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FIX toc capitalization (the wrong way)

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@ -19,6 +19,7 @@
\usepackage{pgfgantt} \usepackage{pgfgantt}
\usepackage{setspace} \usepackage{setspace}
\usepackage{listings} \usepackage{listings}
\usepackage{tocloft}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% my attempt to make MATLAB code look pretty % my attempt to make MATLAB code look pretty
@ -74,15 +75,25 @@
\doublespacing{} \doublespacing{}
\titleformat{\chapter}[block]{\filcenter\bfseries\large} \titleformat{\chapter}[block]{\filcenter\bfseries\Large}
{\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase} {\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase}
% \titleformat{\chapter}[block]{\filcenter\bfseries\large}{}{0pt}{\uppercase}
\titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap} \titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap}
\titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap} \titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap}
\titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap} \titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap}
\setlist[description]{font=$\bullet$~\textbf\normalfont} \setlist[description]{font=$\bullet$~\textbf\normalfont}
\renewcommand*{\contentsname}{TABLE OF CONTENTS}
\renewcommand{\listfigurename}{LIST OF FIGURES}
\renewcommand{\listtablename}{LIST OF TABLES}
\renewcommand{\cfttoctitlefont}{\hspace*{\fill}\Large\bfseries}
\renewcommand{\cftaftertoctitle}{\hspace*{\fill}}
\renewcommand{\cftlottitlefont}{\hspace*{\fill}\Large\bfseries}
\renewcommand{\cftafterlottitle}{\hspace*{\fill}}
\renewcommand{\cftloftitlefont}{\hspace*{\fill}\Large\bfseries}
\renewcommand{\cftafterloftitle}{\hspace*{\fill}}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% acronyms for the lazy % acronyms for the lazy
% %
@ -420,17 +431,41 @@
\hfill Date Approved: \hfill Date Approved:
} }
% \clearpage
% \chapter*{acknowledgements}
% \addcontentsline{toc}{chapter}{Acknowledgements}
% Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational.
\clearpage \clearpage
\chapter*{acknowledgements} \tableofcontents
\addcontentsline{toc}{chapter}{acknowledgements}
Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational. \clearpage
\listoffigures
\addcontentsline{toc}{chapter}{LIST OF FIGURES}
\clearpage
\listoftables
\addcontentsline{toc}{chapter}{LIST OF TABLES}
\clearpage
\chapter*{LIST OF SYMBOLS AND ABBREVIATIONS}
\addcontentsline{toc}{chapter}{LIST OF SYMBOLS AND ABBREVIATIONS}
\printglossary[type=\acronymtype]
\clearpage
\pagenumbering{arabic}
\clearpage \clearpage
\chapter*{summary} \chapter*{summary}
\addcontentsline{toc}{chapter}{summary} \addcontentsline{toc}{chapter}{SUMMARY}
\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but \Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
manufacturing large numbers of high quality cells remains challenging. Currently manufacturing large numbers of high quality cells remains challenging. Currently
@ -458,30 +493,7 @@ method which can be utilized at scale for a clinical trial and beyond.
\clearpage \clearpage
\tableofcontents \chapter{INTRODUCTION}
\clearpage
\listoffigures
\clearpage
\listoftables
\clearpage
% \twocolumn
\chapter*{acronyms}
\addcontentsline{toc}{chapter}{acronyms}
\printglossary[type=\acronymtype]
\clearpage
\pagenumbering{arabic}
\clearpage
\chapter{introduction}
\section*{overview} \section*{overview}
@ -605,11 +617,11 @@ forward. In \cref{aim1,aim2a,aim2b,aim3} we present the work pertaining to Aims
conclusions as well as provide insights for how this work can be extended in the conclusions as well as provide insights for how this work can be extended in the
future. future.
