1057 lines
47 KiB
TeX
1057 lines
47 KiB
TeX
% \documentclass[twocolumn]{article}
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\documentclass{report}
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\usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry}
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\usepackage{siunitx}
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\usepackage{multicol}
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\setlength{\columnsep}{1cm}
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\usepackage[acronym]{glossaries}
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\usepackage[T1]{fontenc}
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\usepackage{enumitem}
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\usepackage{titlesec}
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\usepackage{titlecaps}
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\usepackage{upgreek}
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\usepackage{graphicx}
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\usepackage{subcaption}
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\usepackage{nth}
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\usepackage[capitalize]{cleveref}
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\usepackage[version=4]{mhchem}
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\usepackage{pgfgantt}
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\usepackage{setspace}
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% TODO 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{\chapter}[block]{\filcenter\bfseries\large}{}{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\itshape\/}{}{0pt}{\titlecap}
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\setlist[description]{font=$\bullet$~\textbf\normalfont}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% acronyms for the lazy
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\renewcommand{\glossarysection}[2][]{} % remove glossary title
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\makeglossaries{}
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\newacronym{act}{ACT}{adoptive cell therapies}
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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{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{pdms}{PDMS}{polydimethylsiloxane}
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\newacronym{dc}{DC}{dendritic cell}
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\newacronym{il2}{IL2}{interleukin 2}
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\newacronym{rhil2}{rhIL2}{recombinant human interleukin 2}
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\newacronym{apc}{APC}{antigen presenting cell}
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\newacronym{mhc}{MHC}{major histocompatibility complex}
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\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
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\newacronym{nmr}{NMR}{nuclear magnetic resonance}
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\newacronym{haba}{HABA}{4-hydroxyazobenene-2-carboxylic-acid}
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\newacronym{pbs}{PBS}{phosphate buffered saline}
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\newacronym{bca}{BCA}{bicinchoninic acid assay}
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\newacronym{bsa}{BSA}{bovine serum albumin}
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\newacronym{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{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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% SI units for uber nerds
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% NOTE the \SI macro is depreciated but the arch repo (!!!) hasn't been updated
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% with the latest package yet (texlive-science)
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\sisetup{per-mode=symbol,list-units=single}
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\DeclareSIUnit\IU{IU}
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\DeclareSIUnit\rpm{RPM}
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\DeclareSIUnit\dms{DMS}
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\DeclareSIUnit\cell{cells}
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\DeclareSIUnit\ab{mAb}
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\DeclareSIUnit\molar{M}
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\DeclareSIUnit\gforce{\times{} g}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% commands for lazy farts like me
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\newcommand{\mytitle}{
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\Large{
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\textbf{
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Optimizing T Cell Manufacturing and Quality Using Functionalized
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Degradable Microscaffolds
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}
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}
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}
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\newcommand{\mycommitteemember}[3]{
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\begin{flushleft}
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\noindent
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#1 \\
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#2 \\
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\textit{#3}
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\end{flushleft}
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}
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\newcommand{\invivo}{\textit{in vivo}}
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\newcommand{\invitro}{\textit{in vitro}}
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\newcommand{\exvivo}{\textit{ex vivo}}
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\newcommand{\cd}[1]{CD{#1}}
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\newcommand{\anti}[1]{anti-{#1}}
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\newcommand{\acd}[1]{\anti{\cd{#1}}}
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\newcommand{\pos}[1]{#1+}
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\newcommand{\cdp}[1]{\pos{\cd{#1}}}
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\newcommand{\cdn}[1]{\cd{#1}-}
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\newcommand{\ptmem}{\cdp{62L}\pos{CCR7}}
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\newcommand{\catnum}[2]{(#1, #2)}
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\newcommand{\product}[3]{#1 \catnum{#2}{#3}}
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\newcommand{\thermo}{Thermo Fisher}
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\newcommand{\miltenyi}{Miltenyi Biotech}
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\newcommand{\bl}{Biolegend}
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\newcommand{\inlinecode}{\texttt}
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\newcommand{\subcap}[2]{\subref{#1}) #2}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ditto for environments
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\newenvironment{mytitlepage}{
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\begin{singlespace}
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\begin{center}
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}
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{
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\end{center}
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\end{singlespace}
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% begin document (proceed with caution)
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\begin{document}
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\begin{titlepage}
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\begin{mytitlepage}
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\mytitle{}
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\vfill
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\Large{
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A Dissertation \\
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Presented to \\
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The Academic Faculty \\
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\vspace{1.5em}
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by
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\vspace{1.5em}
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Nathan John Dwarshuis, B.S. \\
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\vfill
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In Partial Fulfillment \\
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of the Requirements for the Degree \\
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Doctor of Philosophy in Biomedical Engineering in the \\
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Wallace H. Coulter Department of Biomedical Engineering
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\vfill
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Georgia Institute of Technology and Emory University \\
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August 2021
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\vfill
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COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
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}
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\end{mytitlepage}
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\end{titlepage}
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\onecolumn \pagenumbering{roman}
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\clearpage
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\begin{mytitlepage}
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\mytitle{}
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\end{mytitlepage}
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\vfill
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\large{
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\noindent
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Committee Members
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||
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\begin{multicols}{2}
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\begin{singlespace}
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\mycommitteemember{Dr.\ Krishnendu\ Roy\ (Advisor)}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Madhav\ Dhodapkar}
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{Department of Hematology and Medical Oncology}
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{Emory University}
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Melissa\ Kemp}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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\columnbreak{}
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\null{}
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\vfill
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\mycommitteemember{Dr.\ Wilbur\ Lam}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Sakis\ Mantalaris}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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\end{singlespace}
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\end{multicols}
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\vspace{1.5em}
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\hfill Date Approved:
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}
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\clearpage
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\chapter*{acknowledgements}
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\addcontentsline{toc}{chapter}{acknowledgements}
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Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational.
