particular are seen in patients with chronic immune activation such as patients
|
||
with chronic cancers.
|
||
|
||
In addition to memory, the other major axis by which T cells may be classified
|
||
is the CD4/CD8 ratio. \Glspl{th} are CD4+ are responsible for secreting
|
||
cytokines which coordinate the immune response while CD8+ \glspl{tc} are
|
||
responsible for killing tumors or infected cells using specialized lytic
|
||
enzymes. Since \glspl{tc} actually perform the killing function, it seems
|
||
intuitive that \glspl{tc} would be most important for anti-tumor
|
||
immunotherapies. However, in mouse models with glioblastoma, survival was
|
||
negatively impacted when \glspl{th} were removed\cite{Wang2018}. Furthermore,
|
||
\glspl{th} have been shown to have cytotoxic properties on their own and also
|
||
show resistance to exhaustion compared to \glspl{tc}\cite{Yang2017}. While T
|
||
cell products with a defined ratio of CD4 and CD8 T cells have been utilized,
|
||
they are more expensive than products with undefined ratios as the T cells need
|
||
to be sorted and recombined, adding additional complexity\cite{Turtle2016}.
|
||
|
||
While less of a focus in this dissertation, other quality markers exists to
|
||
assess the overall killing potential and safety of the T cell product. Numerous
|
||
methods exists to detect the killing capacity of \gls{car} T cells, many of
|
||
which involve either measuring the lysis of a target cell using a dye or a
|
||
radioactive tracer, by measuring the degranulation of the T cells themselves, or
|
||
by measuring a cytokine that is secreted upon T cell activation and killing such
|
||
as \gls{ifng}. Furthermore, the viability of T cells may be assessed using a
|
||
number of methods, including exclusion dyes such as \gls{aopi} or a functional
|
||
assay to detect metabolism. Finally, for the purposes of safety, T cell products
|
||
using retro- or lentiviral vectors as their means of gene-editing must be tested
|
||
for replication competent vectors\cite{Wang2013} and for contamination via
|
||
bacteria or other pathogens.
|
||
|
||
\subsection{T cell Activation Methods}\label{sec:background_activation}
|
||
|
||
In order for T cells to be expanded \exvivo{} they must be activated with a
|
||
stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \Invivo{},
|
||
Signal 1 is administered via the \gls{tcr} and the CD3 receptor when \glspl{apc}
|
||
present a peptide via \gls{mhc} that the T cell in question is able to
|
||
recognize. Signal 2 is administered via CD80 and CD86 which are also present on
|
||
\glspl{apc} and is necessary to prevent the T cell from becoming anergic. While
|
||
these two signal are the bare minimum to trigger a T cell to expand, there are
|
||
many other potential signals present. T cells have many other costimulatory
|
||
receptors such as OX40, 4-1BB and ICOS which are costimulatory along with CD28,
|
||
and \glspl{apc} have corresponding ligands for these depending on the nature of
|
||
the pathogen they have detected\cite{Azuma2019}. Furthermore, T cells exist in
|
||
high cell density within the secondary lymphoid organs, which allows efficient
|
||
cytokine cross-talk in an autocrine and paracrine manner. These cytokines are
|
||
responsible for triggering proliferation (in the case of \il{2}) and subset
|
||
differentiation (in the case of many others)\cite{Luckheeram2012}. By tuning the
|
||
signal strength and duration of Signal 1, Signal 2, the various costimulatory
|
||
signals, and the cytokine milieu, a variety of T cell phenotypes can be
|
||
actualized.
|
||
|
||
\Invitro{}, T cells can be activated in a number of ways but the simplest and
|
||
most common is to use \glspl{mab} that cross-link the CD3 and CD28 receptors,
|
||
which supply Signal 1 and Signal 2 without the need for antigen (which also
|
||
means all T cells are activated and not just a few specific clones). Additional
|
||
signals may be supplied in the form of cytokines (eg \il{2}, \il{7}, or \il{15})
|
||
or feeder cells\cite{Forget2014}.
|
||
|
||
As this is a critical unit operation in the manufacturing of T cell therapies, a
|
||
number of commercial technologies exist to activate T cells\cite{Wang2016,
|
||
Piscopo2017, Roddie2019, Bashour2015}. The simplest is to use \acd{3} and
|
||
\acd{28} \glspl{mab} bound to a 2D surface such as a plate, and this can be
|
||
accomplished in a \gls{gmp} manner (at least from a reagents perspective) as
|
||
soluble \gls{gmp}-grade \glspl{mab} are commericially available. A similar but
|
||
distinct method along these lines is to use multivalent activators such as
|
||
ImmunoCult (StemCell Technologies) or Expamer (Juno Therapeutics) which may have
|
||
increased cross-linking capacity compared to traditional \glspl{mab}. Beyond
|
||
soluble protein, \glspl{mab} against CD3 and CD28 can be mounted on magnetic
|
||
microbeads (\SIrange{3}{5}{\um} in diameter) such as DynaBeads (Invitrogen) and
|
||
MACSbeads (\miltenyi{}), which are easier to separate using magnetic washing
|
||
plates. Magnetic nanobeads such as TransAct (\miltenyi{}) work by a similar
|
||
principle except they can be removed via centrifugation rather than a magnetic
|
||
washing plate. Cloudz (RnD Systems) are another bead-based T cell expansion
|
||
which mounts \acd{3} and \acd{28} \glspl{mab} on alginate microspheres, which
|
||
are not only easily degradable but are also softer, which can have a positive
|
||
impact on T cell activation and phenotype\cite{Lambert2017, OConnor2012}.
|
||
|
||
A problem with all of these commercial solutions is that they only focus on
|
||
Signal 1 and Signal 2 and ignore the many other physiological cues present in
|
||
the secondary lymphoid organs. A variety of novel T cell activation and
|
||
expansion solutions have been proposed to overcome this. One strategy is to use
|
||
modified feeder cell cultures to provide activation signals similar to those of
|
||
\glspl{dc}\cite{Forget2014}. While this has the theoretical capacity to mimic
|
||
several key components of the lymph node, it is hard to reproduce on a large
|
||
scale due to the complexity and inherent variability of using cell lines in a
|
||
fully \gls{gmp}-compliant manner. Others have proposed biomaterials-based
|
||
solutions to circumvent this problem, including lipid-coated
|
||
microrods\cite{Cheung2018}, 3D-scaffolds via either Matrigel\cite{Rio2018} or
|
||
3d-printed lattices\cite{Delalat2017}, ellipsoid beads\cite{meyer15_immun}, and
|
||
\gls{mab}-conjugated \gls{pdms} beads\cite{Lambert2017} that respectively
|
||
recapitulate the cellular membrane, large interfacial contact area,
|
||
3D-structure, or soft surfaces T cells normally experience \textit{in vivo}.
|
||
None have been demonstrated to demonstrably expand high quality T cells as
|
||
outlined in \cref{sec:background_quality}.
|
||
|
||
\subsection{Microcarriers in Bioprocessing}
|
||
|
||
In this work, we explored microcarriers as the basis for an alternative to the
|
||
methods described in \cref{sec:background_activation}.
|
||
|
||
Microcarriers have historically been used to grow a number of adherent cell
|
||
types for a variety of applications. They were introduced in 1967 as a means to
|
||
grow adherent cells `in suspension', effectively turning a 2D flask system into
|
||
a 3D culture system\cite{WEZEL1967}. Microcarriers are generally spherical and
|
||
are several hundred \si{\um} in diameter, which means they collectively have a
|
||
much higher surface area than a traditional flask when matched for volume.
|
||
Consequently, this means that microcarrier-based cultures can operate with much
|
||
lower footprints than flask-like systems. Microcarriers also allow cell culture
|
||
to operate more like a traditional chemical engineering process, wherein a
|
||
\gls{cstr} may be employed to enhance oxygen transfer, maintain pH,
|
||
and continuously supply nutrients\cite{Derakhti2019}.
|
||
|
||
A variety of microcarriers have been designed, primarily differing in their
|
||
choice of material and macroporous structure. Key concerns driving these
|
||
choiceshave been cell attachment at the beginning of culture and cell detachment
|
||
at the harvesting step\cite{Derakhti2019}. Many microcarriers simply use
|
||
polystyrene (the material used for tissue culture flasks and dishes in general)
|
||
with no modification (SoloHill Plastic, Nunc Biosilon), with a cationic surface
|
||
charge (SoloHill Hillex) or coated with an \gls{ecm} protein such as collagen
|
||
(SoloHill Fact III). Other base materials have been used such as dextran (GE
|
||
Healthcare Cytodex), cellulose (GE Healthcare Cytopore), and glass (\sigald{}
|
||
G2767), all with none or similar surface modifications. Additionally, some
|
||
microcarriers such as \gls{cus} and \gls{cug} are composed entirely out of
|
||
protein (in these cases, porcine collagen) which also allows the microcarriers
|
||
to be enzymatically degraded. In the case of non-protein materials, cells may
|
||
still be detached using enzymes but these require similar methods to those
|
||
currently used in flasks such as trypsin which target the cellular \gls{ecm}
|
||
directly. Since trypsin and related enzymes tends to be harsh on cells, an
|
||
advantage of using entirely protein-based microcarriers is that they can be
|
||
degraded using a much safer enzyme such as collagenase, at the cost of being
|
||
more expensive and also being harder to make
|
||
\gls{gmp}-compliant\cite{Derakhti2019}. Going one step further, some
|
||
microcarriers are composed of a naturally degrading scaffold such as alginate,
|
||
which do not need an enzyme for degradation. Finally, microcarriers can differ
|
||
in their overall structure. \gls{cug} and \gls{cus} microcarriers as well as the
|
||
Cytopore microcarriers are macroporous, meaning they have a porous network in
|
||
which cells can attach throughout their interior. This drastically increases the
|
||
effective surface area and consequently the number of cells which may be grown
|
||
per unit volume. Other microcarriers are microporous (eg only to small
|
||
molecules) or not porous at all (eg polystyrene); in either case the cells can
|
||
only grow on the surface.
|
||
|
||
Microcarriers in general have seen the most use in growing \gls{cho} cells and
|
||
hybridomas in the case of protein manufacturing (eg \gls{igg}
|
||
production)\cite{Xiao1999, Kim2011} as well as \glspl{esc} and \glspl{msc} more
|
||
recently in the case of cell manufacturing\cite{Heathman2015, Sart2011,
|
||
Chen2013, Schop2010, Rafiq2016}. Interestingly, some groups have even explored
|
||
using biodegradable microcarriers \invivo{} as a delivery vehicle for stem cell
|
||
therapies in the context of regenerative medicine\cite{Zhang2016, Saltz2016,
|
||
Park2013, Malda2006}. However, the characteristic shared by all the cell types
|
||
in this application is the fact that they are adherent. In this work, we explore
|
||
the use of microcarrier for T cells, which are naturally non-adherent.
|
||
|
||
The microcarriers used in this work were \gls{cus} and \gls{cug} (mostly the
|
||
former) which are both composed of cross-linked gelatin and have a macroporous
|
||
morphology. Their protein-based composition makes functionalization easy; the
|
||
surface is rich in lysine residues which can be easily bonded with a
|
||
base-reactive linker such as \gls{snb}. These specific carriers have been used
|
||
in the past for pancreatic islet cells\cite{Guerra2001},
|
||
\glspl{esc}\cite{Fernandes2007, Storm_2010}, and \glspl{msc}\cite{Eibes2010}.
|
||
Furthermore, they are readily available in over 30 countries and are used in an
|
||
FDA fast-track-approved combination retinal pigment epithelial cell product
|
||
(Spheramine, Titan Pharmaceuticals)\cite{purcellmain}. This regulatory history
|
||
will aid in clinical translation.
|
||
|
||
\subsection{Integrins and T Cell Signaling}
|
||
|
||
Because the microcarriers used in this work are derived from collagen, one key
|
||
question is how these collagen domains may interact with the T cells during
|
||
culture. This question is further explored in \cref{aim2b}.
|
||
|
||
T cells naturally expand in the lymph nodes which have an \gls{ecm} composed of
|
||
collagen\cite{Dustin2001, Ebnet1996, Ohtani2008}. Despite this, T cells don't
|
||
interact with collagen fibers in the lymph node as the collagen fibers are
|
||
sheathed with stromal fibroblasts\cite{Dustin2001, Ebnet1996}. However, the
|
||
\gls{ecm} of peripheral tissues is dense with exposed collagen fibers are
|
||
available to interact with T cells. Furthermore, T cells have been shown
|
||
\invitro{} to crawl along collagen fibers in the presence of \glspl{apc},
|
||
facilitating short encounters with \glspl{apc}\cite{Gunzer2000}. While this may
|
||
not be ideal \invivo{} due to the lack of cumulative signal received by
|
||
\glspl{apc}\cite{Dustin2001}, it may be advantageous to include collagen domains
|
||
\invitro{} as the mode of activation is not specific to any given clone.
|
||
|
||
The major surface receptors for collagen are \gls{a2b1} and
|
||
\gls{a2b2}\cite{Dustin2001, Hemler1990}. These receptors are not expressed on
|
||
naive T cells and thus presence and stimulation of collagen alone is not
|
||
sufficient to cause activation or expansion of T cells\cite{Hemler1990}. These
|
||
receptors have been shown to lead to a number of functions that may be useful in
|
||
the context of T cell expansion. First, they have been shown to act in a
|
||
costimulatory manner which leads to increased proliferation\cite{Rao2000}.
|
||
Furthermore, \gls{a2b1} and \gls{a2b2} have been shown to protect Jurkat cells
|
||
against Fas-mediated apoptosis in the presence of collagen I\cite{Aoudjit2000,
|
||
Gendron2003}. Finally, these receptors have been shown to increase \gls{ifng}
|
||
production \invitro{} when T cells derived from human \glspl{pbmc} are
|
||
stimulated in the presence of collagen I\cite{Boisvert2007}.
|
||
|
||
\subsection{The Role of IL15 in Memory T Cell Proliferation}
|
||
|
||
\il{15} is a cytokine that is involved with the proliferation and homeostasis of
|
||
memory T cells. Its role in the work of this dissertation is the subject of
|
||
further exploration in \cref{aim2b}.
|
||
|
||
Functionally, mice lacking the gene for either \il{15}\cite{Kennedy2000} or its
|
||
high affinity receptor \il{15R$\upalpha$}\cite{Lodolce1998} are generally
|
||
healthy but show a deficit in memory CD8 T cells, thus underscoring its
|
||
importance in manufacturing high-quality memory T cells for immunotherapies. T
|
||
cells themselves express \il{15} and all of its receptor
|
||
components\cite{MirandaCarus2005}. Additionally, blocking \il{15} itself or
|
||
\il{15R$\upalpha$} \invitro{} has been shown to inhibit homeostatic
|
||
proliferation of resting human T cells\cite{MirandaCarus2005}.
|
||
|
||
% ACRO fix the il2R and IL15R stuff
|
||
\il{15} has been puzzling historically because it shares almost the same pathway
|
||
as \il{2} yet has different functions\cite{Stonier2010, Osinalde2014, Giri1994,
|
||
Giri1995}. In particular, both cytokines share the common $\upgamma$ subchain
|
||
(CD132) as well as the \il{2} $\upbeta$ receptor (CD122). The main difference in
|
||
the heterodimeric receptors for \il{2} and \il{15} is the \il{2} $\upalpha$
|
||
receptor (CD25) and the \il{15} $\upalpha$ chain respectively, both of which
|
||
have high affinity for their respective ligands. The \il{2R$\upalpha$} chain
|
||
itself does not have any signaling capacity, and therefore all the signaling
|
||
resulting from \il{2} is mediated thought the $\upbeta$ and $\upgamma$ chains,
|
||
namely via JAK1 and JAK3 leading to STAT5 activation consequently T cell
|
||
activation. \il{15R$\upalpha$} itself has some innate signaling capacity, but
|
||
this is poorly characterized in lymphocytes\cite{Stonier2010}. Thus there is a
|
||
significant overlap between the functions of \il{2} and \il{15} due to their
|
||
receptors sharing the $\upbeta$ and $\upgamma$ chains in their heterodimeric
|
||
receptors, and perhaps the main driver of their differential functions it the
|
||
half life of each respective receptor\cite{Osinalde2014}.
|
||
|
||
Where \il{15} is unique is that many (or possibly most) of its functions derive
|
||
from being membrane-bound to its receptor\cite{Stonier2010}. Particularly,
|
||
\il{15R$\upalpha$} binds to soluble \il{15} which produces a complex that can
|
||
transmit signals to close neighboring cells (so called \textit{trans}
|
||
presentation). This has been demonstrated in adoptive cell models, where T cells
|
||
lacking \il{15R$\upalpha$} were able to generate memory T cells and proliferate
|
||
only when other cells were present which expressed
|
||
\il{15R$\upalpha$}\cite{Burkett2003, Schluns2004}. The implication of this
|
||
mechanism is that cells expression \il{15R$\upalpha$} either need to express
|
||
\il{15} themselves or be near other cells expressing \il{15}, and other cells in
|
||
proximity require the $\upbeta$ and $\upgamma$ chains to receive the signal. In
|
||
addition to \textit{trans} presentation, \il{15} may also work in a \textit{cis}
|
||
manner, where \il{15R$\upalpha$}/\il{15} complexes may bind to the $\upbeta$ and
|
||
$\upgamma$ chains on the same cell, assuming all receptors are expressed and
|
||
soluble \il{15} is available\cite{Olsen2007}. Finally, \il{15R$\upalpha$} itself can exist in
|
||
a soluble form, which can bind to \il{15} and signal to cells which are not
|
||
adjacent to the source independent of the \textit{cis/trans} mechanisms already
|
||
described\cite{Budagian2004}.
|
||
|
||
\subsection{Overview of Design of Experiments}\label{sec:background_doe}
|
||
|
||
The \gls{dms} system described in this dissertation has a number of parameters
|
||
that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal
|
||
framework to test multiple parameters simultaneously and determine which are
|
||
relevant \glspl{cpp}.
|
||
|
||
The goal of \gls{doe} is to answer a data-driven question with the least number
|
||
of resources\cite{Wu2009}. It was developed in many non-biological industries
|
||
throughout the \nth{20} century such as the automotive and semiconductor
|
||
industries where engineers needed to minimize downtime and resource consumption
|
||
on full-scale production lines.
|
||
|
||
At its core, a \gls{doe} is simply a matrix of conditions to test where each row
|
||
(usually called a `run') corresponds to one experimental unit for which the
|
||
conditions are applied, and each column represents a parameter of concern to be
|
||
tested. The values in each cell represent the level at which each parameter is
|
||
to be tested. When the experiment is performed using this matrix of conditions,
|
||
the results are be summarized into one or more `responses' that correspond to
|
||
each run. These responses are then be modeled (usually using linear regression)
|
||
to determine the statistic relationship (also called an `effect') between each
|
||
parameter and the response(s).
|
||
|
||
Collectively, the space spanned by all parameters at their feasible ranges is
|
||
commonly referred to as the `design space', and generally the goal of a
|
||
\gls{doe} is to explore this design space using using the least number of runs
|
||
possible. While there are many types of \glspl{doe} depending on the nature
|
||
of the parameters and the goal of the experimenter, they all share common
|
||
principles:
|
||
|
||
\begin{description}
|
||
\item [randomization --] The order in which the runs are performed should
|
||
ideally be as random as possible. This is to mitigate against any confounding
|
||
factors that may be present which depend on the order or position of the runs.
|
||
For an example in context, the evaporation rate of media in a tissue culture
|
||
plate will be much faster at the perimeter of the plate vs the center. While
|
||
randomization does not eliminate this error, it will ensure the error is
|
||
`spread' evenly across all runs in an unbiased manner.
|
||
\item [replication --] Since the analysis of a \gls{doe} is inherently
|
||
statistical, replicates should be used to ensure that the underlying
|
||
distribution of errors can be estimated. While this is not strictly necessary
|
||
to conclude results using a \gls{doe}, failure to use replications requires
|
||
strong assumptions about the model structure (particularly in the case of
|
||
high-complexity models which could easily fit the data perfectly) and also
|
||
precludes the use of statistical tests such as the lack-of-fit test which can
|
||
be useful in rejecting or accepting a particular analysis. Note that the
|
||
subject of replication is within but not the same as power analysis, which
|
||
concerns the number of runs required to estimate a certain effect size.
|
||
\item [orthogonality --] Orthogonality refers to the independence of each
|
||
parameter in the design matrix. In other words, the levels tested in any given
|
||
parameter add mutually-exclusive information about the response(s). Again,
|
||
while not strictly necessary, orthogonality drastically simplifies the
|
||
analysis of the experiment by allowing each parameter to be treated
|
||
separately. In cases where orthogonality is impossible (which is often true in
|
||
experiments with many categorical variables) strategies exist to maximize
|
||
orthogonality.
|
||
\item [blocking --] In the case where the experiment must be non-randomly spread
|
||
over multiple groups, runs are assigned to `blocks' which are not necessarily
|
||
relevant to the goals of the experiment but nonetheless could affect the
|
||
response. A key assumption that is (usually) made in the case of blocking is
|
||
that there is no interaction between the blocking variable and any of the
|
||
experimental parameters. For example, in T cell expansion, if media lot were a
|
||
blocking variable and expansion method were a parameter, we would by default
|
||
assume that the effect of the expansion method does not depend on the media
|
||
lot (even if the media lot itself might change the mean of the response).
|
||
\end{description}
|
||
|
||
\Glspl{doe} served three purposes in this dissertation. First, we used them as
|
||
screening tools for potential \glspl{cpp}, which allowed us to test many input
|
||
parameters and filter out the few that likely have the greatest effect on the
|
||
response. Second, they were used to make a robust response surface model to
|
||
predict optimums using relatively few resources, especially compared to full
|
||
factorial or one-factor-at-a-time approaches. Third, we used \glspl{doe} to
|
||
discover novel effects and interactions that generated hypotheses that could
|
||
influence the directions for future work. To this end, the types of \glspl{doe}
|
||
we generally used were fractional factorial designs with three levels, which
|
||
enable the estimation of both main effects and second order quadratic effects.
|
||
|
||
While there are advantages of using \glspl{doe}, it is important to recognize
|
||
that they are not necessary or recommended for all experimental aims. In
|
||
particular, \glspl{doe} excel when multiple factors (possible with multiple
|
||
levels) need to be investigated at once and with a known degree of power. This
|
||
is especially important when interaction is expected or needs to be
|
||
investigated. However, it could be the case that one already has data on many of
|
||
the factors of concern. If one only cares about main effects, performing a
|
||
\gls{doe} (particularly a lower-powered screening experiment such as a
|
||
resolution III design) with these factors and a few others may not be
|
||
productive, and one is better off performed a few extra pilot experiments before
|
||
doing a more complex design such as a central composite if desired. Furthermore,
|
||
it should be noted that while the goal of a \gls{doe} is to minimize resources,
|
||
the size necessary to justify a \gls{doe} may not be worth the experimental
|
||
return. For biological work (or any domain with little automation), performing a
|
||
randomized experiment with 20 to 30 runs is not trivial from a logistical
|
||
perspective, especially when considering the number of manual manipulations and
|
||
the chance of human error.
|
||
|
||
Despite these caveats, many of the principles used for a \gls{doe} are important
|
||
in general for experimentation. The most obvious is randomization, which is
|
||
often not employed (and also not explicitly mentioned in papers) even though it
|
||
is empirically obvious that well plates have different evaporation rates
|
||
depending on well position. Assuming the experiment is manual, the largest
|
||
reason to avoid randomization is that the experimentalist has no pattern to
|
||
follow when administering treatment (such as ``add X to row 1 in well plate''),
|
||
thus cognitive burden and the likelihood of mistakes increases. While
|
||
\glspl{doe} are usually bigger with more parameters, the one-factor-at-a-time
|
||
experiment usually performed in biological disciplines is much smaller and only
|
||
has a few parameters, thus these concerns are minimal. There is no reason to
|
||
avoid randomization in these cases, as the added cognitive cost is far offset by
|
||
the guarantee of eliminated bias due to run position.
|
||
|
||
\subsection{Identification and Standardization of CPPs and
|
||
CQAs}\label{sec:background_cqa}
|
||
|
||
% BACKGROUND at least attempt to show that there is alot of work in the space
|
||
% identifying signaling networks
|
||
|
||
In the context of T cell manufacturing, ideally we would have a set of
|
||
non-destructive biomarkers that could both identify functional T cells and
|
||
predict when a process is on track to deliver such functional T cells. T cells
|
||
secrete numerous cytokines and metabolites in the media, which may reflect the
|
||
internal state accurately and thus serve as a potential set of \glspl{cqa}.
|
||
|
||
The complexity of these pathways dictates that we take a big-data approach to
|
||
this problem. To this end, there are several pertinent multi-omic (or simply
|
||
`omic') techniques that can be used to collect such datasets, which can then be
|
||
mined, modeled, and correlated to relevent responses (such as an endpoint
|
||
quantification of memory T cells) to identify pertinent \glspl{cqa}.
|
||
|
||
An overview of the techniques used in this work are:
|
||
|
||
\begin{description}
|
||
\item[Luminex --] This is a multiplexed bead-based assay similar to \gls{elisa} that can measure
|
||
many bulk (not single cell) cytokine concentrations simultaneously
|
||
in a media sample. This is a destructive assay but does not require cells to
|
||
obtain a measurement.
|
||
\item[\gls{nmr} --] It is well known that T cells of different
|
||
lineages have different metabolic profiles; for instance memory T
|
||
cells have larger aerobic capacity and fatty acid
|
||
oxidation\cite{Buck2016, van_der_Windt_2012}. \gls{nmr} is a technique that
|
||
can non-destructively quantify small molecules in a media sample, and thus is
|
||
an attractive method that could be used for inline, real-time monitoring.
|
||
\item[Flow and Mass Cytometry --] Flow cytometry using fluorophores has been
|
||
used extensively for immune cell analysis, but has a practical limit of
|
||
approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
|
||
traditional flow cytometry except that it uses heavy-metal \gls{mab}
|
||
conjugates, which has a practical limit of over 50 markers. While mass
|
||
cytometry is less practical than simple flow cytometers such as the BD Accuri,
|
||
we may find that only a few markers are required to accurately predict
|
||
performance, and thus this could easily translate to industry using relatively
|
||
cost-effective equipment. Both of these destructively analyze the cells
|
||
themselves, but they have the advantage in that they are measuring a direct
|
||
property of the cells and not a secreted product.
|
||
\end{description}
|
||
|
||
% BACKGROUND what about ssRNAseq?
|
||
|
||
Upon collecting these omic datasets, determining the \glspl{cqa} becomes a
|
||
computational problem. Predictions of the final product using data collected
|
||
earlier in time can be made using any number of supervised learning techniques
|
||
(linear and non-linear regression in all its forms) which in turn can be used to
|
||
develop process control models. Unsupervised learning and dimensionality
|
||
reduction techniques such as \gls{tsne}, \gls{umap}, and
|
||
\gls{spade}\cite{Qiu2011, Qiu2017} can be performed to delineate clusters of
|
||
interesting cell types and the markers that define them.
|
||
|
||
Ultimately, identifying \glspl{cqa} will likely be an iterative process, wherein
|
||
putative \glspl{cqa} will be identified, the corresponding \glspl{cpp} will be
|
||
set and optimized to maximize products with these \glspl{cpp}, and then
|
||
additional data will be collected in the clinic as the product is tested on
|
||
various patients with different indications. Additional \glspl{cqa} may be
|
||
identified which better predict specific clinical outcomes, which can be fed
|
||
back into the process model and optimized again.
|
||
|
||
\section{Innovation}
|
||
|
||
Several aspects of the \gls{dms} platform described in this dissertation are
|
||
novel considering the state-of-the-art technology for T cell manufacturing:
|
||
|
||
\begin{itemize}
|
||
\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
|
||
bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
|
||
the licenses for these techniques is controlled by only a few companies
|
||
(Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
|
||
is more expensive to implement and therefore hinders companies from entering
|
||
the rapidly growing T cell manufacturing arena. Providing an alternative will
|
||
provide more options for manufacturers, leading to increased competition,
|
||
lower costs, and higher innovation in the T cell manufacturing space.
|
||
\item This is the first technology for T cell immunotherapies that selectively
|
||
expands memory T cell populations with greater efficiency relative to
|
||
bead-based expansion Others have demonstrated methods that can achieve greater
|
||
expansion of T cells, but not necessarily specific populations that are known
|
||
to be potent.
|
||
\item We used \glspl{doe} to discover and validate novel \glspl{cpp}, which is a
|
||
strategy commonly used in non-biological industries but has yet to gain wide
|
||
usage in the development of cell therapies where research often employs a
|
||
one-factor-at-a-time approach. We believe this method is highly relevant to
|
||
the development of cell therapies, not only for process optimization but also
|
||
hypotheses generation. Furthermore, it is a perfectly natural strategy to use
|
||
even at small scale, where the cost of reagents, cells, and materials often
|
||
precludes large sample sizes.
|
||
\item The \gls{dms} system is be compatible with static bioreactors such as the
|
||
G-Rex which has been adopted throughout the cell therapy industry. Thus this
|
||
technology can be easily incorporated into existing cell therapy process that
|
||
are performed at scale.
|
||
\item We analyzed our system using a multiomics approach, which will enable the
|
||
discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
|
||
has been applied to T cells previously, it has not been done in the context of
|
||
a large \gls{doe}-based model. This approach is aware of the whole design
|
||
space, and thus enables greater understanding of process parameters and their
|
||
effect on cell phenotype.
|
||
\end{itemize}
|
||
|
||
\chapter{AIM 1}\label{aim1}
|
||
|
||
\section{Introduction}
|
||
|
||
The first aim was to develop a microcarrier system that mimics several key
|
||
aspects of the \invivo{} lymph node microenvironment. We compared compare this
|
||
system to state-of-the-art T cell activation technologies for both expansion
|
||
potential and memory cell formation. The governing hypothesis was that
|
||
microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
|
||
provide superior expansion and memory phenotype compared to state-of-the-art
|
||
bead-based T cell expansion technology\footnote{adapted from \dmspaper{}}.
|
||
|
||
\section{Methods}
|
||
|
||
\subsection{DMS Functionalization}\label{sec:dms_fab}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_flowchart.png}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Flowchart]{Overview of \gls{dms} manufacturing process.}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
\product{\gls{cus}}{\gehc}{DG-2001-OO} or \product{\gls{cug}}{\gehc}{DG-0001-OO}
|
||
were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and autoclaved. All
|
||
subsequent steps were done aseptically, and all reactions were carried out at
|
||
\SI{20}{\mg\per\ml} carriers at room temperature and agitated using an orbital
|
||
shaker with a \SI{3}{\mm} orbit diameter. After autoclaving, the microcarriers
|
||
were washed using sterile \gls{pbs} three times in a 10:1 volume ratio.
|
||
\product{\Gls{snb}}{\thermo}{21217} was dissolved at approximately \SI{10}{\uM}
|
||
in sterile ultrapure water, and the true concentration was then determined using
|
||
the \gls{haba} assay (see below). \SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added
|
||
to carrier suspension and allowed to react for \SI{60}{\minute} at
|
||
\SI{700}{\rpm} of agitation. After the reaction, the amount of biotin remaining
|
||
in solution was quantified using the \gls{haba} assay (see below). The carriers
|
||
were then washed three times, which entailed adding sterile \gls{pbs} in a 10:1
|
||
volumetric ratio, agitating at \SI{900}{\rpm} for \SI{10}{\minute}, adding up to
|
||
a 15:1 volumetric ratio (relative to reaction volume) of sterile \gls{pbs},
|
||
centrifuging at \SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid
|
||
back down to the reaction volume.
|
||
|
||
To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson
|
||
Immunoresearch}{016-000-114} was added and allowed to react for
|
||
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant
|
||
was taken for the \product{\gls{bca} assay}{\thermo}{23225}, and the carriers
|
||
were washed analogously to the previous wash step to remove the biotin, except
|
||
two washes were done and the agitation time was \SI{30}{\minute}. Biotinylated
|
||
\glspl{mab} against human CD3 \catnum{\bl}{317320} and CD28 \catnum{\bl}{302904}
|
||
were combined in a 1:1 mass ratio and added to the carriers at
|
||
\SI{0.2}{\ug\of{\ab}\per\mg\of{\dms}}. Along with the \glspl{mab}, sterile
|
||
\product{\gls{bsa}}{\sigald}{A9576} was added to a final concentration of
|
||
\SI{2}{\percent} in order to prevent non-specific binding of the antibodies to
|
||
the reaction tubes. \glspl{mab} were allowed to bind to the carriers for
|
||
\SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding, supernatants were
|
||
sampled to quantify remaining \gls{mab} concentration using an
|
||
\product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}. Fully functionalized
|
||
\glspl{dms} were washed in sterile \gls{pbs} analogous to the previous washing
|
||
step to remove excess \gls{stp}. They were washed once again in the cell culture
|
||
media to be used for the T cell expansion.
