4978 lines
258 KiB
TeX
4978 lines
258 KiB
TeX
\documentclass{report}
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\usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry}
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\usepackage[section]{placeins}
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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,toc]{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[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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\usepackage{threeparttable}
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\usepackage{hyperref} % must be before cleveref
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\usepackage[capitalize]{cleveref}
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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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\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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\setlength{\cftsubsecnumwidth}{0.55in}
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\setlength{\cftfignumwidth}{0.5in}
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\setlength{\cfttabnumwidth}{0.5in}
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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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\newglossary[slg]{symbolslist}{syi}{syg}{Symbolslist}
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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{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{gmcsf}{GM-CSF}{granulocyte-macrophage colony stimulating factor}
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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}
|
||
\newacronym{qbd}{QbD}{quality-by-design}
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||
\newacronym{aws}{AWS}{Amazon Web Services}
|
||
\newacronym{qpcr}{qPCR}{quantitative polymerase chain reaction}
|
||
\newacronym{cstr}{CSTR}{continuously stirred tank bioreactor}
|
||
\newacronym{esc}{ESC}{embryonic stem cell}
|
||
\newacronym{msc}{MSC}{mesenchymal stromal cells}
|
||
\newacronym{scfv}{scFv}{single-chain fragment variable}
|
||
\newacronym{hepes}{HEPES}{4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid}
|
||
\newacronym{nhs}{NHS}{N-hydroxysulfosuccinimide}
|
||
\newacronym{tocsy}{TOCSY}{total correlation spectroscopy}
|
||
\newacronym{hplc}{HPLC}{high-performance liquid chromatography}
|
||
\newacronym{grex}{G-Rex}{Gas Permeable Rapid Expansion}
|
||
|
||
% symbols to make me sound mathier than I really am
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\newcommand{\evalat}[2]{#1\rvert_{#2}}
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|
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\newcommand{\newsymbol}[3]{
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||
\newglossaryentry{sym:#1}{name=\ensuremath{#2},
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description={#3},
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||
type=symbolslist}
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||
}
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||
|
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\newsymbol{diff}{D}{diffusion coefficient of ligand}
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||
\newsymbol{appdiff}{D_{app}}{apparent diffusion coefficient of ligand}
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||
\newsymbol{geodiff}{\beta}{geometric diffusivity, which is a fractional
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||
parameter representing the tortuousity and void fraction of the microcarrier}
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||
\newsymbol{mcligconc}{C_{L,m}}{concentration of ligand in microcarrier}
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||
\newsymbol{bulkligconc}{C_{L,b}}{concentration of ligand outside microcarriers}
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||
\newsymbol{mcrecconc}{C_{R,m}}{concentration of receptor inside microcarriers}
|
||
\newsymbol{flowrate}{Q}{molar flow rate of ligand}
|
||
\newsymbol{mcflux}{N_{m}}{flux of ligand in microcarrier}
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||
\newsymbol{rad}{r}{radial position in the microcarrier}
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||
\newsymbol{interrad}{r_i}{radius of unbound:bound receptor interface in
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||
microcarriers}
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||
\newsymbol{intervol}{V_i}{volume of unbound:bound receptor interface in
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microcarriers}
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||
\newsymbol{mcrad}{R}{average radius of microcarriers}
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||
\newsymbol{mcnum}{n}{number of microcarriers in bulk}
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||
\newsymbol{vol}{V}{volume of bulk liquid in which microcarriers are suspended}
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||
\newsymbol{time}{t}{time}
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||
|
||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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||
% SI units for uber nerds
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||
|
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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\gauge{gauge}
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||
\DeclareSIUnit\dms{DMS}
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\DeclareSIUnit\stp{STP}
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\DeclareSIUnit\snb{SNB}
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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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||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% commands for lazy farts like me
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||
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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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\newcommand{\mycommitteemember}[3]{
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||
\begin{flushleft}
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||
\noindent
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||
#1 \\
|
||
#2 \\
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||
\textit{#3}
|
||
\end{flushleft}
|
||
}
|
||
|
||
% a BME's best friend
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||
\newcommand{\invivo}{\textit{in vivo}}
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||
\newcommand{\invitro}{\textit{in vitro}}
|
||
\newcommand{\exvivo}{\textit{ex vivo}}
|
||
\newcommand{\Invivo}{\textit{In vivo}}
|
||
\newcommand{\Invitro}{\textit{In vitro}}
|
||
\newcommand{\Exvivo}{\textit{Ex vivo}}
|
||
|
||
% various CD-whatever crap
|
||
\newcommand{\cd}[1]{CD{#1}}
|
||
\newcommand{\anti}[1]{anti-{#1}}
|
||
\newcommand{\antih}[1]{anti-human {#1}}
|
||
\newcommand{\antim}[1]{anti-mouse {#1}}
|
||
\newcommand{\acd}[1]{\anti{\cd{#1}}}
|
||
\newcommand{\ahcd}[1]{\antih{\cd{#1}}}
|
||
\newcommand{\amcd}[1]{\antim{\cd{#1}}}
|
||
\newcommand{\pos}[1]{#1+}
|
||
\newcommand{\cdp}[1]{\pos{\cd{#1}}}
|
||
\newcommand{\pcd}[1]{\cdp{#1}~\si{\percent}}
|
||
\newcommand{\cdn}[1]{\cd{#1}-}
|
||
\newcommand{\ptmem}{\cdp{62L}\pos{CCR7}}
|
||
\newcommand{\ptmemp}{\ptmem{}~\si{\percent}}
|
||
\newcommand{\pth}{\cdp{4}}
|
||
\newcommand{\pthp}{\pth{}~\si{\percent}}
|
||
\newcommand{\ptk}{\cdp{8}}
|
||
\newcommand{\ptmemh}{\pth\ptmem}
|
||
\newcommand{\ptmemk}{\ptk\ptmem}
|
||
\newcommand{\dpthp}{$\Updelta$\pthp{}}
|
||
\newcommand{\ptcar}{\gls{car}+}
|
||
\newcommand{\ptcarp}{\ptcar~\si{\percent}}
|
||
|
||
% so I don't need to worry about abbreviating all the different interleukins
|
||
\newcommand{\il}[1]{\gls{il}-#1}
|
||
\newcommand{\ilr}[1]{\gls{il}-#1R}
|
||
|
||
% ...and this one is just plain annoying
|
||
\newcommand{\ilXVra}[1]{\ilr{15}$\upalpha$}
|
||
|
||
% DOE stuff I don't feel like typing ad-nauseam
|
||
\newcommand{\pilII}{\il{2} 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}
|
||
\newcommand{\pltread}{BioTek plate reader}
|
||
|
||
% 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}
|
||
\newcommand{\nVI}{NALM-6}
|
||
|
||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||
% ditto for environments
|
||
|
||
\newenvironment{mytitlepage}{
|
||
\begin{singlespace}
|
||
\begin{center}
|
||
}
|
||
{
|
||
\end{center}
|
||
\end{singlespace}
|
||
}
|
||
|
||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||
% begin document (proceed with caution)
|
||
|
||
\begin{document}
|
||
|
||
\pagenumbering{gobble}
|
||
|
||
\begin{titlepage}
|
||
\begin{mytitlepage}
|
||
\mytitle{}
|
||
|
||
\vfill
|
||
|
||
\Large{
|
||
A Dissertation \\
|
||
Presented to \\
|
||
The Academic Faculty \\
|
||
|
||
\vspace{1.5em}
|
||
|
||
by
|
||
|
||
\vspace{1.5em}
|
||
|
||
Nathan John Dwarshuis \\
|
||
|
||
\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 \\
|
||
December 2021
|
||
|
||
\vfill
|
||
|
||
COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
|
||
}
|
||
\end{mytitlepage}
|
||
\end{titlepage}
|
||
|
||
\onecolumn
|
||
\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: Sep 16, 2021
|
||
}
|
||
|
||
\clearpage
|
||
|
||
\vspace*{\fill}
|
||
|
||
\begin{center}
|
||
You cannot answer a question that you cannot ask, and you cannot ask a
|
||
question for which you have no words.