\chapter{background and significance}\label{background} \chapter{BACKGROUND AND INNOVATION}\label{background}
\section*{background} \section{Background}
\subsection{quality by design in cell manufacturing} \subsection{Quality by Design in Cell Manufacturing}
The challenges for the cell manufacturing field are significant. Unlike other The challenges for the cell manufacturing field are significant. Unlike other
industries which manufacture inanimate products such as automobiles and industries which manufacture inanimate products such as automobiles and
@ -645,7 +657,7 @@ 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 this work are discussed further in \cref{sec:background_doe} and
\cref{sec:background_quality}/\cref{sec:background_cqa} respectively. \cref{sec:background_quality}/\cref{sec:background_cqa} respectively.
\subsection{T cells for immunotherapies} \subsection{T Cells for Immunotherapies}
A variety of T cell therapies have been utilized with varying degrees of 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 success, and we describe a few of the most prominent below. We should note that
@ -716,7 +728,7 @@ date, there are almost 1000 clinical trials using \gls{car} T cells.
% TODO there are other T cells like virus-specific T cells and gd T cells, not % TODO there are other T cells like virus-specific T cells and gd T cells, not
% that they matter... % that they matter...
\subsection{scaling T cell expansion} \subsection{Scaling T Cell Expansion}
In order to scale T cell therapies to meet clinical demands, automation and In order to scale T cell therapies to meet clinical demands, automation and
bioreactors will be necessary. To this end, there are several choices that have bioreactors will be necessary. To this end, there are several choices that have
@ -761,7 +773,7 @@ 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 manufacturing, but novel T cell expansion technologies such as that described in
this work need to consider how it may be used at scale in such a system. this work need to consider how it may be used at scale in such a system.
\subsection{cell sources in T cell manufacturing}\label{sec:background_source} \subsection{Cell Sources in T Cell Manufacturing}\label{sec:background_source}
T cells for cell manufacturing can be obtained broadly via two paradigms: T cells for cell manufacturing can be obtained broadly via two paradigms:
autologous and allogeneic. The former involves obtaining T cells from a patient autologous and allogeneic. The former involves obtaining T cells from a patient
@ -807,7 +819,7 @@ 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 allogeneic T cell therapies edited with \gls{crispr} to reduce the likelihood of
\gls{gvhd}. \gls{gvhd}.
\subsection{overview of T cell quality}\label{sec:background_quality} \subsection{Overview of T Cell Quality}\label{sec:background_quality}
T cells are highly heterogeneous and can exist in a variety of states and 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 subtypes, many of which can be measured (at least indirectly) though biomarkers
@ -864,7 +876,7 @@ using retro- or lentiviral vectors as their means of gene-editing must be tested
for replication competent vectors\cite{Wang2013} and for contamination via for replication competent vectors\cite{Wang2013} and for contamination via
bacteria or other pathogens. bacteria or other pathogens.
\subsection*{T cell activation methods}\label{sec:background_activation} \subsection{T cell Activation Methods}\label{sec:background_activation}
In order for T cells to be expanded \exvivo{} they must be activated with a 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{}, stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \Invivo{},
@ -929,7 +941,7 @@ recapitulate the cellular membrane, large interfacial contact area,
None have been demonstrated to demonstrably expand high quality T cells as None have been demonstrated to demonstrably expand high quality T cells as
outlined in \cref{sec:background_quality}. outlined in \cref{sec:background_quality}.
\subsection{microcarriers in bioprocessing} \subsection{Microcarriers in Bioprocessing}
In this work, we explored microcarriers as the basis for an alternative to the In this work, we explored microcarriers as the basis for an alternative to the
methods described in \cref{sec:background_activation}. methods described in \cref{sec:background_activation}.
@ -999,7 +1011,7 @@ FDA fast-track-approved combination retinal pigment epithelial cell product
(Spheramine, Titan Pharmaceuticals)\cite{purcellmain}. This regulatory history (Spheramine, Titan Pharmaceuticals)\cite{purcellmain}. This regulatory history
will aid in clinical translation. will aid in clinical translation.
\subsection*{integrins and T cell signaling} \subsection{Integrins and T Cell Signaling}
Because the microcarriers used in this work are derived from collagen, one key 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 question is how these collagen domains may interact with the T cells during
@ -1035,7 +1047,7 @@ stimulated in the presence of collagen I\cite{Boisvert2007}.