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Properties of the microcarriers used}
|
||
\label{tab:carrier_props}
|
||
\input{../tables/carrier_properties.tex}
|
||
\end{table}
|
||
|
||
The concentration of the final \gls{dms} suspension was found by taking a
|
||
\SI{50}{\uL} sample, plating in a well, and imaging the entire well. The image
|
||
was then manually counted to obtain a concentration. Surface area for
|
||
\si{\ab\per\um\squared} was calculated using the properties for \gls{cus} and
|
||
\gls{cug} as given by the manufacturer \cref{tab:carrier_props}.
|
||
|
||
\subsection{DMS Quality Control Assays}
|
||
|
||
Biotin was quantified using the \product{\gls{haba} assay}{\sigald}{H2153-1VL}.
|
||
In the case of quantifying \gls{snb} prior to adding it to the microcarriers,
|
||
the sample volume was quenched in a 1:1 volumetric ratio with \SI{1}{\molar}
|
||
NaOH and allowed to react for \SI{1}{\minute} in order to prevent the reactive
|
||
ester linkages from binding to the avidin proteins in the \gls{haba}/avidin
|
||
premix. All quantifications of \gls{haba} were performed on an Eppendorf D30
|
||
Spectrophotometer using \product{\SI{70}{\ul} cuvettes}{BrandTech}{759200}. The
|
||
extinction coefficient at \SI{500}{\nm} for \gls{haba}/avidin was assumed to be
|
||
\SI{34000}{\per\cm\per\molar}.
|
||
|
||
The \gls{stp} binding to the microcarriers was quantified indirectly using a
|
||
\product{\gls{bca} kit}{\thermo}{23227} according to the manufacturer’s
|
||
instructions, with the exception that the standard curve was made with known
|
||
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at
|
||
\SI{592}{\nm} was quantified using a BioTek plate reader.
|
||
|
||
The \gls{mab} binding to the microcarriers was quantified indirectly using an
|
||
\gls{elisa} assay per the manufacturer’s instructions, with the exception that
|
||
the same \glspl{mab} used to coat the carriers were used as the standard for the
|
||
\gls{elisa} standard curve.
|
||
|
||
Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
|
||
quantified indirectly using \product{\gls{fitcbt}}{\thermo}{B10570}.
|
||
Briefly, \SI{400}{\pmol\per\ml} \gls{fitcbt} were added to \gls{stp}-coated
|
||
carriers and allowed to react for \SI{20}{\minute} at room temperature under
|
||
constant agitation. The supernatant was quantified against a standard curve of
|
||
\gls{fitcbt} using a BioTek plate reader.
|
||
|
||
\Gls{stp} binding was verified after the \gls{stp}-binding step visually by
|
||
adding \gls{fitcbt} to the \gls{stp}-coated \glspl{dms}, resuspending in
|
||
\SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
|
||
microscope}{Zeiss}{Z.1}. Overall \gls{mab} binding was verified visually
|
||
by first staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001},
|
||
incubating for \SI{30}{\minute}, washing with \gls{pbs}, and imaging on a
|
||
confocal microscope.
|
||
|
||
\subsection{T Cell Culture}\label{sec:tcellculture}
|
||
|
||
Cryopreserved primary human T cells were either obtained as sorted
|
||
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
|
||
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection
|
||
\product{\cdp{3} \gls{macs} kit}{\miltenyi}{130-096-535}. T cells were activated
|
||
using \glspl{dms} or \product{\SI{3.5}{\um} CD3/CD28 magnetic
|
||
beads}{\miltenyi}{130-091-441}. In the case of beads, T cells were activated
|
||
at the manufacturer recommended cell:bead ratio of 2:1. In the case of
|
||
\glspl{dms}, cells were activated using \SI{2500}{\dms\per\cm\squared} unless
|
||
otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
|
||
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
|
||
\product{OpTmizer}{\thermo}{A1048501} or
|
||
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
|
||
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02} unless
|
||
otherwise noted. Cell cultures were expanded for \SI{14}{\day} as counted from
|
||
the time of initial seeding and activation. Cell counts and viability were
|
||
assessed using \product{trypan blue}{\thermo}{T10282} or
|
||
\product{\gls{aopi}}{Nexcelom Bioscience}{CS2-0106-5} and a \product{Countess
|
||
Automated Cell Counter}{Thermo Fisher}{Countess 3 FL}. Media was added to
|
||
cultures every \SIrange{2}{3}{\day} depending on media color or a
|
||
\SI{300}{\mg\per\deci\liter} minimum glucose threshold. Media glucose was
|
||
measured using a \product{GlucCell glucose meter}{Chemglass}{CLS-1322-02}.
|
||
|
||
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
|
||
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
|
||
\product{\acd{45}-\gls{af647}}{\bl}{368538}, incubating for \SI{1}{\hour}, and
|
||
imaging on a spinning disk confocal microscope.
|
||
|
||
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
|
||
Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}.
|
||
|
||
\subsection{Quantifying Cells on DMS Interior}
|
||
|
||
To visualize T cells on the interior of the \glspl{dms}, we stained them with
|
||
\gls{mtt}. \glspl{dms} with attached and loosely attached cells were sampled as
|
||
desired and filtered through a \SI{40}{\um} cell strainer. While still in the
|
||
cell strainer, \glspl{dms} were washed twice with \gls{pbs} and then dried by
|
||
pulling liquid through the bottom of the cell strainer via a micropipette and
|
||
dabbing with a KimWipe. \glspl{dms} were transferred to a 24 well plate with
|
||
\SI{400}{\ul} media. \SI{40}{\ul} \gls{mtt} was added to each well and allowed
|
||
to incubate for \SI{3}{\hour}, after which \glspl{dms} with cell were visualized
|
||
via a brightfield microscope.
|
||
|
||
To quantify cells on the interior of \glspl{dms}, cells and \glspl{dms} were
|
||
isolated analogously to those for the \gls{mtt} stain up until the drying step.
|
||
Cells were then transferred to a tube containing \SI{400}{\ul} at
|
||
\SI{5}{\mg\per\ml} dispase solution. \glspl{dms} were incubated and rotated for
|
||
\SI{45}{\minute} at \SI{37}{\degreeCelsius}, after which cells were counted as
|
||
already described in \cref{sec:tcellculture}.
|
||
|
||
\subsection{Quantification of Apoptosis Using Annexin-V}
|
||
|
||
Apoptosis was quantified using \gls{anv} according to the manufacturer's
|
||
instructions. Briefly, cells were transferred to flow tubes and washed twice
|
||
with \gls{pbs} by adding \SI{3}{\ml} to each tube, centrifuging for
|
||
\SI{400}{\gforce}, and aspirating the liquid down to \SI{200}{\ul}. The cells
|
||
were analogously washed a third time with staining buffer (\SI{10}{\mM}
|
||
\gls{hepes}, \SI{140}{\mM} NaCl, \SI{2.5}{\mM} \ce{CaCl2}) and aspirated down to
|
||
a final volume of \SI{100}{\ul}. Cells were stained in this volume with
|
||
\SI{1}{\ul} \product{\gls{anv}-\gls{fitc}}{\bl}{640906} and \SI{5}{\ul}
|
||
\product{\gls{pi}}{\thermo}{P3566} and incubated for \SI{15}{\minute} at
|
||
\gls{rt} in the dark. After incubation, \SI{400}{\ul} staining buffer was added
|
||
to each tube. Each tube was then analyzed via flow cytometry.
|
||
|
||
\subsection{Quantification of Caspase-3/7}
|
||
|
||
\Gls{cas37} was quantified using \product{CellEvent dye}{\thermo}{C10723}
|
||
according the manufacturer's instructions. Briefly, a 2X (\SI{8}{\mM}) working
|
||
solution of CellEvent dye was added to \SI{100}{\ul} cell suspension (at least
|
||
\num{5e4} cells) and incubated at \SI{37}{\degreeCelsius} for \SI{30}{\minute}.
|
||
After incubation, cells were immediately analyzed via flow cytometry.
|
||
|
||
\subsection{Quantification of BCL-2}
|
||
|
||
\Gls{bcl2} was quantified using an \product{Human Total Bcl-2 DuoSet \gls{elisa}
|
||
kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
|
||
supplemented with \product{\gls{tmb} substrate
|
||
solution}{eBioscience}{00-4201-56}, \product{5X diluent buffer}{\bl}{421203},
|
||
and \SI{2}{\normal} \ce{H2SO4} stop solution made in house. Briefly, cells were
|
||
lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and the lysate
|
||
was quantified for protein using a \product{\gls{bca} assay}{\thermo}{23225} as
|
||
directed. Standardized lysates were measured using the \gls{elisa} kit as
|
||
directed.
|
||
|
||
\subsection{Chemotaxis Assay}
|
||
|
||
Migratory function was assayed using a transwell chemotaxis assay as previously
|
||
described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
|
||
\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
|
||
basolateral chamber loaded with \SI{600}{\ul} media and 0, 250, or
|
||
\SI{1000}{\ng\per\mL} \product{CCL21}{Peprotech}{250-13}. The plate was
|
||
incubated for \SI{4}{\hour} after loading, and the basolateral chamber of each
|
||
transwell was quantified for total cells using \product{countbright
|
||
beads}{\thermo}{C36950}. The final readout was normalized using the
|
||
\SI{0}{\ng\per\mL} concentration as background.
|
||
|
||
\subsection{Degranulation Assay}
|
||
|
||
Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
|
||
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
|
||
were incubated with \num{1.5e5} target cells consisting of either \product{K562
|
||
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
|
||
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}.
|
||
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
|
||
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar}
|
||
\product{monensin}{eBioscience}{ 00-4505-51}, and \SI{1}{\ug\per\ml}
|
||
\product{\acd{28}}{eBioscience}{302914} (all functional grade \glspl{mab}) with
|
||
\SI{250}{\ul} total volume. After \SI{4}{\hour} incubation at
|
||
\SI{37}{\degreeCelsius}, cells were stained for CD3, CD4, and CD107a and
|
||
analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent
|
||
\cdp{107a} cells of the total \cdp{8} fraction.
|
||
|
||
\subsection{CAR Expression}
|
||
|
||
\gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously
|
||
described\cite{Zheng2012}. Briefly, cells were washed once and stained with
|
||
\product{biotinylated \gls{ptnl}}{\thermo}{29997}. After a subsequent wash,
|
||
cells were stained with \product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again,
|
||
and analyzed on a \bd{} Accuri. Readout was percent \gls{pe}+ cells as compared
|
||
to secondary controls (\gls{pe}-\gls{stp} with no \gls{ptnl}).
|
||
|
||
\gls{car} expression of the \anti{\gls{bcma}} \gls{car} was quantified using a
|
||
\product{\gls{fitc}-labeled \gls{bcma} protein}{Acro}{Bca-hf254}. \SI{100}{\ng}
|
||
was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute}
|
||
prior to analyzing on a \bd{} Accuri
|
||
|
||
\subsection{CAR Plasmid and Lentiviral Transduction}
|
||
|
||
The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$
|
||
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
|
||
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core.
|
||
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the
|
||
Cincinnati Children's Hospital Medical Center Viral Vector Core. RNA titer was
|
||
calculated using a \product{Lenti-X \gls{qpcr} titer kit}{Takara}{631235}. To
|
||
transduce primary human T cells, \product{retronectin}{Takara}{T100A} was coated
|
||
onto non-TC treated 96 well plates and used to immobilize lentiviral vector
|
||
particles according to the manufacturer's instructions. Briefly, retronectin
|
||
solution was adsorbed overnight at \SI{4}{\degreeCelsius} and blocked the next
|
||
day using \gls{bsa}. Prior to transduction, lentiviral supernatant was
|
||
spinoculated at \SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}.
|
||
T cells were activated in 96 well plates using beads or \glspl{dms} for
|
||
\SI{24}{\hour}, and then cells and beads/\glspl{dms} were transferred onto
|
||
lentiviral vector coated plates and incubated for another \SI{24}{\hour}. Cells
|
||
and beads/\glspl{dms} were removed from the retronectin plates using vigorous
|
||
pipetting and transferred to another 96 well plate wherein expansion continued.
|
||
|
||
% METHOD fill in missing product numbers
|
||
\gls{bcma} \gls{car} lentiviral vector was synthesized in house as
|
||
follows\footnote{lentiviral synthesis was performed by Ritika Jain in our
|
||
laboratory and included here for reference}. \SI{10}{\ng} of
|
||
\anti{\gls{bcma}}-CD8-CD137-CD3$\upzeta$ plasmid (generously provided by Jim
|
||
Kochenderfer at the NIH)\cite{Lam2020} was added to \SI{50}{\ul}
|
||
\product{DH5$\upalpha$ cells}{\thermo}{18265017} and incubated for
|
||
\SI{30}{\minute} on ice. The cell mixture was then heat-shocked at
|
||
\SI{42}{\degreeCelsius} for \SI{20}{\minute} before being placed on ice for
|
||
another \SI{2}{\minute}. \SI{950}{\ul} luria broth was added to the cells which
|
||
were then centrifuged for \SI{1}{\hour} at \SI{225}{\rpm}. \SI{20}{\ul} of the
|
||
cell mixture was then spread over selection plates and incubated overnight at
|
||
\SI{37}{\degreeCelsius}. Colonies were selected the following day and incubated
|
||
in luria broth with \product{ampicillin}{\sigald{}}{A9518-5G} at
|
||
\SI{37}{\degreeCelsius} for \SIrange{12}{16}{\hour} prior to using the
|
||
\product{miniprep kit}{Qiagen}{27104} as per the manufacturer's instructions to
|
||
isolate the plasmid DNA. Transfer plasmid along with
|
||
\product{pMDLg/pRRE}{Addgene}{12251}, \product{pRSV-Rev}{Addgene}{12253}, and
|
||
\product{pMD2.G}{Addgene}{12259} (generously provided by the Sloan lab at Emory
|
||
University) in \product{Opti-Mem}{\thermo}{31-985-070} with
|
||
\product{lipfectamine 2000}{\thermo}{11668019} were added dropwise to HEK 293T
|
||
cells and incubated for \SI{6}{\hour}, after which all media was replaced with
|
||
fresh fresh media. After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was
|
||
collected, pooled, and concentrated using a \product{Lenti-X
|
||
concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}.
|
||
|
||
\subsection{Sulfo-NHS-Biotin Hydrolysis Quantification}
|
||
|
||
The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by
|
||
\cref{chem:snb_hydrolysis}.
|
||
|
||
\begin{equation}
|
||
\label{chem:snb_hydrolysis}
|
||
\ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
|
||
\end{equation}
|
||
|
||
Measuring the hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb}
|
||
was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well
|
||
plate. Kinetic analysis using a BioTek plate reader began immediately after
|
||
prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
|
||
|
||
\subsection{Reaction Kinetics Quantification}
|
||
|
||
The diffusion of \gls{stp} into biotin-coated microcarriers was determined
|
||
experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of
|
||
biotin-coated microcarriers, and supernatents were taken at fixed intervals and
|
||
quantified for \gls{stp} protein using the \gls{bca} assay.
|
||
|
||
The geometric diffusivity of the microcarriers was determined using a
|
||
pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere
|
||
with a fixed number of uniformly distributed `\gls{stp} binding sites' equal to
|
||
the number of \gls{stp} molecules experimentally determined to bind to the
|
||
microcarriers. Because the reaction rate between biotin and \gls{stp} is so fast
|
||
(it is the strongest non-covalent bond in known existence), we assumed that the
|
||
interface of free biotin shrunk as a function of \gls{stp} diffusing to the
|
||
unbound biotin interface until the center of the microcarriers was reached. We
|
||
also assumed that the pores in the microcarriers were large enough that the
|
||
interactions between the \gls{stp} and surfaces would be small, thus the
|
||
geometric diffusivity could be represented as a fraction of the diffusion
|
||
coefficient of \gls{stp} in water. This model was given by
|
||
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
|
||
|
||
% TODO actually derive these equations, eg state the initial conditions and
|
||
% governing equation
|
||
\begin{equation}
|
||
\label{eqn:stp_diffusion_1}
|
||
\frac{dr}{dt} = \frac{-D_{app}C_b}{Br(1-r/R)}
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_diffusion_2}
|
||
\frac{dC_b}{dt} = \frac{-4 \pi n D_{app} C_b}{V(1/r-1/R)}
|
||
\end{equation}
|
||
|
||
\noindent where
|
||
\begin{itemize}[label={}]
|
||
\item $D_{app}$ is the apparent diffusion rate of species $X$ which is equal to
|
||
$D\beta$
|
||
\item $D$ the diffusion rate of species $X$ in water at room temperature
|
||
(where $X$ is \gls{stp} in this example and \glspl{mab} later in this section)
|
||
\item $\beta$ a fractional parameter representing the tortuousity and void
|
||
fraction of the microcarriers (here called the `geometric diffusivity')
|
||
\item $r$ is the interfatial radius of the unbound binding sites for species $X$
|
||
within a microcarrier
|
||
\item $t$ is the reaction time
|
||
\item $C_b$ is the concentration of species $X$ in the bulk solution
|
||
\item $V$ is the volume of the bulk medium
|
||
\item $R$ is the average radius of the microcarriers
|
||
\item $n$ is the number of microcarriers in the reaction volume
|
||
\end{itemize}
|
||
|
||
The diffusion rate of \gls{stp} was assumed to be
|
||
\SI{6.2e-7}{\cm\squared\per\second}\cite{Kamholz2001}. Since all but $\beta$ was
|
||
known, the experimental data was fit using these equations using
|
||
\inlinecode{ode45} in MATLAB and least squares as the fitting error. These
|
||
equations were then used analogously to describe the reaction profile of
|
||
\glspl{mab} assuming a diffusion rate of
|
||
\SI{4.8e-7}{\cm\squared\per\second}\cite{Sherwood1992}.
|
||
|
||
To model the washing of the microcarriers, they once again were assumed to be
|
||
porous spheres filled with whatever amount of reagent was left unbound from the
|
||
previous step (which was assumed to be equal to concentration in the
|
||
supernatent). The diffusion out of the microcarriers is given by the following
|
||
partial differential equation and boundary conditions:
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing}
|
||
\frac{\partial C_i}{\partial t} = \frac{1}{r^2}\frac{\partial}{\partial
|
||
r}\left(r^2 D_{app} \frac{\partial C_i}{\partial r}\right)
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_left_bc}
|
||
C_i(r, 0) = C_{i,0}
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_left_bc}
|
||
N_i(0, t) = 0
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_right_bc}
|
||
C_i(R, t) = (C_{b,0}+C_{b,\infty}) / 2
|
||
\end{equation}
|
||
|
||
\noindent where (in addition to the variables given already for
|
||
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2})
|
||
\begin{itemize}[label={}]
|
||
\item $N_i$ is the radial flux of species $X$ inside the microcarriers
|
||
\item $C_i$ is the concentration of species $X$ inside the microcarriers
|
||
\item $C_{i,0}$ is the initial concentration of species $X$ inside
|
||
the microcarriers (which is assumed to be the concentration in the bulk before
|
||
the wash volume is added)
|
||
\item $C_{b,0}$ is the initial bulk concentration of species $X$ outside the
|
||
microcarriers after the initial wash volume has been added
|
||
\item $C_{b,\infty}$ is the final bulk concentration of species $X$ outside the
|
||
microcarriers
|
||
\end{itemize}
|
||
|
||
Note that in order to avoid solving a moving boundary value problem, the
|
||
concentration at the boundary of the microcarriers was fixed at the average of
|
||
the final and initial concentration expected to be observed in bulk. This should
|
||
be a reasonable assumption given that the volume inside the microcarriers is
|
||
tiny compared to the amount of volume added in the wash, thus the boundary
|
||
concentration should change little.
|
||
|
||
The same diffusion coefficients were used in determining the kinetics of the
|
||
washing steps, and \SI{5.0e-6}{\cm\squared\per\second}\cite{Niether2020} was
|
||
used as the diffusion coefficient for free biotin (which should be the only
|
||
reactive species left in solution after all the \gls{snb} has hydrolyzed).
|
||
|
||
All diffusion coefficients were taken to be valid at \gls{rt} and in \gls{di}
|
||
water, which is a safe assumption given that our reaction medium was 1X
|
||
\gls{pbs}.
|
||
|
||
See \cref{sec:appendix_binding} and \cref{sec:appendix_washing} for the MATLAB
|
||
code (and output in the case of the washing steps) used.
|
||
|
||
\subsection{Luminex Analysis}\label{sec:luminex_analysis}
|
||
|
||
Luminex was performed using a \product{ProcartaPlex kit}{\thermo}{custom} for
|
||
the markers outlined in \cref{tab:luminex_panel} with modifications (note that
|
||
some markers were run in separate panels to allow for proper dilutions).
|
||
Briefly, media supernatents from cells were sampled as desired and immediately
|
||
placed in a \SI{-80}{\degreeCelsius} freezer until use. Before use, samples were
|
||
thawed at \gls{rt} and vortexed to ensure homogeneity. To run the plate,
|
||
\SI{25}{\ul} of magnetic beads were added to the plate and washed 3X using
|
||
\SI{300}{\ul} of wash buffer. \SI{25}{\ul} of samples or standard were added to
|
||
the plate and incubated for \SI{120}{\minute} at \SI{850}{\rpm} at \gls{rt}
|
||
before washing analogously 3X with wash buffer. \SI{12.5}{\ul} detection
|
||
\glspl{mab} and \SI{25}{\ul} \gls{stppe} were sequentially added, incubated for
|
||
\SI{30}{\minute} and vortexed, and washed analogously to the sample step.
|
||
Finally, samples were resuspended in \SI{120}{\ul} reading buffer and analyzed
|
||
via a BioRad Bioplex 200 plate reader. An 8 point log\textsubscript{2} standard
|
||
curve was used, and all samples were run with single replicates.
|
||
|
||
Luminex data was preprocessed using R for inclusion in downstream analysis as
|
||
follows. Any cytokine level that was over-range (`OOR >' in output spreadsheet)
|
||
was set to the maximum value of the standard curve for that cytokine. Any value
|
||
that was under-range (`OOR <' in output spreadsheet) was set to zero. All values
|
||
that were extrapolated from the standard curve were left unchanged.