|
||
|
||
\medskip
|
||
|
||
\textit{Judea Pearl -- The Book of Why}
|
||
|
||
\nocite{Pearl2018}
|
||
\end{center}
|
||
|
||
\vspace{1in}
|
||
\vspace*{\fill}
|
||
|
||
\clearpage
|
||
|
||
\pagenumbering{roman}
|
||
\setcounter{page}{4}
|
||
|
||
\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, and 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
|
||
because reasons.
|
||
|
||
\clearpage
|
||
|
||
\tableofcontents
|
||
|
||
\clearpage
|
||
|
||
\listoftables
|
||
\addcontentsline{toc}{chapter}{LIST OF TABLES}
|
||
|
||
\clearpage
|
||
|
||
\listoffigures
|
||
\addcontentsline{toc}{chapter}{LIST OF FIGURES}
|
||
|
||
\clearpage
|
||
|
||
\printglossary[type=\acronymtype,title=LIST OF ABBREVIATIONS,toctitle=LIST OF
|
||
ABBREVIATIONS,style=index]
|
||
|
||
\clearpage
|
||
|
||
\printglossary[type=symbolslist,title=LIST OF SYMBOLS,toctitle=LIST OF SYMBOLS,
|
||
style=index]
|
||
|
||
\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 developed and characterized the \gls{dms} system, including
|
||
\gls{qc} steps. We also demonstrated the feasibility of expanding
|
||
high-quality T cells. In \cref{aim2a,aim2b}, we used \gls{doe} methodology to
|
||
optimize the \gls{dms} platform, and we developed a computational pipeline to
|
||
identify and model the effects of measurable \glspl{cqa} and \glspl{cpp} on the
|
||
final product. In \cref{aim3}, we demonstrated 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 clinical trials and beyond.
|
||
|
||
\clearpage
|
||
|
||
\pagenumbering{arabic}
|
||
|
||
\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, 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
|
||
\il{2}. 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. These include 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 activate 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 are 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 for expanding T
|
||
cells using porous microcarriers functionalized with \acd{3} and \acd{28}
|
||
\glspl{mab}. Microcarriers have historically been used in 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
|
||
increase quantity and quality of T cells as compared to state-of-the-art
|
||
bead-based expansion. We also hypothesized that such 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 \gls{qc} steps that are necessary for translation of this
|
||
platform into a scalable manufacturing setting. We also demonstrated 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 showed \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 demonstrated 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 \acrfull{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). 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 are 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 to control the outcome of process
|
||
and the quality of the final product. Examples include the type of media used
|
||
and the amount of \il{2} added. While these can be easy to control, the effect
|
||
they have on the final outcome is generally unknown. 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 excising tumor fragments
|
||
from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{} from
|
||
within these fragments, and then administered these lymphocytes 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 against melanoma \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}. \Acrlong{car} T cells overcome
|
||
this limitation by 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} for antigen recognition, a CD3$\upzeta$ domain for the stimulatory
|
||
signal, and a CD28, OX-40, or 4-1BB domains for the costimulatory signal. 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, automation and bioreactors will be
|
||
necessary. To this end, several choices 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, 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 is less prone to mistakes,
|
||
since most 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
|
||
\gls{grex} bioreactor (Wilson Wolf). This is effectively a tall tissue-culture
|
||
plate with a porous membrane at the bottom. This allows large volumes of media
|
||
to be loaded without suffocating the cells, which can exchange gas through the
|
||
membrane. 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. \gls{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 they 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 cells need to be shipped 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.
|
||
Sourcing 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 have an inhibitory effect on
|
||
immunotherapies\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 makes the
|
||
overall process more expensive as an additional separation step is 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,
|
||
and they can be stored easily under liquid nitrogen. Donors can also be screened
|
||
to find those with optimal anti-tumor cells. The key is overcoming \gls{gvhd}.
|
||
Obviously this could be done analogously to 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 (eg \glspl{zfn}, \glspl{talen},
|
||
or \gls{crispr}) to remove the native \gls{tcr} and thus 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 lymphoid
|
||
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 possess 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 safety, retro- or lentivirally
|
||
transduced T cell products must be tested for replication competent
|
||
vectors\cite{Wang2013}, and all cell products in general should be tested for
|
||
bacterial or fungal contamination.
|
||
|
||
\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 other 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.
|
||
|
||
There are many ways to activate T cells \invitro{}, but the simplest and most
|
||
common is to use \glspl{mab} that cross-link CD3 and CD28, 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 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 easy 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 can theoretically mimic many components
|
||
of the lymph node, it is hard to scale due to the complexity and inherent
|
||
variability of using cell lines in a \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 \invivo{}. None of these have been shown to 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 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 cultures
|
||
to operate more like traditional chemical engineering processes, 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 choices
|
||
have 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 similar surface modifications (if any). 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
|
||
permeable to small molecules) or not porous at all; in either case, cells can
|
||
only grow on the outer surface.
|
||
|
||
Microcarriers have been mainly used for 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,
|
||
all these cell types 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 where 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 \gls{tn} cells and thus presence and stimulation of collagen alone is not
|
||
sufficient for activation or expansion\cite{Hemler1990}; however, they have been
|
||
shown to possess many functions that may be useful for T cell expansion. First,
|
||
they can act in a costimulatory manner which leads to increased
|
||
proliferation\cite{Rao2000}. Furthermore, \gls{a2b1} and \gls{a2b2} seem to
|
||
protect Jurkat cells against Fas-mediated apoptosis in the presence of collagen
|
||
I\cite{Aoudjit2000, Gendron2003}. Finally, these receptors can 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 \ilXVra{}\cite{Lodolce1998} are generally
|
||
healthy but show a deficit in memory CD8 T cells, thus underscoring this
|
||
cytokine's importance in producing 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
|
||
\ilXVra{} \invitro{} has been shown to inhibit homeostatic
|
||
proliferation of resting human T cells\cite{MirandaCarus2005}.
|
||
|
||
\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 bond with heterotrimeric receptors
|
||
which share the common $\upgamma$ subchain (CD132) as well as the \il{2}
|
||
$\upbeta$ receptor (CD122). The difference is the third subchain which is either
|
||
the \il{2} $\upalpha$ receptor (CD25) or the \il{15} $\upalpha$ chain
|
||
respectively, both of which have high affinity for their respective ligands. The
|
||
\ilXVra{} 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, which leads to STAT5 activation,
|
||
which leads to T cell activation). \ilXVra{} 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, 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,
|
||
\ilXVra{} 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 \ilXVra{}
|
||
were able to generate memory T cells and proliferate only when other cells were
|
||
present which expressed \ilXVra{} \cite{Burkett2003, Schluns2004}. The
|
||
implication of this mechanism is that cells expression \ilXVra{} 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 \ilXVra{}/\il{15} complexes may bind to the $\upbeta$
|
||
and $\upgamma$ chains on the same cell, assuming each subchain is expressed and
|
||
soluble \il{15} is available\cite{Olsen2007}. Finally, \ilXVra{} itself can
|
||
exist in 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 many 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,'' which is the term used throughout this work)
|
||
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 of each parameter. 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 statistical 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 be
|
||
randomized. This is to guarantee that the tested parameters are independent of
|
||
any unobserved influences to the response, and thus allows the causal effect
|
||
of each parameter to be isolated completely\footnote{this is why \glspl{doe}
|
||
are sometimes called ``black box models;'' one can can safely say ``this
|
||
parameter causes that'' without paying attention to the causal structure of
|
||
the experiment}. 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'' 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 model. 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 performing 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). 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 multi-omic (or simply ``omic'')
|
||
techniques that can be used to collect such datasets, which can then be fit 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 to maximize high-quality products, 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 are 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 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
|
||
\gls{grex} 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}
|
||
|
||
This aim was to develop a functionalized 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[\Acrshort{dms} Manufacturin 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} in a 15, 50, or 250
|
||
\si{\ml} conical tube. The mass of the tube with the \gls{pbs} and microcarriers
|
||
were recorded to the nearest millimeter (subsequently referred to here as
|
||
``reaction mass''). The tube was centrifuged for \SI{30}{\second} at
|
||
\SI{4500}{\gforce} to ensure all microcarriers were at the bottom of the tube.