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\clearpage
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\chapter*{summary}
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\addcontentsline{toc}{chapter}{summary}
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\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
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manufacturing large numbers of high quality cells remains challenging. Currently
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approved T cell expansion technologies involve \anti-cd{3} and \anti-cd{28}
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\glspl{mab}, usually mounted on magnetic beads. This method fails to
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recapitulate many key signals found \invivo{} and is also heavily licensed by a
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few companies, limiting its long-term usefulness to manufactures and clinicians.
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Furthermore, we understand that highly potent T cells are generally
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less-differentiated subtypes such as central memory and stem memory T cells.
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Despite this understanding, little has been done to optimize T cell expansion
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for generating these subtypes, including measurement and feedback control
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strategies that are necessary for any modern manufacturing process.
|
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|
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The goal of this thesis was to develop a microcarrier-based \gls{dms} T cell
|
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expansion system as well as determine biologically-meaningful \glspl{cqa} and
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\glspl{cpp} that could be used to optimize for highly-potent T cells. In Aim 1,
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we develop and characterized the \gls{dms} system, including quality control
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steps. We also demonstrate the feasiblity of expanding highly-potent memory and
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CD4+ T cells, and showing compatibility with existing \gls{car} transduction
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methods. In aim 2, we use \gls{doe} methodology to optimize the \gls{dms}
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platform, and develop a computational pipeline to identify and model the effect
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of measurable \glspl{cqa} and \glspl{cpp} on the final product. In aim 3, we
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demonstrate the effectiveness of the \gls{dms} platform \invivo{}. This
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thesis lays the groundwork for a novel T cell expansion method which can be used
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in a clinical setting, and also provides a path toward optimizing for product
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quality in an industrial setting.
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\clearpage
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\tableofcontents
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\clearpage
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\listoffigures
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\clearpage
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\listoftables
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\clearpage
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% \twocolumn
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\chapter*{acronyms}
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\addcontentsline{toc}{chapter}{acronyms}
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\printglossary[type=\acronymtype]
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\clearpage
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\pagenumbering{arabic}
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\clearpage
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\chapter{introduction}
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\section*{overview}
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% TODO this is basically the same as the first part of the backgound, I guess I
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% can just trim it down
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T cell-based immunotherapies have received great interest from clinicians and
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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
|
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expensive and difficult-to-scale manufacturing process with little control on
|
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cell quality and phenotype3,4. State-of-the-art T cell manufacturing techniques
|
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focus on \acd{3} and \acd{28} activation and expansion, typically
|
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presented on superparamagnetic, iron-based microbeads (Invitrogen Dynabead,
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Miltenyi MACS beads), on nanobeads (Miltenyi TransACT), or in soluble tetramers
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(Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016, Piscopo2017, Bashour2015}.
|
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These strategies overlook many of the signaling components present in the
|
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secondary lymphoid organs where T cells expand \invivo{}. Typically, T cells are
|
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activated under close cell-cell contact, which allows for efficient
|
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autocrine/paracrine signaling via growth-stimulating cytokines such as
|
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\gls{il2}. Additionally, the lymphoid tissues are comprised of \gls{ecm}
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components such as collagen, which provide signals to upregulate proliferation,
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cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
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Boisvert2007, Ben-Horin2004}. We hypothesized that culture conditions that
|
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better mimic these \invivo{} expansion conditions of T cells, can significantly
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improve the quality and quantity of manufactured T cells and provide better
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control on the resulting T cell phenotype.