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Luminex Panel}
|
||
\label{tab:luminex_panel}
|
||
\input{../tables/luminex_panel.tex}
|
||
\end{table}
|
||
|
||
\subsection{Data Aggregation and Meta-Analysis}
|
||
|
||
In order to perform meta-analysis on all experimental data generate using the
|
||
\gls{dms} system, we developed a program to curate and aggregate the raw
|
||
datafiles into a \gls{sql} database (\cref{sec:appendix_meta}).
|
||
|
||
The data files to be aggregated included Microsoft Excel files which held
|
||
timeseries measurements for cell cultures (eg cell counts, viability, glucose,
|
||
\gls{il2} added, media added, and media removed), \gls{fcs} files for cellular
|
||
phenotypes, and FlowJo files which held gating parameters and statistics based
|
||
on the \gls{fcs} files. Additional information which was held in electronic lab
|
||
notebooks (eg OneNote files) was not easily parsable, and thus this data was
|
||
summarized in YAML files. The data included in these YAML files included reagent
|
||
characteristics (vendor, catalog number, lot number, manufacturing date), cell
|
||
donor characteristics (age, \gls{bmi}, phenotype, demographic, gender), and all
|
||
experimental parameters such as the number of bead or \gls{dms} added.
|
||
|
||
To aggregate the data in a database, we wrote a program using Python, R, and
|
||
Docker which retrieved the data from its source location and inserted the data
|
||
in a Postgres database (specifically Aurora implementation hosted on \gls{aws}).
|
||
This program included checks to ensure the integrity of source data (for
|
||
example, flagging entries which had a reagent whose manufacturing date was after
|
||
the date the experiment started, which signifies a human input error).
|
||
|
||
|
||
\subsection{Statistical Analysis}\label{sec:statistics}
|
||
|
||
For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
|
||
significance was assessed using the \inlinecode{stat\_compare\_means} function
|
||
with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R.
|
||
For 2-way \gls{anova} analysis, the significance of main and interaction effects
|
||
was determined using the car library in R.
|
||
|
||
For least-squares linear regression, statistical significance was evaluated the
|
||
\inlinecode{lm} function in R. All results with categorical variables are
|
||
reported relative to baseline reference. Each linear regression was assessed for
|
||
validity using residual plots (to assess constant variance and independence
|
||
assumptions), QQplots and Shapiro-Wilk normality test (to assess normality
|
||
assumptions), Box-Cox plots (to assess need for power transformations), and
|
||
lack-of-fit tests where replicates were present (to assess model fit in the
|
||
context of pure error). Significance was evaluated at $\upalpha$ = 0.05.
|
||
|
||
\subsection{Flow Cytometry}\label{sec:flow_cytometry}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/gating_strategy.png}
|
||
|
||
\endgroup
|
||
\caption[Gating Strategy]
|
||
{Gating strategy for quantifying \ptmemp{}, \pthp{}, and \ptcarp{}.}
|
||
\label{fig:gating_strategy}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{\glspl{mab} used for flow cytometry}
|
||
\label{tab:flow_mabs}
|
||
\input{../tables/flow_mabs.tex}
|
||
\end{table}
|
||
|
||
All \glspl{mab} used for flow cytometry are shown in \cref{tab:flow_mabs}. Other
|
||
reagents for specialized assays such as degranulation are described in their
|
||
respective sections. Cells were gated according to \cref{fig:gating_strategy}.
|
||
|
||
\section{Results}
|
||
|
||
\subsection{DMSs Can be Fabricated in a Controlled Manner}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_coating.png}
|
||
\phantomsubcaption\label{fig:cug_vs_cus}
|
||
\phantomsubcaption\label{fig:biotin_coating}
|
||
\phantomsubcaption\label{fig:stp_coating}
|
||
\phantomsubcaption\label{fig:mab_coating}
|
||
\phantomsubcaption\label{fig:stp_carrier_fitc}
|
||
\phantomsubcaption\label{fig:mab_carrier_fitc}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Coating]
|
||
{\gls{dms} functionalization results.
|
||
\subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
|
||
density on either \gls{cus} or \gls{cug} microcarriers. Surface density
|
||
was estimated using the properties in~\cref{tab:carrier_props}}.
|
||
Total binding curve of \subcap{fig:biotin_coating}{biotin},
|
||
\subcap{fig:stp_coating}{\gls{stp}}, and
|
||
\subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added for
|
||
batches manufactured on different dates.
|
||
\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
|
||
treated with \gls{fitcbt} and imaged using a lightsheet microscope.}
|
||
\subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
|
||
\glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
|
||
lightsheet microscope.}
|
||
}
|
||
\label{fig:dms_coating}
|
||
\end{figure*}
|
||
|
||
Two types of gelatin-based microcariers, \gls{cus} and \gls{cug}, were
|
||
covalently conjugated with varying concentration of \gls{snb} and then coated
|
||
with \gls{stp} and \glspl{mab} to make \glspl{dms}. Aside from slight
|
||
differences in swelling ratio and crosslinking chemistry\cite{purcellmain} the
|
||
properties of \gls{cus} and \gls{cug} were the same (\cref{tab:carrier_props}).
|
||
We chose to continue with the \gls{cus}-based \glspl{dms}, which showed higher
|
||
overall \gls{stp} binding compared to \gls{cug}-based \glspl{dms}
|
||
(\cref{fig:cug_vs_cus}). We showed that by varying the concentration of
|
||
\gls{snb}, we were able to precisely control the amount of attached biotin
|
||
(\cref{fig:biotin_coating}), mass of attached \gls{stp}
|
||
(\cref{fig:stp_coating}), and mass of attached \glspl{mab}
|
||
(\cref{fig:mab_coating}). Furthermore, we showed that the microcarriers were
|
||
evenly coated with \gls{stp} on the surface and throughout the interior as
|
||
evidenced by the presence of biotin-binding sites occupied with \gls{fitcbt} on
|
||
the microcarrier surfaces after the \gls{stp}-coating step
|
||
(\cref{fig:stp_carrier_fitc}). Finally, we confirmed that biotinylated
|
||
\glspl{mab} were bound to the \glspl{dms} by staining either \gls{stp}- or
|
||
\gls{stp}/\gls{mab}-coated carriers with \antim{\gls{igg}-\gls{fitc}} and
|
||
imaging on a confocal microscope (\cref{fig:mab_carrier_fitc}). Taking this
|
||
together, we noted that the maximal \gls{mab} binding capacity occurred near
|
||
\SI{50}{\nmol} biotin input (which corresponded to
|
||
\SI{2.5}{\nmol\per\mg\of{\dms}}) thus we used this in subsequent processes.
|
||
|
||
We then asked how sensitive the \gls{dms} manufacturing process was to a variety
|
||
of variables. In particular, we focused on the biotin-binding step, since it
|
||
appeared that the \gls{mab} binding was quadratically related to biotin binding
|
||
(\cref{fig:mab_coating}) and thus controlling the biotin binding step would be
|
||
critical to controlling the amount and \glspl{mab} and thus the amount of signal
|
||
the T cells receive downstream.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_qc.png}
|
||
\phantomsubcaption\label{fig:dms_qc_doe}
|
||
\phantomsubcaption\label{fig:dms_qc_ph}
|
||
\phantomsubcaption\label{fig:dms_qc_washes}
|
||
\phantomsubcaption\label{fig:dms_snb_decay_curves}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Process Parameters]
|
||
{Investigation of influential parameters for the \gls{dms} process.
|
||
\subcap{fig:dms_qc_doe}{\gls{doe} investigating the effect of initial mass
|
||
of microcarriers, reaction temperature, and biotin concentration on
|
||
biotin attachment.}
|
||
\subcap{fig:dms_qc_ph}{Effect of reaction ph on biotin attachment.}
|
||
\subcap{fig:dms_qc_washes}{effect of post-autoclave washing of the
|
||
microcarriers on biotin attachment.}
|
||
\subcap{fig:dms_snb_decay_curves}{Hydrolysis curves of \gls{snb} in
|
||
\gls{pbs} or \gls{di} water.}
|
||
All statistical tests where p-values are noted are given by two-tailed t
|
||
tests.
|
||
}
|
||
\label{fig:dms_qc}
|
||
\end{figure*}
|
||
|
||
To answer this question, we first performed a \gls{doe} to understand the effect
|
||
of reaction parameters on biotin binding. The parameters included in this
|
||
\gls{doe} were temperature, microcarrier mass, and \gls{snb} input mass. These
|
||
were parameters that we specifically controlled but hypothesized might have some
|
||
sensitivity on the final biotin mass attachment rate depending on their noise
|
||
and uncertainty. In particular, temperature was `controlled' only by allowing
|
||
the microcarrier suspension to come to \gls{rt} after autoclaving. After
|
||
performing a full factorial \gls{doe} with three center points as the target
|
||
reaction conditions, we found that the final biotin binding mass is only highly
|
||
dependent on biotin input concentration (\cref{fig:dms_qc_doe}). Overall,
|
||
temperature had no effect and carrier mass had no effect at higher temperatures,
|
||
but carrier mass had a slightly positive effect when temperature was low. This
|
||
could be because lower temperature might make spontaneous decay of \gls{snb}
|
||
occur slower, which would give \gls{snb} molecule more opportunity to diffuse
|
||
into the microcarriers and react with amine groups to form attachments. It seems
|
||
that concentration only has a linear effect and doesn't interact with any of the
|
||
other variables, which is not surprisingly given the behavior observed in
|
||
(\cref{fig:biotin_coating})
|
||
|
||
We also observed that the reaction pH does not influence the amount of biotin
|
||
attached (\cref{fig:dms_qc_ph}), which indicates that while higher pH might
|
||
increase the number of deprotonated amines on the surface of the microcarrier,
|
||
it also increases the number of \ce{OH-} groups which can spontaneously
|
||
hydrolyze the \gls{snb} in solution (\cref{chem:snb_hydrolysis}).
|
||
|
||
Furthermore, we observed that washing the microcarriers after autoclaving
|
||
increases the biotin binding rate (\cref{fig:dms_qc_washes}). While we did not
|
||
explore this further, one possible explanation for this behavior is that the
|
||
microcarriers have some loose protein in the form of powder or soluble peptides
|
||
that competes for \gls{snb} binding against the surface of the microcarriers,
|
||
and when measuring the supernatent using the \gls{haba} assay, these soluble or
|
||
lightly-suspended peptides/protein fragments are also measured and therefore
|
||
inflate the readout.
|
||
|
||
Lastly, we asked what the effect on reaction pH had on spontaneous degradation
|
||
of \gls{snb} while in solution (\cref{chem:snb_hydrolysis}). If the \gls{snb}
|
||
significantly degrades within minutes of preparation, then it is important to
|
||
carefully control the timing between \gls{snb} solution preparation and addition
|
||
to the microcarriers. We found that in the presence of \gls{di} water, \gls{snb}
|
||
is extremely stable (\cref{fig:dms_snb_decay_curves}) where it decays rapidly in
|
||
the presence of \gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water
|
||
curve actually decreases slightly, possibly due to \gls{snb} absorbing to the
|
||
plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but
|
||
the lack of hydrolysis in \gls{di} water can be explained by the fact that
|
||
biotin itself is acidic, and thus the reaction is self-inhibitory in an
|
||
unbuffered and neutral pH system. Because we dissolve our \gls{snb} in \gls{di}
|
||
water prior to adding it to the microcarrier suspension (which itself is in
|
||
\gls{pbs}) this result indicated that hydrolysis is not of concern when adding
|
||
\gls{snb} within minutes.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_timing.png}
|
||
\phantomsubcaption\label{fig:dms_biotin_rxn_mass}
|
||
\phantomsubcaption\label{fig:dms_biotin_rxn_frac}
|
||
\phantomsubcaption\label{fig:dms_stp_per_time}
|
||
\phantomsubcaption\label{fig:dms_mab_per_time}
|
||
\phantomsubcaption\label{fig:dms_biotin_washed}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Reaction kinetics]
|
||
{Reaction kinetics for microcarrier functionalization.
|
||
\subcap{fig:dms_biotin_rxn_mass}{Biotin mass bound per time}
|
||
\subcap{fig:dms_biotin_rxn_frac}{Fraction of input biotin bound per time}
|
||
\subcap{fig:dms_stp_per_time}{\Gls{stp} bound per time. Each dot is an
|
||
experimental run and the line is the fitted model.}
|
||
\subcap{fig:dms_mab_per_time}{Simulated \glspl{mab} bound per time.}
|
||
\subcap{fig:dms_biotin_washed}{Biotin quantification via the \gls{haba}
|
||
assay after washing.}
|
||
}
|
||
\label{fig:dms_kinetics}
|
||
\end{figure*}
|
||
|
||
\subsection{Reaction Kinetics for Coating the DMSs}
|
||
|
||
We investigated the reaction kinetics of all three coating steps (accompanying
|
||
MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the
|
||
reaction kinetics of the biotin binding step, we reacted multiple batches of
|
||
\SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt} with \gls{snb} in
|
||
parallel and sacrificially analyzed each at varying timepoints using the
|
||
\gls{haba} assay. This was performed at two different concentrations. We
|
||
observed that for either concentration, the reaction was over in
|
||
\SIrange{20}{30}{\minute} (\cref{fig:dms_biotin_rxn_mass}). Furthermore, when
|
||
put in terms of fraction of input \gls{snb}, we observed that the curves are
|
||
almost identical (\cref{fig:dms_biotin_rxn_frac}). Given this, the reaction step
|
||
for biotin attached can be set to \SI{30}{\minute}\footnote{we actually used
|
||
\SI{60}{\minute} as outlined in methods, which shouldn't make any difference
|
||
except save for being excessive according to this result}.
|
||
|
||
Next, we quantified the amount of \gls{stp} reacted with the surface of the
|
||
biotin-coated microcarriers. Different batches of biotin-coated \glspl{dms} were
|
||
coated with \SI{40}{\ug\per\ml} \gls{stp} and sampled at intermediate timepoints
|
||
using the \gls{bca} assay to indirectly quantify the amount of attached
|
||
\gls{stp} mass. We found this reaction took approximately \SI{30}{\minute}
|
||
(\cref{fig:dms_stp_per_time}). Assuming a quasi-steady-state paradigm, we used
|
||
this experimental binding data to compute the geometric diffusivity of the
|
||
microcarriers and fit a continuous model for the \gls{stp} binding reaction. We
|
||
computed the number of `binding sites' using the maximum mass observed to bind
|
||
to the \gls{dms}, which should describe the upper-bound for reaction time
|
||
(\cref{fig:stp_coating}). Using the diffusion rate of the \gls{stp}
|
||
(\SI{6.2e-7}{\cm\squared\per\second}), we then calculated the geometric
|
||
diffusivity of the microcarriers to be 0.190 (see
|
||
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}).
|
||
|
||
Using this geometric diffusivity and the known diffusion coefficient of a
|
||
\gls{mab} protein in water, we calculated the binding of \glspl{mab} per time
|
||
onto the microcarriers (this obviously assumes that the effectively diffusivity
|
||
is independent of the protein used, which should be reasonable given that the
|
||
pores of the microcarriers are huge compared to the proteins, and we don't
|
||
expect any significant reaction between the protein and the microcarrier surface
|
||
save for the \gls{stp}-biotin binding reaction). Once again, we used the maximum
|
||
number of \glspl{mab} observed to determine the number of `binding sites' for
|
||
\glspl{mab} on the microcarriers, which should correspond to the upper-bound for
|
||
the reaction time (\cref{fig:mab_coating}). According to this model, the
|
||
\gls{mab} binding reaction should be complete within \SI{75}{\minute} under the
|
||
conditions used for our protocol (\cref{fig:dms_mab_per_time})\footnote{We
|
||
actually used \SI{60}{\minute} as describe in the method section as this model
|
||
was not updated with new parameters until recently; however, we should point
|
||
out that even at \SI{60}{\minute} the reaction appears to be
|
||
>\SI{95}{\percent} complete}.
|
||
|
||
Finally, we calculated the number of wash steps needed to remove the reagents
|
||
between each step, including the time for each wash which required the geometric
|
||
diffusivity of the microcarriers as calculated above. This is important, as
|
||
failing to wash out residual free \gls{snb} (for example) could occupy binding
|
||
sites on the \gls{stp} molecules, lowering the effective binding capacity of the
|
||
\gls{mab} downstream. Each wash was a 1:15 dilution (\SI{1}{\ml} reaction volume
|
||
in a \SI{15}{\ml} conical tube), and in the case of \gls{snb} we wished to wash
|
||
out enough biotin such that less than \SI{1}{\percent} of the binding sites in
|
||
\gls{stp} would be occupied. Given this dilution factor, a maximum of
|
||
\SI{20}{\nmol} of biotin remaining \cref{fig:biotin_coating} \SI{2.9}{\nmol}
|
||
biotin binding sites on \SI{40}{\ug} \gls{stp} (assuming 4 binding sites per
|
||
\gls{stp} protein), this turned out to be 3 washes. By similar logic, using 2
|
||
washes after the \gls{stp} binding step will ensure that the number of free
|
||
\gls{stp} binding sites is less than 20X the number of \gls{mab} molecules
|
||
added\footnote{This step may benefit from an additional wash, as the number of
|
||
washes used here was develop when \SI{40}{\ug} rather than \SI{4}{\ug}
|
||
\gls{mab} was used to coat the \gls{dms}, yielding a much wider margin.
|
||
However, it is also not clear to what extent this matters, as the \gls{mab}
|
||
have multiple biotin molecules per \gls{mab} protein, and thus one \gls{mab}
|
||
would require binding to several \gls{stp} molecules to be prevented from
|
||
binding at all.}
|
||
|
||
To determine the length of time required for each wash, we again assumed the
|
||
microcarriers to be porous spheres, this time with an initial concentration of
|
||
\gls{snb}, \gls{stp}, or \glspl{mab} equal to the final concentration of the
|
||
bulk concentration of the previous binding step, and calculated the amount of
|
||
time it would take for the concentration profile inside the microcarriers to
|
||
equilibrate to the bulk in the wash step. Using this model, we found that the
|
||
wash times for \gls{snb}, \gls{stp}, and \glspl{mab} was \SI{3}{\minute},
|
||
\SI{15}{\minute}, and \SI{17}{\minute} respectively. We verified that the
|
||
\gls{snb} was totally undetectable after washing (\cref{fig:dms_biotin_washed}).
|
||
The other two species need to be verified in a similar manner; however, we
|
||
should not that the washing time for both the \gls{stp} and \gls{mab} coating
|
||
steps were \SI{30}{\minute}, which is a significant margin of safety (albeit
|
||
one that could be optimized).
|
||
|
||
MATLAB code and output for all the wash step calculations are given in
|
||
\cref{sec:appendix_washing}.
|
||
|
||
\subsection{DMSs can efficiently expand T cells compared to beads}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/cells_on_dms.png}
|
||
\phantomsubcaption\label{fig:dms_cells_phase}
|
||
\phantomsubcaption\label{fig:dms_cells_fluor}
|
||
|
||
\endgroup
|
||
\caption[T cells growing on \glspl{dms}]
|
||
{Cells grow in tight clusters in and around functionalized \gls{dms}.
|
||
\subcap{fig:dms_cells_phase}{Phase-contrast image of T cells growing on
|
||
\glspl{dms}}
|
||
\subcap{fig:dms_cells_fluor}{Confocal images of T cells in varying z-planes
|
||
growing on \glspl{dms} on day 9. \Glspl{dms} were stained using
|
||
\gls{stppe} (red) and T cells were stained using \acd{45}-\gls{af647}.}
|
||
Images are from day 7 of culture.
|
||
}
|
||
\label{fig:dms_cells}
|
||
\end{figure*}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_expansion.png}
|
||
\phantomsubcaption\label{fig:dms_expansion_bead}
|
||
\phantomsubcaption\label{fig:dms_expansion_isotype}
|
||
|
||
\endgroup
|
||
\caption[\glspl{dms} selectively expand T cells]
|
||
{T cells are selectively expanded on \gls{dms}.
|
||
\subcap{fig:dms_expansion_bead}{T cells expanded with either \glspl{dms} or
|
||
bead for 12 days. Significance was assessed using a two-tailed
|
||
heteroschodastic T test.}
|
||
\subcap{fig:dms_expansion_isotype}{T cells grown on \glspl{dms} coated with
|
||
either activating \glspl{mab} or \gls{igg} isotype control \glspl{mab}.}
|
||
}
|
||
\label{fig:dms_expansion}
|
||
\end{figure*}
|
||
|
||
We next sought to determine how our \glspl{dms} could expand T cells compared to
|
||
state-of-the-art methods used in industry. All bead expansions were performed as
|
||
per the manufacturer’s protocol, with the exception that the starting cell
|
||
densities were matched between the beads and carriers to
|
||
~\SI{2.5e6}{\cell\per\ml}. Throughout the culture we observed that T cells in
|
||
\gls{dms} culture grew in tight clumps on the surface of the \glspl{dms} as well
|
||
as inside the pores of the \glspl{dms}
|
||
(\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore, we observed that
|
||
the \glspl{dms} conferred greater expansion compared to traditional beads, and
|
||
significantly greater expansion after \SI{12}{\day} of culture
|
||
(\cref{fig:dms_expansion_bead}). We also observed no T cell expansion using
|
||
\glspl{dms} coated with an isotype control mAb compared to \glspl{dms} coated
|
||
with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}), confirming
|
||
specificity of the expansion method. Given that \il{2} does not lead to
|
||
expansion on its own, we know that the expansion of the T cells shown here is
|
||
due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/apoptosis.png}
|
||
\phantomsubcaption\label{fig:apoptosis_annV}
|
||
\phantomsubcaption\label{fig:apoptosis_cas}
|
||
\phantomsubcaption\label{fig:apoptosis_bcl2}
|
||
|
||
\endgroup
|
||
\caption[Apoptosis Quantification for \glspl{dms}]
|
||
{\glspl{dms} produce cells with lower apoptosis marker expression on average
|
||
compared to bead.
|
||
\subcap{fig:apoptosis_annV}{Quantification of apoptosis and necrosis by
|
||
\gls{anv} and \gls{pi}.}
|
||
\subcap{fig:apoptosis_cas}{Quantification of Caspase-3/7 expression using
|
||
CellEvent dye.}
|
||
\subcap{fig:apoptosis_bcl2}{Quantification of BCL-2 expression using
|
||
\gls{elisa}. All statistical tests shown are two-tailed homoschodastic
|
||
t-tests. All cells were harvested at day 8.}
|
||
}
|
||
\label{fig:dms_apoptosis}
|
||
\end{figure*}
|
||
|
||
Given that the \gls{dms} system seemed to expand T cells more effectively, we
|
||
asked if this difference was due to a reduction in apoptosis or an increase in
|
||
proliferation rate (or both). We assessed the apoptotic state of T cells grown
|
||
using either bead or \gls{dms} harvested on day 8 using \gls{pi} and \gls{anv}.
|
||
\gls{anv} is a marker which stains phospholipid phosphatidylserine, which is
|
||
usually present only on the cytoplasmic surface of the cell membrane, but flips
|
||
to the outside when the cell becomes apoptotic. \gls{pi} stains the nucleus of
|
||
the cell, but only penetrates necrotic cells which have a perforated cell
|
||
membrane. When staining for these two markers and assessing via flow cytometry,
|
||
we observe that the \gls{dms}-expanded T cells have lower frequencies of
|
||
apoptotic and necrotic cells (\cref{fig:apoptosis_annV}). Furthermore, we
|
||
stained our cultures with CellEvent dye, which is an indicator of \gls{cas37},
|
||
which is activated in apoptotic cells. In line with the \gls{pi}/\gls{anv}
|
||
results, we observed that the \gls{dms} T cells had lower frequency of
|
||
\gls{cas37} expression, indicating less apoptosis for our method
|
||
(\cref{fig:apoptosis_cas}). Finally, we lysed our cells and stained for
|
||
\gls{bcl2}, which is also upregulated in apoptosis. In this case, some (but not
|
||
all) of the bead-expanded cultures showed higher \gls{bcl2} expression, which
|
||
could indicate more apoptosis in those groups (\cref{fig:apoptosis_bcl2}). None
|
||
of the \gls{dms} cultures showed similar heightened expression. Taken together,
|
||
these data suggest that the \gls{dms} platform at least in part achieves higher
|
||
expansion by lowering apoptosis of the cells in culture.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_inside.png}
|
||
\phantomsubcaption\label{fig:dms_inside_bf}
|
||
\phantomsubcaption\label{fig:dms_inside_regression}
|
||
|
||
\endgroup
|
||
\caption[A subset of T cells grow in interior of \glspl{dms}]
|
||
{A percentage of T cells grow in the interior of \glspl{dms}.
|
||
\subcap{fig:dms_inside_bf}{T cells stained dark with \gls{mtt} after
|
||
growing on either coated or uncoated \glspl{dms} for 15 days visualized
|
||
with brightfield microscope.}
|
||
\subcap{fig:dms_inside_regression}{Linear regression performed on T cell
|
||
percentages harvested on the interior of the \glspl{dms} vs the initial
|
||
starting cell density.}
|
||
}
|
||
\label{fig:dms_inside}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Regression for fraction of cells in \gls{dms} at day 14}
|
||
\label{tab:inside_regression}
|
||
\input{../tables/inside_fraction_regression.tex}
|
||
\end{table}
|
||
|
||
% RESULT state the CI of what are inside the carriers
|
||
We also asked how many cells were inside the \glspl{dms} vs. free-floating in
|
||
suspension and/or loosely attached to the surface. We qualitatively verified the
|
||
presence of cells inside the \glspl{dms} using a \gls{mtt} stain to opaquely
|
||
mark cells and enable visualization on a brightfield microscope
|
||
(\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different densities and
|
||
expanding for \SI{14}{\day}, we filtered the \glspl{dms} out of the cell
|
||
suspension and digested them using dispase to free any cells attached on the
|
||
inner surface. We observed that approximately \SI{15}{\percent} of the total
|
||
cells after \SI{14}{\day} were on the interior surface of the \glspl{dms}
|
||
(\cref{fig:dms_inside_regression,tab:inside_regression}). Performing linear
|
||
regression on this data revealed that the percentage of T cells inside the
|
||
\glspl{dms} does not depend on the initial starting cell density (at least when
|
||
harvested after \SI{14}{\day}) (\cref{tab:inside_regression}).
|
||
|
||
\subsection{DMSs lead to greater expansion and memory and CD4+ phenotypes}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_vs_bead_expansion.png}
|
||
\phantomsubcaption\label{fig:dms_exp_fold_change}
|
||
\phantomsubcaption\label{fig:dms_exp_mem}
|
||
\phantomsubcaption\label{fig:dms_exp_cd4}
|
||
\phantomsubcaption\label{fig:dms_exp_mem4}
|
||
\phantomsubcaption\label{fig:dms_exp_mem8}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} vs bead expansion]
|
||
{\gls{dms} lead to superior expansion of T cells compared to beads across
|
||
multiple donors.
|
||
\subcap{fig:dms_exp_fold_change}{Longitudinal fold change of T cells grown
|
||
using either \glspl{dms} or beads. Significance was evaulated using t
|
||
tests at each timepoint}
|
||
Fold change of subpopulations expanded using either \gls{dms} or beads at
|
||
day 14, including
|
||
\subcap{fig:dms_exp_mem}{\ptmem{} cells},
|
||
\subcap{fig:dms_exp_cd4}{\pth{} cells},
|
||
\subcap{fig:dms_exp_mem4}{\ptmemh{} cells}, and
|
||
\subcap{fig:dms_exp_mem8}{\ptmemk{} cells}. \sigkey{}
|
||
}
|
||
\label{fig:dms_exp}
|
||
\end{figure*}
|
||
|
||
After observing differences in expansion, we further hypothesized that the
|
||
\gls{dms} cultures could lead to a different T cell phenotype. In particular, we
|
||
were interested in the formation of \glspl{tn}, \gls{tscm}, and \glspl{tcm} as
|
||
these represent a subset with higher capacity to replicate and therefore
|
||
improved clinical prognosis\cite{Gattinoni2011, Wang2018}. We measured the
|
||
frequency of these subtypes by staining for CCR7 and CD62L. Using three donor
|
||
lots, we noted again \glspl{dms} produced more T cells over a \SI{14}{\day}
|
||
expansion than beads, with significant differences in number appearing as early
|
||
after \SI{5}{\day} (\cref{fig:dms_exp_fold_change}). Furthermore, we noted that
|
||
\glspl{dms} produced more memory/naïve cells after \SI{14}{\day} when compared
|
||
to beads for all donors (\cref{fig:dms_exp_mem,fig:dms_exp_cd4}) showing that
|
||
the \gls{dms} platform is able to selectively expand potent, early
|
||
differentiation T cells.
|
||
|
||
Of additional interest was the preservation of the CD4+ compartment. In healthy
|
||
donor samples (such as those used here), the typical CD4:CD8 ratio is 2:1. We
|
||
noted that \glspl{dms} produced more CD4+ T cells than bead cultures as well as
|
||
naïve/memory, showing that the \gls{dms} platform can selectively expand CD4 T
|
||
cells to a greater degree than beads \cref{fig:dms_exp_cd4}. The trends held
|
||
true when observing the CD4+ and CD8+ fractions of the naïve/memory subset
|
||
(\ptmem{}) (\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_phenotypes.png}
|
||
\phantomsubcaption\label{fig:dms_phenotype_mem}
|
||
\phantomsubcaption\label{fig:dms_phenotype_cd4}
|
||
|
||
\endgroup
|
||
\caption[Representative flow plots of \ptmem{} and \pth{} T cells]
|
||
{Representative flow plots of \ptmem{} and \pth{} T cells at day 14 of
|
||
expansion using either beads or \glspl{dms}. For three representative donor
|
||
samples, phenotypes are shown for \subcap{fig:dms_phenotype_mem}{\ptmem{}}
|
||
and \subcap{fig:dms_phenotype_cd4}{\pth}. Each population was also gated on
|
||
\cdp{3} T cells.
|
||
}
|
||
\label{fig:dms_phenotype}
|
||
\end{figure*}
|
||
|
||
We also observed that, at least with the donors and conditions tested in these
|
||
experiments\footnote{these results were not always consistent, see the
|
||
metaanalysis at the end of this aim for an in-depth quantification of this
|
||
observation} that the fraction of \ptmem{} and \pth{} T cells was higher in
|
||
the \gls{dms} groups compared to the bead groups
|
||
(\cref{fig:dms_phenotype})\footnote{these where not the same donors as used for
|
||
\cref{fig:dms_exp}}. This result was seen for multiple donors. We should note
|
||
that in the case of \pthp{}, the donors we used had an initial \pthp{} that was
|
||
much higher (healthy donors generally have a CD4:CD8 ratio of 2:1), so the
|
||
proper interpretation of this is that the \pthp{} decreases less over the
|
||
culture period with the \gls{dms} platform as opposed to the beads (or
|
||
alternatively, the \gls{dms} has less preferential expansion for CD8 T cells).
|
||
We cannot say the same about the \ptmemp{} since we did not have the initial
|
||
data for this phenotype; however (although it should be the vast majority of
|
||
cells given that cryopreserved T cells from a healthy donor should generally be
|
||
composed of circulated memory and naive T cells). Taken together, these data
|
||
indicate the \gls{dms} platform has the capacity to expand higher numbers and
|
||
percentages of highly potent \ptmem{} and \pth{} T cells compared to
|
||
state-of-the-art bead technology.
|
||
|
||
\subsection*{DMSs can be used to produce functional CAR T cells}
|
||
|
||
After optimizing for naïve/memory and CD4 yield, we sought to determine if the
|
||
\glspl{dms} were compatible with lentiviral transduction protocols used to
|
||
generate \gls{car} T cells\cite{Tumaini2013, Lamers2014}. We added a
|
||
\SI{24}{\hour} transduction step on day 1 of the \SI{14}{\day} expansion to
|
||
insert an anti-CD19 \gls{car}\cite{Milone2009} and subsequently measured the
|
||
surface expression of the \gls{car} on day 14
|
||
(\cref{fig:car_cd19_flow,fig:car_cd19_endpoint}). We noted
|
||
that there was robust \gls{car} expression in over \SI{25}{\percent} of expanded
|
||
T cells, and there was no observable difference in \gls{car} expression between
|
||
beads and \glspl{dms}.
|
||
|
||
We also verified the functionality of expanded \gls{car} T cells using a
|
||
degranulation assay\cite{Zheng2012}. Briefly, T cells were cocultured with
|
||
target cells (either wild-type K562 or CD19-expressing K562 cells) for
|
||
\SI{4}{\hour}, after which the culture was analyzed via flow cytometry for the
|
||
appearance of CD107a on CD8+ T cells. CD107a is found on the inner-surface of
|
||
cytotoxic granules and will emerge on the surface after cytotoxic T cells are
|
||
activated and degranulate. Indeed, we observed degranulation in T cells expanded
|
||
with both beads and \glspl{dms}, although not to an observably different degree
|
||
(\cref{fig:car_degran_flow,fig:car_degran_endpoint}).
|
||
Taken together, these results indicated that the \glspl{dms} provide similar
|
||
transduction efficiency compared to beads.
|
||
|
||
We also verified that expanded T cells were migratory using a chemotaxis assay
|
||
for CCL21; since \glspl{dms} produced a larger percentage of naïve and memory T
|
||
cells (which have CCR7, the receptor for CCL21) we would expect higher migration
|
||
in \gls{dms}-expanded cells vs.\ their bead counterparts. Indeed, we noted a
|
||
significantly higher migration percentage for T cells grown using \glspl{dms}
|
||
versus beads (\cref{fig:car_degran_migration}). Interestingly, there also
|
||
appeared to be a decrease in CCL21 migration between transduced and untransduced
|
||
T cells expanded using beads, but this interaction effect was only weakly
|
||
significant (p = 0.068). No such effect was seen for \gls{dms}-expanded T cells,
|
||
showing that migration was likely independent of \gls{car} transduction.