|
||
The tube was then autoclaved using a \SI{15}{\minute} cycle at
|
||
\SI{121}{\degreeCelsius} and \SI{100}{\kPa\of{\gauge}}.
|
||
|
||
All subsequent steps were done aseptically, and all reactions were carried out
|
||
at \SI{20}{\mg\of{\carrier}\per\ml} 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. The volume after these washes was corrected by massing the tube and its
|
||
contents and adding or removing \gls{pbs} until the ``reaction mass'' was
|
||
reached.
|
||
|
||
\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{2.5}{\nmol\of{\snb}\per\mg\of{\carrier}}
|
||
(unless otherwise noted) 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 attached to the microcarriers was determined indirectly by
|
||
measuring the biotin in solution via 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. The volume of the \gls{pbs} was
|
||
corrected by massing the tube and its contents and adding or removing \gls{pbs}
|
||
until the tube mass matched the ``reaction mass.''
|
||
|
||
To coat the microcarriers with \product{\gls{stp}}{Jackson
|
||
Immunoresearch}{016-000-114}, \SI{2}{\ug\of{\stp}\per\mg\of{\carrier}} was
|
||
added and allowed to react for \SI{60}{\minute} at \SI{700}{RPM} of agitation.
|
||
After the reaction, \SI{400}{\ul} supernatant (regardless of tube size) was
|
||
taken for the \product{\gls{bca} assay}{\thermo}{23225} in order to indirectly
|
||
quantify \gls{stp} attachment. Prior to the assay, the supernatent was filtered
|
||
through a \SI{40}{\um} cell strainer to remove any stray microcarriers, which
|
||
could increase the \gls{bca} readout as the assay is protein-agnostic and each
|
||
microcarrier is approximately \SI{1}{\ug}. The carriers were washed analogously
|
||
to the previous wash step to remove biotin, except two wash cycles were used,
|
||
the agitation time was \SI{30}{\minute}, and the first cycle had an extra
|
||
\SI{400}{\ul} \gls{pbs} to make up for the volume removed for the \gls{bca}
|
||
assay.
|
||
|
||
To coat with \glspl{mab}, sterile \product{\gls{bsa}}{\sigald}{A9576} was first
|
||
added to a final concentration of \SI{2}{\percent} in order to prevent
|
||
non-specific binding of the \glspl{mab} to the reaction tubes. 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{\carrier}}. \glspl{mab} were allowed to bind to
|
||
the carriers for \SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding,
|
||
\SI{400}{\ul} supernatant was sampled to indirectly quantify \gls{mab}
|
||
attachment 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}.
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Microcarrier properties}
|
||
\label{tab:carrier_props}
|
||
\input{../tables/carrier_properties.tex}
|
||
\end{table}
|
||
|
||
Finished \glspl{dms} were washed once again in the cell culture media (analogous
|
||
to previous washing steps) to be used for the T cell expansion. 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}\footnote{\SI{500}{\nm} is normally used for the
|
||
\gls{haba} assay, but the spectrophotometer to which we had access only had
|
||
\SI{490}{\nm} as the closest wavelength; the extinction coefficient should
|
||
change little}.
|
||
|
||
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 \pltread{}.
|
||
|
||
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. This assay was quantified using a \pltread{}.
|
||
|
||
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 \pltread{}.
|
||
|
||
\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{1500}{\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{recombinant human
|
||
\il{2}}{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 \gls{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 \cdp{19} 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}\label{sec:transduction}
|
||
|
||
The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} sequence with the EF1$\upalpha$
|
||
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
|
||
\product{FUGW transfer plasmid}{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 as the
|
||
extinction coefficient of \ce{NHS-} is well-known. \gls{snb} was added to either
|
||
\gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic analysis
|
||
using a \pltread{} began immediately after prep, and readings at \SI{260}{\nm}
|
||
were taken every minute for \SI{2}{\hour}. The extinction coefficient of
|
||
\ce{NHS-} at \SI{260}{\nm} was assumed to be \SI{8600}{\per\cm\per\molar}.
|
||
|
||
\subsection{Reaction Kinetics Quantification}
|
||
|
||
The reaction kinetics of \gls{stp} attaching to 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 as
|
||
described above.
|
||
|
||
To model diffusion in the microcarriers, we assumed that its pores were large
|
||
enough that the interactions between the \gls{stp} and surfaces would be small.
|
||
This means that the apparent, macroscropic diffusion of a given species within
|
||
the microcarriers would only depend on the aqueous diffusion coefficient of
|
||
\gls{stp} and a fractional factor (the ``geometric diffusivity'') representing
|
||
the additional path length an \gls{stp} molecule would take in the microcarriers
|
||
due to the tortuousity and void fraction of its pore network. This is given in
|
||
\cref{eqn:stp_diffusion_3}.
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_diffusion_3}
|
||
\gls{sym:appdiff}=\gls{sym:diff} \gls{sym:geodiff}
|
||
\end{equation}
|
||
|
||
This 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 ``receptors'' equal to the number
|
||
of \gls{stp} molecules (here called ``ligands'') 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 unbound receptors (free biotin) shrunk as a
|
||
function of \gls{stp} diffusing to the unbound biotin interface until the center
|
||
of the microcarriers was reached. This model was given by
|
||
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_diffusion_1}
|
||
\frac{d\gls{sym:rad}}{d\gls{sym:time}} =
|
||
\frac{- \gls{sym:appdiff} \gls{sym:bulkligconc}}
|
||
{\gls{sym:rad} (1 - \gls{sym:rad} / \gls{sym:mcrad})
|
||
\evalat{\gls{sym:mcrecconc}}{\gls{sym:time} = 0}}
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_diffusion_2}
|
||
\frac{d\gls{sym:bulkligconc}}{d\gls{sym:time}} =
|
||
\frac{-4 \pi \gls{sym:mcnum} \gls{sym:appdiff}\gls{sym:bulkligconc}}
|
||
{\gls{sym:vol} (1 / \gls{sym:rad} - 1 / \gls{sym:mcrad})}
|
||
\end{equation}
|
||
|
||
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
|
||
fitted equations were then used to simulate 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 fitted geometric diffusivity from above was used in these
|
||
washing calculations, and \SI{5.0e-6}{\cm\squared\per\second}\cite{Niether2020}
|
||
was used as the diffusion coefficient for free biotin. 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 \gls{sym:mcligconc}}{\partial \gls{sym:time}} =
|
||
\frac{1}{\gls{sym:rad}^2}
|
||
\frac{\partial}{\partial \gls{sym:rad}}
|
||
\left(\gls{sym:rad}^2
|
||
\gls{sym:appdiff}
|
||
\frac{\partial \gls{sym:mcligconc}}{\partial \gls{sym:rad}}
|
||
\right)
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_time_bc}
|
||
\evalat{\gls{sym:mcligconc}}{\gls{sym:time}=0} =
|
||
\evalat{\gls{sym:bulkligconc}}{\gls{sym:time}=0}
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_left_bc}
|
||
\gls{sym:mcflux}\rvert_{\gls{sym:rad}=0} = 0
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_washing_right_bc}
|
||
\evalat{\gls{sym:mcligconc}}{\gls{sym:rad} = \gls{sym:mcrad}} =
|
||
(\evalat{\gls{sym:bulkligconc}}{\gls{sym:time} = 0} +
|
||
\evalat{\gls{sym:bulkligconc}}{\gls{sym:time} = \infty}) / 2
|
||
\end{equation}
|
||
|
||
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.