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|
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% TODO mention the Cloudz stuff that's in my presentation
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A variety of solutions have been proposed to make the T cell expansion process
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more physiological. One strategy is to use modified feeder cell cultures to
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provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
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While this has the theoretical capacity to mimic many components of the lymph
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node, it is hard to reproduce on a large scale due to the complexity and
|
||
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
|
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Others have proposed biomaterials-based solutions to circumvent this problem,
|
||
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
|
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Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
|
||
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
|
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beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
|
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large interfacial contact area, 3D-structure, or soft surfaces T cells normally
|
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experience \invivo{}. While these have been shown to provide superior expansion
|
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compared to traditional microbeads, none of these methods has been able to show
|
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preferential expansion of functional naïve/memory and CD4 T cell populations.
|
||
Generally, T cells with a lower differentiation state such as naïve and memory
|
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cells have been shown to provide superior anti-tumor potency, presumably due to
|
||
their higher potential to replicate, migrate, and engraft, leading to a
|
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long-term, durable response\cite{Xu2014, Fraietta2018, Gattinoni2011,
|
||
Gattinoni2012}. Likewise, CD4 T cells are similarly important to anti-tumor
|
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potency due to their cytokine release properties and ability to resist
|
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exhaustion\cite{Wang2018, Yang2017}. Therefore, methods to increase naïve/memory
|
||
and CD4 T cells in the final product are needed, a critical consideration being
|
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ease of translation to industry and ability to interface with scalable systems
|
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such as bioreactors.
|
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|
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% TODO probably need to address some of the modeling stuff here as well
|
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|
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This thesis describes a novel degradable microscaffold-based method derived from
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porous microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab}
|
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for use in T cell expansion cultures. Microcarriers have historically been used
|
||
throughout the bioprocess industry for adherent cultures such as stem cells and
|
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\gls{cho} cells, but not with suspension cells such as T
|
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cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to make the DMSs in
|
||
this study have a microporous structure that allows T cells to grow inside and
|
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along the surface, providing ample cell-cell contact for enhanced autocrine and
|
||
paracrine signaling. Furthermore, the carriers are composed of gelatin, which is
|
||
a collagen derivative and therefore has adhesion domains that are also present
|
||
within the lymph nodes. Finally, the 3D surface of the carriers provides a
|
||
larger contact area for T cells to interact with the \glspl{mab} relative to
|
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beads; this may better emulate the large contact surface area that occurs
|
||
between T cells and \glspl{dc}. These microcarriers 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) {\#}[Purcell
|
||
documentation]. This regulatory history will aid in clinical translation. We
|
||
show that compared to traditional microbeads, \gls{dms}-expanded T cells not
|
||
only provide superior expansion, but consistently provide a higher frequency of
|
||
naïve/memory and CD4 T cells (CCR7+CD62L+) across multiple donors. We also
|
||
demonstrate functional cytotoxicity using a CD19 \gls{car} and a superior
|
||
performance, even at a lower \gls{car} T cell dose, of \gls{dms}-expanded
|
||
\gls{car}-T cells \invivo{} in a mouse xenograft model of human B cell
|
||
\gls{all}. Our results indicate that \glspl{dms} provide a robust and scalable
|
||
platform for manufacturing therapeutic T cells with higher naïve/memory
|
||
phenotype and more balanced CD4+ T cell content.
|
||
|
||
\section*{hypothesis}
|
||
|
||
The hypothesis of this dissertation was that using \glspl{dms} created from
|
||
off-the-shelf microcarriers and coated with activating \glspl{mab} would lead to
|
||
higher quantity and quality T cells as compared to state-of-the-art bead-based
|
||
expansion. The objective of this dissertation was to develop this platform, test
|
||
its effectiveness both \invivo{} and \invivo{}, and develop computational
|
||
pipelines that could be used in a manufacturing environment.
|
||
|
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\section*{specific aims}
|
||
|
||
The specific aims of this dissertation are outlined in
|
||
\cref{fig:graphical_overview}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics[width=\textwidth]{example-image-a}
|
||
|
||
\endgroup
|
||
\caption[Project Overview]{High-level workflow.}
|
||
\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}
|
||
|
||
% TODO this might be easier to break apart in separate aims
|
||
|
||
In this first aim, we demonstrated the process for manufacturing \glspl{dms},
|
||
including quality control steps that are necessary for translation of this
|
||
platform into a scalable manufacturing setting. We also demonstrate that the
|
||
\gls{dms} platform leads to higher overall expansion of T cells and higher
|
||
overall fractions of potent memory and CD4+ subtypes desired for T cell
|
||
therapies. Finally, we demonstrate \invitro{} that the \gls{dms} platform can be
|
||
used to generate functional \gls{car} T cells targeted toward CD19.