|
||
|
||
% FIGURE break this up to give the text more flexibility
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_cd19.png}
|
||
\phantomsubcaption\label{fig:car_cd19_flow}
|
||
\phantomsubcaption\label{fig:car_cd19_endpoint}
|
||
|
||
\endgroup
|
||
\caption[\glspl{dms} lead to efficient CD19 transduction]
|
||
{\glspl{dms} lead to efficient CD19 transduction.
|
||
\subcap{fig:car_cd19_flow}{Representative flow cytometry plot for
|
||
transduced or untransduced T cells stained with \gls{ptnl}.}
|
||
\subcap{fig:car_cd19_endpoint}{Endpoint plots with \gls{anova} for
|
||
transduced or untransduced T cells stained with \gls{ptnl}.}
|
||
All data is from T cells expanded for \SI{14}{\day}.
|
||
}
|
||
\label{fig:car_production}
|
||
\end{figure*}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_degranulation.png}
|
||
\phantomsubcaption\label{fig:car_degran_flow}
|
||
\phantomsubcaption\label{fig:car_degran_endpoint}
|
||
\phantomsubcaption\label{fig:car_degran_migration}
|
||
|
||
\endgroup
|
||
\caption[\glspl{dms} produce functional \gls{car} T cells]
|
||
{\glspl{dms} produce functional \gls{car} T cells.
|
||
\subcap{fig:car_degran_flow}{Representative flow plot for
|
||
degenerating T cells.}
|
||
\subcap{fig:car_degran_endpoint}{Endpoint plots for transduced or
|
||
untransduced T cells stained with \cd{107a} for the degranulation assay.}
|
||
\subcap{fig:car_degran_migration}{Endpoint plot for transmigration assay
|
||
with \gls{anova}.} All data is from T cells expanded for \SI{14}{\day}.
|
||
}
|
||
\label{fig:car_production}
|
||
\end{figure*}
|
||
|
||
In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms}
|
||
platform can be used to expand \gls{car} T cells against \gls{bcma}. Analogously
|
||
to the case with CD19, \gls{dms} and bead produced similar fractions of \ptcar{}
|
||
cells (albeit in this case at day 7 and with an undefined \gls{moi})
|
||
(\cref{fig:car_bcma_percent}). Also consistent with CD19 and non-\gls{car} data,
|
||
we also found that the number of \ptcar{} T cells was greater for \gls{dms} than
|
||
for bead (\cref{fig:car_bcma_total}).
|
||
|
||
% FIGURE the right half if bigger than the left half
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_bcma.png}
|
||
\phantomsubcaption\label{fig:car_bcma_percent}
|
||
\phantomsubcaption\label{fig:car_bcma_total}
|
||
|
||
\endgroup
|
||
\caption[BMCA Transduction Results]
|
||
{\glspl{dms} produce larger numbers of \gls{bcma} \gls{car} T cells compared
|
||
to beads.
|
||
\subcap{fig:car_bcma_percent}{\ptcarp{} at day 14.}
|
||
\subcap{fig:car_bcma_total}{Total number of \ptcarp{} cells at day 14.}
|
||
}
|
||
\label{fig:car_bcma}
|
||
\end{figure*}
|
||
|
||
\subsection{DMSs efficiently expand T cells in Grex bioreactors}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/grex_results.png}
|
||
\phantomsubcaption\label{fig:grex_results_fc}
|
||
\phantomsubcaption\label{fig:grex_results_viability}
|
||
\phantomsubcaption\label{fig:grex_mem}
|
||
\phantomsubcaption\label{fig:grex_cd4}
|
||
|
||
\endgroup
|
||
\caption[Grex bioreactor results]
|
||
{\glspl{dms} expand T cells robustly in Grex bioreactors.
|
||
\subcap{fig:grex_results_fc}{Fold change of T cells over time.}
|
||
\subcap{fig:grex_results_viability}{Viability of T cells over time.}
|
||
\subcap{fig:grex_mem}{\ptmemp{}} and
|
||
\subcap{fig:grex_cd4}{\pthp{}} of T cells after \SI{14}{\day}
|
||
of expansion. Significance tests were performed using the Wilcoxon
|
||
non-parametric test.
|
||
}
|
||
\label{fig:grex_results}
|
||
\end{figure*}
|
||
|
||
We also asked if the \gls{dms} platform could expand T cells in a static
|
||
bioreactor such a Grex. We incubated T cells in a Grex analogously to that for
|
||
plates and found that T cells in Grex bioreactors expanded as efficiently as
|
||
bead over \SI{14}{\day} and had similar viability
|
||
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent
|
||
with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to
|
||
beads and higher \ptmemp{} compared to beads (\cref{fig:grex_mem,fig:grex_cd4}).
|
||
Overall the \ptmemp{} was much lower than that seen from cultures grown in
|
||
tissue-treated plates (\cref{fig:dms_phenotype_mem}).
|
||
|
||
These discrepancies might be explained in light of our other data as follows.
|
||
The Grex bioreactor has higher media capacity relative to its surface area, and
|
||
we did not move the T cells to a larger bioreactor as they grew in contrast with
|
||
our plate cultures. This means that the cells had higher growth area
|
||
constraints, which may have nullified any advantage to the expansion that we
|
||
seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
|
||
area could mean higher signaling and higher differentiation rate to
|
||
\glspl{teff}, which was why the \ptmemp{} was so low compared to other data
|
||
(\cref{fig:dms_phenotype_mem}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/grex_luminex.png}
|
||
|
||
\endgroup
|
||
\caption[Grex luminex results]
|
||
{\gls{dms} lead to higher cytokine production in Grex bioreactors.}
|
||
\label{fig:grex_luminex}
|
||
\end{figure*}
|
||
|
||
We also quantified the cytokines released during the Grex expansion using
|
||
Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
|
||
released higher concentrations of cytokines compared to beads
|
||
(\cref{fig:grex_luminex}). This included higher concentrations of
|
||
pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This
|
||
demonstrates that \gls{dms} could lead to more robust activation and fitness.
|
||
|
||
Taken together, these data suggest that \gls{dms} also lead to robust expansion
|
||
in Grex bioreactors, although more optimization may be necessary to maximize the
|
||
media feed rate and growth area to get comparable results to those seen in
|
||
tissue-culture plates.
|
||
|
||
\subsection{DMSs do not leave antibodies attached to cell product}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/nonstick.png}
|
||
|
||
\endgroup
|
||
\caption[\glspl{mab} do not detach from \glspl{dms}]
|
||
{\glspl{mab} do not detach from microcarriers onto T cells in a detectable
|
||
manner. Plots are representative manufacturing runs harvest after
|
||
\SI{14}{\day} of expansion and stained with \anti{\gls{igg}}.
|
||
}
|
||
\label{fig:nonstick}
|
||
\end{figure*}
|
||
|
||
% DISCUSSION alude to this figure
|
||
We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
|
||
and could be detected on the final T cell product. This test is important for
|
||
clinical translation as any residual \glspl{mab} on T cells injected into the
|
||
patient could elicit an undesirable \antim{\gls{igg}} immune response. We did
|
||
not detect the presence of either \ahcd{3} or \ahcd{28} \glspl{mab} (both of
|
||
which were \gls{igg}) on the final T cell product after \SI{14}{\day} of
|
||
expansion (\cref{fig:nonstick}).
|
||
|
||
\subsection{DMSs consistently outperform bead-based expansion compared to
|
||
beads in a variety of conditions}
|
||
|
||
In order to establish the robustness of our method, we combined all experiments
|
||
performed in our lab using beads or \glspl{dms} and combined them into one
|
||
dataset. Since each experiment was performed using slightly different process
|
||
conditions, we hypothesized that performing causal inference on such a dataset
|
||
would not only indicate if the \glspl{dms} indeed led to better results under a
|
||
variety of conditions, but would also indicate other process parameters that
|
||
influence the outcome. The dataset was curated by compiling all experiments and
|
||
filtering those that ended at day 14 and including flow cytometry results for
|
||
the \ptmem{} and \pth{} populations. We further filtered our data to only
|
||
include those experiments where the surface density of the CD3 and CD28
|
||
\gls{mab} were held constant (since some of our experiments varied these on the
|
||
\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16
|
||
experiments between early 2017 and early 2021.
|
||
|
||
Since the aim of the analysis was to perform causal inference, we determined 6
|
||
possible treatment variables which we controlled when designing the experiments
|
||
included in this dataset. Obviously the principle treatment parameter was
|
||
‘activation method’ which represented the effect of activating T cells with
|
||
either beads or our \gls{dms} method. We also included ‘bioreactor’ which was a
|
||
categorical for growing the T cells in a Grex bioreactor vs polystyrene plates,
|
||
‘feed criteria’ which represented the criteria used to feed the cells (using
|
||
media color or a glucose meter), ‘IL2 Feed Conc’ as a continuous parameter for
|
||
the concentration of IL2 added each feed cycle, and ‘CD19-CAR Transduced’
|
||
representing if the cells were lentivirally transduced or not. Unfortunately,
|
||
many of these parameters correlated with each other highly despite the large
|
||
size of our dataset, so the only two parameters for which causal relationships
|
||
could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
|
||
note that these were not the only set of theoretical treatment parameters that
|
||
we could have used. For example, media feed rate is an important process
|
||
parameter, but in our experiments this was dependent on the feeding criteria and
|
||
the growth rate of the cells, which in turn is determined by activation method.
|
||
Therefore, ‘media feed rate’ (or similar) is a ‘post-treatment parameter’ and
|
||
would have violated the backdoor criteria and severely biased our estimates of
|
||
the treatment parameters themselves.
|
||
|
||
In addition to these treatment parameters, we also included covariates to
|
||
improve the precision of our model. Among these were donor parameters including
|
||
age, \gls{bmi}, demographic, and gender, as well as the initial viability and
|
||
CD4/CD8 ratio of the cryopreserved cell lots used in the experiments
|
||
(\cref{tab:meta_donors}). We also included the age of key reagents such as IL2,
|
||
media, and the anti-aggregate media used to thaw the T cells prior to
|
||
activation. Each experiment was performed by one of three operators, so this was
|
||
included as a three-level categorical parameter. Lastly, some of our experiments
|
||
were sampled longitudinally, so we included a boolean categorical to represented
|
||
this modification as removing conditioned media as the cell are expanding could
|
||
disrupt signaling pathways.
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables only}
|
||
\label{tab:ci_treat}
|
||
\input{../tables/causal_inference_treat.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables and control variables}
|
||
\label{tab:ci_controlled}
|
||
\input{../tables/causal_inference_control.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables and control variables (single
|
||
donor)}
|
||
\label{tab:ci_single}
|
||
\input{../tables/causal_inference_single.tex}
|
||
\end{table}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/metaanalysis_effects.png}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_exp}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_mem}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_cd4}
|
||
|
||
\endgroup
|
||
\caption[Meta-analysis effect sizes]
|
||
{\glspl{dms} exhibit superior performance compared to beads controlling for
|
||
many experimental and process conditions. Effect sizes for
|
||
\subcap{fig:metaanalysis_fx_exp}{fold change},
|
||
\subcap{fig:metaanalysis_fx_mem}{\ptmemp{}}, and
|
||
\subcap{fig:metaanalysis_fx_cd4}{\dpthp{}}. The dotted line represents
|
||
the mean of the bead population. The red and blue dots represent the effect
|
||
size of using \gls{dms} instead of beads only considering treatment
|
||
variables (\cref{tab:ci_treat}) or treatment and control variables
|
||
(\cref{tab:ci_controlled}) respectively.
|
||
}
|
||
\label{fig:metaanalysis_fx}
|
||
\end{figure*}
|
||
|
||
We first asked what the effect of each of our treatment parameters was on the
|
||
responses of interest, which were fold change of the cells, the \ptmemp{}, and
|
||
\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
|
||
performed a linear regression using activation method and bioreactor as
|
||
predictors (the only treatments that were shown to be balanced)
|
||
(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the
|
||
exponential nature of T cell growth. We observe that the treatments are
|
||
significant in all cases except for the \dpthp{}; however, we also observe that
|
||
relatively little of the variability is explained by these simple models ($R^2$
|
||
between 0.17 and 0.44).
|
||
|
||
We then included all covariates and unbalanced treatment parameters and
|
||
performed linear regression again
|
||
(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
|
||
controlling for additional noise, the models explained much more variability
|
||
($R^2$ between 0.76 and 0.87). Furthermore, the coefficient for activation
|
||
method in the case of fold change changed very little but still remained quite
|
||
high (note the log-transformation) with \SI{131}{\percent} increase in fold
|
||
change compared to beads. Furthermore, the coefficient for \ptmemp{} dropped to
|
||
a \SI{3.5}{\percent} increase and almost became non-significant at $\upalpha$ =
|
||
0.05, and the \dpthp{} response increased to a \SI{7.4}{\percent} increase and
|
||
became highly significant. Looking at the bioreactor treatment, we see that
|
||
using the bioreactor in the case of fold change and \ptmemp{} is actually
|
||
harmful to the response, while at the same time it seems to increase the
|
||
\dpthp{} response. We should note that this parameter merely represents whether
|
||
or not the choice was made experimentally to use a bioreactor or not; it does
|
||
not indicate why the bioreactor helped or hurt a certain response. For example,
|
||
using a Grex entails changing the cell surface and feeding strategy for the T
|
||
cells, and any one of these ‘mediating variables’ might actually be the cause of
|
||
the responses.
|
||
|
||
Finally, we stratified on the most common donor (vendor ID 338 from Astarte
|
||
Biotech) as this was responsible for almost half the data (80 runs) and repeated
|
||
the regression (\Cref{tab:ci_single}). Note that in this case, we did not
|
||
include any of the donor-dependent variables as well as any of the variables
|
||
that were the same value for these 80 runs. In this analysis, fold change and
|
||
\dpthp{} remained high (but slightly lowered from the full analysis) and
|
||
\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may
|
||
have been due to other donors besides this one, and that high fold change and
|
||
\dpthp{} may have been driven by this single donor but more extreme in other
|
||
donors.
|
||
|
||
\section{Discussion}
|
||
|
||
% DISCUSSION this is fluffy
|
||
We have developed a T cell expansion shows superior expansion with higher number
|
||
of naïve/memory and CD4+ T cells compared to state-of-the-art microbead
|
||
technology (\cref{fig:dms_exp}). Other groups have used biomaterials approaches
|
||
to mimic the \invivo{} microenvironment\cite{Cheung2018, Rio2018, Delalat2017,
|
||
Lambert2017, Matic2013}; however, to our knowledge this is the first system
|
||
that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
|
||
potentially bioreactor-compatible manufacturing process.
|
||
|
||
Memory and naïve T cells have been shown to be important clinically. Compared to
|
||
\glspl{teff}, they have a higher proliferative capacity and are able to engraft
|
||
for months; thus they are able to provide long-term immunity with smaller
|
||
doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
|
||
led to greater survival both in mouse tumor models and human
|
||
patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
|
||
response rates have been positively correlated with T cell expansion, implying
|
||
that highly-proliferative naïve and memory T cells are a significant
|
||
contributor\cite{Xu2014, Besser2010}. Circulating memory T cells have also been
|
||
found in complete responders who received CAR T cell therapy\cite{Kalos2011}.
|
||
|
||
Similarly, CD4 T cells have been shown to play an important role in CAR T cell
|
||
immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4
|
||
and CD8 T cells confer greater tumor cytotoxicity than only CD8 T
|
||
cells\cite{Wang2018, Sommermeyer2015}. There are several possible reasons for
|
||
these observations. First, CD4 T cells secrete proinflammatory cytokines upon
|
||
stimulation which may have a synergistic effect on CD8 T cells. Second, CD4 T
|
||
cells may be less prone to exhaustion and may more readily adopt a memory
|
||
phenotype compared to CD8 T cells\cite{Wang2018}. Third, CD8 T cells may be more
|
||
susceptible than CD4 T cells to dual stimulation via the \gls{car} and
|
||
endogenous \gls{tcr} , which could lead to overstimulation, exhaustion, and
|
||
apoptosis\cite{Yang2017}. Despite evidence for the importance of CD4 T cells,
|
||
more work is required to determine the precise ratios of CD4 and CD8 T cell
|
||
subsets to be included in CAR T cell therapy given a disease state.
|
||
|
||
% DISCUSSION this mentions the DOE which is in the next aim
|
||
When analyzing all our experiments comprehensively using causal inference, we
|
||
found that all three of our responses were significantly increased when
|
||
controlling for covariates (\cref{fig:metaanalysis_fx,tab:ci_controlled}). By
|
||
extension, this implies that not only will \glspl{dms} lead to higher fold
|
||
change overall, but also much higher fold change in absolute numbers of memory
|
||
and CD4+ T cells. Furthermore, we found that using a Grex bioreactor is
|
||
detrimental to fold change and memory percent while helping CD4+. Since there
|
||
are multiple consequences to using a Grex compared to tissue-treated plates, we
|
||
can only speculate as to why this might be the case. Firstly, when using a Grex
|
||
we did not expand the surface area on which the cells were growing in a
|
||
comparable way to that of polystyrene plates. One possible explanation is that
|
||
the T cells spent longer times in highly activating conditions (since the beads
|
||
and DMSs would have been at higher per-area concentrations in the Grex vs
|
||
polystyrene plates) which has been shown to skew toward \gls{teff}
|
||
populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent
|
||
more time at high surface densities could simply mean that the T cells didn’t
|
||
expands as much due to spacial constraints. This would all be despite the fact
|
||
that Grex bioreactors are designed to lead to better T cell expansion due to
|
||
their gas-permeable membranes and higher media-loading capacities. If anything,
|
||
our data suggests we were using the bioreactor sub-optimally, and the
|
||
hypothesized causes for why our T cells did not expand could be verified with
|
||
additional experiments varying the starting cell density and/or using larger
|
||
bioreactors.
|
||
|
||
A key question in the space of cell manufacturing is that of donor variability.
|
||
To state this precisely, this is a second order interaction effect that
|
||
represents the change in effect of treatment (eg bead vs \gls{dms}) given the
|
||
donor. While our meta-analysis was relatively large compared to many published
|
||
experiments usually seen for technologies at this developmental stage, we have a
|
||
limited ability in answering this question. We can control for donor as a
|
||
covariate, and indeed our models show that many of the donor characteristics are
|
||
strongly associated with each response on average, but these are first order
|
||
effects and represent the association of age, gender, demographic, etc given
|
||
everything else in the model is held constant. Second order interactions require
|
||
that our treatments be relatively balanced and random across each donor, which
|
||
is a dubious assumption for our dataset. However, this can easily be solved by
|
||
performing more experiments with these restrictions in mind, which will be a
|
||
subject of future work.
|
||
|
||
Furthermore, this dataset offers an interesting insight toward novel hypothesis
|
||
that might be further investigated. One limitation of our dataset is that we
|
||
were unable to investigate the effects of time using a method such as
|
||
autoregression, and instead relied on aggregate measures such as the total
|
||
amount of a reagent added over the course of the expansion. Further studies
|
||
should be performed to investigate the temporal relationship between phenotype,
|
||
cytokine concentrations, feed rates, and other measurements which may perturb
|
||
cell cultures, as this will be the foundation of modern process control
|
||
necessary to have a fully-automated manufacturing system.
|
||
|
||
% It is important to note that all T cell cultures in this study were performed up
|
||
% to 14 days. Others have demonstrated that potent memory T cells may be obtained
|
||
% simply by culturing T cells as little as 5 days using traditional
|
||
% beads\cite{Ghassemi2018}. It is unknown if the naïve/memory phenotype of our DMS
|
||
% system could be further improved by reducing the culture time, but we can
|
||
% hypothesize that similar results would be observed given the lower number of
|
||
% doublings in a 5 day culture. We should also note that we investigated one
|
||
% subtype (\ptmem{}) in this study. Future work will focus on other memory
|
||
% subtypes such as tissue resident memory and stem memory T cells, as well as the
|
||
% impact of using the DMS system on the generation of these subtypes.
|
||
|
||
% DISCUSSION this sounds sketchy
|
||
% Another advantage is that the DMS system appears to induce a faster growth rate
|
||
% of T cells given the same IL2 concentration compared to beads (Supplemental
|
||
% Figure 8) along with retaining naïve and memory phenotype. This has benefits in
|
||
% multiple contexts. Firstly, some patients have small starting T cell populations
|
||
% (such as infants or those who are severely lymphodepleted), and thus require
|
||
% more population doublings to reach a usable dose. Our data suggests the time to
|
||
% reach this dose would be reduced, easing scheduling a reducing cost. Secondly,
|
||
% the allogeneic T cell model would greatly benefit from a system that could
|
||
% create large numbers of T cells with naïve and memory phenotype. In contrast to
|
||
% the autologous model which is currently used for Kymriah and Yescarta,
|
||
% allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
|
||
% across many doses for multiple patients\cite{Harrison2019}. Since it is
|
||
% economically advantageous to grow as many T cells as possible in one batch in
|
||
% the allogeneic model (reduced start up and harvesting costs, fewer required cell
|
||
% donations), the DMSs offer an advantage over current technology.
|
||
|
||
The \gls{dms} system could be used as a drop in replacement for beads in many of
|
||
current allogeneic therapies. Indeed, given its higher potential for expansion
|
||
(\cref{fig:dms_exp,tab:ci_controlled}), it may work in cases where the beads
|
||
fail (although this would need to be tested by gathering data with many
|
||
unhealthy donors). However, in the autologous setting patients only need a fixed
|
||
dose, and thus any expansion beyond the indicated dose would be wasted. Given
|
||
this, it will be interesting to apply this technology in an allogeneic paradigm
|
||
where this increased expansion potential would be well utilized.
|
||
|
||
Finally, we should note that while we demonstrated a method providing superior
|
||
performance compared to bead-based expansion, the cell manufacturing field would
|
||
tremendously benefit from simply having an alternative to state-of-the-art bead
|
||
based expansion. The patents for bead-based expansion are owned by few companies
|
||
and licensed accordingly; having an alternative would provide more competition
|
||
in the market, reducing costs and improving access for academic researchers and
|
||
manufacturing companies.
|
||
|
||
\chapter{AIM 2A}\label{aim2a}
|
||
|
||
\section{Introduction}
|
||
|
||
The purpose of this sub-aim was to develop computational methods to identify
|
||
novel \glspl{cqa} and \glspl{cpp} that could be used for release criteria,
|
||
process control, and process optimization for the \gls{dms} platform. We
|
||
hypothesized that T cells grown using the \gls{dms} system would produce
|
||
detectable biological signatures in the media supernatent which corresponded to
|
||
clinically relevent responses such as fold expansion or phenotype. We tested
|
||
this hypothesis by activating T cells under a variety of conditions using a
|
||
\gls{doe}, sampling the media at intermediate timepoints, and creating models to
|
||
predict the outcome of the cultures. We should stress that the specific
|
||
\glspl{cpp} and \glspl{cqa} determined by this aim are not necessarily
|
||
universal, as this was not performed with equipment that would normally be used
|
||
at scale. However, the process outlined here is one that can easily be adaptable
|
||
to any system, and the specific findings themselves offer interesting insights
|
||
that warrant further study\footnote{adapted from \modelpaper{}}.
|
||
|
||
\section{Methods}
|
||
|
||
\subsection{Study Design}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/modeling_overview.png}
|
||
\phantomsubcaption\label{fig:mod_overview_flow}
|
||
\phantomsubcaption\label{fig:mod_overview_doe}
|
||
|
||
\endgroup
|
||
\caption[Modeling Overview]
|
||
{Overview of modeling experiments.
|
||
\subcap{fig:mod_overview_flow}{Relationship
|
||
between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
|
||
be used to determine optimal process input conditions, and longitudinal
|
||
multiomics data will be used to fit predictive models. Together, these
|
||
will reveal predictive species that may be used for \glspl{cqa} and
|
||
\glspl{cpp}.}
|
||
\subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
|
||
initial \gls{doe} is given by the blue points and the augmented \gls{doe}
|
||
is given by the blue points.}
|
||
}
|
||
\label{fig:mod_overview}
|
||
\end{figure*}
|
||
|
||
The overall workflow of this aim is shown in \cref{fig:mod_overview_flow}.
|
||
Experimental conditions within the design space were explored using a \gls{doe},
|
||
and longitudinal samples were collected for each condition as the cultures
|
||
progressed. Data from inputs and/or longitudinal samples were used to predict
|
||
the endpoint response. The fusion of cytokine and \gls{nmr} profiles from media
|
||
to model these responses included 30 cytokines from a custom Thermo Fisher
|
||
ProcartaPlex Luminex kit and 20 \gls{nmr} features. These 20 spectral features
|
||
from \gls{nmr} media analysis were selected out of approximately 250 peaks
|
||
through the implementation of a variance-based feature selection approach and
|
||
some manual inspection steps.
|
||
|
||
The first \gls{doe} resulted in a randomized 18-run I-optimal custom design
|
||
where each \gls{dms} parameter was evaluated at three levels: \pilII{} (10, 20,
|
||
and 30 U/uL), \pdms{} (500, 1500, 2500 \si{\dms\per\ul}), and \pmab{} (60, 80,
|
||
100 \si{\percent}). These 18 runs consisted of 14 unique parameter combinations
|
||
where 4 of them were replicated twice to assess prediction error. To further
|
||
optimize the initial region explored, an \gls{adoe} was designed with 10 unique
|
||
parameter combinations, two of these replicated twice for a total of 12
|
||
additional samples (\cref{fig:mod_overview_doe}). Process parameters for the
|
||
\gls{adoe} were evaluated at multiple levels: \pilII{} (30, 35, and 40
|
||
\si{\IU\per\ml}), \pdms{} (500, 1000, 1500, 2000, 2500, 3000, 3500
|
||
\si{\dms\per\ml}), and \pmab{} (\SI{100}{\percent}) (\cref{fig:mod_overview}).
|
||
|
||
\subsection{DMS fabrication}
|
||
|
||
\glspl{dms} were fabricated as described in \cref{sec:dms_fab} with the
|
||
following modifications in order to obtain a variable functional \gls{mab}
|
||
surface density. During the \gls{mab} coating step, the \acd{3}/\acd{28}
|
||
\gls{mab} mixture was further combined with a biotinylated isotype control to
|
||
reduce the overall fraction of targeted \glspl{mab} (for example the
|
||
\SI{60}{\percent} \gls{mab} surface density corresponded to 3 mass parts
|
||
\acd{3}, 3 mass parts \acd{28}, and 4 mass parts isotype control).
|
||
|
||
\subsection{T Cell Culture}
|
||
|
||
T cell culture was performed as described in \cref{sec:tcellculture} with the
|
||
following modifications. At days 4, 6, 8, and 11, \SI{100}{\ul} media were
|
||
collected for the Luminex assay and \gls{nmr} analysis. The volume of removed
|
||
media was equivalently replaced during the media feeding step, which took place
|
||
immediately after sample collection. Additionally, the same media feeding
|
||
schedule was followed for the \gls{doe} and \gls{adoe} to improve consistency,
|
||
and the same donor lot was used for both experiments. All cell counts were
|
||
performed using \gls{aopi}.
|
||
|
||
\subsection{Flow Cytometry}
|
||
|
||
Flow cytometry was performed analogously to \cref{sec:flow_cytometry}.
|
||
|
||
\subsection{Cytokine Quantification}
|
||
|
||
Cytokines were quantified via Luminex as described in
|
||
\cref{sec:luminex_analysis}.
|
||
|
||
\subsection{NMR Metabolomics}
|
||
|
||
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
|
||
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris\footnote{all
|
||
\gls{nmr} analysis was done by our collaborators Max Colonna and Art Edison at
|
||
the University of Georgia; methods included here for reference}. \SI{5}{\ul} of
|
||
100/3 \si{\mM} DSS-D6 in deuterium oxide (Cambridge Isotope Laboratories) were
|
||
added to \SI{1.7}{\mm} \gls{nmr} tubes (Bruker BioSpin), followed by
|
||
\SI{45}{\ul} of media from each sample that was added and mixed, for a final
|
||
volume of \SI{50}{\ul} in each tube. Samples were prepared on ice and in
|
||
predetermined, randomized order. The remaining volume from each sample in the
|
||
rack (approx. \SI{4}{\ul}) was combined to create an internal pool. This
|
||
material was used for internal controls within each rack as well as metabolite
|
||
annotation.
|
||
|
||
\gls{nmr} spectra were collected on a Bruker Avance III HD spectrometer at
|
||
\SI{600}{\MHz} using a \SI{5}{\mm} TXI cryogenic probe and TopSpin software
|
||
(Bruker BioSpin). One-dimensional spectra were collected on all samples using
|
||
the noesypr1d pulse sequence under automation using ICON NMR software.
|
||
Two-dimensional \gls{hsqc} and \gls{tocsy} spectra were collected on internal
|
||
pooled control samples for metabolite annotation.
|
||
|
||
One-dimensional spectra were manually phased and baseline corrected in TopSpin.
|
||
Two-dimensional spectra were processed in NMRpipe\cite{Delaglio1995}. One
|
||
dimensional spectra were referenced, water/end regions removed, and normalized
|
||
with the PQN algorithm\cite{Dieterle2006} using an in-house MATLAB (The
|
||
MathWorks, Inc.) toolbox.