|
||
|
||
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 derivations, as well as output in the case of the washing
|
||
steps.
|
||
|
||
\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}[!ht] \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,
|
||
\il{2} 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 beads or \glspl{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 PostgreSQL database (specifically the 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, significance of main and interaction effects
|
||
was determined using the \inlinecode{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}[!ht] \centering
|
||
\caption{Antibodies 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[\acrshort{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 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[\acrshort{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} molecules more opportunity to diffuse
|
||
into the microcarriers and react with amine groups to form attachments. It
|
||
seemed that concentration only has a linear effect and doesn't interact with any
|
||
of the other variables, which is not surprising 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
|
||
increased 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 decreased slightly, possibly due to \gls{snb} absorbing to the
|
||
plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}
|
||
groups, 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[\acrshort{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{DMS Process Has Defined Reaction Kinetics}
|
||
|
||
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 for costing more time}.
|
||
|
||
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 ``receptors'' 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
|
||
\glspl{mab} 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 receptors 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
|
||
\glspl{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 determined 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 time 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 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 \acrshortpl{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[\acrshortpl{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 \glspl{dms} to
|
||
~\SI{2.5e6}{\cell\per\ml}. 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 \acrshortpl{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 observed 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, 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.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_inside.png}
|
||
\phantomsubcaption\label{fig:dms_inside_bf}
|
||
\phantomsubcaption\label{fig:dms_inside_regression}
|
||
|
||
\endgroup
|
||
\caption[T Cells Growing on Interior of \acrshortpl{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}[!ht] \centering
|
||
\caption{Regression for fraction of cells in \acrshortpl{dms} at day 14}
|
||
\label{tab:inside_regression}
|
||
\input{../tables/inside_fraction_regression.tex}
|
||
\end{table}
|
||
|
||
We also asked how many cells were inside the \glspl{dms} instead of
|
||
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{15}{\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{15}{\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{15}{\day}) (\cref{tab:inside_regression}).
|
||
|
||
\subsection{DMSs Lead to Greater Expansion and High-Quality 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[\acrshort{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
|
||
as \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 among some donors and conditions\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}}. 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 \cdp{8} T cells). We cannot say the same about the \ptmemp{} since
|
||
we did not have the initial data for this phenotype; (although memory and naive
|
||
cells should be the vast majority of cells given that \glspl{pbmc} is taken from
|
||
blood which has mostly these cell types). 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 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} with a \gls{moi} of 10 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.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_cd19.png}
|
||
\phantomsubcaption\label{fig:car_cd19_flow}
|
||
\phantomsubcaption\label{fig:car_cd19_endpoint}
|
||
|
||
\endgroup
|
||
\caption[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_cd19}
|
||
\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[\acrshort{car} T Cell Functionality]
|
||
{\glspl{dms} produce functional \gls{car} T cells.
|
||
\subcap{fig:car_degran_flow}{Representative flow plot for
|
||
degranulating 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_degran}
|
||
\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}. Analogous
|
||
to 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}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_bcma.png}
|
||
\phantomsubcaption\label{fig:car_bcma_percent}
|
||
\phantomsubcaption\label{fig:car_bcma_total}
|
||
|
||
\endgroup
|
||
\caption[\acrshort{bcma} Transduction]
|
||
{\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 G-Rex 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[\acrshort{grex} Expansion]
|
||
{\glspl{dms} expand T cells robustly in \gls{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 \gls{grex}
|
||
bioreactor. We incubated T cells in a \gls{grex} analogously to plates and found
|
||
that T cells in \gls{grex} bioreactors expanded as efficiently as beads over
|
||
\SI{14}{\day} with similar viability
|
||
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Consistent with past
|
||
results, \glspl{dms}-expanded T cells had higher \pthp{} and \ptmemp{} compared
|
||
to beads (\cref{fig:grex_mem,fig:grex_cd4}). Overall the \ptmemp{} was lower
|
||
than that seen in standard plates (\cref{fig:dms_phenotype_mem}).
|
||
|
||
These discrepancies might be explained in light of other data as follows. The
|
||
\gls{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 seen in
|
||
standard plates (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
|
||
area could mean increased signaling and \gls{teff} differentiation, which was
|
||
why the \ptmemp{} was low compared to past data (\cref{fig:dms_phenotype_mem}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/grex_luminex.png}
|
||
|
||
\endgroup
|
||
\caption[\acrshort{grex} Luminex Results]
|
||
{\gls{dms} lead to higher cytokine production in \gls{grex} bioreactors.}
|
||
\label{fig:grex_luminex}
|
||
\end{figure*}
|
||
|
||
We also quantified the cytokines released during the \gls{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}), including higher concentrations of pro-inflammatory
|
||
cytokines such as \gls{gmcsf}, \gls{ifng}, and \gls{tnfa}. This demonstrates
|
||
that \glspl{dms} could lead to more robust activation.
|
||
|
||
Taken together, these data suggest that \gls{dms} also lead to robust expansion
|
||
in \gls{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[\acrshort{dms} \acrshort{mab} Detachment]
|
||
{\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*}
|
||
|
||
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 Outperform 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} into one dataset. Since each
|
||
experiment was performed using slightly different process conditions, we
|
||
hypothesized that performing causal inference on such a dataset would indicate
|
||
if the \glspl{dms} indeed led to better results under a variety of conditions.
|
||
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 \glspl{dms}. We also included ``bioreactor'' which was a
|
||
categorical variable for growing the T cells in a \gls{grex} bioreactor or
|
||
polystyrene plates, ``feed criteria'' which represented the criteria used to
|
||
feed the cells (media color or a glucose meter), ``\il{2} Feed Conc.'' as a
|
||
continuous parameter for the concentration of \il{2} 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
|
||
despite the large size of our dataset, so the only two parameters for which
|
||
causal relationships could be evaluated were ``activation method'' and
|
||
``bioreactor''. 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 including it 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 (in days) of \il{2}, growth
|
||
media, and thaw media; for \il{2} this was the time elapsed since
|
||
reconstitution, and for the others it was the elapsed time since the
|
||
manufacturing date according to the vendor. 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}[!ht] \centering
|
||
\caption{Causal inference on treatment variables}
|
||
\label{tab:ci_treat}
|
||
\input{../tables/causal_inference_treat.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Causal inference on all variables}
|
||
\label{tab:ci_controlled}
|
||
\input{../tables/causal_inference_control.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Causal inference on all 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{} (\pthp{} at day 14 compared to its day 0 value). 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 \gls{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 accounted for almost half the data (80 runs) and repeated the
|
||
regression (\Cref{tab:ci_single}). In this case, we did not include any
|
||
donor-dependent variables or any 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 other donors may have had high \ptmemp{}, and that high
|
||
fold change and \dpthp{} may have been driven by this single donor but more
|
||
extreme in other donors.
|
||
|
||
\section{Discussion}
|
||
|
||
We have developed a method for activating T cells which leads to 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}. 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.