% with fibronectin and has been shown to lead to higher IL2 production (Iwata et % with fibronectin and has been shown to lead to higher IL2 production (Iwata et
% al 2000). % al 2000).
\subsection*{the role of IL15 in memory T cell proliferation} \subsection{The Role of IL15 in Memory T Cell Proliferation}
\il{15} is a cytokine that is involved with the proliferation and homeostasis of \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 memory T cells. Its role in the work of this dissertation is the subject of
@ -1087,7 +1099,7 @@ a soluble form, which can bind to \il{15} and signal to cells which are not
adjacent to the source independent of the \textit{cis/trans} mechanisms already adjacent to the source independent of the \textit{cis/trans} mechanisms already
described\cite{Budagian2004}. described\cite{Budagian2004}.
\subsection*{overview of design of experiments}\label{sec:background_doe} \subsection{Overview of Design of Experiments}\label{sec:background_doe}
The \gls{dms} system described in this dissertation has a number of parameters The \gls{dms} system described in this dissertation has a number of parameters
that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal
@ -1165,7 +1177,7 @@ 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 we generally used were fractional factorial designs with three levels, which
enable the estimation of both main effects and second order quadratic effects. enable the estimation of both main effects and second order quadratic effects.
\subsection*{identification and standardization of CPPs and \subsection{Identification and Standardization of CPPs and
CQAs}\label{sec:background_cqa} CQAs}\label{sec:background_cqa}
% BACKGROUND at least attempt to show that there is alot of work in the space % BACKGROUND at least attempt to show that there is alot of work in the space
@ -1267,9 +1279,9 @@ novel considering the state-of-the-art technology for T cell manufacturing:
effect on cell phenotype. effect on cell phenotype.
\end{itemize} \end{itemize}
\chapter{aim 1}\label{aim1} \chapter{AIM 1}\label{aim1}
\section{introduction} \section{Introduction}
The first aim was to develop a microcarrier system that mimics several key The first aim was to develop a microcarrier system that mimics several key
aspects of the \invivo{} lymph node microenvironment. We compared compare this aspects of the \invivo{} lymph node microenvironment. We compared compare this
@ -1279,9 +1291,9 @@ microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
provide superior expansion and memory phenotype compared to state-of-the-art provide superior expansion and memory phenotype compared to state-of-the-art
bead-based T cell expansion technology. bead-based T cell expansion technology.
\section{methods} \section{Methods}
\subsection{DMS functionalization}\label{sec:dms_fab} \subsection{DMS Functionalization}\label{sec:dms_fab}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -1342,7 +1354,7 @@ 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 \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}. \gls{cug} as given by the manufacturer \cref{tab:carrier_props}.
\subsection{DMS quality control assays} \subsection{DMS Quality Control Assays}
Biotin was quantified using the \product{\gls{haba} assay}{\sigald}{H2153-1VL}. 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, In the case of quantifying \gls{snb} prior to adding it to the microcarriers,
@ -1380,7 +1392,7 @@ 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 incubating for \SI{30}{\minute}, washing with \gls{pbs}, and imaging on a
confocal microscope. confocal microscope.
\subsection{t cell culture}\label{sec:tcellculture} \subsection{T Cell Culture}\label{sec:tcellculture}
Cryopreserved primary human T cells were either obtained as sorted Cryopreserved primary human T cells were either obtained as sorted
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from \product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
@ -1412,7 +1424,7 @@ imaging on a spinning disk confocal microscope.
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}. Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}.
\subsection{Quantifying cells on DMS interior} \subsection{Quantifying Cells on DMS Interior}
% TODO add a product number to MTT (if I can find it) % TODO add a product number to MTT (if I can find it)
To visualize T cells on the interior of the \glspl{dms}, we stained them with To visualize T cells on the interior of the \glspl{dms}, we stained them with
@ -1432,7 +1444,7 @@ Cells were then transferred to a tube containing \SI{400}{\ul} at
\SI{45}{\minute} at \SI{37}{\degreeCelsius}, after which cells were counted as \SI{45}{\minute} at \SI{37}{\degreeCelsius}, after which cells were counted as
already described in \cref{sec:tcellculture}. already described in \cref{sec:tcellculture}.