|
||
|
||
To reduce the total number of spectral features from approximately 250 peaks and
|
||
enrich for those that would be most useful for statistical modeling, a
|
||
variance-based feature selection was performed within MATLAB. For each digitized
|
||
point on the spectrum, the variance was calculated across all experimental
|
||
samples and plotted. Clearly-resolved features corresponding to peaks in the
|
||
variance spectrum were manually binned and integrated to obtain quantitative
|
||
feature intensities across all samples. In addition to highly variable features,
|
||
several other clearly resolved and easily identifiable features were selected
|
||
(glucose, \gls{bcaa} region, etc). Some features were later discovered to belong
|
||
to the same metabolite but were included in further analysis.
|
||
|
||
Two-dimensional spectra collected on pooled samples were uploaded to COLMARm web
|
||
server, where \gls{hsqc} peaks were automatically matched to database peaks.
|
||
\gls{hsqc} matches were manually reviewed with additional 2D and proton spectra
|
||
to confirm the match. Annotations were assigned a confidence score based upon
|
||
the levels of spectral data supporting the match as previously
|
||
described\cite{Dashti2017}. Annotated metabolites were matched to previously
|
||
selected features used for statistical analysis.
|
||
|
||
Several low abundance features selected for analysis did not have database
|
||
matches and were not annotated. Statistical total correlation spectroscopy41
|
||
suggested that some of these unknown features belonged to the same molecules
|
||
(not shown). Additional multidimensional \gls{nmr} experiments will be required
|
||
to determine their identity.
|
||
|
||
\subsection{Machine Learning and Statistical Analysis}
|
||
|
||
Linear regression analysis of the \glspl{doe} was performed as described in
|
||
\cref{sec:statistics}.
|
||
|
||
Seven \gls{ml} techniques were implemented to predict three responses related to
|
||
the memory phenotype of the cultured T cells under different process
|
||
conditions (\rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods
|
||
executed were \gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso}, \gls{plsr},
|
||
\gls{svm}, and DataModeler’s \gls{sr}\footnote{\gls{sr} was performed by Theresa
|
||
Kotanchek at Evolved Analytics, \gls{rf}, \gls{gbm}, \gls{cif}, \gls{plsr},
|
||
\gls{svm} were performed by Valerie Odeh-Couvertier at UPRM. Methods included
|
||
here for reference}. Primarily, \gls{sr} models were used to optimize process
|
||
parameter values based on \ptmem{} phenotype and to extract early predictive
|
||
variable combinations from the multi-omics experiments. Furthermore, all
|
||
regression methods were executed, and the high-performing models were used to
|
||
perform a consensus analysis of the important variables to extract potential
|
||
critical quality attributes and critical process parameters predictive of T cell
|
||
potency, safety, and consistency at the early stages of the manufacturing
|
||
process.
|
||
|
||
\gls{sr} was done using Evolved Analytics’ DataModeler software (Evolved
|
||
Analytics LLC, Midland, MI). DataModeler utilizes genetic programming to evolve
|
||
symbolic regression models (both linear and non-linear) rewarding simplicity and
|
||
accuracy. Using the selection criteria of highest accuracy
|
||
($R^2$>\SI{90}{\percent}) and lowest complexity, the top-performing models were
|
||
identified. Driving variables, variable combinations, and model dimensionality
|
||
tables were generated. The top-performing variable combinations were used to
|
||
generate model ensembles. In this analysis, DataModeler’s
|
||
\inlinecode{SymbolicRegression} function was used to develop explicit algebraic
|
||
(linear and nonlinear) models. The fittest models were analyzed to identify the
|
||
dominant variables using the \inlinecode{VariablePresence} function, the
|
||
dominant variable combinations using the \inlinecode{VariableCombinations}
|
||
function, and the model dimensionality (number of unique variables) using the
|
||
\inlinecode{ModelDimensionality} function. \inlinecode{CreateModelEnsemble} was
|
||
used to define trustable model ensembles using selected variable combinations
|
||
and these were summarized (model expressions, model phenotype, model tree plot,
|
||
ensemble quality, model quality, variable presence map, \gls{anova} tables,
|
||
model prediction plot, exportable model forms) using the
|
||
\inlinecode{ModelSummaryTable} function. Ensemble prediction and residual
|
||
performance were assessed via the \inlinecode{EnsemblePredictionPlot} and
|
||
\inlinecode{EnsembleResidualPlot} subroutines respectively. Model maxima
|
||
(\inlinecode{ModelMaximum} function) and model minima (\inlinecode{ModelMinimum}
|
||
function) were calculated and displayed using the
|
||
\inlinecode{ResponsePlotExplorer} function. Trade-off between multiple
|
||
responses was explored using \inlinecode{MultiTargetResponseExplorer} and
|
||
\inlinecode{ResponseComparisonExplorer} with additional insights derived from
|
||
\inlinecode{ResponseContourPlotExplorer}. Graphics and tables were generated by
|
||
DataModeler. These model ensembles were used to identify predicted response
|
||
values, potential optima in the responses, and regions of parameter values where
|
||
the predictions diverge the most.
|
||
|
||
Non-parametric tree-based ensembles were done through the
|
||
\inlinecode{randomForest}, inlinecode{gbm}, and \inlinecode{cforest} regression
|
||
functions in R, for \gls{rf}, \gls{gbm}, and \gls{cif} models, respectively.
|
||
Both \gls{rf} and \gls{cif} construct multiple decision trees in parallel, by
|
||
randomly choosing a subset of features at each decision tree split, in the
|
||
training stage. \gls{rf} individual decision trees are split using the Gini
|
||
Index, while conditional inference forest uses a statistical significance test
|
||
procedure to select the variables at each split, reducing correlation bias. In
|
||
contrast, \gls{gbm} construct regression trees in series through an iterative
|
||
procedure that adapts over the training set. This model learns from the mistakes
|
||
of previous regression trees in an iterative fashion to correct errors
|
||
(\gls{mse}) from its precursors’ trees. Prediction performance was evaluated
|
||
using \gls{loocv} and permutation-based variable importance scores assessing
|
||
percent increase of \gls{mse}, relative influence based on the increase of
|
||
prediction error, coefficient values for \gls{rf}, \gls{gbm}, and \gls{cif},
|
||
respectively. \gls{plsr} was executed using the \inlinecode{plsr} function from
|
||
the \inlinecode{pls} package in R while \gls{lasso} regression was performed
|
||
using the \inlinecode{cv.glmnet} R package, both using \gls{loocv}. Finally, the
|
||
\inlinecode{kernlab} R package was used to construct the \gls{svm} models.
|
||
|
||
Parameter tuning was done for all models in a grid search manner using the train
|
||
function from the \inlinecode{caret} R package using \gls{loocv} as the
|
||
optimization criteria. Specifically, the number of features randomly sampled as
|
||
candidates at each split (\inlinecode{mtry}) and the number of trees to grow
|
||
(\inlinecode{ntree}) were tuned parameters for random forest and conditional
|
||
inference forest. In particular, minimum sum of weights in a node to be
|
||
considered for splitting and the minimum sum of weights in a terminal node were
|
||
manually tuned for building the \gls{cif} models. Moreover, \gls{gbm} parameters
|
||
such as the number of trees to grow, maximum depth of each tree, learning rate,
|
||
and the minimal number of observations at the terminal node, were tuned for
|
||
optimum \gls{loocv} performance as well. For \gls{plsr}, the optimal number of
|
||
components to be used in the model was assessed based on the standard error of
|
||
the cross-validation residuals using the function \inlinecode{selectNcomp} from
|
||
the \inlinecode{pls} package. Moreover, \gls{lasso} regression was performed
|
||
using the \inlinecode{cv.glmnet} package with $\upalpha$ = 1. The best
|
||
$\uplambda$ for each response was chosen using the minimum error criteria.
|
||
Lastly, a fixed linear kernel (\inlinecode{svmLinear}) was used to build
|
||
the \gls{svm} regression models evaluating the cost parameter value with best
|
||
\gls{loocv}. Prediction performance was measured for all models using the final
|
||
model with \gls{loocv} tuned parameters.
|
||
|
||
\subsection{Consensus Analysis}
|
||
|
||
Consensus analysis of the relevant variables extracted from each machine
|
||
learning model was done to identify consistent predictive features of quality at
|
||
the early stages of manufacturing. First importance scores for all features were
|
||
measured across all \gls{ml} models using \inlinecode{varImp} with
|
||
\inlinecode{caret} R package except for scores for \gls{svm} which
|
||
\inlinecode{rminer} R package was used. These importance scores were percent
|
||
increase in \gls{mse}, relative importance through average increase in
|
||
prediction error when a given predictor is permuted, permuted coefficients
|
||
values, absolute coefficient values, weighted sum of absolute coefficients
|
||
values, and relative importance from sensitivity analysis determined for
|
||
\gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso}, \gls{plsr}, and \gls{svm},
|
||
respectively. Using these scores, key predictive variables were selected if
|
||
their importance scores were within the \nth{80} percentile ranking for the
|
||
following \gls{ml} methods: \gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso},
|
||
\gls{plsr}, \gls{svm} while for \gls{sr} variables present in >\SI{30}{\percent}
|
||
of the top-performing \gls{sr} models from DataModeler ($R^2\ge$
|
||
\SI{90}{\percent}, Complexity $\ge$ 100) were chosen to investigate consensus
|
||
except for \gls{nmr} media models at day 4 which considered a combination of the
|
||
top-performing results of models excluding lactate ppms, and included those
|
||
variables which were in >\SI{40}{\percent} of the best performing models. Only
|
||
variables with those high percentile scoring values were evaluated in terms of
|
||
their logical relation (intersection across \gls{ml} models) and depicted using
|
||
a Venn diagram from the \inlinecode{venn} R package.
|
||
|
||
\section{Results}
|
||
|
||
\subsection{T Cells Can be Grown on DMSs with Lower IL2 Concentrations}
|
||
|
||
Prior to the main experiments in this aim, we performed a preliminary experiment
|
||
to assess the effect of lowering the \gls{il2} concentration on the T cells
|
||
grown with either bead or \gls{dms}. One of the hypotheses for the \gls{dms}
|
||
system was that the higher cell density would enable more efficient cross-talk
|
||
between T cells. Since \gls{il2} is secreted by activated T cells themselves,
|
||
T cells in the \gls{dms} system may need less or no \gls{il2} if this hypothesis
|
||
were true.
|
||
|
||
% FIGURE this plots proportions look dumb
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il2_modulation.png}
|
||
\phantomsubcaption\label{fig:il2_mod_timecourse}
|
||
\phantomsubcaption\label{fig:il2_mod_total}
|
||
\phantomsubcaption\label{fig:il2_mod_mem}
|
||
\phantomsubcaption\label{fig:il2_mod_flow}
|
||
|
||
\endgroup
|
||
\caption[T cells grown at varying IL2 concentrations]
|
||
{\glspl{dms} grow T cells effectively at lower IL2 concentrations.
|
||
\subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown
|
||
with either bead or \glspl{dms} using varying IL2 concentrations}
|
||
Day 14 counts of either \subcap{fig:il2_mod_total}{total cells} or
|
||
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \gls{il2}
|
||
concentration.
|
||
\subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated
|
||
populations at day 14 of culture for each \gls{il2} concentration.}
|
||
}
|
||
\label{fig:il2_mod}
|
||
\end{figure*}
|
||
|
||
We varied the concentration of \gls{il2} from \SIrange{0}{100}{\IU\per\ml} and
|
||
expanded T cells as described in \cref{sec:tcellculture}. T cells grown with
|
||
either method expanded robustly as \gls{il2} concentration was increased
|
||
(\cref{fig:il2_mod_timecourse}). Surprisingly, neither the bead or the \gls{dms}
|
||
group expanded at all with \SI{0}{\IU\per\ml} \gls{il2}. When examining the
|
||
endpoint fold change after \SI{14}{\day}, we observe that the difference between
|
||
the bead and \gls{dms} appears to be greater at lower \gls{il2} concentrations
|
||
(\cref{fig:il2_mod_total}).
|
||
% This is further supported by fitting a non-linear
|
||
% least squares equation to the data following a hyperbolic curve (which should be
|
||
% a plausible model given that this curve describes receptor-ligand kinetics,
|
||
% which we can assume \gls{il2} to follow).
|
||
Furthermore, the same trend can be
|
||
seen when only examining the \ptmem{} cell expansion at day 14
|
||
(\cref{fig:il2_mod_mem}). In this case, the \ptmemp{} of the T cells seemed to
|
||
be relatively close at higher \gls{il2} concentrations, but separated further at
|
||
lower concentrations (\cref{fig:il2_mod_flow})
|
||
|
||
Taken together, these data do not support the hypothesis that the \gls{dms}
|
||
system does not need \gls{il2} at all; however, it appears to have a modest
|
||
advantage at lower \gls{il2} concentrations compared to beads. For this reason,
|
||
we decided to investigate the lower range of \gls{il2} concentrations starting
|
||
at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
|
||
|
||
\subsection{DOE Shows Optimal Conditions for Expanded Potent T Cells}
|
||
|
||
% TABLE not all of these were actually used, explain why by either adding columns
|
||
% or marking with an asterisk
|
||
\begin{table}[!h] \centering
|
||
\caption{DOE Runs}
|
||
\label{tab:doe_runs}
|
||
\input{../tables/doe_runs.tex}
|
||
\end{table}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_responses_first.png}
|
||
\phantomsubcaption\label{fig:doe_response_first_mem}
|
||
\phantomsubcaption\label{fig:doe_response_first_cd4}
|
||
|
||
\endgroup
|
||
\caption[Response plots for first DOE]
|
||
{Response plots from the first \gls{doe} experiment for
|
||
\subcap{fig:doe_response_first_mem}{\ptmemp{}} and
|
||
\subcap{fig:doe_response_first_cd4}{\pthp{}}. Each point is one run.
|
||
}
|
||
\label{fig:doe_response_first}
|
||
\end{figure*}
|
||
|
||
% RESULT maybe add regression tables to this, although it doesn't really matter
|
||
% since we end up doing regression on the full thing later anyways.
|
||
We conducted two consecutive \glspl{doe} to optimize the \pth{} and \ptmem{}
|
||
responses for the \gls{dms} system. In the first \gls{doe} we, tested \pilII{} in
|
||
the range of \SIrange{10}{30}{\IU\per\ml}, \pdms{} in the range of
|
||
\SIrange{500}{2500}{\dms\per\ml}, and \pmab{} in the range of
|
||
\SIrange{60}{100}{\percent}. When looking at the total \ptmemp{} output, we
|
||
observed that \pilII{} showed a positive linear trend with the \pdms{} and
|
||
\pmab{} showing possible second-order effects with maximums and minimums at the
|
||
intermediate level (\cref{fig:doe_response_first_mem}). In the case of \pth{},
|
||
we observed that all parameters seemed to have a positive linear response, with
|
||
\pilII{} and \pdms{} showing slight second order effects that suggest a maximum
|
||
might exist at a higher value for each.
|
||
|
||
After performing the first \gls{doe} we augmented the original design matrix
|
||
with an \gls{adoe} which was built with three goals in mind. Firstly we wished
|
||
to validate the first \gls{doe} by assessing the strength and responses of each
|
||
effect. Secondly, we wished to improve our confidence in regions that showed
|
||
high complexity, such as the peak in the \gls{dms} concentration for the total
|
||
\ptmem{} cell response. Thirdly, we wished to explore additional ranges of each
|
||
response. Since \pilII{} and \pdms{} appeared to continue positively influence
|
||
multiple responses beyond our tested range, we were curious if there was an
|
||
optimum at some higher setting of either of these values. For this reason, we
|
||
increased the \pilII{} to include \SI{40}{\IU\per\ml} and the \pdms{} to
|
||
\SI{3500}{\dms\per\ml}. Note that it was impossible to go beyond
|
||
\SI{100}{\percent} for the \pmab{}, so runs were positioned for this parameter
|
||
with validation and confidence improvements in mind. The runs for each \gls{doe}
|
||
were shown in \cref{tab:doe_runs}\footnote{Not all runs in this table were used.
|
||
It was determined later that the total \glspl{mab} surface density may not be
|
||
consistent across each batch of \gls{dms} used, primarily due to the fact that a
|
||
subset were created at different scale and with a different operator. To remove
|
||
this bias in our data, these runs were not used.}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_responses.png}
|
||
\phantomsubcaption\label{fig:doe_responses_mem}
|
||
\phantomsubcaption\label{fig:doe_responses_cd4}
|
||
\phantomsubcaption\label{fig:doe_responses_mem4}
|
||
\phantomsubcaption\label{fig:doe_responses_ratio}
|
||
|
||
\endgroup
|
||
\caption[T cell optimization through Design of Experiments]
|
||
{\gls{doe} methodology reveals optimal conditions for expanding T cell
|
||
subsets. Responses vs IL2 concentration, \gls{dms} concentration, and
|
||
functional \gls{mab} percentage are shown for
|
||
\subcap{fig:doe_responses_mem}{total \ptmem{} T cells},
|
||
\subcap{fig:doe_responses_cd4}{total \pth{} T cells},
|
||
\subcap{fig:doe_responses_mem4}{total \ptmemh{} T cells}, and
|
||
\subcap{fig:doe_responses_ratio}{ratio of CD4 and CD8 T cells in the
|
||
\ptmem{} compartment}. Each point represents one run.
|
||
}
|
||
\label{fig:doe_responses}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD62L+CCR7+ T cell response (first order regression)}
|
||
\label{tab:doe_mem1.tex}
|
||
\input{../tables/doe_mem1.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD62L+CCR7+ T cell response (third order regression)}
|
||
\label{tab:doe_mem2.tex}
|
||
\input{../tables/doe_mem2.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD4+ T cell response}
|
||
\label{tab:doe_cd4.tex}
|
||
\input{../tables/doe_cd4.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Linear regression for total \ptmemh{} cells}
|
||
\label{tab:doe_mem4.tex}
|
||
\input{../tables/doe_mem4.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Linear regression for CD4:CD8 ratio in the \ptmem{} compartment}
|
||
\label{tab:doe_ratio.tex}
|
||
\input{../tables/doe_ratio.tex}
|
||
\end{table}
|
||
|
||
The response plots from both \glspl{doe} are shown in \cref{fig:doe_responses}
|
||
for total \ptmem{} cells, total \pth{} cells, total \ptmemh{} cells, and CD4:CD8
|
||
ratio in the \ptmem{} compartment. In general, the responses for the first and
|
||
second \gls{doe} seemed to overlap, although not perfectly. Interestingly, only
|
||
the \ptmem{} response seemed to have anything more complex than a linear
|
||
relationship, particularly in the case of \pilII{} and \pdms{}, which showed
|
||
intermediate optimums (\cref{fig:doe_responses_mem}). In the case of \pilII{},
|
||
it was not clear if this optimum was simply due to a batch effect of being from
|
||
the first or second \gls{doe}. The optimum for \pdms{} appeared in the same
|
||
location albeit more pronounced in the second \gls{doe} so, giving more
|
||
confidence to the location of this second order feature. The remainder of the
|
||
responses showed mostly linear relationships in all parameter cases
|
||
(\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}).
|
||
|
||
% RESULT it seems arbitrary that I went straight to a third order model, the real
|
||
% reason is because it seemed weird that a second order model didn't find
|
||
% anything to be significant
|
||
We performed linear regression on the three input parameters as well as a binary
|
||
parameter representing if a given run came from the first or second \gls{doe}
|
||
(called `dataset'). Starting with the total \ptmem{} cells response, we fit a
|
||
first order regression model using these four parameters
|
||
(\cref{tab:doe_mem1.tex}). While \pilII{} was found to be a significant
|
||
predictor, the model fit was extremely poor ($R^2$ of 0.331). This was not
|
||
surprising given the apparent complexity of this response
|
||
(\cref{fig:doe_responses_mem}). To obtain a better fit, we added second and
|
||
third degree terms (\cref{tab:doe_mem2.tex}). Note that the dataset parameter
|
||
was not included in the second order interaction as this was treated as a
|
||
blocking variable, which are typically not assumed to have interaction effects.
|
||
Also note that the response was log-transformed, which yielded a better fit. In
|
||
this model many more parameters emerged as being significant, including the
|
||
quadratic terms for \pdms{} and \pilII{}, in agreement with what can be
|
||
qualitatively observed in the response plot (\cref{fig:doe_responses_mem}).
|
||
Furthermore, the dataset parameter was weakly significant, indicating a possible
|
||
batch effect between the \glspl{doe}. We should also note that despite many
|
||
parameters being significant, this model was still only mediocre in describing
|
||
this response; the $R^2$ was 0.741 but the $R_{adj}^2$ was 0.583, indicating
|
||
that our data might be underpowered for a model this complex. Further
|
||
experiments beyond what was performed here may be needed to fully describe this
|
||
response.
|
||
|
||
% TABLE combine these tables into one
|
||
We performed linear regression on the other three responses, all of which
|
||
performed much better than the \ptmem{} response as expected given the much
|
||
lower apparent complexity in the response plots
|
||
(\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}).
|
||
All these models appeared to fit will, with $R^2$ and $R_{adj}^2$ upward of
|
||
0.8. In all but the CD4:CD8 \ptmem{} ratio, the dataset parameter emerged as
|
||
significant, indicating a batch effect between the \glspl{doe}. All other
|
||
parameters except \pilII{} in the case of CD4:CD8 \ptmem{} ratio were
|
||
significant predictors.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_sr_contour.png}
|
||
\phantomsubcaption\label{fig:doe_sr_contour_mem4}
|
||
\phantomsubcaption\label{fig:doe_sr_contour_ratio}
|
||
|
||
\endgroup
|
||
\caption[Contour plots for DOE responses]
|
||
{Symbolic regression and contour plots reveal optimal conditions for
|
||
\subcap{fig:doe_sr_contour_mem4}{\ptmemh{} cells} and
|
||
\subcap{fig:doe_sr_contour_ratio}{CD4:CD8 ratio in the \ptmem{}
|
||
compartment}.
|
||
}
|
||
\label{fig:doe_sr_contour}
|
||
\end{figure*}
|
||
|
||
We then visualized the total \ptmemh{} cells and \rratio{} using the response
|
||
explorer in DataModeler to create contour plots around the maximum responses.
|
||
For both, it appeared that maximizing all three input parameters resulted in the
|
||
maximum value for either response (\cref{fig:doe_sr_contour}). While not all
|
||
combinations at and around this optimum were tested, the model nonetheless
|
||
showed that there were no other optimal values or regions elsewhere in the
|
||
model.
|
||
|
||
\subsection{Modeling With Artificial Intelligence Methods Reveals Potential
|
||
CQAs}
|
||
|
||
Due to the heterogeneity of the multivariate data collected and knowing that no
|
||
single model is perfect for all applications, we implemented an agnostic
|
||
modeling approach to better understand these \ptmem{} responses. To achieve
|
||
this, a consensus analysis using seven \gls{ml} techniques, \gls{rf}, \gls{gbm},
|
||
\gls{cif}, \gls{lasso}, \gls{plsr}, \gls{svm}, and DataModeler’s \gls{sr}, was
|
||
implemented to molecularly characterize \ptmem{} cells and to extract predictive
|
||
features of quality early in their expansion process.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_luminex.png}
|
||
|
||
\endgroup
|
||
\caption[Cytokine release profile of T cells from DOE]
|
||
{T cells show robust and varying cytokine responses over time}
|
||
\label{fig:doe_luminex}
|
||
\end{figure*}
|
||
|
||
We collected secretome data via luminex for days 4, 6, 8, 11, and 14.
|
||
Plotting the concentrations of these cytokines showed a large variation over all
|
||
runs and between different timepoints, demonstrated that these could potentially
|
||
be used to differentiate between different process conditions qualitatively
|
||
simply based on variance (\cref{fig:doe_luminex}). These were also much higher
|
||
in most cases that a set of bead based runs which were run in parallel, in
|
||
agreement with the luminex data obtained previously in the Grex system (these
|
||
data were collected in plates) (\cref{fig:grex_luminex}).
|
||
|
||
% TABLE this table looks like crap, break it up into smaller tables
|
||
\begin{table}[!h] \centering
|
||
\caption[Results for data-driven modeling]
|
||
{Results for data-driven modeling using process parameters (PP) with
|
||
only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome
|
||
on day 6 (S6), or various combindation of each for all seven \gls{ml}
|
||
techniques}
|
||
\label{tab:mod_results}
|
||
\input{../tables/model_results.tex}
|
||
\end{table}
|
||
|
||
\gls{sr} models achieved the highest predictive performance
|
||
($R^2$>\SI{93}{\percent}) when using multi-omics predictors for all endpoint
|
||
responses (\cref{tab:mod_results}). \gls{sr} achieved $R^2$>\SI{98}{\percent}
|
||
while \gls{gbm} ensembles showed \gls{loocv} $R^2$ > \SI{95}{\percent} for
|
||
\rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, \gls{plsr}, and
|
||
\gls{svm} methods showed consistently high \gls{loocv}, (\SI{92.9}{\percent},
|
||
\SI{99.7}{\percent}, and \SI{90.5}{\percent} respectively), to predict the
|
||
\rratio{}. Yet, about \SI{10}{\percent} reduction in \gls{loocv},
|
||
\SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with these three
|
||
methods. Lastly, \gls{sr} and \gls{plsr} achieved $R^2$>\SI{90}{\percent} while
|
||
other \gls{ml} methods exhibited exceedingly variable \gls{loocv}
|
||
(\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for \gls{lasso}) for
|
||
\rmemk{}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/sr_omics.png}
|
||
|
||
\endgroup
|
||
\caption[Symbolic Regression Cytokine Dependencies]
|
||
{Multi-omics culturing media prediction profiles at day 6 using symbolic
|
||
regression.}
|
||
\label{fig:sr_omics}
|
||
\end{figure*}
|
||
|
||
The top-performing technique, \gls{sr}, showed that the median aggregated
|
||
predictions for \rmemh{} \rmemk{} increases when IL2 concentration, IL15, and
|
||
IL2R increase while IL17a decreases in conjunction with other features. These
|
||
patterns combined with low values of \pdms{} and GM-CSF uniquely characterized
|
||
maximum \rmemk{}. Meanwhile, higher glycine but lower IL13 in combination with
|
||
others showed maximum \rmemh{} predictions (\cref{fig:sr_omics}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/modeling_flower.png}
|
||
\phantomsubcaption\label{fig:mod_flower_48r}
|
||
\phantomsubcaption\label{fig:mod_flower_cd4}
|
||
|
||
\endgroup
|
||
\caption[Data-Driven \gls{cqa} identification]
|
||
{Data-driven modeling using techniques with regularization reveals species
|
||
predictive species which are candidates for \glspl{cqa}. Flower plots are
|
||
shown for \subcap{fig:mod_flower_48r}{CD4:CD8 ratio} and
|
||
\subcap{fig:mod_flower_cd4}{total \ptmemh{} cells}. The left and right
|
||
columns includes models that were trained only on the secretome and
|
||
metabolome respectively. Each flower on each plot represents one model,
|
||
moving toward the center indicates higher agreement between models.}
|
||
\label{fig:mod_flower}
|
||
\end{figure*}
|
||
|
||
Selecting \gls{cpp} and \glspl{cqa} candidates consistently for T cell memory is
|
||
desired. Here, \gls{tnfa} was found in consensus across all seven \gls{ml}
|
||
methods for predicting \rratio{} when considering features with the highest
|
||
importance scores across models (\cref{fig:mod_flower_48r}). Other features,
|
||
IL2R, IL4, IL17a, and \pdms{}, were commonly selected in $\ge$ 5 \gls{ml}
|
||
methods (\cref{fig:mod_flower_48r}). When restricting the models only to include
|
||
metabolome, formate emerged as the dominant predictor shared across all seven
|
||
models.
|
||
|
||
% Moreover, IL13 and IL15 were found predictive in combination
|
||
% with these using \gls{sr} (Supp.Table.S4).
|
||
|
||
When performing similar analysis on \rmemh{}, we observe that no species for
|
||
either the secretome or metabolome was agreed upon by all seven models
|
||
(\cref{fig:mod_flower_cd4}). We also observed that these models did not fit as
|
||
well as they did for \rratio{} (\cref{tab:mod_results}). For the secretome, the
|
||
species that were agreed upon by $\ge$ 5 models were IL4, IL17a, and IL2R. For
|
||
the metabolome, formate once again was agreed upon by $\ge$ 5 models as well as
|
||
lactate.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/nmr_cors.png}
|
||
\phantomsubcaption\label{fig:nmr_cors_lactate}
|
||
\phantomsubcaption\label{fig:nmr_cors_formate}
|
||
\phantomsubcaption\label{fig:nmr_cors_glucose}
|
||
\phantomsubcaption\label{fig:nmr_cors_matrix}
|
||
|
||
\endgroup
|
||
\caption[NMR Day 4 correlations]
|
||
{\gls{nmr} features at day 4 are strongly correlated with each other and the
|
||
response variables. Highly correlated relationships are shown for
|
||
\subcap{fig:nmr_cors_lactate}{lactate},
|
||
\subcap{fig:nmr_cors_formate}{formate}, and
|
||
\subcap{fig:nmr_cors_glucose}{glucose}. Blue and blue connections indicate
|
||
positive and negative correlations respectively. The threshold for
|
||
visualizing connections in all cases was 0.8.