|
||
|
||
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 \gls{grex} bioreactor is
|
||
detrimental to fold change and memory percent while helping CD4+. Since there
|
||
are multiple consequences to using a \gls{grex} compared to tissue-treated
|
||
plates, we can only speculate as to why this might be the case. Firstly, when
|
||
using a \gls{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 \gls{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 expand as much due to spacial constraints. This would
|
||
all be despite the gas-permeable membrane and tell design of the \gls{grex},
|
||
which are meant to enhance growth and not impede it. Given this, 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 (indeed, one donor was used for nearly
|
||
half of it). 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.
|
||
|
||
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.
|
||
|
||
While our method is superior in several ways compared to beads, the cell
|
||
manufacturing field would tremendously benefit from simply having an alternative
|
||
to the state-of-the-art. The licenses for bead-based expansion are controlled by
|
||
few companies; 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 would correspond
|
||
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 using
|
||
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\ml}), 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 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
|
||
spectral data levels 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
|
||
spectroscopy\cite{Holmes2006} 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, high-performing
|
||
models from each method were used in consensus analysis to extract potential
|
||
\glspl{cqa} and \glspl{cpp} 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 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{DMSs Grow T Cells With Lower IL2 Concentrations}
|
||
|
||
Prior to the main experiments in this aim, we assessed the effect of lowering
|
||
the \il{2} concentration on the T cells grown with either bead or \gls{dms}.
|
||
One of our hypotheses for the \gls{dms} system was that higher cell density
|
||
would enhance cross-talk between T cells. Since \il{2} is secreted by
|
||
activated T cells themselves, T cells in the \gls{dms} system may need less or
|
||
no \il{2} if this is true.
|
||
|
||
\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 \il{2} concentrations.
|
||
\subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown
|
||
with either bead or \glspl{dms} using varying \il{2} concentrations.}
|
||
Day 14 counts of either \subcap{fig:il2_mod_total}{total cells} or
|
||
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \il{2}
|
||
concentration.
|
||
\subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated
|
||
populations at day 14 of culture for each \il{2} concentration.}
|
||
}
|
||
\label{fig:il2_mod}
|
||
\end{figure*}
|
||
|
||
We varied the concentration of \il{2} 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 \il{2} 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} \il{2}. When examining the
|
||
endpoint fold change after \SI{14}{\day}, we observed that the difference
|
||
between the bead and \gls{dms} appears to be greater at lower \il{2}
|
||
concentrations (\cref{fig:il2_mod_total}). 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 \il{2} 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 \il{2} at all; however, it appears to have a modest
|
||
advantage at lower \il{2} concentrations compared to beads. For this reason,
|
||
we decided to investigate the lower range of \il{2} concentrations starting
|
||
at \SI{10}{\IU\per\ml} in the remainder of this aim.
|
||
|
||
\subsection{DOE Shows Optimal Conditions for Potent T Cells}
|
||
|
||
\begin{table}[!ht]
|
||
\centering
|
||
\begin{threeparttable}
|
||
\caption{DOE Runs}
|
||
\label{tab:doe_runs}
|
||
\input{../tables/doe_runs.tex}
|
||
\begin{tablenotes}
|
||
\item[a] It was determined later that the total \glspl{mab} surface density
|
||
may not be consistent across each batch of \gls{dms} used. Thus, these
|
||
runs were taken out as they were created at different scale and with a
|
||
different operator compared to the rest. Leaving them in may produce
|
||
unobserved confounding factors
|
||
\end{tablenotes}
|
||
\end{threeparttable}
|
||
\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 \acrshort{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*}
|
||
|
||
We conducted two consecutive \glspl{doe} to optimize the \pth{} and \ptmem{}
|
||
responses for the \gls{dms} system. In the first, 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 total \ptmemp{} cells, \pilII{}
|
||
showed a positive linear trend and \pdms{} and \pmab{} showed possible
|
||
second-order effects with intermediate maximums and minimums respectively
|
||
(\cref{fig:doe_response_first_mem}). In the case of \pth{}, all parameters
|
||
showed a positive, suggesting 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. Notably, \pilII{} appeared to increase beyond our tested range, thus
|
||
we were curious if there was an optimum at some higher setting. 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}.
|
||
|
||
\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 \acrshortpl{doe}]
|
||
{\gls{doe} methodology reveals optimal conditions for expanding T cell
|
||
subsets. Responses vs \il{2} 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}[!ht] \centering
|
||
\caption{Regression for total \ptmem{} cells (first order)}
|
||
\label{tab:doe_mem1.tex}
|
||
\input{../tables/doe_mem1.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Regression for total \ptmem{} cells (third order)}
|
||
\label{tab:doe_mem2.tex}
|
||
\input{../tables/doe_mem2.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Regression for total \pth{} cells}
|
||
\label{tab:doe_cd4.tex}
|
||
\input{../tables/doe_cd4.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Regression for total \ptmemh{} cells}
|
||
\label{tab:doe_mem4.tex}
|
||
\input{../tables/doe_mem4.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption{Regression for \ptmem{} CD4:CD8 ratio}
|
||
\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}).
|
||
|
||
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 ($R^2 = 0.331$). 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.
|
||
|
||
We performed linear regression on the other three responses, all of which
|
||
performed much better than the \ptmem{} response as expected given the
|
||
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 \acrshort{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, maximizing all input parameters maximized both responses
|
||
(\cref{fig:doe_sr_contour}). While not all combinations at and around this
|
||
optimum were tested, these plots suggest that there were no other optimal values
|
||
elsewhere.
|
||
|
||
\subsection{Modeling with Machine Learning Reveals Putative 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 \acrshort{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, demonstrating that these could 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 \gls{grex} system (these data were
|
||
collected in plates) (\cref{fig:grex_luminex}).
|
||
|
||
\begin{table}[!ht] \centering
|
||
\caption[Machine Learning Model Results]
|
||
{Results for \gls{ml} 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 \il{2} concentration, \il{15},
|
||
and \ilr{2} increase while \il{17a} decreases in conjunction with other
|
||
features. These patterns combined with low values of \pdms{} and \gls{gmcsf}
|
||
uniquely characterized maximum \rmemk{}. Meanwhile, higher glycine but lower
|
||
\il{13} 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[\acrshort{cqa} Consensus Plots]
|
||
{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,
|
||
\ilr{2}, \il{4}, \il{17a}, 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 was the sole predictor shared by all.
|
||
|
||
When performing similar analysis on \rmemh{}, no species for either secretome or
|
||
metabolome was shared by all models (\cref{fig:mod_flower_cd4}). These models
|
||
also had worse fits compared to those for \rratio{} (\cref{tab:mod_results}).
|
||
For the secretome, \il{4}, \il{17a}, and \ilr{2} were agreed upon by $\ge$ 5
|
||
models. For the metabolome, formate once again was shared 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. 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 red connections indicate
|
||
positive and negative correlations respectively. The threshold for
|
||
visualizing connections in all cases was 0.8.
|
||
\subcap{fig:nmr_cors_matrix}{Correlation matrix for all features and total
|
||
\ptmemh{} yield.} }
|
||
\label{fig:nmr_cors}
|
||
\end{figure*}
|
||
|
||
We also asked if day 4 \gls{nmr} features could predict \ptmemh{}; these models
|
||
generally fit well despite being 2 days earlier in the process
|
||
(\cref{fig:nmr_cors}). Lactate and formate correlated with each other and
|
||
\rmemh{}. Furthermore, lactate positively correlated with \pdms{} and negatively
|
||
correlated 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
|
||
have life-saving implications in the context of cell therapy. The challenges for
|
||
effective modeling grow with the increasing process complexity due to high
|
||
dimensionality, interactions between parameters, nonlinearity. Another critical
|
||
challenge is the limited amount of available data. \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 few prior assumptions\cite{Kotancheka}.
|
||
|
||
Since the model search process lets the data determine the model, diverse and
|
||
competitive model structures are typically discovered. An diverse ensemble will
|
||
contain models that agree in regions constrained by observable data and diverge
|
||
in regions without data. Collecting data in divergent regions ensures the system
|
||
is accurately modeled and its optimum accurately located\cite{Kotancheka}.