|
||
|
||
\subsection*{aim 2: develop methods to control and predict T cell quality}
|
||
|
||
For this second aim, we investigated methods to identify and control \glspl{cqa}
|
||
and glspl{cpp} for manufacturing T cells using the \gls{dms} platform. This was
|
||
accomplished through two sub-aims:
|
||
|
||
\begin{itemize}
|
||
\item[A --] Develop computational methods to control and predict T cell
|
||
expansion and quality
|
||
\item[B --] Perturb \gls{dms} expansion to identify additional mechanistic
|
||
controls for expansion and quality
|
||
\end{itemize}
|
||
|
||
\subsection*{aim 3: confirm potency of T cells from novel T cell expansion
|
||
process using \invivo{} xenograft mouse model}
|
||
|
||
In this final aim, we demonstrate the effectiveness of \gls{dms}-expanded T
|
||
cells compared to state-of-the-art beads using \invivo{} mouse models for
|
||
\gls{all}.
|
||
|
||
\section*{outline}
|
||
|
||
In Chapter~\ref{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 Chapters~\ref{aim1},~\ref{aim2}, and~\ref{aim3} we present the
|
||
work pertaining to Aims 1, 2, and 3 respectively. Finally, we present our final
|
||
conclusions in Chapter~\ref{conclusions}.
|
||
|
||
\chapter{background and significance}\label{background}
|
||
\section*{background}
|
||
|
||
% TODO break this apart into mfg tech and T cell phenotypes/quality
|
||
% TODO consider adding a separate section on microcarriers and their use in
|
||
% bioprocess
|
||
% TODO add stuff about T cell licensing
|
||
\subsection*{current T cell manufacturing technologies}
|
||
|
||
\Gls{car} T cell therapy has received great interest from both academia and
|
||
industry due to its potential to treat cancer and other
|
||
diseases\cite{Fesnak2016, Rosenberg2015}. In 2017, Novartis and Kite Pharma
|
||
acquired FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
|
||
two \gls{car} T cell therapies against B cell malignancies. Despite these
|
||
successes, \gls{car} T cell therapies are constrained by an expensive and
|
||
difficult-to-scale manufacturing process\cite{Roddie2019, Dwarshuis2017}.
|
||
|
||
Of critical concern, state-of-the-art manufacturing techniques focus only on
|
||
Signal 1 and Signal 2-based activation via \acd{3} and \acd{28} \glspl{mab},
|
||
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
|
||
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
|
||
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
|
||
strategies overlook many of the signaling components present in the secondary
|
||
lymphoid organs where T cells normally expand. Typically, T cells are activated
|
||
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
|
||
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
|
||
signals. These close quarters allow for efficient autocrine/paracrine signaling
|
||
among the expanding T cells, which secrete gls{il2} and other cytokines to
|
||
assist their own growth. Additionally, the lymphoid tissues are comprised of
|
||
\gls{ecm} components such as collagen, 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. One strategy is to use modified feeder cell cultures to
|
||
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
|
||
While this has the theoretical capacity to mimic several key components of the
|
||
lymph node, it is hard to reproduce on a large scale due to the complexity and
|
||
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
|
||
Others have proposed biomaterials-based solutions to circumvent this problem,
|
||
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
|
||
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
|
||
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
|
||
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
|
||
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
|
||
experience \textit{in vivo}. While these have been shown to provide superior
|
||
expansion compared to traditional microbeads, no method has been able to show
|
||
preferential expansion of functional memory and CD4 T cell populations.
|
||
Generally, T cells with a lower differentiation state such as memory cells have
|
||
been shown to provide superior anti-tumor potency, presumably due to their
|
||
higher potential to replicate, migrate, and engraft, leading to a long-term,
|
||
durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
|
||
Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
|
||
cytokine release properties and ability to resist exhaustion\cite{Wang2018,
|
||
Yang2017}, and no method exists to preferentially expand the CD4 population
|
||
compared to state-of-the-art systems.