|
||
\subcap{fig:nmr_cors_matrix}{The correlation matrix for all predictive
|
||
features and the total \ptmemh{} response.}
|
||
}
|
||
\label{fig:nmr_cors}
|
||
\end{figure*}
|
||
|
||
We also investigated the \gls{nmr} features extracted from day of expansion to
|
||
assess if there was any predictive power for \ptmemh{}; in general these models
|
||
had almost as good of fit despite being 2 days earlier in the process
|
||
(\cref{fig:nmr_cors}). Lactate and formate were observed to correlate with each
|
||
other, and both correlated with \rmemh{}. Furthermore, lactate was observed to
|
||
positively correlate with \pdms{} and negatively correlate with glucose
|
||
(\cref{fig:nmr_cors_lactate}). Formate also had the same correlation patterns
|
||
(\cref{fig:nmr_cors_formate}). Glucose was only negatively correlated with
|
||
formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest
|
||
that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked.
|
||
|
||
\section{Discussion}
|
||
|
||
\gls{cpp} modeling and understanding are critical to new product development and
|
||
in cell therapy development, it can have life-saving implications. The
|
||
challenges for effective modeling grow with the increasing complexity of
|
||
processes due to high dimensionality, and the potential for process interactions
|
||
and nonlinear relationships. Another critical challenge is the limited amount of
|
||
available data, mostly small \gls{doe} datasets. \gls{sr} has the necessary
|
||
capabilities to resolve the issues of process effects modeling and has been
|
||
applied across multiple industries\cite{Kordona}. \gls{sr} discovers
|
||
mathematical expressions that fit a given sample and differs from conventional
|
||
regression techniques in that a model structure is not defined \textit{a
|
||
priori}\cite{Koza1992}. Hence, a key advantage of this methodology is that
|
||
transparent, human-interpretable models can be generated from small and large
|
||
datasets with no prior assumptions\cite{Kotancheka}.
|
||
|
||
Since the model search process lets the data determine the model, diverse and
|
||
competitive model structures are typically discovered. An ensemble of diverse
|
||
models can be formed where its constituent models will tend to agree when
|
||
constrained by observed data yet diverge in new regions. Collecting data in
|
||
these regions helps to ensure that the target system is accurately modeled, and
|
||
its optimum is accurately located\cite{Kotancheka}. Exploiting these features
|
||
allows adaptive data collection and interactive modeling. Consequently, this
|
||
\gls{adoe} approach is useful in a variety of scenarios, including maximizing
|
||
model validity for model-based decision making, optimizing processing parameters
|
||
to maximize target yields, and developing emulators for online optimization and
|
||
human understanding\cite{Kotancheka}.
|
||
|
||
An in-depth characterization of potential \gls{dms} based T cell \glspl{cqa}
|
||
includes a list of cytokine and \gls{nmr} features from media samples that are
|
||
crucial in many aspects of T cell fate decisions and effector functions of
|
||
immune cells. Cytokine features were observed to slightly improve prediction and
|
||
dominated the ranking of important features and variable combinations when
|
||
modeling together with \gls{nmr} media analysis and process parameters
|
||
(\cref{fig:mod_flower}).
|
||
|
||
Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and
|
||
IL15 were biologically assessed in terms of their known functions and activities
|
||
associated with T cells. T helper cells secrete more cytokines than T cytotoxic
|
||
cells, as per their main functions, and activated T cells secrete more cytokines
|
||
than resting T cells. It is possible that some cytokines simply reflect the
|
||
\rratio{} and the activation degree by proxy proliferation. However, the exact
|
||
ratio of expected cytokine abundance is less clear and depends on the subtypes
|
||
present, and thus examination of each relevant cytokine is needed.
|
||
|
||
IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
|
||
dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was much greater
|
||
than IL2 in solution, this might reduce the overall effect of IL2, which could
|
||
be further investigated by blocking IL2R with an antibody. In T cells, TNF can
|
||
increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may
|
||
also induce apoptosis depending on concentration and alter the CD4+ to CD8+
|
||
ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound
|
||
form, this may either increase or decrease CD4+ ratio and/or memory T cells
|
||
depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since
|
||
only soluble TNF was measured, membrane TNF is needed to understand its impact
|
||
on both CD4+ ratio and memory T cells. Furthermore, IL13 is known to be critical
|
||
for \gls{th2} response and therefore could be secreted if there are significant
|
||
\glspl{th2} already present in the starting population\cite{Wong2011}. This
|
||
cytokine has limited signaling in T cells and is thought to be more of an
|
||
effector than a differentiation cytokine\cite{Junttila2018}. It might be
|
||
emerging as relevant due to an initially large number of \glspl{th2} or because
|
||
\glspl{th2} were preferentially expanded; indeed, IL4, also found important, is
|
||
the conical cytokine that induces \gls{th2} differentiation
|
||
(\cref{fig:mod_flower}). The role of these cytokines could be investigated by
|
||
quantifying \glspl{th1}, \glspl{th2}, or \glspl{th17} both in the starting
|
||
population and longitudinally. Similar to IL13, IL17 is an effector cytokine
|
||
produced by \glspl{th17}\cite{Amatya2017} thus may reflect the number of
|
||
\glspl{th17} in the population. GM-CSF has been linked with activated T cells,
|
||
specifically \glspl{th17}, but it is not clear if this cytokine is inducing
|
||
differential expansion of CD8+ T cells or if it is simply a covariate with
|
||
another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15 has
|
||
been shown to be essential for memory signaling and effective in skewing
|
||
\gls{car} T cells toward \glspl{tscm} when using membrane-bound IL15Ra and
|
||
IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to
|
||
induce large numbers of memory T cells by functioning in an autocrine/paracrine
|
||
manner and could be explored by blocking either the cytokine or its receptor.
|
||
|
||
Moreover, many predictive metabolites found here are consistent with metabolic
|
||
activity associated with T cell activation and differentiation, yet it is not
|
||
clear how the various combinations of metabolites relate with each other in a
|
||
heterogeneous cell population. Formate and lactate were found to be highly
|
||
predictive and observed to positively correlate with higher values of total live
|
||
\rmemh{} cells (~\cref{fig:nmr_cors}). Formate is a byproduct of the
|
||
one-carbon cycle implicated in promoting T cell activation\cite{RonHarel2016}.
|
||
Importantly, this cycle occurs between the cytosol and mitochondria of cells and
|
||
formate excreted\cite{Pietzke2020}. Mitochondrial biogenesis and function are
|
||
shown necessary for memory cell persistence\cite{van_der_Windt_2012,
|
||
Vardhana2020}. Therefore, increased formate in media could be an indicator of
|
||
one-carbon metabolism and mitochondrial activity in the culture.
|
||
|
||
In addition to formate, lactate was found as a putative \gls{cqa} of \ptmem{}
|
||
cells. Lactate is the end-product of aerobic glycolysis, characteristic of
|
||
highly proliferating cells and activated T cells\cite{Lunt2011, Chang2013}.
|
||
Glucose import and glycolytic genes are immediately upregulated in response to T
|
||
cell stimulation, and thus generation of lactate. At earlier time-points, this
|
||
abundance suggests a more robust induction of glycolysis and higher overall T
|
||
cell proliferation. Interestingly, our models indicate that higher lactate
|
||
predicts higher CD4+, both in total and in proportion to CD8+, seemingly
|
||
contrary to previous studies showing that CD8+ T cells rely more on glycolysis
|
||
for proliferation following activation\cite{Cao2014}. It may be that glycolytic
|
||
cells dominate in the culture at the early time points used for prediction, and
|
||
higher lactate reflects more cells.
|
||
|
||
Metabolites that consistently decreased over time are consistent with the
|
||
primary carbon source (glucose) and essential amino acids (\gls{bcaa},
|
||
histidine) that must be continually consumed by proliferating cells. Moreover,
|
||
the inclusion of glutamine in our predictive models also suggests the importance
|
||
of other carbon sources for certain T cell subpopulations. Glutamine can be used
|
||
for oxidative energy metabolism in T cells without the need for
|
||
glycolysis\cite{Cao2014}. Overall, these results are consistent with existing
|
||
literature that show different T cell subtypes require different relative levels
|
||
of glycolytic and oxidative energy metabolism to sustain the biosynthetic and
|
||
signaling needs of their respective phenotypes\cite{Almeida2016,Wang_2012}. It
|
||
is worth noting that the trends of metabolite abundance here are potentially
|
||
confounded by the partial replacement of media that occurred periodically during
|
||
expansion, thus likely diluting some metabolic byproducts (such as formate,
|
||
lactate) and elevating depleted precursors (such as glucose and amino acids).
|
||
More definitive conclusions of metabolic activity across the expanding cell
|
||
population can be addressed by a closed system, ideally with on-line process
|
||
sensors and controls for formate, lactate, along with ethanol and glucose.
|
||
|
||
Practically, knowledge of how cytokines and/or metabolites are related to
|
||
outcome can be utilized for process control, which involves measuring the
|
||
current state of the culture, comparing it to a desired state, and intervening
|
||
if it is outside an acceptable range. In the case of lactate and formate, a
|
||
benchtop \gls{nmr} can be utilized to sample the media in real time during
|
||
culture. This \gls{nmr} can be tuned to automatically quantify the presence of
|
||
lactate and formate. Formate is part of the one-carbon pathway, and thus culture
|
||
fate may be controlled by altering the inputs to this pathway (glycine, serine,
|
||
choline) and/or adding folic acid inhibitors\cite{Ducker2017}. Since lactate is
|
||
a direct byproduct of glycolysis, this may be controlled by altering the
|
||
concentration of glucose in solution. Each of these control schemes would need
|
||
further study to assess if they have enough precision and temporal resolution to
|
||
reasonably ensure product quality. In the case of cytokines, there is currently
|
||
no analogue to a benchtop \gls{nmr}; however, research is underway to develop
|
||
protein-specific sensors using aptamers\cite{Parolo2020}. Even without these
|
||
developments, one could still use \gls{elisa} or Luminex to assess protein
|
||
levels in a semi-automated manner, but the disadvantage is that these assays are
|
||
temporally discrete and impose a significant time lag before the intervention
|
||
can be performed.
|
||
|
||
\chapter{AIM 2B}\label{aim2b}
|
||
|
||
\section{Introduction}
|
||
|
||
The purpose of this sub-aim was to further explore the \gls{dms} platform,
|
||
specifically for mechanisms and pathways that could be the basis for additional
|
||
\glspl{cpp} that could be optimized to yield higher quantity and quality of T
|
||
cells. Our strategy in general was to perturb the \gls{dms} system from the
|
||
normal operating conditions at which it was used up until this point either
|
||
through temporal modulation of activation signal or by blocking pathways of
|
||
interest using \glspl{mab}.
|
||
|
||
\section{Methods}
|
||
|
||
\subsection{DMSs Temporal Modulation}
|
||
|
||
% METHOD The concentration for the surface marker cleavage experiment was much
|
||
% higher, if that matters
|
||
\glspl{dms} were digested in active T cell cultures via addition of sterile
|
||
\product{\gls{colb}}{\sigald}{11088807001} or
|
||
\product{\gls{cold}}{\sigald}{11088858001}. Collagenase was dissolved in
|
||
\product{\gls{hbss}}{Gibco}{14025-076} or
|
||
\product{TexMACS}{\miltenyi}{170-076-307} at approximately \SI{100}{\ug\per\ml}.
|
||
This solution was added to T cell cultures at a 1:1 ratio in place of plain
|
||
media normally used to feed the cells during the regular media addition cycle at
|
||
day 4. Cultures were then incubated as described in \cref{sec:tcellculture}, and
|
||
the \glspl{dms} were verified to have been digested after \SI{24}{\hour}.
|
||
|
||
Adding \glspl{dms} was relatively much simpler; the number of \gls{dms} used per
|
||
area on day 0 was scaled up by 3 on day 4 to match the change from a 96 well
|
||
plate to a 24 well plate, effectively producing a constant activation signal.
|
||
|
||
\subsection{Mass Wytometry and Clustering Analysis}
|
||
|
||
T cells were stained using a \product{34 \gls{cytof} marker
|
||
panel}{Fluidigm}{201322} and \product{cisplatin}{Fluidigm}{201064} which were
|
||
used according to the manufacturer’s instructions. \numrange{2e6}{3e6} stained
|
||
cells per group were analyzed on a Fluidigm Helios.
|
||
|
||
Unbiased cell clusters were obtained using \gls{spade} analysis by pooling three
|
||
representative \gls{fcs} files and running the \gls{spade} pipeline with k-means
|
||
clustering (k = 100), arcsinh transformation with cofactor 5, density
|
||
calculation neighborhood size of 5 and local density approximation factor of
|
||
1.5, target density of 20000 cells, and outlier density cutoff of
|
||
\SI{1}{\percent}\cite{Qiu2017}. All markers in the \gls{cytof} panel were used
|
||
in the analysis
|
||
|
||
\subsection{Integrin Blocking Experiments}
|
||
|
||
To block \gls{a2b1} and \gls{a2b2}, active T cell cultures with \gls{dms} were
|
||
supplemented with \product{\anti{\gls{a2b1}}}{\sigald}{MAB1973Z} and
|
||
\product{\anti{\gls{a2b2}}}{\sigald}{MAB1950Z} (both \gls{leaf}) at indicated
|
||
concentrations and timepoints. T cells were grown as described in
|
||
\cref{sec:tcellculture}.
|
||
|
||
\gls{a2b1} and \gls{a2b2} were verified to be present on active T cell cultures
|
||
by staining with \product{\anti{\gls{a2b1}}-\gls{apc}}{\bl}{328313} and
|
||
\product{\anti{\gls{a2b2}}-\gls{fitc}}{\bl}{359305} on day 6 of culture and
|
||
analyzing via a \bd{} Accuri flow cytometer.
|
||
|
||
\subsection{IL15 Blocking Experiments}
|
||
|
||
To block the \gls{il15r}, we supplemented T cell
|
||
cultures activated with \gls{dms} with either
|
||
\product{\anti{\gls{il15r}}}{Rnd}{AF247} or \product{\gls{igg} isotype
|
||
control}{RnD}{AB-108-C} at the indicated timepoints and concentrations. T
|
||
cells were grown as otherwise described in \cref{sec:tcellculture} with the
|
||
exception that volumes were split by $\frac{1}{3}$ to keep the culture volume
|
||
constant and minimize the amount of \gls{mab} required.
|
||
|
||
To block soluble \gls{il15}, we supplemented analogously with
|
||
\product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
|
||
control}{\bl}{B236633}.
|
||
|
||
\section{Results}
|
||
|
||
\subsection{Adding or Removing DMSs Alters Expansion and Phenotype}
|
||
|
||
We hypothesized that adding or removing \gls{dms} in the middle of an active
|
||
culture would alter the activation signal and hence the growth trajectory and
|
||
phenotype of T cells. While adding \glspl{dms} was simple, the easiest way to
|
||
remove \glspl{dms} was to use enzymatic digestion. Collagenase is an enzyme that
|
||
specifically targets collagen proteins. Since our \glspl{dms} are composed of
|
||
porcine-derived collagen, this enzyme should target the \gls{dms} while sparing
|
||
the cells along with any markers we wish to analyze. We tested this specific
|
||
hypothesis using either \gls{colb}, \gls{cold} or \gls{hbss}, and stained the
|
||
cells using a typical marker panel to assess if any of the markers were cleaved
|
||
off by the enzyme which would bias our final readout. We observed that the
|
||
marker histograms in the \gls{cold} group were similar to that of the buffer
|
||
group, while the \gls{colb} group visibly lowered CD62L and CD4, indicating
|
||
partial enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we
|
||
used \gls{cold} moving forward.
|
||
|
||
% FIGURE this figure is tall and skinny like me
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/collagenase.png}
|
||
|
||
\endgroup
|
||
\caption[Effects Collagenase Treatment on T cells]
|
||
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
|
||
stained for various surface markers and analyzing via flow cytometry.}
|
||
\label{fig:collagenase_fx}
|
||
\end{figure*}
|
||
|
||
When either adding more \glspl{dms}, removing \glspl{dms} using \gls{cold}, or
|
||
doing nothing, we observed that, counterintuitively, cell growth seemed to be
|
||
inhibited in the \textit{added} group while the cells seemed to grow faster in
|
||
the \textit{removed} group relative to the \textit{no change} group
|
||
(\cref{fig:add_rem_growth}). Additionally, the \textit{removed} group seemed to
|
||
have a negative growth rate in the final \SI{4}{\day} of culture, indicating
|
||
that either the lack activation signal had slowed the cell growth down or that
|
||
the cells were growing fast enough to outpace the media feeding schedule. The
|
||
viability was the same between all groups, indicating that this negative growth
|
||
rate and the lower growth rate in the \textit{added} group were likely not due
|
||
to cell death (\cref{fig:add_rem_viability}). Interestingly, the \textit{added}
|
||
group had significantly higher \pth{} cells compared to the \textit{no change}
|
||
group, and the inverse was true for the \textit{removed} group
|
||
(\cref{fig:add_rem_cd4}). These results show that the growth rate and phenotype
|
||
are fundamentally altered by changing the number of \glspl{dms} temporally.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/add_remove_endpoint.png}
|
||
\phantomsubcaption\label{fig:add_rem_growth}
|
||
\phantomsubcaption\label{fig:add_rem_viability}
|
||
\phantomsubcaption\label{fig:add_rem_cd4}
|
||
|
||
\endgroup
|
||
\caption[Endpoint results from adding/removing \gls{dms} on day 4]
|
||
{Changing \gls{dms} concentration on day 4 has profound effects on phenotype
|
||
and growth.
|
||
\subcap{fig:add_rem_growth}{Longitudinal fold change},
|
||
\subcap{fig:add_rem_viability}{longitudinal viability}, and
|
||
\subcap{fig:add_rem_cd4}{day 14 \pthp{}} of T cell cultures with \glspl{dms}
|
||
added, removed, or kept the same on day 4.
|
||
}
|
||
\label{fig:add_rem}
|
||
\end{figure*}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/spade_gates.png}
|
||
|
||
\endgroup
|
||
\caption[SPADE Gating Strategy]
|
||
{Gating strategy for quantifying early-differentiated T cells via
|
||
\gls{spade}.}
|
||
\label{fig:spade_gates}
|
||
\end{figure*}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/add_remove_spade.png}
|
||
\phantomsubcaption\label{fig:spade_msts}
|
||
\phantomsubcaption\label{fig:spade_quant}
|
||
\phantomsubcaption\label{fig:spade_tsne_all}
|
||
\phantomsubcaption\label{fig:spade_tsne_stem}
|
||
|
||
\endgroup
|
||
\caption[SPADE and tSNE analysis temporally-modified DMS concentration]
|
||
{Removing \glspl{dms} leads to a higher fraction of potent stem-memory T
|
||
cells compared to both adding and not changing the \gls{dms} concentration
|
||
at day 4.
|
||
\subcap{fig:spade_msts}{SPADE plots of CD4, CD45RA, CD27, and CD45RO
|
||
expression on T cells. All cells from the added, removed, or no change
|
||
groups were pooled and clustered at once.}
|
||
\subcap{fig:spade_quant}{T cells from SPADE plots clustered by expression in
|
||
(\subref{fig:spade_msts}) quantified against total cell number from each
|
||
group.}
|
||
\subcap{fig:spade_tsne_all}{\gls{tsne} plots of all cells pooled from all
|
||
groups.}
|
||
\subcap{fig:spade_tsne_stem}{\gls{tsne} plots of T cells from all groups
|
||
manually gated on \cdp{8}\cdp{27}\cdp{45RO}.}
|
||
}
|
||
\label{fig:spade}
|
||
\end{figure*}
|
||
|
||
We next asked what the effect of removing the \glspl{dms} would have on other
|
||
phenotypes, specifically \gls{tcm} and \gls{tscm} cells. To this end we stained
|
||
cells using a 34-marker mass cytometry panel and analyzed them using a Fluidigm
|
||
Helios. After pooling the \gls{fcs} file events from each group and analyzing
|
||
them via \gls{spade} we see that there is a strong bifurcation of CD4 and CD8 T
|
||
cells. We also observe that among CD27, CD45RA, and CD45RO (markers commonly
|
||
used to identify \gls{tcm} and \gls{tscm} subtypes) we see clear `metaclusters'
|
||
composed of individual \gls{spade} clusters which are high for that marker
|
||
(\cref{fig:spade_msts,fig:spade_gates}). We then gated each of these
|
||
metaclusters according to their marker levels and assigned them to one of three
|
||
phenotypes for both the CD4 and CD8 compartments: \gls{tcm} (high CD45RO, low
|
||
CD45RA, high CD27), \gls{tscm} (low CD45RO, high CD45RA, high CD27), and
|
||
`transitory' \gls{tscm} cells (mid CD45RO, mid CD45RA, high CD27). Together
|
||
these represent low differentiated cells which should be highly potent as
|
||
anti-tumor therapies.
|
||
|
||
When quantifying the number of cells from each experimental group in these
|
||
phenotypes, we clearly see that the number of lower differentiated cells is much
|
||
higher in the \textit{no change} or \textit{removed} groups compared to the
|
||
\textit{added} group (\cref{fig:spade_quant}). Furthermore, the \textit{removed}
|
||
group had a much higher fraction of \gls{tscm} cells compared to the \textit{no
|
||
change} group, which had more `transitory \gls{tscm} cells'. The majority of
|
||
these cells were \cdp{8} cells. When analyzing the same data using \gls{tsne},
|
||
we observe a higher fraction of CD27 and lower fraction of CD45RO in the the
|
||
\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the
|
||
CD27+CD45RO- population, we see there is higher density in the \textit{removed}
|
||
group, indicating more of this population (\cref{fig:spade_tsne_stem}).
|
||
Together, these data indicate that removing \glspl{dms} at lower timepoints
|
||
leads to potentially higher expansion, lower \pthp{}, and higher fraction of
|
||
lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to
|
||
do the inverse.
|
||
|
||
\subsection{Blocking Integrin Binding Does not Alter Expansion or Phenotype}
|
||
|
||
One of the reasons the \gls{dms} platform might perform better than the beads is
|
||
the fact that they are composed of gelatin, which is a collagen derivative. The
|
||
beads are simply \gls{mab} attached to a polymer resin coated onto an iron oxide
|
||
core, and thus have no analogue for collagen. Collagen domains present on the
|
||
\gls{dms} group could be creating pro-survival and pro-expansion signals to the
|
||
T cells through \gls{a2b1} and \gls{a2b2}, causing them to grow better in the
|
||
\gls{dms} system.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/integrin_1.png}
|
||
\phantomsubcaption\label{fig:inegrin_1_overview}
|
||
\phantomsubcaption\label{fig:inegrin_1_fc}
|
||
\phantomsubcaption\label{fig:inegrin_1_mem}
|
||
\phantomsubcaption\label{fig:inegrin_1_cd49}
|
||
|
||
\endgroup
|
||
\caption[Integrin blocking I]
|
||
{Blocking with integrin does not lead to differences in memory or growth.
|
||
\subcap{fig:inegrin_1_overview}{Experimental overview}
|
||
\subcap{fig:inegrin_1_fc}{Fold change of \gls{dms}-activated T cell over
|
||
time with each blocking condition.}
|
||
\subcap{fig:inegrin_1_mem}{\ptmemp{} at day 14 for each blocked condition.}
|
||
\subcap{fig:inegrin_1_cd49}{\gls{a2b1} and \gls{a2b2} expression over time.}
|
||
`A' and `B' refer to the inclusion of \anti{\gls{a2b1}} or \anti{\gls{a2b2}}
|
||
respectively.
|
||
}
|
||
\label{fig:integrin_1}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Linear regression for day 14 phenotype shown in \cref{fig:integrin_1}}
|
||
\label{tab:integrin_1_reg}
|
||
\input{../tables/integrin_1_reg.tex}
|
||
\end{table}
|
||
|
||
We tested this hypothesis by adding blocking \glspl{mab} against \gls{a2b1}
|
||
and/or \gls{a2b2} to running T cell cultures activated using the \glspl{dms}.
|
||
These block \glspl{mab} were added at day 6 of culture when \gls{a2b1} and
|
||
\gls{a2b2} were known to be expressed\cite{Hemler1990}. We found that the fold
|
||
expansion was identical in all the blocked groups vs the unblocked control group
|
||
(\cref{fig:inegrin_1_fc}). Furthermore, we observed that the \ptmemp{} (total
|
||
and across the CD4/CD8 compartments) was not significantly different between any
|
||
of the groups (\cref{fig:inegrin_1_mem,tab:integrin_1_reg}). We also noted that
|
||
\gls{a2b1} and \gls{a2b2} were present on the surface of a significant subset of
|
||
T cells at day 6, showing that the target we wished to block was present
|
||
(\cref{fig:inegrin_1_cd49}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/integrin_2.png}
|
||
\phantomsubcaption\label{fig:inegrin_2_overview}
|
||
\phantomsubcaption\label{fig:inegrin_2_fc}
|
||
\phantomsubcaption\label{fig:inegrin_2_mem}
|
||
|
||
\endgroup
|
||
\caption[Integrin blocking II]
|
||
{Blocking with integrin does not lead to differences in memory or growth.
|
||
\subcap{fig:inegrin_1_fc}{Fold change of \gls{dms}-activated T cell over
|
||
time with each blocking condition.}
|
||
\subcap{fig:inegrin_1_mem}{\ptmemp{} at day 14 for each blocked condition.}
|
||
`A' and `B' refer to the inclusion of \anti{\gls{a2b1}} or \anti{\gls{a2b2}}
|
||
respectively.
|
||
}
|
||
\label{fig:integrin_2}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Linear regression for day 14 phenotype shown in \cref{fig:integrin_2}}
|
||
\label{tab:integrin_2_reg}
|
||
\input{../tables/integrin_2_reg.tex}
|
||
\end{table}
|
||
|
||
Since this last experiment gave a negative result, we decided to block
|
||
\gls{a2b1} and \gls{a2b2} harder by adding \glspl{mab} at more timepoints
|
||
between day 0 and day 6, hypothesizing that the majority of the signaling would
|
||
be during the period of culture where the \gls{dms} surface concentration was at
|
||
its maximum. Once again, we observed no difference between any of the blocked
|
||
conditions and the unblocked controls in regard to expansion
|
||
(\cref{fig:inegrin_2_fc}). Furthermore, none of the \ptmemp{} readouts (total,
|
||
CD4, or CD8) were statistically different between groups
|
||
(\cref{fig:inegrin_2_mem,tab:integrin_2_reg}).
|
||
|
||
Taken together, these data suggest that the advantage of the \gls{dms} platform
|
||
is not due to signaling through \gls{a2b1} or \gls{a2b2}.
|
||
|
||
\subsection{Blocking IL15 Signaling does not Alter Expansion or Phenotype}
|
||
|
||
\gls{il15} is a cytokine responsible for memory T cell survival and maintenance.
|
||
Furthermore, we observed in other experiments that it is secreted to a much
|
||
greater extend in \gls{dms} compared to bead cultures (\cref{fig:doe_luminex}).
|
||
One of our driving hypotheses in designing the \gls{dms} system was that the
|
||
higher cell density would lead to greater local signaling. Since we observed
|
||
higher \ptmemp{} across many conditions, we hypothesized that \gls{il15} may be
|
||
responsible for this, and further that the unique \textit{cis/trans} activity of
|
||
\gls{il15} may be more active in the \gls{dms} system due to higher cell
|
||
density.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il15_blockade_1.png}
|
||
\phantomsubcaption\label{fig:il15_1_overview}
|
||
\phantomsubcaption\label{fig:il15_1_fc}
|
||
\phantomsubcaption\label{fig:il15_1_viability}
|
||
\phantomsubcaption\label{fig:il15_1_mem}
|
||
|
||
\endgroup
|
||
\caption[IL15 blocking I]
|
||
{Blocking IL15Ra does not lead to differences in memory or growth.
|
||
\subcap{fig:il15_1_overview}{Experimental overview}
|
||
Longitudinal measurements of
|
||
\subcap{fig:il15_1_fc}{fold change} and
|
||
\subcap{fig:il15_1_viability}{viability} for blocked and unblocked
|
||
conditions expanded with either beads or \glspl{dms}.
|
||
\subcap{fig:il15_1_mem}{Flow cytometry markers for \gls{dms}-expanded T
|
||
cells at day 14 for blocked and unblocked groups.}.
|
||
}
|
||
\label{fig:il15_1}
|
||
\end{figure*}
|
||
|
||
We first tested this hypothesis by blocking \gls{il15r} with either a specific
|
||
\gls{mab} or an \gls{igg} isotype control at
|
||
\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. We observed no difference in the
|
||
expansion rate of blocked or unblocked cells (this experiment also had
|
||
bead-based groups but they did not expand well and thus were not included)
|
||
(\cref{fig:il15_1_fc}). Furthermore, there were no differences in viability
|
||
between any group (\cref{fig:il15_1_viability}). We also performed flow
|
||
cytometry to asses the \ptmemp{} and \pthp{} outputs. Without even gating the
|
||
samples, simply lining up their histograms showed no difference between any of
|
||
the markers, and by extension showing no difference in phenotype
|
||
(\cref{fig:il15_1_mem}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il15_blockade_2.png}
|
||
\phantomsubcaption\label{fig:il15_2_overview}
|
||
\phantomsubcaption\label{fig:il15_2_fc}
|
||
\phantomsubcaption\label{fig:il15_2_viability}
|
||
\phantomsubcaption\label{fig:il15_2_mem}
|
||
|
||
\endgroup
|
||
\caption[IL15 blocking II]
|
||
{Blocking soluble IL15 does not lead to differences in memory or growth.
|
||
\subcap{fig:il15_2_overview}{Experimental overview}
|
||
Longitudinal measurements of
|
||
\subcap{fig:il15_2_fc}{fold change} and
|
||
\subcap{fig:il15_2_viability}{viability} for blocked and unblocked
|
||
conditions expanded with \glspl{dms}.