\subsection{quantification of apoptosis using Annexin-V} \subsection{Quantification of Apoptosis Using Annexin-V}
Apoptosis was quantified using \gls{anv} according to the manufacturer's Apoptosis was quantified using \gls{anv} according to the manufacturer's
instructions. Briefly, cells were transferred to flow tubes and washed twice instructions. Briefly, cells were transferred to flow tubes and washed twice
@ -1446,7 +1458,7 @@ a final volume of \SI{100}{\ul}. Cells were stained in this volume with
\gls{rt} in the dark. After incubation, \SI{400}{\ul} staining buffer was added \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. to each tube. Each tube was then analyzed via flow cytometry.
\subsection{quantification of Caspase-3/7} \subsection{Quantification of Caspase-3/7}
\Gls{cas37} was quantified using \product{CellEvent dye}{\thermo}{C10723} \Gls{cas37} was quantified using \product{CellEvent dye}{\thermo}{C10723}
according the manufacturer's instructions. Briefly, a 2X (\SI{8}{\mM}) working according the manufacturer's instructions. Briefly, a 2X (\SI{8}{\mM}) working
@ -1454,7 +1466,7 @@ 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}. \num{5e4} cells) and incubated at \SI{37}{\degreeCelsius} for \SI{30}{\minute}.
After incubation, cells were immediately analyzed via flow cytometry. After incubation, cells were immediately analyzed via flow cytometry.
\subsection{quantification of BCL-2} \subsection{Quantification of BCL-2}
\Gls{bcl2} was quantified using an \product{Human Total Bcl-2 DuoSet \gls{elisa} \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 kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
@ -1466,7 +1478,7 @@ 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. Standardized lysates were measured using the \gls{elisa} kit as
directed. directed.
\subsection{chemotaxis assay} \subsection{Chemotaxis Assay}
% TODO not sure about the transwell product number % TODO not sure about the transwell product number
Migratory function was assayed using a transwell chemotaxis assay as previously Migratory function was assayed using a transwell chemotaxis assay as previously
@ -1479,7 +1491,7 @@ transwell was quantified for total cells using \product{countbright
beads}{\thermo}{C36950}. The final readout was normalized using the beads}{\thermo}{C36950}. The final readout was normalized using the
\SI{0}{\ng\per\mL} concentration as background. \SI{0}{\ng\per\mL} concentration as background.
\subsection{degranulation assay} \subsection{Degranulation Assay}
Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
@ -1495,7 +1507,7 @@ Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent
\cdp{107a} cells of the total \cdp{8} fraction. \cdp{107a} cells of the total \cdp{8} fraction.
\subsection{CAR expression} \subsection{CAR Expression}
\gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously \gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously
described\cite{Zheng2012}. Briefly, cells were washed once and stained with described\cite{Zheng2012}. Briefly, cells were washed once and stained with
@ -1509,7 +1521,7 @@ to secondary controls (\gls{pe}-\gls{stp} with no \gls{ptnl}).
was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute} was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute}
prior to analyzing on a \bd{} Accuri prior to analyzing on a \bd{} Accuri
\subsection{car plasmid and lentiviral transduction} \subsection{CAR Plasmid and Lentiviral Transduction}
The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$ The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
@ -1555,7 +1567,7 @@ fresh fresh media. After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was
collected, pooled, and concentrated using a \product{Lenti-X collected, pooled, and concentrated using a \product{Lenti-X
concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}. concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}.
\subsection{sulfo-NHS-biotin hydrolysis quantification} \subsection{Sulfo-NHS-Biotin Hydrolysis Quantification}
The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by
\cref{chem:snb_hydrolysis}. \cref{chem:snb_hydrolysis}.
@ -1570,7 +1582,7 @@ was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well
plate. Kinetic analysis using a BioTek plate reader began immediately after plate. Kinetic analysis using a BioTek plate reader began immediately after
prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}. prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
\subsection{reaction kinetics quantification} \subsection{Reaction Kinetics Quantification}
The diffusion of \gls{stp} into biotin-coated microcarriers was determined The diffusion of \gls{stp} into biotin-coated microcarriers was determined
experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of
@ -1718,7 +1730,7 @@ that were extrapolated from the standard curve were left unchanged.