|
||
|
||
Here we propose a method using microcarriers functionalized with \acd{3} and
|
||
\acd{28} \glspl{mab} for use in T cell expansion cultures. Microcarriers have
|
||
historically been used throughout the bioprocess industry for adherent cultures
|
||
such as stem cells and \gls{cho} cells, but not with suspension cells such as T
|
||
cells\cite{Heathman2015, Sart2011}. The carriers have a macroporous 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 carriers are composed of gelatin, which is a collagen derivative and
|
||
therefore has adhesion domains that are also present within the lymph nodes.
|
||
Finally, 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}.
|
||
|
||
\subsection*{strategies to optimize cell manufacturing}
|
||
|
||
The \gls{dms} system has a number of parameters that can be optimized, and a
|
||
\gls{doe} is an ideal framework to test multiple parameters simultaneously. The
|
||
goal of \gls{doe} is to answer a data-driven question with the least number of
|
||
resources. 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.
|
||
|
||
% TODO add a bit more about the math of a DOE here
|
||
|
||
\Glspl{doe} served three purposes in this dissertation. First, we used them as
|
||
screening tools, 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.
|
||
|
||
\subsection*{strategies to characterize cell manufacturing}
|
||
|
||
A number of multiomics strategies exist which can generate rich datasets for T
|
||
cells. We will consider several multiomics strategies within this proposal:
|
||
|
||
\begin{description}
|
||
\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
|
||
many bulk (not single cell) cytokine concentrations simultaneously
|
||
in a media sample. Since this only requires media (as opposed to
|
||
destructively measuring cells) we will use this as a longitudinal
|
||
measurement.
|
||
\item[Metabolomics:] 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}. We will interrogate
|
||
key metabolic species using \gls{nmr} in collaboration with the
|
||
Edison Lab at the University of Georgia. This will be both a
|
||
longitudinal assay using media samples (since some metabolites may
|
||
be expelled from cells that are indicative of their phenotype) and
|
||
at endpoint where we will lyse the cells and interogate their entire
|
||
metabolome.
|
||
\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. This will be
|
||
useful in determining precise subpopulations and phenotypes that may be
|
||
influencing responses, especially when one considers that many cell types can
|
||
be defined by more than one marker combination. We will perform this at
|
||
endpoint. 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.
|
||
\end{description}
|
||
|
||
% TODO add a computational section
|
||
|
||
% TODO add a section explaining causal inference since this is a big part of
|
||
% the end of aim 1
|
||
|
||
\section{Innovation}
|
||
|
||
\subsection{Innovation}
|
||
|
||
Several aspects of this work are novel considering the state-of-the-art
|
||
technology for T cell manufacturing:
|
||
|
||
\begin{itemize}
|
||
\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
|
||
bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
|
||
the licenses for these techniques is controlled by only a few companies
|
||
(Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
|
||
is more expensive to implement and therefore hinders companies from entering
|
||
the rapidly growing T cell manufacturing arena. Providing an alternative as we
|
||
are doing will add more options, increase competition among both raw material
|
||
and T cell manufacturers, and consequently drive down cell therapy market
|
||
prices and increase innovation throughout the industry.
|
||
\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 propose to optimize our systems using \gls{doe} methodology, which is a
|
||
strategy commonly used in other industries and disciplines but has yet to gain
|
||
wide usage in the development of cell therapies. \Glspl{doe} are advantageous
|
||
as they allow the inspection of multiple parameters simultaneously, allowing
|
||
efficient and comprehensive analysis of the system vs 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.
|
||
Of further note, most \textit{in vivo} experiments are not done using a
|
||
\gls{doe}-based approach; however, a \gls{doe} is perfectly natural for a
|
||
large mouse study where one naturally desires to use as few animals as
|
||
possible.
|
||
\item The \gls{dms} system is be compatible with static bioreactors such as the
|
||
G-Rex which has been adopted throughout the cell therapy industry. Thus this
|
||
technology can be easily incorporated into existing cell therapy process that
|
||
are performed at scale.
|
||
\item We analyzed our system using a multiomics approach, which will enable the
|
||
discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
|
||
has been applied to T cells previously, it has not been done in the context of
|
||
a large \gls{doe}-based model. This approach is aware of the whole design
|
||
space, and thus enables greater understanding of process parameters and their
|
||
effect on cell phenotype.
|
||
\end{itemize}
|
||
|
||
\chapter{aim 1}\label{aim1}
|
||
|
||
\section{introduction}
|
||
|
||
The first aim was to develop a microcarrier system that mimics several key
|
||
aspects of the \invivo{} lymph node microenvironment. We compared compare this
|
||
system to state-of-the-art T cell activation technologies for both expansion
|
||
potential and memory cell formation. The governing hypothesis was that
|
||
microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
|
||
provide superior expansion and memory phenotype compared to state-of-the-art
|
||
bead-based T cell expansion technology.