|
||
\subcap{fig:il15_2_mem}{Flow cytometry markers for \gls{dms}-expanded T
|
||
cells at day 14 for blocked and unblocked groups.}.
|
||
}
|
||
\label{fig:il15_2}
|
||
\end{figure*}
|
||
|
||
We next tried blocking soluble \gls{il15} itself using either a \gls{mab} or an
|
||
\gls{igg} isotype control. \anti{\gls{il15}} or \gls{igg} isotype control was
|
||
added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in
|
||
excess of the \gls{il15} concentration seen in past experiments by over
|
||
\num{20000} times. Similarly, we observed no difference between fold change,
|
||
viability, or marker histograms between any of these markers, showing that
|
||
blocking \gls{il15} led to no difference in growth or phenotype.
|
||
|
||
% RESULT this can probably be worded more specifically in terms of the cis/trans
|
||
% action of IL15
|
||
In summary, this data did not support the hypothesis that the \gls{dms} platform
|
||
gains its advantages via the \gls{il15} pathway.
|
||
|
||
\section{Discussion}
|
||
|
||
This work provides insight for how the \gls{dms} operates and may be optimized
|
||
further. The data showing increased \pthp{} when \glspl{dms} are added and the
|
||
reverse when removed is consistent with other data we produced via \gls{doe}
|
||
showing that higher \gls{dms} concentrations lead to higher \pthp{}
|
||
(\cref{fig:doe_responses_cd4,fig:add_rem_cd4}). The difference in this case is
|
||
that we showed that altering activation signal analogously affects the \pthp{}
|
||
in the dimension of time as well as space. A similar trend was observed with
|
||
memory T cells in this aim. Our previous \gls{doe} data showed that, to a point,
|
||
lower \gls{dms} concentration leads to higher \ptmemp{}
|
||
(\cref{fig:doe_responses_mem}). In this aim, we showed that decreasing
|
||
activation signal temporally by removing \glspl{dms} leads to the same effect in
|
||
the \gls{tcm}, \gls{tscm} and `transitory' \gls{tscm} populations, (all of which
|
||
are included in the \ptmem{} phenotype). Taken together, these imply that
|
||
temporally or spatially altering the \gls{dms} concentration, and thus the
|
||
activation signal, has similar effects.
|
||
|
||
% BACKGROUND this sounds like background?
|
||
% There are several plausible explanations for the observed phenotypic differences
|
||
% between beads and DMSs. First, the DMSs are composed of a collagen derivative
|
||
% (gelatin); collagen has been shown to costimulate activated T cells via
|
||
% \gls{a2b1} and \gls{a2b2}, leading to enhanced proliferation, increased
|
||
% \gls{ifng} production, and upregulated CD25 (IL2R$\upalpha$) surface
|
||
% expression8,10,11,41,42.
|
||
|
||
While we did not find support for our hypothesis that the \gls{dms} signal
|
||
through the \gls{a2b1} and/or \gls{a2b2} receptors, we can speculate as to why
|
||
either this experiment failed and may be done better in the future, or why these
|
||
receptors may simply be irrelevant for our system.
|
||
|
||
On the first point, we did not verify that these \glspl{mab} indeed blocked the
|
||
receptor we were targeting. There has been evidence from other groups that these
|
||
particular clones work at the concentrations we used\cite{MirandaCarus2005}.
|
||
This does not necessarily mean that the \glspl{mab} we obtained were functional
|
||
in blocking their intended targets (although they were from a reputable
|
||
manufacturer, \bl). Furthermore, we can safely rule out the possibility that the
|
||
\glspl{mab} never reached their targets, as they were added immediately after
|
||
the T cells were resuspended as required for cell counting, hence their resting
|
||
clustered state was disrupted.
|
||
|
||
On the second point, the collagen domains may not even be relevant to our system
|
||
depending on the nature of the \gls{stp} coating. We intended by design for the
|
||
system to be fully coated or nearly fully-coated with \gls{stp}
|
||
(\cref{fig:stp_coating}). Thus the domains that \gls{a2b1} and \gls{a2b2} may be
|
||
targeting could be sterically hindered by a layer of \gls{stp}, and if not that,
|
||
also a layer of CD3/CD28 \glspl{mab}. The other possibility is that these
|
||
domains are simply denatured to beyond recognition due to the fabrication
|
||
process for the microcarriers we used (which involves a proprietary
|
||
cross-linking step to make the material autoclave-safe). Either of these could
|
||
be tested and verified by staining the \glspl{dms} with a fluorescently-tagged
|
||
\gls{mab} and verifying binding via confocal microscopy or indirect protein
|
||
quantification as we do for the \gls{qc} of the \gls{dms}. If this test came
|
||
back negative, we would be fairly confident that the \gls{a2b1} and \gls{a2b1}
|
||
domains are either unreachable or unrecognizable. Even if it turned out that
|
||
collagen binding domains are irrelevant in the \gls{dms} system, previous
|
||
studies show that these domains can enhance proliferation and survival, and thus
|
||
adding them along with with the \glspl{mab} could enhance T cell
|
||
expansion\cite{Aoudjit2000, Gendron2003, Boisvert2007}.
|
||
|
||
We also failed to uphold our hypothesis that the \gls{dms} system gains its
|
||
advantage via \gls{il15} signaling. There could be multiple reasons for why
|
||
blocking either \il{15} itself or its receptor would not influence the response
|
||
at all. First, it could be that \il{15} is not important in our system, which is
|
||
not likely given the importance of \il{15} in T cells expansion and particularly
|
||
memory phenotypes\cite{Lodolce1998,Kennedy2000}. Second, in the case of the
|
||
receptor it could be that that \glspl{mab} we purchased did not actually block,
|
||
which also seems unlikely given that this clone has been observed to inhibit
|
||
proliferation in the past (although like the integrin blocking experiments we
|
||
did not verify that it blocked ourselves), albeit of resting T
|
||
cells\cite{MirandaCarus2005}. Third, it could be that turnover of the receptor
|
||
was so high that there were not enough \glspl{mab} to block (the key difference
|
||
between our experiment and that of \cite{MirandaCarus2005} was that they used
|
||
resting T cells, which are not expressing protein to nearly as high of a
|
||
degree). The way to test this would be to simply titrate increasing
|
||
concentrations of \gls{mab} (which we did not do in our case because the
|
||
\gls{mab} was already very expensive in the concentrations employed for our
|
||
experiment). Fourth, the blocking the soluble protein may not have worked
|
||
because the \il{15} may have been secreted and immediately captured via
|
||
\il{15R$\upalpha$} either by the cell that secreted it or by a neighboring cell.
|
||
|
||
Regardless of whether or not \il{15} is important for the overall mechanism that
|
||
differentiates the \glspl{dms} from the beads, adding \il{15} or its receptor
|
||
complex to the surface of the \gls{dms} might produce interesting and positive
|
||
results on expansion and memory phenotype. Essentially this would turn the
|
||
\glspl{dms} into stromal cells that present \il{15}, as seen to be important in
|
||
the early work with \il{15} in mice\cite{Lodolce1998}.
|
||
|
||
% DISCUSSION not sure if this belongs here, although it might make sense to offer
|
||
% alternative explanations of why the DMSs "work" given this negative data
|
||
% Second, there is evidence that providing a larger
|
||
% contact area for T cell activation provides greater stimulation16,43; the DMSs
|
||
% have a rougher interface than the 5 µm magnetic beads, and thus could facilitate
|
||
% these larger contact areas. Third, the DMSs may allow the T cells to cluster
|
||
% more densely compared to beads, as evidenced by the large clusters on the
|
||
% outside of the DMSs (Figure 1f) as well as the significant fraction of DMSs
|
||
% found within their interiors (Supplemental Figure 2a and b). This may alter the
|
||
% local cytokine environment and trigger different signaling pathways.
|
||
% Particularly, IL15 and IL21 are secreted by T cells and known to drive memory
|
||
% phenotype44–46. We noted that the IL15 and IL21 concentration was higher in a
|
||
% majority of samples when comparing beads and DMSs across multiple timepoints
|
||
% (Supplemental Figure 18) in addition to many other cytokines. IL15 and IL21 are
|
||
% added exogenously to T cell cultures to enhance memory frequency,45,47 and our
|
||
% data here suggest that the DMSs are better at naturally producing these
|
||
% cytokines and limiting this need. Furthermore, IL15 unique signals in a trans
|
||
% manner in which IL15 is presented on IL15R to neighboring cells48. The higher
|
||
% cell density in the DMS cultures would lead to more of these trans interactions,
|
||
% and therefore upregulate the IL15 pathway and lead to more memory T cells.
|
||
|
||
\chapter{AIM 3}\label{aim3}
|
||
|
||
\section{Introduction}
|
||
|
||
% DO NOT COMMENT OUT THIS LINE: the real purpose of this aim was to appease
|
||
% Nature Biotech because they think that animal models are necessary for good
|
||
% science. This entire aim exists because of their foolishness.
|
||
|
||
The purpose of this aim was to verify that \gls{car} T cells produced using the
|
||
\gls{dms} system will show potent anti-tumor properties in a complex \invivo{}
|
||
system compared to state-of-the-art bead technology\footnote{adapted from
|
||
\dmspaper{}}. We hypothesized that due to the increased \ptmem{} and \pth{}
|
||
phenotypes as shown in \cref{aim1}, that \gls{dms}-expanded T cells would show
|
||
longer survival and lower tumor burden than those expanded with beads. We
|
||
explored the effect of dosing at different levels and the effect of harvesting T
|
||
cells at early timepoints in the culture, which has been shown to produce
|
||
lower-differentiated T cells with higher potency\cite{Ghassemi2018}.
|
||
|
||
\section{Methods}
|
||
|
||
\subsection{CD19-CAR T Cell Generation}
|
||
|
||
\subsection{T Cell Culture}
|
||
|
||
T cells were grown as described in \cref{sec:tcellculture}.
|
||
|
||
|
||
\subsection{\Invivo{} Therapeutic Efficacy in NSG Mice Model}
|
||
|
||
% METHOD describe how the luciferase cells were generated (eg the kwong lab)
|
||
% METHOD use actual product numbers for mice
|
||
All mice in this study were male \gls{nsg} mice from Jackson Laboratories. At
|
||
day 0 (\SI{-7}{\day} relative to T cell injection), 1e6 firefly
|
||
luciferase-expressing \product{Nalm-6 cells}{ATCC}{CRL-3273} suspended in
|
||
ice-cold \gls{pbs} were injected via tail vein into each mouse. At day 7, saline
|
||
or \gls{car} T cells at the indicated doses from either bead or
|
||
\gls{dms}-expanded T cell cultures (for \SI{14}{\day}) were injected into each
|
||
mouse via tail vein. Tumor burden was quantified longitudinally via an
|
||
\gls{ivis} Spectrum (Perkin Elmer). Briefly, \SI{200}{\ug} luciferin at
|
||
\SI{15}{\mg\per\ml} in \gls{pbs} was injected intraperitoneally under isoflurane
|
||
anesthesia into each mouse and allowed to circulate for at least
|
||
\SI{10}{\minute} before imaging. Mice were anesthetized again and imaged using
|
||
the \gls{ivis}. Mice from each treatment group/dose were anesthetized, injected,
|
||
and imaged together, and exposure time of the \gls{ivis} was limited to avoid
|
||
saturation based on the signal from the saline group. \gls{ivis} images were
|
||
processed by normalizing them to common minimum and maximum photon counts and
|
||
total flux was estimated in terms of photons/second. Endpoint for each mouse was
|
||
determined by \gls{iacuc} euthanasia criteria (hunched back, paralysis,
|
||
blindness, lethargy, and weight loss). Mice were euthanized according to these
|
||
endpoint criteria using carbon dioxide asphyxiation.
|
||
|
||
\subsection{Statistics}
|
||
|
||
For the \invivo{} model, the survival curves were created and statistically
|
||
analyzed using GraphPad Prism using the Mantel-Cox test to assess significance
|
||
between survival groups.
|
||
|
||
\section{Results}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_overview.png}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing Experimental Overview]
|
||
{Overview of \invivo{} experiment to test \gls{car} T cells expanded with
|
||
either \glspl{dms} at different doses. }
|
||
\label{fig:mouse_dosing_overview}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Results for \gls{car} T cell \invivo{} dose study}
|
||
\label{tab:mouse_dosing_results}
|
||
\input{../tables/mouse_dose_car.tex}
|
||
\end{table}
|
||
|
||
\subsection{DMS-expanded T Cells Show Greater Anti-Tumor Activity \invivo{}
|
||
Compared to Beads}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_qc.png}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_mem}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_cd4}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_growth}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing T cell Characteristics]
|
||
{Characteristics of T cells harvested at day 14 injected into NSG
|
||
mice at varying doses.
|
||
Fractions of T cell subtypes in the day 14 product including
|
||
\subcap{fig:mouse_dosing_qc_mem}{\ptmemp{}}.
|
||
\subcap{fig:mouse_dosing_qc_cd4}{\pthp{}}, and
|
||
\subcap{fig:mouse_dosing_qc_growth}{Fold change of T cells.}
|
||
}
|
||
\label{fig:mouse_dosing_qc}
|
||
\end{figure*}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_ivis.png}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_images}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_plots}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival_comp}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival_full}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing IVIS and Survival Results]
|
||
{T cells expanded with \glspl{dms} confer greater anti-tumor potency \invivo{}
|
||
even at lower doses.
|
||
\subcap{fig:mouse_dosing_ivis_images}{IVIS images of Nalm-6 tumor-bearing
|
||
\gls{nsg} mice injected with varying doses of T cells}
|
||
\subcap{fig:mouse_dosing_ivis_plots}{Plots showing quantified photon counts
|
||
of the results from (\subref{fig:mouse_dosing_ivis_plots}).}
|
||
\subcap{fig:mouse_dosing_ivis_survival}{Survival plots of mice}
|
||
\subcap{fig:mouse_dosing_ivis_survival_comp}{Survival plots of mice showing
|
||
only those that received a comparable number of \gls{car} T cells.}
|
||
\subcap{fig:mouse_dosing_ivis_survival_full}{The same data as
|
||
\subref{fig:mouse_dosing_ivis_survival} except showing the full time until
|
||
euthanasia for all mice (including those that died via \gls{gvhd}). Survival
|
||
curves were statistically analyzed using the logrank test in GraphPad
|
||
Prism.}
|
||
}
|
||
\label{fig:mouse_dosing_ivis}
|
||
\end{figure*}
|
||
|
||
We asked if the higher memory/naive phenotype and more balanced CD4/CD8 ratio of
|
||
our \gls{dms}-expanded \gls{car} T cells would lead to better anti-tumor potency
|
||
in vivo compared to bead-expanded \gls{car} T cells. We also asked if this
|
||
superior anti-tumor potency would hold true at lower doses of \gls{car}
|
||
expressing T cells in the DMS group vs the bead group. To test this, we used a
|
||
human xenograft model of B cell \gls{all} by intravenously injecting \gls{nsg}
|
||
mice with \num{1e6} Nalm-6 tumor cells expression firefly
|
||
luciferase\cite{Fraietta2018}. After \SI{7}{\day} of tumor cell growth
|
||
(\cref{fig:mouse_dosing_overview}), we intravenously injected saline or three
|
||
doses (high, medium, and low) of \gls{car} T cells from either bead or \gls{dms}
|
||
cultures expanded for \SI{14}{\day}. We quantified \ptcarp{} bead and \gls{dms}
|
||
groups using the \gls{ptnl} assay (\cref{tab:mouse_dosing_results}).
|
||
|
||
Before injecting the T cells into the mice, we quantified their phenotype and
|
||
growth. We observed that for this expansion, the bead and \gls{dms} T cells
|
||
produced similar numbers of \ptmem{} T cells, and the beads even had a higher
|
||
fraction of CD45RA, which is present on lower-differentiated \glspl{tn} and
|
||
\glspl{tscm} (\cref{fig:mouse_dosing_qc_mem}). However, the \pthp{} of
|
||
the final product was higher in \gls{dms} (\cref{fig:mouse_dosing_qc_cd4}). The
|
||
\gls{dms} T cells also expanded more robustly than the beads
|
||
(\cref{fig:mouse_dosing_qc_growth}).
|
||
|
||
In the Nalm-6/\gls{nsg} xenograft model, we observed lower tumor burden and
|
||
significantly longer survival of bead and \gls{dms}-treated mice at all doses
|
||
compared to the saline groups (\cref{fig:mouse_dosing_ivis}). Importantly, at
|
||
each dose we observed that the \gls{dms}-treated mice had much lower tumor
|
||
burden and significantly higher survival than their bead-treated counterparts
|
||
(\cref{fig:mouse_dosing_ivis_survival}). When factoring the percentage T cells
|
||
in each dose that expressed the \gls{car}, we note that survival of the low
|
||
\gls{dms} dose (which had similar total \gls{car} T cells compared to the bead
|
||
medium dose and less than the bead high dose) is significantly higher than that
|
||
of both the bead medium dose and the bead high dose
|
||
(\cref{fig:mouse_dosing_ivis_survival_comp}). Overall, the Kaplan-Meier survival
|
||
of Nalm-6 tumor bearing \gls{nsg} mice shown in the
|
||
\cref{fig:mouse_dosing_ivis_survival} was up to day 40 as reported
|
||
elsewhere\cite{Fraietta2018}. However, we also included a Kaplan-Meier figure up
|
||
to day 46 (\cref{fig:mouse_dosing_ivis_survival_full}) where most of the mice
|
||
euthanized from day 40 through day 46 from \gls{dms} groups showed no or very
|
||
small fragment of spleen which was due to \gls{gvhd} responses. Similar
|
||
\gls{gvhd} responses were reported earlier in \gls{nsg} mice where the mice
|
||
injected with human \gls{pbmc} exhibited acute \gls{gvhd} between
|
||
\SIrange{40}{50}{\day} post intravenous injection\cite{Ali2012}. Notably, both
|
||
survival analyses (up to day 40 in \cref{fig:mouse_dosing_ivis_survival} and up
|
||
to day 46 in \cref{fig:mouse_dosing_ivis_survival_full}) confirmed that
|
||
\gls{dms}-expanded groups outperformed bead-expanded groups in terms of
|
||
prolonging survival of Nalm-6 tumor challenged \gls{nsg} mice.
|
||
|
||
Together, these data suggested that \glspl{dms} produce T cells that are not
|
||
only more potent that bead-expanded T cells (even when accounting for
|
||
differences in \gls{car} expression) but also showed that \gls{dms} expanded T
|
||
cells are effective at lower doses. Given the quality control data of the T
|
||
cells prior to injecting into the mice, it seems that this advantage is either
|
||
due to the higher \pthp{} or the overall fitness of the T cells given the higher
|
||
expansion in the case of \gls{dms}
|
||
(\cref{fig:mouse_dosing_qc_cd4,fig:mouse_dosing_qc_growth}). It was likely not
|
||
due to the memory phenotype given that it was actually slightly higher in the
|
||
case of beads (\cref{fig:mouse_dosing_qc_mem}).
|
||
|
||
\subsection{Beads and DMSs Perform Similarly at Earlier Timepoints}
|
||
|
||
We then asked how T cells harvested using either beads or \gls{dms} performed
|
||
when harvested at earlier timepoints\cite{Ghassemi2018}. We performed the same
|
||
experiments as described in \cref{fig:mouse_dosing_overview} with the
|
||
modification that T cells were only grown and harvested after \SI{6}{\day},
|
||
\SI{10}{\day}, or \SI{14}{\day} of expansion
|
||
(\cref{fig:mouse_timecourse_overview}). T cells were frozen after harvest, and
|
||
all timepoints were thawed at the same time prior to injection. The dose of T
|
||
cells injected was \num{1.25e6} cells per mouse (the same as the high dose in
|
||
the first experiment). All other characteristics of the experiment were the
|
||
same.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_overview.png}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse Experimental Overview]
|
||
{Overview of \invivo{} experiment to test \gls{car} T cells using either
|
||
\glspl{dms} or bead harvested at varying timepoints.
|
||
}
|
||
\label{fig:mouse_timecourse_overview}
|
||
\end{figure*}
|
||
|
||
As was the case with the first \invivo{} experiment, T cells activated with
|
||
\glspl{dms} expanded much more efficiently compared to those expanded with beads
|
||
(\cref{fig:mouse_timecourse_qc_growth}). When we quantified the \ptcarp{} of T
|
||
cells harvested at each timepoint, we noted that the bead group had much higher
|
||
\ptcar{} expression at earlier timpoints compared to \gls{dms}, while they
|
||
equalized at later timepoints (\cref{fig:mouse_timecourse_qc_car}). In addition,
|
||
overall \ptcar{} expression decreased at later timepoints, indicating that
|
||
\gls{car} transduced T cells either grow slower or died faster compared to
|
||
untransduced cells. The \pthp{} of the harvested T cells was higher overall in
|
||
\gls{dms} expanded T cells but decreased with increasing timepoints
|
||
(\cref{fig:mouse_timecourse_qc_cd4}). The \ptmemp{} was similar at day 6
|
||
between bead and \gls{dms} groups but the \gls{dms} group had higher \ptmemp{}
|
||
at day 14 despite the overall \ptmemp{} decreasing with time as shown elsewhere
|
||
(\cref{fig:mouse_timecourse_qc_mem})\cite{Ghassemi2018}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_qc.png}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_growth}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_car}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_cd4}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_mem}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse T cell Characteristics]
|
||
{Characteristics of T cells harvested at varying timepoints injected into NSG
|
||
mice.
|
||
\subcap{fig:mouse_timecourse_qc_growth}{Fold change of T cells (each
|
||
timepoint only includes the runs that were harvested at day 14).}
|
||
Fractions of T cell subtypes in the day 14 product including
|
||
\subcap{fig:mouse_timecourse_qc_car}{\ptcarp{}},
|
||
\subcap{fig:mouse_timecourse_qc_cd4}{\pthp{}}, and
|
||
\subcap{fig:mouse_timecourse_qc_mem}{\ptmemp{}}.
|
||
}
|
||
\label{fig:mouse_timecourse_qc}
|
||
\end{figure*}
|
||
|
||
We analyzed the tumor burden using \gls{ivis} which showed that mice that
|
||
received T cells from any group performed better than those that received only
|
||
saline (\cref{fig:mouse_timecourse_ivis}). Note that unlike the previous
|
||
experiment, many of the mice survived until day 40 at which point \gls{gvhd}
|
||
began to take effect (after euthanizing the mice at day 42, most had small or no
|
||
spleen). When comparing bead and \gls{dms} groups, the \gls{dms} T cells still
|
||
seemed superior to the bead group, at least initially (note that in this case
|
||
they had similar numbers of \ptcar{} cells). At day 6, both \gls{dms} and bead
|
||
groups seemed to eradicate the tumor initially, after which it came back after
|
||
day 21 for the bead and day 28 for the \gls{dms} group. The day 10 groups
|
||
performed somewhere in between, where they increased linearly unlike the day 6
|
||
groups but not as quickly as the day 14 groups. In the case of the \gls{dms} day
|
||
10 group, it also appeared like a few mice actually performed better than all
|
||
other groups in regard to the final tumor burden.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_ivis.png}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_ivis_images}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_ivis_plots}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse IVIS Results]
|
||
{\glspl{dms} exhibit superior anti-tumor activity \invivo{} at day 14 compared
|
||
to beads but are similar to beads at lower timepoints.
|
||
\subcap{fig:mouse_timecourse_ivis_images}{IVIS images for day 6 to day 42 of
|
||
mice treated with varying doses of \gls{car} T cells grown with beads or
|
||
\glspl{dms}.}
|
||
\subcap{fig:mouse_timecourse_ivis_plots}{Quantified dotplots of the images
|
||
in (\subref{fig:mouse_timecourse_ivis_images}). Numbers beneath each dot
|
||
represent the number of mice at that timepoint.},
|
||
}
|
||
\label{fig:mouse_timecourse_ivis}
|
||
\end{figure*}
|
||
|
||
\section{Discussion}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_summary.png}
|
||
\phantomsubcaption\label{fig:mouse_summary_1}
|
||
\phantomsubcaption\label{fig:mouse_summary_2}
|
||
|
||
\endgroup
|
||
\caption[Mouse Summary]
|
||
{Summary of cells injected into mice during for
|
||
\subcap{fig:mouse_summary_1}{the first mouse experiment} and
|
||
\subcap{fig:mouse_summary_2}{the second mouse experiment}. The y axis
|
||
maximum is set to the maximum number of cells injected between both
|
||
experiments (\num{1.25e6}). Note that the \gls{car} was quantified using a
|
||
separate panel than the rest of the markers.
|
||
}
|
||
\label{fig:mouse_summary}
|
||
\end{figure*}
|
||
|
||
The total number of T cells injected for each \invivo{} experiment are shown in
|
||
\cref{fig:mouse_summary}.
|
||
|
||
When we tested bead and \gls{dms} expanded \gls{car} T cells, we found that the
|
||
\gls{dms} expanded \gls{car} T cells outperformed bead groups in prolonging
|
||
survival of Nalm-6 tumor challenged (intravenously injected) \gls{nsg} mice.
|
||
\gls{dms} expanded CAR-T cells were very effective in clearing tumor cells as
|
||
early as \SI{7}{\day} post \gls{car} T injection even at low total T cell dose
|
||
compared to the bead groups where tumor burden was higher than \gls{dms} groups
|
||
across all the total T cell doses tested here. More interestingly, when only
|
||
\gls{car}-expressing T cell doses between bead and \gls{dms} groups were
|
||
compared, \gls{dms} group had significantly higher survival effects over similar
|
||
or higher CAR expression T cell doses from bead group. All these results suggest
|
||
that the T cells in \gls{dms} groups (compared to bead group) resulted in highly
|
||
effective \gls{car} T cells that can efficiently kill tumor cells.
|
||
|
||
When comparing the total number of T cells of different phenotypes, we observed
|
||
that when comparing low-dose \gls{dms} group to the mid- bead groups (which had
|
||
similar numbers of \gls{car} T cells), the number of \ptmem{} (both with and
|
||
without CD45RA) T cells injected was much lower in the \gls{dms} group
|
||
(\cref{fig:mouse_summary_1}). This could mean several things. First, the
|
||
\ptmem{} phenotype may have nothing to do with the results seen here, at least
|
||
in this model. While this may have been the case in our hands, this would
|
||
contradict previous evidence suggesting that \gls{tn} and \gls{tcm} cells work
|
||
better in almost the same model (the only difference being Raji cells in place
|
||
of Nalm-6 cells, both of which express CD19)\cite{Sommermeyer2015}. Second, the
|
||
distribution of \gls{car} T cells across different subtypes of T cells was
|
||
different between the \gls{dms} and bead groups (with possibly higher
|
||
correlation of \gls{car} expression and the \ptmem{} phenotype). It is hard to
|
||
assess this without strong assumptions as the \gls{car} was quantified using a
|
||
separate flow panel relative to the other markers.
|
||
|
||
We can also make a similar observation for the number of \pth{} T cells injected
|
||
(\cref{fig:mouse_summary_1}). In this case, either the \pth{} phenotype doesn't
|
||
matter in this model (or the \ptk{} population matters much more), or the
|
||
distribution of \gls{car} is different between CD4 and CD8 T cells in a manner
|
||
that favors the \gls{dms} group. While in a glioblastoma model and not a B-cell
|
||
\gls{all} model, previous groups have shown that \pthp{} T cells are important
|
||
for response\cite{Wang2018}.
|
||
|
||
When testing \gls{car} T cells at earlier timepoints relative to day 14 as used
|
||
in the first \invivo{} experiment, we noted that none of the \gls{car}
|
||
treatments seemed to work as well as they did in the first experiment. However,
|
||
the total number of \gls{car} T cells was generally much lower in this second
|
||
experiment relative to the first (\cref{fig:mouse_summary}). Only the day 6
|
||
group had \gls{car} T cell numbers comparable to the weakest dose of bead cells
|
||
given in the first experiment, and these T cells were harvested at earlier
|
||
timepoints than the first mouse experiment and thus may not be safely
|
||
comparable. Furthermore, the \ptcarp{} decreased over time, which suggested that
|
||
the transduced T cells grew slower. This has been observed elsewhere and could
|
||
be due to tonic signaling\cite{GomesSilva2017}. The lower overall \gls{car}
|
||
doses may explain why at best, the tumor seemed to be in remission only
|
||
temporarily. Even so, the \gls{dms} group seemed to perform better at day 6 as
|
||
it held off the tumor longer, and also slowed the tumor progression relative to
|
||
the bead group at day 14 (\cref{fig:mouse_timecourse_ivis_plots}).
|
||
|
||
Taken together, these data suggest that the \gls{dms} platform produces T cells
|
||
that have an advantage \invivo{} over beads. While we may not know the exact
|
||
mechanism, our data suggests that the responses are unsurprisingly influenced by
|
||
the \ptcarp{} of the final product. Followup experiments would need to be
|
||
performed to determine the precise phenotype responsible for these responses in
|
||
our hands.
|
||
|
||
\chapter{CONCLUSIONS AND FUTURE WORK}\label{conclusions}
|
||
|
||
\section{Conclusions}
|
||
|
||
This dissertation describes the development of a novel T cell expansion
|
||
platform, including the fabrication, quality control, and biological validation
|
||
of its performance both \invitro{} and \invivo{}. Development of such a system
|
||
would be meaningful even if it only performed as well as current methods, as
|
||
adding another method to the arsenal of the growing T cell manufacturing
|
||
industry would reduce the reliance on a small number of companies that currently
|
||
license magnetic bead-based T cell expansion technology. However, we
|
||
additionally show that the \gls{dms} platform expands more T cells on average,
|
||
including highly potent \ptmem{} and \pth{} T cells, and produces higher
|
||
percentages of both. If commercialized, this would be a compelling asset the T
|
||
cell manufacturing industry.