\input{../tables/luminex_panel.tex} \input{../tables/luminex_panel.tex}
\end{table} \end{table}
\subsection{data aggregation and meta-analysis} \subsection{Data Aggregation and Meta-Analysis}
In order to perform meta-analysis on all experimental data generate using the 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 \gls{dms} system, we developed a program to curate and aggregate the raw
@ -1743,7 +1755,7 @@ example, flagging entries which had a reagent whose manufacturing date was after
the date the experiment started, which signifies a human input error). the date the experiment started, which signifies a human input error).
\subsection{statistical analysis}\label{sec:statistics} \subsection{Statistical Analysis}\label{sec:statistics}
For 1-way \gls{anova} analysis with Tukey multiple comparisons test, For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
significance was assessed using the \inlinecode{stat\_compare\_means} function significance was assessed using the \inlinecode{stat\_compare\_means} function
@ -1761,7 +1773,7 @@ lack-of-fit tests where replicates were present (to assess model fit in the
context of pure error). Statistical significance was evaluated at $\upalpha$ = context of pure error). Statistical significance was evaluated at $\upalpha$ =
0.05. 0.05.
\subsection{flow cytometry}\label{sec:flow_cytometry} \subsection{Flow Cytometry}\label{sec:flow_cytometry}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -1784,9 +1796,9 @@ 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 reagents for specialized assays such as degranulation are described in their
respective sections. Cells were gated according to \cref{fig:gating_strategy}. respective sections. Cells were gated according to \cref{fig:gating_strategy}.
\section{results} \section{Results}
\subsection{DMSs can be fabricated in a controlled manner} \subsection{DMSs Can be Fabricated in a Controlled Manner}
% FIGURE flip the rows of this figure (right now the text is backward) % FIGURE flip the rows of this figure (right now the text is backward)
\begin{figure*}[ht!] \begin{figure*}[ht!]
@ -1948,7 +1960,7 @@ water prior to adding it to the microcarrier suspension (which itself is in
\label{fig:dms_kinetics} \label{fig:dms_kinetics}
\end{figure*} \end{figure*}
\subsection{reaction kinetics for coating the DMSs} \subsection{Reaction Kinetics for Coating the DMSs}
We investigated the reaction kinetics of all three coating steps (accompanying We investigated the reaction kinetics of all three coating steps (accompanying
MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the
@ -2577,7 +2589,7 @@ bioreactor helped or hurt a certain response. For example, using a Grex entails
changing the cell surface and feeding strategy for the T cells, and any one of 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. these ‘mediating variables’ might actually be the cause of the responses.
\section{discussion} \section{Discussion}
% DISCUSSION this is fluffy % DISCUSSION this is fluffy
We have developed a T cell expansion shows superior expansion with higher number We have developed a T cell expansion shows superior expansion with higher number
@ -2708,9 +2720,9 @@ and licensed accordingly; having an alternative would provide more competition
in the market, reducing costs and improving access for academic researchers and in the market, reducing costs and improving access for academic researchers and
manufacturing companies. manufacturing companies.
\chapter{aim 2a}\label{aim2a} \chapter{AIM 2A}\label{aim2a}
\section{introduction} \section{Introduction}
The purpose of this sub-aim was to develop computational methods to identify 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, novel \glspl{cqa} and \glspl{cpp} that could be used for release criteria,
@ -2727,9 +2739,9 @@ 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 to any system, and the specific findings themselves offer interesting insights
that warrant further study. that warrant further study.
\section{methods} \section{Methods}
\subsection{study design} \subsection{Study Design}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2787,7 +2799,7 @@ reduce the overall fraction of targeted \glspl{mab} (for example the
\SI{60}{\percent} \gls{mab} surface density corresponded to 3 mass parts \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). \acd{3}, 3 mass parts \acd{28}, and 4 mass parts isotype control).