|
||
|
||
% TODO this doesn't flow that well and is repetitive with what comes above
|
||
|
||
Microcarriers have been used throughout the bioprocess industry for adherent
|
||
cell cultures such as \gls{cho} cells and stem cells, as they are able to
|
||
achieve much greater surface area per unit volume than traditional 2D
|
||
cultures\cite{Heathman2015, Sart2011}. Adding adhesive \glspl{mab} to the
|
||
microcarriers will adapt them for suspension cell cultures such as T cells.
|
||
Consequently, the large macroporous structure will allow T cells to cluster more
|
||
closely, which in turn will enable better autocrine and paracrine signaling.
|
||
Specifically, two cytokines that are secreted by T cells, IL-2 and IL-15, are
|
||
known to drive expansion and memory phenotype respectively\cite{Buck2016}.
|
||
Therefore, the proposed microcarrier system should enable greater expansion and
|
||
better retention of memory phenotype compared to current bead-based methods.
|
||
|
||
\section{methods}
|
||
\subsection{dms functionalization}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_flowchart.png}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Flowchart]{Overview of \gls{dms} manufacturing process.}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
Gelatin microcarriers (\gls{cus} or \gls{cug}, GE Healthcare, DG-2001-OO and
|
||
DG-0001-OO) were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and
|
||
autoclaved. All subsequent steps were done aseptically, and all reactions were
|
||
carried out at \SI{20}{\mg\per\ml} carriers at room temperature and agitated
|
||
using an orbital shaker with a \SI{3}{\mm} orbit diameter. After autoclaving,
|
||
the microcarriers were washed using sterile \gls{pbs} three times in a 10:1
|
||
volume ratio. \product{\Gls{snb}}{\thermo}{21217} was dissolved at
|
||
approximately \SI{10}{\micro\molar} in sterile ultrapure water, and the true
|
||
concentration was then determined using the \gls{haba} assay (see below).
|
||
\SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added to carrier suspension and allowed
|
||
to react for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the
|
||
reaction, the amount of biotin remaining in solution was quantified using the
|
||
\gls{haba} assay (see below). The carriers were then washed three times, which
|
||
entailed adding sterile \gls{pbs} in a 10:1 volumetric ratio, agitating at
|
||
\SI{900}{\rpm} for \SI{10}{\minute}, adding up to a 15:1 volumetric ratio
|
||
(relative to reaction volume) of sterile \gls{pbs}, centrifuging at
|
||
\SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid back down to the
|
||
reaction volume.
|
||
|
||
To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson
|
||
Immunoresearch}{ 016-000-114} was added and allowed to react for
|
||
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant
|
||
was taken for the \gls{bca} assay, and the carriers were washed analogously to
|
||
the previous wash step to remove the biotin, except two washes were done and the
|
||
agitation time was \SI{30}{\minute}. Biotinylated \glspl{mab} against human CD3
|
||
\catnum{\bl}{317320} and CD28 \catnum{\bl}{302904} were combined in a 1:1 mass
|
||
ratio and added to the carriers at \SI{0.2}{\ug\of{\ab}\per\mg\of{\dms}}. Along
|
||
with the \glspl{mab}, sterile \product{\gls{bsa}}{Sigma}{A9576} was added to a
|
||
final concentration of \SI{2}{\percent} in order to prevent non-specific binding
|
||
of the antibodies to the reaction tubes. \glspl{mab} were allowed to bind to the
|
||
carriers for \SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding,
|
||
supernatants were sampled to quantify remaining \gls{mab} concentration using an
|
||
\product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}. Fully functionalized
|
||
\glspl{dms} were washed in sterile \gls{pbs} analogous to the previous washing
|
||
step to remove excess \gls{stp}. They were washed once again in the cell culture
|
||
media to be used for the T cell expansion.
|
||
|
||
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 {Table X}.
|
||
|
||
%TODO this bit belongs in the next aim
|
||
% In the case of the \gls{doe} experiment where
|
||
% variable mAb surface density was utilized, the anti-CD3/anti-CD28 mAb mixture
|
||
% was further combined with a biotinylated isotype control to reduce the overall
|
||
% fraction of targeted mAbs (for example the 60\% mAb surface density corresponded
|
||
% to 3 mass parts anti-CD3, 3 mass parts anti-CD8, and 4 mass parts isotype
|
||
% control).
|
||
|
||
\subsection{dms quality control assays}
|
||
|
||
Biotin was quantified using the \product{\gls{haba} assay}{Sigma}{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}.