|
||
Consequently, this \gls{adoe} approach is useful in a many scenarios, including
|
||
maximizing model validity for model-based decision making, optimizing processing
|
||
parameters to maximize yield, 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 to fate and effector functions of immune cells. Cytokine features
|
||
slightly improved 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}, \ilr{2}, \il{4}, \il{17a},
|
||
\il{13}, and \il{15} 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, thus examination of each relevant
|
||
cytokine is needed.
|
||
|
||
\ilr{2} is secreted by activated T cells and binds to \il{2}, acting as a sink
|
||
to dampen its effect on T cells\cite{Witkowska2005}. Since \ilr{2} was more
|
||
abundant than \il{2} in solution, this might reduce the overall effect of
|
||
\il{2}, which could be further investigated by blocking \ilr{2} with an
|
||
antibody. In T cells, \gls{tnfa} can increase \ilr{2}, proliferation, and
|
||
cytokine production\cite{Mehta2018}. It may also induce apoptosis depending on
|
||
concentration and alter the CD4:CD8 ratio\cite{Vudattu2005}. Given that TNF has
|
||
both a soluble and membrane-bound form, this may either increase or decrease
|
||
CD4:CD8 ratio and/or memory T cells depending on the ratio of the membrane to
|
||
soluble TNF\cite{Mehta2018}. Since only soluble \gls{tnfa} was measured,
|
||
membrane \gls{tnfa} is needed to understand its impact on both CD4:CD8 ratio and
|
||
memory T cells. Furthermore, \il{13} 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 here due to an
|
||
initially large number of \glspl{th2} or because \glspl{th2} were preferentially
|
||
expanded; indeed, \il{4}, also found important, is the canonical 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
|
||
\il{13}, \il{17} is an effector cytokine produced by
|
||
\glspl{th17}\cite{Amatya2017} thus may reflect the number of \glspl{th17} in the
|
||
population. \gls{gmcsf} 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, \il{15} has been shown to be
|
||
essential for memory signaling and effective in skewing \gls{car} T cells toward
|
||
\glspl{tscm} when using membrane-bound \ilXVra{} and \ilr{15}\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, from which formate is
|
||
excreted\cite{Pietzke2020}. Mitochondrial biogenesis and function are shown to
|
||
be 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 upregulated in response to T cell
|
||
stimulation, thus leading to 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 sensors and
|
||
controls for formate, lactate, 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 tuned to quantify lactate and formate to sample the
|
||
media in real time during culture. 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. For 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, \gls{elisa} or Luminex can still quantify cytokines in a
|
||
semi-automated manner. However, these are temporally discrete and impose a
|
||
non-trivial delay 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}
|
||
|
||
\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 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 Cytometry 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 \gls{spade} with k-means clustering
|
||
(k = 100), arcsinh transformation with cofactor 5, density calculation
|
||
neighborhood size of 5, 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 \ilXVra{}, we supplemented T cell
|
||
cultures activated with \gls{dms} with either
|
||
\product{\anti{\ilXVra{}}}{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 \il{15}, we supplemented analogously with
|
||
\product{\anti{\il{15}}}{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 hypothesis
|
||
using \gls{colb}, \gls{cold} or \gls{hbss}, and then analyzed the cells via flow
|
||
cytometry to assess if the enzymes would cleave off markers of interest. The
|
||
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.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/collagenase.png}
|
||
|
||
\endgroup
|
||
\caption[Effects of Collagenase Treatment on T cells]
|
||
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
|
||
stained for various surface markers and analyzed 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 cell growth 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[Results of Adding/Removing \acrshort{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[\acrshort{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[\acrshort{spade} and \acrshort{tsne} Analysis of Temporally Modulated
|
||
\acrshort{dms} Cultures]
|
||
{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 saw a strong bifurcation of CD4 and CD8 T cells. When
|
||
looking at CD27, CD45RA, and CD45RO (markers commonly used to identify \gls{tcm}
|
||
and \gls{tscm} subtypes) we saw clear ``metaclusters'' composed of individual
|
||
\gls{spade} clusters which are high for these markers
|
||
(\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, the number of lower differentiated cells was 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
|
||
observed a higher fraction of CD27 and lower fraction of CD45RO in the
|
||
\textit{removed} group (\cref{fig:spade_tsne_all}). Manually gating on the
|
||
CD27+CD45RO- population more cells with this phenotype in the \textit{removed}
|
||
group (\cref{fig:spade_tsne_stem}). Together, these data indicate that removing
|
||
\glspl{dms} at lower timepoints leads to higher expansion, lower \pthp{}, and
|
||
higher fraction of lower differentiated T cells such as \gls{tscm}, and adding
|
||
\gls{dms} does the inverse.
|
||
|
||
\subsection{Blocking Integrin 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 provide 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}[!ht] \centering
|
||
\caption{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 blocking \glspl{mab} were added at day 6 of culture when \gls{a2b1} and
|
||
\gls{a2b2} were known to be expressed\cite{Hemler1990}. The fold expansion was
|
||
identical between the blocked and unblocked groupds (\cref{fig:inegrin_1_fc}).
|
||
Furthermore, 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}). Furthermore, \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}[!ht] \centering
|
||
\caption{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 initial 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, there was no difference between the blocked and
|
||
unblocked conditions 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 Does Not Alter Expansion or Phenotype}
|
||
|
||
\il{15} is a cytokine responsible for memory T cell survival and maintenance.
|
||
Furthermore, previous experiments showed 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 \il{15} may be
|
||
responsible for this, and further that the unique \textit{cis/trans} activity of
|
||
\il{15} 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 \ilXVra{} 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 \ilXVra{} with either a specific
|
||
\gls{mab} or an \gls{igg} isotype control at
|
||
\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. There was 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 \il{15} 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 \il{15} itself using either a \gls{mab} or an
|
||
\gls{igg} isotype control. Anti-\il{15} 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 \il{15} concentration seen in past experiments by over
|
||
\num{20000} times. Similarly, there was no difference between fold change,
|
||
viability, or marker histograms between any of these markers, showing that
|
||
blocking \il{15} led to no difference in growth or phenotype.
|
||
|
||
In summary, this data did not support the hypothesis that the \gls{dms} platform
|
||
gains its advantages via the \il{15} pathway.
|
||
|
||
\section{Discussion}
|
||
|
||
This work provides insight for how the \gls{dms} platform operates and how it
|
||
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 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 decreasing the \gls{dms} concentration, and thus the
|
||
activation signal, increases memory and lowers CD4+ fractions.
|
||
|
||
While we did not find support for our hypothesis that \glspl{dms} signal via the
|
||
\gls{a2b1} and/or \gls{a2b2} receptors, we can speculate that either the
|
||
experiment failed to block the targeted pathways or that this mechanism is
|
||
simply not relevant for our system.
|
||
|
||
On the first point, we did not verify that these \glspl{mab} actually blocked
|
||
their target receptors (although they were from a reputable manufacturer, \bl).
|
||
However, other groups have shown that these particular clones work at the
|
||
concentrations we used\cite{MirandaCarus2005}. 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. Therefore, the most
|
||
likely failure mode was that the \glspl{mab} we obtained were somehow defective
|
||
in their intended purpose, which we could experimentally verify using adhesion
|
||
assays.
|
||
|
||
On the second point, collagen domains may not even be relevant to our system
|
||
depending on the extent of \gls{stp} coating. We intended by design for the
|
||
system to be 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 (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 non-existent in the \gls{dms}
|
||
system, previous studies have shown 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 \il{15} 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 for ourselves that it blocked), 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, blocking the soluble protein may not have worked because
|
||
\il{15} may have been secreted and immediately captured via \ilXVra{} 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}.