|
||
|
||
In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
|
||
\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
|
||
the fabrication process to ensure that the \gls{dms} can be made within a
|
||
targeted specification. These \gls{qc} steps all rely on common, relatively
|
||
cost-effective assays such as the \gls{haba} assay, \gls{bca} assay, and
|
||
\glspl{elisa}, thus other labs and commercial entities should be able to perform
|
||
them. The microcarriers themselves are an off-the-shelf product available from
|
||
reputable vendors, and they have a regulatory history in human cell therapies
|
||
that will aid in clinical translation\cite{purcellmain}. Both these will help
|
||
in translatability. On average, we demonstrated that the \gls{dms} outperforms
|
||
state-of-the-art bead-based T cell expansion technology in terms of total fold
|
||
expansion, \ptmemp{}, and \pthp{} by \SI{131}{\percent}, \SI{3.5}{\percent}, and
|
||
\SI{7.4}{\percent} controlling for donor, operator, and a variety of process
|
||
conditions.
|
||
|
||
In addition to larger numbers of potent T cells, other advantages of our
|
||
\gls{dms} approach are that the \glspl{dms} are large enough to be filtered
|
||
(approximately \SI{300}{\um}) using standard \SI{40}{\um} cell filters or
|
||
similar. If the remaining cells inside that \glspl{dms} are also desired,
|
||
digestion with dispase or collagenase may be used. Collagenase D may be
|
||
selective enough to dissolve the \gls{dms} yet preserve surface markers which
|
||
may be important to measure as critical quality attributes \glspl{cqa}
|
||
(\cref{fig:collagenase_fx}). Furthermore, our system should be compatible with
|
||
large-scale static culture systems such as the G-Rex bioreactor or perfusion
|
||
culture systems, which have been previously shown to work well for T cell
|
||
expansion\cite{Forget2014, Gerdemann2011, Jin2012}.
|
||
|
||
In \cref{aim2a}, we developed a modeling pipeline that can be used by commercial
|
||
entities as the scale up this process to identify \glspl{cqa} and \gls{cpp}.
|
||
These are highly important for a variety of reasons. First, understanding
|
||
pertinent \glspl{cpp} allow manufacturers to operate their process at optimal
|
||
conditions. This is important for anti-tumor cell therapies, where the prospects
|
||
of a patient can urgently depend on receiving therapy in a timely manner.
|
||
Optimal process conditions allow T cells to be expanded as quickly as possible
|
||
for the patient, while also minimizing cost for the manufacturer. Second,
|
||
\glspl{cqa} can be used to define process control schemes as well as release
|
||
criteria. Process control, and with it the ability to predict future outcomes
|
||
based on data obtained at the present, is highly important for cell therapies
|
||
given that batch failures are extremely expensive\cite{Harrison2019}, and
|
||
predicting a batch failure would allow manufacturers to restart the batch in a
|
||
timely manner without wasting resources. Furthermore, \glspl{cqa} can be used to
|
||
define what a `good' vs `bad' product is, which will important help anticipate
|
||
dosing and followup procedures in the clinic if the T cells are administered. In
|
||
the aim, we cannot claim to have found the ultimate set of \glspl{cqa} and
|
||
\glspl{cpp}, as we used tissue culture plates instead of a bioreactor and we
|
||
only used one donor. However, we have indeed outlined a process that others may
|
||
use to find these for their process. In particular, the 2-phase modeling process
|
||
we used (starting with a \gls{doe} and collecting data longitudinally) is a
|
||
strategy that manufacturers can easily implement. Also, collecting secretome and
|
||
metabolome is easily generalized to any setting and to most bioreactors and
|
||
expansion systems, as they can be obtained with relatively inexpensive equipment
|
||
(Luminex assay, benchtop \gls{nmr}, etc) without disturbing the cell culture.
|
||
|
||
In \cref{aim2b}, we further explored additional tuning knobs that could be used
|
||
to control and optimize the \gls{dms} system. We determined that altering the
|
||
\gls{dms} concentration temporally has profound effects on the phenotype and
|
||
expansion rate. This agrees with other data we obtained in \cref{aim2a} and with
|
||
what others have generally reported about signal strength and T cell
|
||
differentiation\cite{Gattinoni2012, Lozza2008, Lanzavecchia2005, Corse2011}. We
|
||
did not find any mechanistic relationship between either integrin signaling or
|
||
\gls{il15} signaling. In the case of the former, it may be more likely that the
|
||
\glspl{dms} surfaces are saturated to the point of sterically hindering any
|
||
integrin interactions with the collagen surface. In the case of \gls{il15} more
|
||
experiments likely need to be done in order to plausibly rule out this mechanism
|
||
and/or determine if it is involved at all.
|
||
|
||
In \cref{aim3} we determined that the \glspl{dms} expand T cells that also
|
||
performed better than beads \invivo{}. In the first experiment we performed, the
|
||
results were very clearly in favor of the \glspl{dms}. In the second experiment,
|
||
even the \gls{dms} group failed to fully control the tumor burden, but this is
|
||
not surprising given the low \ptcarp{} across all groups. Also, despite this,
|
||
the \gls{dms} group appeared to control the tumor better on average for early,
|
||
mid, and late T cell harvesting timepoints. It was not clear if this effect was
|
||
due to increased \pthp{}, \ptmemp{}, or fitness of the \gls{dms}-expanded T
|
||
cells given their higher expansion rate. More data is needed to establish which
|
||
phenotype is responsible for the results we observed, as we did not include the
|
||
\gls{car} in the same panel as the other phenotype surface markers, making it
|
||
difficult to reliably say the identity of the \ptcar{} cells.
|
||
|
||
Finally, while we have demonstrated the \gls{dms} system in the context of
|
||
\gls{car} T cells, this method can theoretically be applied to any T cell
|
||
immunotherapy which responds to \acd{3}/\acd{28} \gls{mab} and cytokine
|
||
stimulation. These include \glspl{til}, virus-specific T cells, T cells
|
||
engineered to express $\upgamma\updelta$ \glspl{tcr}, $\upgamma\updelta$ T
|
||
cells, T cells with transduced-\gls{tcr}, and \gls{car}-\gls{tcr} T
|
||
cells\cite{Cho2015, Straetemans2018, Robbins2011, Brimnes2012, Baldan2015,
|
||
Walseng2017}. Similar to \glspl{car} against CD19 used in liquid tumors, these
|
||
T cell immunotherapies would similarly benefit from the increased proliferative
|
||
capacity, metabolic fitness, migration, and engraftment potential characteristic
|
||
of naïve and memory phenotypes\cite{Blanc2018, Lalor2016, Rosato2019}. Indeed,
|
||
since these T cell immunotherapies are activated and expanded with either
|
||
soluble \glspl{mab} or bead-immobilized \glspl{mab}, our system will likely
|
||
serve as a drop-in substitution to provide these benefits.
|
||
|
||
\section{Future Directions}
|
||
|
||
There are several important next steps to perform with this work, many of which
|
||
will be relevent to using this technology in a clinical trial:
|
||
|
||
\subsection{Using GMP Materials}
|
||
|
||
While this work was done with translatability and \gls{qc} in mind, an important
|
||
feature that is missing from the process currently is the use of \gls{gmp}
|
||
materials. The microcarriers themselves are made from porcine-derived collagen,
|
||
which itself is not \gls{gmp}-compliant due to its non-human animal origins.
|
||
However, using any other source of collagen should work so long as the structure
|
||
of the microcarriers remains relatively similar and it has lysine groups that
|
||
can react with the \gls{snb} to attach \gls{stp} and \glspl{mab}. Obviously
|
||
these would need to be tested and verified, but these should not be
|
||
insurmountable. Furthermore, the \gls{mab} binding step requires \gls{bsa} to
|
||
prevent adsorption to the non-polar polymer walls of the reaction tubes. A human
|
||
carrier protein such as \gls{hsa} could be used in its place to eliminate the
|
||
non-human animal origin material, but this could be much more expensive.
|
||
Alternatively, the use of protein could be replaced altogether by a non-ionic
|
||
detergent such as Tween-20 or Tween-80, which are already used for commercial
|
||
\gls{mab} formulations for precisely this purpose\cite{Kerwin2008}. Validating
|
||
the process with Tween would be the best next step to eliminate \gls{bsa} from
|
||
the process. The \gls{stp} and \glspl{mab} in this work were not
|
||
\gls{gmp}-grade; however, they are commonly used in clinical technology such as
|
||
dynabeads and thus the research-grade proteins used here could be easily
|
||
replaced. The \gls{snb} is a synthetic small molecule and thus does not have any
|
||
animal-origin concerns.
|
||
|
||
\subsection{Mechanistic Investigation}
|
||
|
||
Despite the improved outcomes in terms of expansion and phenotype relative to
|
||
beads, we don't have a good understanding of why they \gls{dms} platform works
|
||
as well as it does. The following are several plausible hypotheses and a
|
||
proposed experiment for testing them:
|
||
|
||
\subsubsection{Cytokine Cross-talk}
|
||
|
||
As hypothesized in the beginning of this work, the \gls{dms} may derive their
|
||
advantage through increased cytokine cross-talk. While this work found that
|
||
blocking \il{15} did not lead to differences in outcome, other cytokines could
|
||
be explored in a similar vein.
|
||
|
||
An efficient test of this hypothesis would be to simply incubate T cells grown
|
||
with either bead or \glspl{dms} with a cocktail of \glspl{mab} each feeding
|
||
cycle that target the cytokines seen in \cref{fig:doe_luminex}, assuming that at
|
||
least a few of the targeted cytokines will cause a difference. The experiment
|
||
should be sized appropriately such that the second order interaction effect can
|
||
be resolved (that is, the effect of adding the cocktail conditional on the
|
||
activation method). In these terms, we hypothesize that the growth and phenotype
|
||
will be more similar between the beads and \glspl{dms} when the cocktail is
|
||
added, while the \gls{dms} will have better expansion and phenotype when the
|
||
cocktail is not added. If this experiment shows any effects, the cytokines
|
||
responsible can be resolved by testing individually (or in small pools).
|
||
|
||
One caveat with this approach is that it assumes that the \gls{mab} cocktail
|
||
will completely quench their target cytokines between each feed cycle. This assumption
|
||
can be tested by running luminex with each cocktail addition. If a given
|
||
cytokine is undetectable, this indicates that the blocking \gls{mab} completely
|
||
quenched all target cytokine at the time of addition and in the time between
|
||
feeding cycles.
|
||
|
||
\subsubsection{Interior cell phenotype}
|
||
|
||
Unlike the beads, the \glspl{dms} have interior and exterior surfaces. We
|
||
demonstrated that some T cell expand on the interior of the \glspl{dms}, and is
|
||
plausible that these cells are phenotypically different than those growing on
|
||
the exterior or completely detached from the microcarriers, and that this leads
|
||
to an asymmetric cytokine cross-talk which accounts for the population-level
|
||
differences seen in comparison to the beads.
|
||
|
||
Experimentally, the first step involves separating the \glspl{dms} from the
|
||
loosely or non-adhered T cells and digesting the \glspl{dms} with \gls{cold}
|
||
(concentrations of \SI{10}{\ug\per\ml} will completely the \glspl{dms} within
|
||
\SIrange{30}{45}{\min}) isolate the interior T cells. Unfortunately, only
|
||
\SIrange{10}{20}{\percent} of all cells will be on the interior, so the interior
|
||
group may only have cells on the order of \num{1e3} to \num{1e4} for analysis. A
|
||
good first pass experiment would be to analyze both populations with a T cell
|
||
differentiation/activation state flow panel first (since flow cytometry is
|
||
relatively cheap and doesn't require a large number of cells) to simply
|
||
establish if the two groups are different phenotypes or are in a different state
|
||
of activation. From there, more in-depth analysis using \gls{cytof} or another
|
||
high-dimensionality method may be used to evaluate differential cytokine
|
||
expression.
|
||
|
||
\subsubsection{Antibody Surface Density}
|
||
|
||
While our \gls{doe} experiments showed a relationship between activating
|
||
\gls{mab} density and number of cells, we don't know how the \gls{mab} surface
|
||
density of the \gls{dms} compares to that of the beads. In all likelihood, the
|
||
\gls{mab} density on the \gls{dms} surface is lower (given the number of total
|
||
binding sites on \gls{stp} and the number of \glspl{mab} that actually bind)
|
||
which may lead to differences in performance\cite{Lozza2008}.
|
||
|
||
Before attempting this experiment, it will be vital to improve the \gls{dms}
|
||
manufacturing process such that \gls{mab} binding is predictable and
|
||
reproducible (see below). Once this is established, we can then determine the
|
||
amount of \glspl{mab} that bind to the beads, which could be performed much like
|
||
the \gls{mab} binding step is quantified in the \gls{dms} process (eg with
|
||
ELISA, \cref{fig:dms_flowchart}). Knowing this, we can vary the
|
||
\gls{mab} surface density for both the bead and the \glspl{dms} using a dummy
|
||
\gls{mab} as done previously with the \gls{doe} experiments in \cref{aim2a}.
|
||
Using varying surface densities that are matched per-area between the beads and
|
||
\glspl{dms} we can then activate T cells and assess their growth/phenotype as a
|
||
function of surface density and the presentation method.
|
||
|
||
\subsection{Reducing Ligand Variance}
|
||
|
||
While we have robust quality control steps to quantify each step of the
|
||
\gls{dms} coating process, we still see high variance across time and personnel
|
||
(\cref{fig:dms_coating}). This is less than ideal for translation.
|
||
|
||
When investigating the \gls{mab} and \gls{stp} binding, it appears that there is
|
||
a significant variance both between and within different experiments (even
|
||
within the same operator). The following are a list of variance sources and
|
||
potential mitigation strategies:
|
||
|
||
\begin{description}
|
||
\item[Mass loss during autoclaving --] In order to ensure a consistent reaction
|
||
volume, we mass the tube after adding carriers and \gls{pbs} prior to
|
||
autoclaving. Autoclaving and washing will cause variations in the liquid
|
||
level, and these are corrected using the pre-recorded tube mass. However, this
|
||
assumes that the mass of the tube never changes, which may or may not be true
|
||
in an autoclave where the temperature easily causes deformation of the plastic
|
||
tube material. This can easily be tested by autoclaving empty tubes and
|
||
observing a mass change. If there is a mass change, it may be mitigated by
|
||
pre-autoclaving tubes (assuming that autoclaving is idempotent with respect to
|
||
mass loss), or alternatively we could estimate the bias by autoclaving a
|
||
set of tubes, recording the mean mass loss, and using this to correct the tube
|
||
mass for downstream calculations.
|
||
\item[Errors in initial microcarrier massing --] The massing of microcarriers at
|
||
the very beginning of the process requires care due to the low target mass and
|
||
the propensity for both the plastic tubes and microcarriers to accumulate
|
||
static. Oddly, the biotin attachment readout does not seem to be much affected
|
||
by the mass of carriers (\cref{fig:dms_qc_doe}); however, this merely means
|
||
that errors in carrier mass lead to different biotin surface densities, which
|
||
downstream causes different ratios of \gls{stp} and \gls{mab} attachment since
|
||
these relationships are non-linear with respect to biotin surface density
|
||
(\cref{fig:stp_coating,fig:mab_coating}) (this is in addition to the fact that
|
||
having more or less carriers will bias the total amount of \gls{stp} and
|
||
\gls{mab} able to bind). A quick survey of operators revealed that acceptable
|
||
margins for error in mass range from \SIrange{2.5}{5.0}{\percent} (eg, a
|
||
target value $X$ \si{\mg} will be accepted as $X$ at plus or minus these
|
||
margins). These could easily be reduced and standardized via protocol.
|
||
Additionally, we do not currently record the exact mass of microcarriers
|
||
weighed for each batch. Knowing this would allow us to pinpoint how much of
|
||
this variance is due to our acceptable measurement margins and what errors may
|
||
arise from static and other instrument noise.
|
||
\item[Centrifugation after washing --] After coating the \gls{dms} with \gls{snb},
|
||
\gls{stp}, or \glspl{mab}, they must be washed. After washing, they must be
|
||
massed in order to ensure the reaction volume is consistent. Ideally, the
|
||
tubes are centrifuged after washing to ensure that all liquid is at the bottom
|
||
prior to beginning the next coating step. Upon survey, not all operators
|
||
follow this protocol, and the protocols are not written such to make this
|
||
obvious. Therefore, protocols will be revised followed by additional training.
|
||
\item[Accidental microcarrier removal --] When washing the microcarriers after a
|
||
coating step, liquid is aspirated using a stripette. The carriers should be at
|
||
the bottom of the tube during this aspiration step. Depending on the skill and
|
||
care of the operator, carriers may be aspirated with the liquid during this
|
||
step. If this happens, downstream \gls{qc} assays will not reflect the true
|
||
binding magnitude, as these assays assume the number of carriers is constant.
|
||
\item[\gls{bsa} binding kinetics --] Prior to \gls{mab} addition, \gls{bsa} is
|
||
added to the \gls{mab} to block binding to the tubes. \glspl{mab} are added
|
||
immediately after adding the \gls{bsa}, which means the \gls{bsa} has almost
|
||
no time to mix completely and thus the \gls{mab} could come into contact with
|
||
the sides of the tube unshielded. In theory this could cause the \gls{mab}
|
||
reading to be lower on the \gls{elisa} during \gls{qc}. This problem may be
|
||
minor since significant binding would only occur if the \gls{mab}/plastic
|
||
adhesion was quite fast and happened in the seconds prior to beginning
|
||
agitation. However, this problem is easily mitigated by agitating the tubes
|
||
with \gls{bsa} for several minutes prior to adding \gls{mab} to ensure even
|
||
mixing.
|
||
\item[Improving protein detection --] While the \gls{bca} assay and \gls{elisa}
|
||
are quite precise, they both have problems that could lead to systemic bias as
|
||
well as increases in random noise. The \gls{bca} assay is non-specific. All
|
||
our data shows consistent small (\SI{0.5}{\ug}) but negative readings when
|
||
adding zero \gls{snb}, which indicates that some background protein (or
|
||
something that behaves like a protein) may be present that the \gls{bca} assay
|
||
is detecting. The \gls{elisa} is specific to \gls{mab}; however, in our case
|
||
we need to run a blank (just \gls{pbs}, \gls{bsa}, and \glspl{mab} without
|
||
carriers) and subtract this from the reading, effectively doubling the assay
|
||
variance. Using \gls{hplc} would mitigate both of these issues. \gls{hplc} can
|
||
specifically detect species based on differences in charge and size, so it
|
||
will likely be able to resolve \gls{stp} without the extraneous bias
|
||
introduced via the \gls{bca} assay. In the case of \gls{elisa} it will not
|
||
have remove the need to run a blank, but it likely will have lower variance
|
||
due to its automated nature.
|
||
\end{description}
|
||
|
||
|
||
\subsubsection{Surface Stiffness}
|
||
|
||
The beads and \gls{dms} are composed of different materials: iron/polymer in the
|
||
former case and cross-linked gelatin in the latter. These materials likely have
|
||
different stiffnesses, and stiffness could play a role in T cell
|
||
activation\cite{Lambert2017}.
|
||
|
||
This hypotheses will be difficult to test directly, so it is advised to
|
||
eliminate other hypothesis before proceeding here. Direct testing could be
|
||
performed using a force probe to determine the Young's modulus of each
|
||
material\cite{Ju2017}. Since the microcarriers are porous and the cells will be
|
||
interacting with the bulk material itself, the void fraction and pore size will
|
||
need to be taken into account to find the bulk material properties of the
|
||
cross-linked gelatin\cite{Wang1984}.
|
||
|
||
\subsection{Additional Ligands and Signals on the DMSs}
|
||
|
||
In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated
|
||
on the surface of the \gls{dms}. The chemistry used for the \gls{dms} is very
|
||
general, and any molecule or protein that could be engineered with a biotin
|
||
ligand could be attached without any further modification. There are many other
|
||
ligands (in addition to integrin-binding domains and \il{15} complexes as
|
||
described at the end of \cref{aim2b}) that could have profound effects on the
|
||
expansion and quality of T cells which may be utilized. The simplest next step
|
||
is to simply vary the ratio of \acd{3} and \acd{28} signal. Another obvious
|
||
example is to attach \il{15}/\il{15R$\upalpha$} complexes to the surface to
|
||
mimic \textit{trans} presentation from other cell types\cite{Stonier2010}. Other
|
||
adhesion ligands or peptides such as GFOGER could be used to stimulate T cells
|
||
and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral
|
||
delivery systems could theoretically be attached to the \gls{dms}, greatly
|
||
simplifying the transduction step.
|
||
|
||
\subsection{Assessing Performance Using Unhealthy Donors}
|
||
|
||
All the work presented in this dissertation was performed using healthy donors.
|
||
This was mostly due to the fact that it was much easier to obtain healthy donor
|
||
cells and was much easier to control. However, it is indisputable that the most
|
||
relevant test cases of the \glspl{dms} will be for unhealthy patient T cells, at
|
||
least in the case of autologous therapies. In particular, it will be interesting
|
||
to see how the \gls{dms} performs when assessed head-to-head with bead-based
|
||
expansion technology given that even in healthy donors, we observed the
|
||
\gls{dms} platform to work where the beads failed
|
||
(\cref{fig:dms_exp_fold_change}).
|
||
|
||
\subsection{Translation to Bioreactors}
|
||
|
||
In this work we performed some preliminary experiments demonstrating that the
|
||
\gls{dms} platform can work in a Grex bioreactor. While an important first step,
|
||
more work needs to be done to optimize how this system will or can work in a
|
||
scalable environment using bioreactors. There are several paths to explore.
|
||
Firstly, the Grex itself has additional automation accessories which could be
|
||
tested, which would allow continuous media exchange and cytokine
|
||
administration. While this is an improvement from the work done here, it is
|
||
still a Grex and has all the disadvantages of an open system. Secondly, other
|
||
static bioreactors such as the Quantum hollow fiber bioreactor (Terumo) could be
|
||
explored. Essentially the \gls{dms} would be an additional matrix that could be
|
||
supplied to this system which would enhance its compatibility with T cells.
|
||
Finally, suspension bioreactors such as the classic \gls{cstr} or WAVE
|
||
bioreactors could be tried. The caveat with these is that the T cells only seem
|
||
to be loosely attached to the \gls{dms} throughout culture, so an initial
|
||
activation/transduction step in static culture might be necessary before moving
|
||
to a suspension system (alternatively the \gls{dms} could be coated with
|
||
additional adhesion ligands to make the T cells attach more strongly).
|
||
|
||
\onecolumn
|
||
\clearpage
|
||
|
||
\appendix
|
||
\chapter{META ANALYSIS DATABASE CODE}\label{sec:appendix_meta}
|
||
|
||
The code used to aggregate all experimental data was written primarily in
|
||
Python, with a subprocess running R in a Docker container to handle the flow
|
||
cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
|
||
hosted using \gls{aws} using their proprietary Aurora implementation.
|
||
|
||
% FIGURE explain what the colors mean
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/metaanalysis.png}
|
||
|
||
\endgroup
|
||
\caption[Meta-analysis overview]
|
||
{Overview of strategy used for meta-analysis. Colors: notebook (pink), input
|
||
files (green), analysis framework (blue), data store (cyan), analysis
|
||
pipeline (orange).}
|
||
\label{fig:meta_overview}
|
||
\end{figure*}
|
||
|
||
The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
|
||
|
||
\chapter{META ANALYSIS DONORS}\label{sec:appendix_donors}
|
||
|
||
\begin{table}[!h]
|
||
\caption{Donors used in meta-analysis}
|
||
\begin{subtable}[t]{\textwidth} \centering
|
||
\caption{characteristics}
|
||
\input{../tables/donor_chars.tex}
|
||
\phantomsubcaption\label{tab:meta_donors_chars}
|
||
\end{subtable}
|
||
|
||
\bigskip
|
||
|
||
\begin{subtable}[t]{\textwidth} \centering
|
||
\caption{phenotype (all in percents)}
|
||
\input{../tables/donor_phenotypes.tex}
|
||
\phantomsubcaption\label{tab:meta_donors_phenotypes}
|
||
\end{subtable}
|
||
\label{tab:meta_donors}
|
||
\end{table}
|
||
|
||
\chapter{BINDING KINETICS}\label{sec:appendix_binding}
|
||
|
||
\newcommand{\lig}{\textit{ligand}}
|
||
\newcommand{\rcp}{\textit{receptor}}
|
||
\newcommand{\ligs}{\textit{ligands}}
|
||
\newcommand{\rcps}{\textit{receptors}}
|
||
|
||
% TODO make notation consistent
|
||
|
||
To model binding kinetics of either \gls{stp} or \glspl{mab} (here called
|
||
\ligs{}), each microcarrier was assumed to be a porous sphere with a given
|
||
number of binding sites for the \ligs{} (here called \rcps{}). The \rcp{}/\lig{}
|
||
reaction was assumed to be instantaneous (which is reasonable given that these
|
||
are reactions between biotin and \gls{stp} which are extremely strong). From
|
||
this, we further assumed a spherical interface within each microcarrier and
|
||
aligned at the center wherein all \rcps{} in the interior were unbound and all
|
||
on the exterior were bound. At $t=0$ this interface was assumed to start with a
|
||
radius equal to that of the microcarrier, and shrunk down to radius of zero as
|
||
\ligs{} flowed into the porous microcarriers and bound. We assumed the
|
||
concentration of \lig{} to be zero at the interface and equal to the bulk
|
||
concentration at the exterior surface of the microcarrier. Furthermore, we
|
||
assumed that the interface move slowly relative to the diffusion of \lig{} into
|
||
the microcarriers, and thus we used a quasi-steady-state model to avoid solving
|
||
a boundary value problem with two movable boundaries (the interface radius and
|
||
the concentration in bulk).
|
||
|
||
The concentration profile of \lig{} in the microcarriers is given by Fick's
|
||
Second Law in spherical coordinates assuming only radial flux and steady state.
|
||
This with the boundary conditions as stated is:
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_ficks}
|
||
0 = \frac{1}{r^2}\frac{d}{dr}\left(r^2\frac{dC_L}{dr}\right)
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_bc_left}
|
||
C_L\rvert_{r_i} = 0
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_bc_right}
|
||
C_L\rvert_R = C_{L,b}
|
||
\end{equation}
|
||
|
||
Solving \cref{eqn:binding_ficks} we find the a relation for the concentration
|
||
profile in terms of the interfacial radius:
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_conc}
|
||
C_L = \frac{C_{L,b}}{r(1/r_i - 1/R)}\left(\frac{1}{r_i} - \frac{1}{r}\right)
|
||
\end{equation}
|
||
|
||
Solving \cref{eqn:binding_conc} for flux, the molar flow rate into the
|
||
microcarriers is given by:
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_molar_flow}
|
||
Q = 4\pi R^2N\rvert_R = \frac{-4\pi DC}{1/r_i - 1/R}
|
||
\end{equation}
|
||
|
||
Using the quasi-steady-state assumption, we can now find time-dependent
|
||
equations for the interfatial radius and the bulk concentration. The interfacial
|
||
volume in terms of molar flow rate is given by:
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_volume_change}
|
||
C_{R,0}\frac{dV_i}{dt} = -Q
|
||
\end{equation}
|
||
|
||
Substituting volume of a sphere and applying the chain rule:
|
||
|
||
\begin{equation}
|
||
\label{eqn:radial_radial_change}
|
||
\frac{dr_i}{dt} = \frac{-Q}{4\pi r_i^2C_{R,0}}
|
||
\end{equation}
|
||
|
||
The change in bulk concentration is simply given by:
|
||
|
||
\begin{equation}
|
||
\label{eqn:radial_conc_change}
|
||
\frac{dC_{L,b}}{dt} = \frac{-nQ}{V}
|
||
\end{equation}
|
||
|
||
Substituting \cref{eqn:binding_molar_flow} into \cref{eqn:radial_radial_change}
|
||
and \cref{eqn:radial_conc_change} yields \cref{eqn:stp_diffusion_1} and
|
||
\cref{eqn:stp_diffusion_2}.
|
||
|
||
The \gls{stp} binding kinetic profile was fit and calculated using the following
|
||
MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to
|
||
minimize the \inlinecode{SSE} output.
|
||
|
||
\lstinputlisting{../code/diffusion_stp.m}
|
||
|
||
The geometric diffusivity from above (the \inlinecode{geometry} variable) was
|
||
used in the below code to obtain the reaction profile for the \gls{mab} binding
|
||
step. The model is the same except for the parameters which were changes to
|
||
reflect the \gls{mab} coating process.
|
||
|
||
\lstinputlisting{../code/diffusion_mab.m}
|
||
|
||
\chapter{WASHING KINETICS CODE}\label{sec:appendix_washing}
|
||
|
||
The wash steps for the \gls{dms} were modeled using the following code:
|
||
|
||
\lstinputlisting{../code/microcarrier_diffusion_washing.m}
|
||
|
||
Complete output from this code is shown below:
|
||
|
||
\input{../code/washing_out.tex}
|
||
|
||
\chapter{REFERENCES}
|
||
\renewcommand{\chapter}[2]{} % noop the original bib section header
|
||
|
||
\bibliography{references}
|
||
|
||
\bibliographystyle{naturemag}
|
||
|
||
\end{document}
|