|
||
|
||
\gls{stp} binding to the carriers was quantified indirectly using a
|
||
\product{\gls{bca} kit }{\thermo}{23227} according to the manufacturer’s
|
||
instructions, with the exception that the standard curve was made with known
|
||
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at
|
||
\SI{592}{\nm} was quantified using a Biotek plate reader.
|
||
|
||
\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 antibodies used to coat the carriers were used as the standard for the
|
||
\gls{elisa} standard curve.
|
||
|
||
Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
|
||
quantified indirectly using \product{FITC-biotin}{\thermo}{B10570}.
|
||
Briefly, \SI{400}{\pmol\per\ml} FITC-biotin 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
|
||
FITC-biotin using a Biotek plate reader.
|
||
|
||
\Gls{stp} binding was verified after the \gls{stp}-binding step visually by
|
||
adding biotin-FITC to the \gls{stp}-coated \glspl{dms}, resuspending in
|
||
\SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
|
||
microscope}{Zeiss}{Z.1}. \Gls{mab} binding was verified visually by first
|
||
staining with \product{\anti{gls{igg}}-FITC}{\bl}{406001}, incubating for
|
||
\SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope.
|
||
|
||
\subsection{t cell culture}
|
||
|
||
% TODO verify countess product number
|
||
Cryopreserved primary human T cells were either obtained as sorted
|
||
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
|
||
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection
|
||
\product{\cdp{3} \gls{macs} kit}{\miltenyi}{130-096-535}. T cells were activated
|
||
using \glspl{dms} or \product{\SI{3.5}{\um} CD3/CD28 magnetic
|
||
beads}{\miltenyi}{130-091-441}. In the case of beads, T cells were activated
|
||
at the manufacturer recommended cell:bead ratio of 2:1. In the case of
|
||
\glspl{dms}, cells were activated using \SI{2500}{\dms\per\cm\squared} unless
|
||
otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
|
||
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
|
||
\product{OpTmizer}{\thermo}{A1048501} or
|
||
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
|
||
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02}. 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}.
|
||
|
||
% TODO this belongs in aim 2
|
||
% In order to remove \glspl{dms} from
|
||
% culture, collagenase D (Sigma Aldrich) was sterile filtered in culture media and
|
||
% added to a final concentration of \SI{50}{\ug\per\ml} during media addition.
|
||
|
||
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.
|
||
|
||
\subsection{chemotaxis assay}
|
||
|
||
% TODO not sure about the transwell product number
|
||
Migratory function was assayed using a transwell chemotaxis assay as previously
|
||
described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
|
||
\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
|
||
basolateral chamber loaded with \SI{600}{\ul} media and 0, 250, or
|
||
\SI{1000}{\ng\per\mL} \product{CCL21}{Peprotech}{250-13}. The plate was
|
||
incubated for \SI{4}{\hour} after loading, and the basolateral chamber of each
|
||
transwell was quantified for total cells using \product{countbright
|
||
beads}{\thermo}{C36950}. The final readout was normalized using the
|
||
\SI{0}{\ng\per\mL} concentration as background.
|
||
|
||
\subsection{degranulation assay}
|
||
|
||
Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
|
||
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
|
||
were incubated with \num{1.5e5} target cells consisting of either \product{K562
|
||
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
|
||
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}.
|
||
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
|
||
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar} \product{monensin}{eBioscience}{
|
||
00-4505-51}, and \SI{1}{\ug\per\ml} \product{\acd{28}}{eBioscience}{302914} (all
|
||
functional grade \glspl{mab}) with \SI{250}{\ul} total volume. After
|
||
\SI{4}{\hour} incubation at \SI{37}{\degreeCelsius}, cells were stained for CD3,
|
||
CD4, and CD107a and analyzed on a BD LSR Fortessa. Readout was calculated as the
|
||
percent \cdp{107a} cells of the total \cdp{8} fraction.
|
||
|
||
\subsection{car expression}
|
||
|
||
\gls{car} expression 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}).
|
||
|
||
\subsection{car plasmid and lentiviral transduction}
|
||
|
||
The anti-CD19-CD8-CD137-CD3z \gls{car} with the EF1$\upalpha$
|
||
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
|
||
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core.
|
||
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the
|
||
Cincinnati Children's Hospital Medical Center Viral Vector Core. 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.
|
||
|
||
\subsection{statistical analysis}
|
||
|
||
For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
|
||
significance was assessed using the \inlinecode{stat\_compare\_means} function
|
||
with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R.
|
||
For 2-way \gls{anova} analysis, the significance of main and interaction effects
|
||
was determined using the car library in R.