|
||
|
||
\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{T Cell Culture}
|
||
|
||
T cells were grown as described in \cref{sec:tcellculture}.
|
||
|
||
\subsection{CD19-CAR T Cell Generation}
|
||
|
||
T cells were grown as described in \cref{sec:transduction}.
|
||
|
||
\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), \num{1e6} firefly
|
||
luciferase-expressing\footnote{luciferase transduction was performed and
|
||
verified by Ian Miller in the Kwong Lab at Georgia Tech} \product{\nVI{}
|
||
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; exposure time of the \gls{ivis} was limited to
|
||
avoid saturation based on the signal from the saline group. \gls{ivis} images
|
||
were scaled to common minimum and maximum photon counts. 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}
|
||
|
||
Survival curves were created and statistically analyzed using GraphPad Prism
|
||
using the Mantel-Cox test to assess significance between survival groups.
|
||
|
||
\section{Results}
|
||
|
||
\subsection{DMSs Lead to Greater \invivo{} Anti-Tumor Activity}
|
||
|
||
\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}[!ht] \centering
|
||
\caption{Cells injected for \acrshort{car} T cell \invivo{} dose study}
|
||
\label{tab:mouse_dosing_results}
|
||
\input{../tables/mouse_dose_car.tex}
|
||
\end{table}
|
||
|
||
|
||
\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 \nVI{} 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 Mantel-Cox 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
|
||
\invivo{} 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} \nVI{} tumor cells expressing 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 \cdp{45RA} cells, 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 \nVI{}/\gls{nsg} xenograft model, bead and \gls{dms}-treated mice at all
|
||
doses had lower tumor burden and significantly longer survival compared to the
|
||
saline groups (\cref{fig:mouse_dosing_ivis}). Importantly, at each dose 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}, 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) was 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 \nVI{} 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, 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
|
||
(\cref{fig:mouse_dosing_ivis_survival_full}). Similar \gls{gvhd} responses
|
||
\SIrange{40}{50}{\day} after injection have been reported by others in \gls{nsg}
|
||
mice injected with human \gls{pbmc}\cite{Ali2012}. 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
|
||
\nVI{} 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 \gls{qc} data of T cells prior to
|
||
injection, it seems that this advantage for \gls{dms} groups was either due to
|
||
higher \pthp{} or greater overall fitness (implied by higher fold change)
|
||
(\cref{fig:mouse_dosing_qc_cd4,fig:mouse_dosing_qc_growth}). It was likely not
|
||
due to memory phenotype given that this was actually slightly higher for the
|
||
bead culture (\cref{fig:mouse_dosing_qc_mem}).
|
||
|
||
\subsection{Beads and DMSs Perform Similarly at Earlier Timepoints}
|
||
|
||
We then asked how T cells activated using 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 expanded 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 simultaneously 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, \gls{dms} cultures expanded
|
||
much more efficiently than bead cultures
|
||
(\cref{fig:mouse_timecourse_qc_growth}). When we quantified the \ptcarp{} at
|
||
each timepoint, 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 transduced cells either grew
|
||
slower or died faster compared to untransduced cells. The \pthp{} was higher
|
||
overall in \gls{dms} groups 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
|
||
(\cref{fig:mouse_timecourse_qc_mem}).
|
||
|
||
\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*}
|
||
|
||
Analyzing the tumor burden using \gls{ivis} showed that mice who received T
|
||
cells from any group had less tumor than those that received only saline
|
||
(\cref{fig:mouse_timecourse_ivis}). Unlike the previous experiment, most mice
|
||
survived until day 40 after which \gls{gvhd} began to take effect (upon
|
||
euthanization at day 42, most had little or no spleen). When comparing bead and
|
||
\gls{dms} groups, the \gls{dms} groups had lower tumor than the bead group, at
|
||
least initially (note that in this experiment they had similar numbers of
|
||
\ptcar{} cells). For day 6 groups, both treatments seemed to eradicate the tumor
|
||
initially, then it came back after \SI{21}{\day} for the beads and \SI{28}{\day}
|
||
for \glspl{dms}. 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, a few mice actually had less
|
||
tumor burden overall than all other groups.
|
||
|
||
\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 T cells injected into mice for the
|
||
\subcap{fig:mouse_summary_1}{first} and \subcap{fig:mouse_summary_2}{second}
|
||
experiments. The y-axis maximum is set to the maximum cell number
|
||
injected between both experiments (\num{1.25e6}). NOTE: the \gls{car} was
|
||
quantified using a separate panel from the other markers. }
|
||
\label{fig:mouse_summary}
|
||
\end{figure*}
|
||
|
||
When we tested bead- and \gls{dms}-expanded \gls{car} T cells, the latter
|
||
prolonged survival compared to the former in \nVI{} tumor challenged
|
||
(intravenously injected) \gls{nsg} mice. This held true when matching groups for
|
||
absolute \gls{car} dose. Furthermore, \gls{dms}-expanded \gls{car} T cells were
|
||
effective in clearing tumor cells as early as \SI{7}{\day} post T injection even
|
||
at low dose compared to the bead groups where tumor burden was higher than
|
||
\gls{dms} groups across all the total T cell doses tested here. These suggest
|
||
that \glspl{dms} (compared to beads) produced highly effective \gls{car} T cells
|
||
that can efficiently kill tumor cells.
|
||
|
||
When comparing total number of injected T cells with different phenotypes, the
|
||
number of \ptmem{} (both with and without CD45RA) cells was lower in the
|
||
low-dose \gls{dms} group compared to the med-dose bead group (which had similar
|
||
numbers of \gls{car} T cells) (\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 \nVI{} 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 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. Previous groups have shown that \pthp{} T cells
|
||
are important for response (albeit for a glioblastoma model and not a B-cell
|
||
\gls{all} model)\cite{Wang2018}.
|
||
|
||
When testing \gls{car} T cells at earlier timepoints relative to day 14 as used
|
||
in the first \invivo{} experiment, 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 are not directly 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 are needed to determine
|
||
the precise phenotype responsible for these results.
|
||
|
||
\chapter{CONCLUSIONS AND FUTURE WORK}\label{conclusions}
|
||
|
||
\section{Conclusions}
|
||
|
||
This dissertation describes the development of a novel T cell expansion
|
||
platform, including the fabrication, \gls{qc}, and biological validation of its
|
||
performance both \invitro{} and \invivo{}. Development of such a system would
|
||
have been meaningful even if it only performed as well as current technology, 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 reagents. However, we additionally
|
||
demonstrated 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 developed the \gls{dms} platform and verified its efficacy
|
||
\invitro{}. Importantly, this included \gls{qc} at every critical step of the
|
||
fabrication process to ensure that the \glspl{dms} can be made within a targeted
|
||
specification. These \gls{qc} steps all rely on common, cost-effective,
|
||
easy-to-use assays such as the \gls{haba} assay, \gls{bca} assay, and
|
||
\gls{elisa}. 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}. On average,
|
||
we demonstrated that the \glspl{dms} outperforms bead-based 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
|
||
approach are that the \glspl{dms} are large enough to be filtered (approximately
|
||
\SI{300}{\um}) using standard \SI{40}{\um} cell strainers or similar. If the
|
||
remaining cells inside that \glspl{dms} are also desired, digestion with dispase
|
||
or collagenase may be used. \gls{cold} 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 \gls{grex} 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 to identify \glspl{cqa} and \gls{cpp} during scale-up. 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, from which dosing and followup procedures in the clinic can
|
||
be planned more accurately. In the aim, we cannot claim to have found the
|
||
universal 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 method that others may use to find \glspl{cqa} and \glspl{cpp} for
|
||
their process. In particular, the 2-phase modeling approach 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
|
||
generalizable 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
|
||
\il{15} 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 \il{15}, 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 \gls{dms}-expanded T cells that also performed
|
||
better than beads \invivo{}. In the first experiment we performed, the results
|
||
were clearly in favor of the \glspl{dms}. In the second experiment, even the
|
||
\gls{dms}-expanded cells 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. We did not include the
|
||
\gls{car} in the same panel as the other phenotype surface markers, making it
|
||
difficult to reliably assess 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, \gls{gmp}
|
||
are still absent from the fabrication process. 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 the \gls{dms} platform works as
|
||
well as it does. The following are several plausible hypotheses and testing
|
||
strategies:
|
||
|
||
\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 each \gls{mab} in the
|
||
cocktail is in sufficient quantity to quench their target cytokine 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
|
||
these cells may be phenotypically different than those growing on the exterior.
|
||
This could lead 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}) to isolate the interior T cells. Unfortunately, only
|
||
\SIrange{10}{20}{\percent} of all cells will be on the interior, so this
|
||
population 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
|
||
flow cytometry (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{dms} \gls{mab}
|
||
surface density compares to that of the beads. The \gls{mab} surface density on
|
||
the \glspl{dms} is likely 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 quantified much
|
||
like the \gls{mab} binding step 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 \gls{qc} for 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. The following are a list of variance sources
|
||
and potential mitigation strategies:
|
||
|
||
\subsubsection{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 (assuming that autoclaving is idempotent
|
||
with respect to mass loss), or by statistically estimating the bias by recording
|
||
the mean mass loss for a set of tubes and using this as a correction factor.
|
||
|
||
\subsubsection{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 showed that
|
||
operators had acceptable margins for error 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.
|
||
|
||
\subsubsection{Centrifugation after washing}
|
||
|
||
After coating the \glspl{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 do this, and the protocol is not written to
|
||
make this obvious. This protocol can be revised followed by additional training.
|
||
|
||
\subsubsection{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. Equipment can be modified (such as
|
||
aspirators with guides to ensure fixed depth of suction) to mitigate this issue.
|
||
|
||
\subsubsection{BSA binding kinetics}
|
||
|
||
Prior to \gls{mab} addition, \gls{bsa} is added to the reaction volume 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 without
|
||
competition. This could cause the \gls{mab} \gls{elisa} reading to be lower.
|
||
This problem may be minor since significant binding would only occur if the
|
||
\gls{mab}/plastic adhesion was fast and happened in the seconds prior to
|
||
beginning agitation. We can mitigate this by agitating the tubes with \gls{bsa}
|
||
for several minutes prior to adding \gls{mab} to ensure mixing.
|
||
|
||
\subsubsection{Improving protein detection}
|
||
|
||
While the \gls{bca} assay and \gls{elisa} are relatively precise, they both have
|
||
problems that could lead to systemic bias or excess random noise. The \gls{bca}
|
||
assay is non-specific. All our data shows consistent small (\SI{0.5}{\ug}) but
|
||
negative readings for blank carriers, 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 \glspl{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 issues.
|
||
\gls{hplc} can specifically detect species based on differences in charge and
|
||
size, so it should be able to quantify \gls{stp} without the extraneous bias of
|
||
the \gls{bca} assay. In the case of \gls{elisa} it will not remove the need to
|
||
run a blank, but it should lower variance due to its automated nature.
|
||
|
||
\subsection{Surface Stiffness}
|
||
|
||
The beads and \glspl{dms} are composed of different materials: iron/polymer for
|
||
the former and cross-linked gelatin for 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 \glspl{dms}. The chemistry used for the \glspl{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}/\ilXVra{} 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 transduction.
|
||
|
||
\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 for 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, the \gls{dms} platform worked
|
||
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 \gls{grex} bioreactor. While an important first
|
||
step, more work needs to be done to optimize how the \gls{dms} system will or
|
||
can function in a scalable environment using bioreactors. There are several
|
||
paths to explore. Firstly, the \gls{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 \gls{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 PostgreSQL database itself was
|
||
hosted using \gls{aws} using their proprietary Aurora implementation.
|
||
|
||
\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}[!ht]
|
||
\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}
|
||
|
||
The binding kinetics of \gls{stp} or \glspl{mab} were simulated using a
|
||
receptor/ligand model, where the free-floating species in question was the
|
||
ligand which bound to receptors attached to the microcarriers. Each microcarrier
|
||
was assumed to be a porous sphere with a fixed number of receptors uniformly
|
||
distributed throughout its interior matrix. The receptor/ligand 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 aligned at the center within each microcarrier
|
||
wherein all receptors in the interior were unbound and all on the exterior were
|
||
bound. At $\gls{sym:time}=0$ this interface was assumed to start with a radius
|
||
equal to that of the microcarrier, and shrunk down to radius of zero as ligand
|
||
flowed into the porous microcarrier and bound. We assumed the concentration of
|
||
ligand 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
|
||
moved slowly relative to the diffusion of ligand 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 ligand 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}{\gls{sym:rad}^2} \frac{d}{d\gls{sym:rad}} \left( \gls{sym:rad}^2
|
||
\frac{d\gls{sym:mcligconc}}{d\gls{sym:rad}} \right)
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_bc_left}
|
||
\evalat{\gls{sym:mcligconc}}{\gls{sym:rad}=\gls{sym:interrad}} = 0
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:binding_bc_right}
|
||
\evalat{\gls{sym:mcligconc}}{\gls{sym:rad}=\gls{sym:mcrad}} =
|
||
\gls{sym:bulkligconc}
|
||
\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}
|
||
\gls{sym:mcligconc} =
|
||
\frac{\gls{sym:bulkligconc}}
|
||
{(1 / \gls{sym:interrad} - 1 / \gls{sym:mcrad})}
|
||
\left( \frac{1}{\gls{sym:interrad}} - \frac{1}{\gls{sym:rad}} \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}
|
||
\gls{sym:flowrate} = 4 \pi \gls{sym:mcrad}^2
|
||
\evalat{\gls{sym:mcflux}}{\gls{sym:rad} = \gls{sym:mcrad}} =
|
||
\frac{-4 \pi \gls{sym:appdiff} \gls{sym:bulkligconc}}
|
||
{1 / \gls{sym:interrad} - 1 / \gls{sym:mcrad}}
|
||
\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}
|
||
\evalat{\gls{sym:mcrecconc}}{\gls{sym:time} = 0}
|
||
\frac{d\gls{sym:intervol}}{d\gls{sym:time}} = -\gls{sym:flowrate}
|
||
\end{equation}
|
||
|
||
Substituting volume of a sphere and applying the chain rule:
|
||
|
||
\begin{equation}
|
||
\label{eqn:radial_radial_change}
|
||
\frac{d\gls{sym:interrad}}{d\gls{sym:time}} =
|
||
\frac{-\gls{sym:flowrate}}
|
||
{4 \pi \gls{sym:interrad}^2 \evalat{\gls{sym:mcrecconc}}{\gls{sym:time} = 0}}
|
||
\end{equation}
|
||
|
||
The change in bulk concentration is simply given by:
|
||
|
||
\begin{equation}
|
||
\label{eqn:radial_conc_change}
|
||
\frac{d\gls{sym:bulkligconc}}{d\gls{sym:time}} =
|
||
\frac{-\gls{sym:mcnum}\gls{sym:flowrate}}{\gls{sym:vol}}
|
||
\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 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 changed 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 \glspl{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}
|
||
\addcontentsline{toc}{chapter}{REFERENCES}
|
||
\renewcommand{\chapter}[2]{} % noop the original bib section header
|
||
|
||
\bibliography{references}
|
||
|
||
\bibliographystyle{naturemag}
|
||
|
||
\end{document}
|