|
||
|
||
% TODO not all of this stuff applied to my regressions
|
||
For least-squares linear regression, statistical significance was evaluated the
|
||
\inlinecode{lm} function in R. Stepwise regression models were obtained using
|
||
the \inlinecode{stepAIC} function from the \inlinecode{MASS} package with
|
||
forward and reverse stepping. 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). Statistical significance was evaluated at $\upalpha$ =
|
||
0.05.
|
||
|
||
% TODO add meta-analysis section
|
||
|
||
\section{results}
|
||
|
||
\subsection{DMSs can be fabricated in a controlled manner}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_coating.png}
|
||
\phantomsubcaption\label{fig:stp_carrier_fitc}
|
||
\phantomsubcaption\label{fig:mab_carrier_fitc}
|
||
\phantomsubcaption\label{fig:cug_vs_cus}
|
||
\phantomsubcaption\label{fig:biotin_coating}
|
||
\phantomsubcaption\label{fig:stp_coating}
|
||
\phantomsubcaption\label{fig:mab_coating}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Coating]
|
||
{\gls{dms} functionalization results.
|
||
\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
|
||
treated with biotin-FITC 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.} \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. }
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
% TODO these caption titles suck
|
||
% TODO combine this DOE figure into one interaction plot
|
||
\begin{table}[!h] \centering
|
||
\caption{Properties of the microcarriers used}
|
||
\label{tab:carrier_props}
|
||
\input{../tables/carrier_properties.tex}
|
||
\end{table}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_qc.png}
|
||
\phantomsubcaption\label{fig:dms_qc_doe}
|
||
\phantomsubcaption\label{fig:dms_qc_ph}
|
||
\phantomsubcaption\label{fig:dms_qc_washes}
|
||
\phantomsubcaption\label{fig:dms_snb_decay_curves}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Quality Control]
|
||
{\gls{dms} quality control investigation and development
|
||
\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} of differing pH.}
|
||
All statistical tests where p-values are noted are given by two-tailed t
|
||
tests.
|
||
}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Properties of the microcarriers used}
|
||
\label{tab:carrier_props}
|
||
\input{../tables/carrier_properties.tex}
|
||
\end{table}
|
||
|
||
\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}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Reaction timing]
|
||
{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.}
|
||
}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
\subsection{DMSs can efficiently expand T cells compared to beads}
|
||
|
||
% TODO make sure the day on these is correct
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/cells_on_dms.png}
|
||
\phantomsubcaption\label{fig:dms_cells_phase}
|
||
\phantomsubcaption\label{fig:dms_cells_fluor}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Reaction timing]
|
||
{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} on day 7}
|
||
\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}.}
|
||
}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
% 3-donor expansion figure
|
||
|
||
\subsection{DMSs lead to greater expansion and memory and CD4+ phenotypes}
|
||
|
||
% phenotype expansion plots
|
||
% phenotype flow plots
|
||
|
||
\subsection*{DMSs can be used to produce functional CAR T cells}
|
||
|
||
% protein L + degranulation + migration
|
||
|
||
\subsection{DMSs do not leave antibodies attached to cell product}
|
||
|
||
% non-leaching figure
|
||
|
||
\subsection{DMSs consistently outperform bead-based expansion compared to
|
||
beads in a variety of conditions}
|
||
|
||
% TODO these tables have extra crap in them that I don't need to show
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables only}
|
||
\label{tab:ci_treat}
|
||
\input{../tables/causal_inference_treat.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables and control variables}
|
||
\label{tab:ci_controlled}
|
||
\input{../tables/causal_inference_control.tex}
|
||
\end{table}
|
||
|
||
% meta-analysis regression effect sizes
|
||
|
||
\section{discussion}
|
||
|
||
\chapter{aim 2}\label{aim2}
|
||
|
||
\section{introduction}
|
||
\section{methods}
|
||
\section{results}
|
||
\section{discussion}
|
||
|
||
\chapter{aim 3}\label{aim3}
|
||
|
||
\section{introduction}
|
||
\section{methods}
|
||
\section{results}
|
||
\section{discussion}
|
||
|
||
\chapter{conclusions and future work}\label{conclusions}
|
||
\section{conclusions}
|
||
\section{future work}
|
||
|
||
\onecolumn
|
||
\clearpage
|
||
|
||
% TODO some people put appendices here....not sure if I need to
|
||
|
||
\chapter{References}
|
||
\renewcommand{\section}[2]{} % noop the original bib section header
|
||
|
||
\bibliography{references}
|
||
|
||
\bibliographystyle{naturemag}
|
||
|
||
\end{document}
|