\subsection{T cell culture} \subsection{T Cell Culture}
T cell culture was performed as described in \cref{sec:tcellculture} with the 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 following modifications. At days 4, 6, 8, and 11, \SI{100}{\ul} media were
@ -2798,16 +2810,16 @@ 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 and the same donor lot was used for both experiments. All cell counts were
performed using \gls{aopi}. performed using \gls{aopi}.
\subsection{flow cytometry} \subsection{Flow Cytometry}
Flow cytometry was performed analogously to \cref{sec:flow_cytometry}. Flow cytometry was performed analogously to \cref{sec:flow_cytometry}.
\subsection{Cytokine quantification} \subsection{Cytokine Quantification}
Cytokines were quantified via Luminex as described in Cytokines were quantified via Luminex as described in
\cref{sec:luminex_analysis}. \cref{sec:luminex_analysis}.
\subsection{NMR metabolomics} \subsection{NMR Metabolomics}
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris\footnote{all \SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris\footnote{all
@ -2875,14 +2887,14 @@ suggested that some of these unknown features belonged to the same molecules
(not shown). Additional multidimensional \gls{nmr} experiments will be required (not shown). Additional multidimensional \gls{nmr} experiments will be required
to determine their identity. to determine their identity.
\subsection{machine learning and statistical analysis} \subsection{Machine Learning and Statistical Analysis}
Linear regression analysis of the \glspl{doe} was performed as described in Linear regression analysis of the \glspl{doe} was performed as described in
\cref{sec:statistics}. \cref{sec:statistics}.
Seven \gls{ml} techniques were implemented to predict three responses related to Seven \gls{ml} techniques were implemented to predict three responses related to
the memory phenotype of the cultured T cells under different process parameters the memory phenotype of the cultured T cells under different process
conditions (i.e. \rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods conditions (\rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods
executed were \gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso}, \gls{plsr}, 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 \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}, Kotanchek at Evolved Analytics, \gls{rf}, \gls{gbm}, \gls{cif}, \gls{plsr},
@ -2972,7 +2984,7 @@ model with \gls{loocv} tuned parameters.
% Table M2 shows the parameter values evaluated per model % Table M2 shows the parameter values evaluated per model
% at the final stages of results reporting. % at the final stages of results reporting.
\subsection{consensus analysis} \subsection{Consensus Analysis}
Consensus analysis of the relevant variables extracted from each machine Consensus analysis of the relevant variables extracted from each machine
learning model was done to identify consistent predictive features of quality at learning model was done to identify consistent predictive features of quality at
@ -2998,9 +3010,9 @@ variables with those high percentile scoring values were evaluated in terms of
their logical relation (intersection across \gls{ml} models) and depicted using their logical relation (intersection across \gls{ml} models) and depicted using
a Venn diagram from the \inlinecode{venn} R package. a Venn diagram from the \inlinecode{venn} R package.
\section{results} \section{Results}
\subsection{T cells can be grown on DMSs with lower IL2 concentrations} \subsection{T Cells Can be Grown on DMSs with Lower IL2 Concentrations}
Prior to the main experiments in this aim, we performed a preliminary experiment Prior to the main experiments in this aim, we performed a preliminary experiment
to assess the effect of lowering the \gls{il2} concentration on the T cells to assess the effect of lowering the \gls{il2} concentration on the T cells
@ -3059,7 +3071,7 @@ advantage at lower \gls{il2} concentrations compared to beads. For this reason,
we decided to investigate the lower range of \gls{il2} concentrations starting we decided to investigate the lower range of \gls{il2} concentrations starting
at \SI{10}{\IU\per\ml} throughout the remainder of this aim. at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
\subsection{DOE shows optimal conditions for expanded potent T cells} \subsection{DOE Shows Optimal Conditions for Expanded Potent T Cells}
% TABLE not all of these were actually used, explain why by either adding columns % TABLE not all of these were actually used, explain why by either adding columns
% or marking with an asterisk % or marking with an asterisk
@ -3247,7 +3259,7 @@ combinations at and around this optimum were tested, the model nonetheless
showed that there were no other optimal values or regions elsewhere in the showed that there were no other optimal values or regions elsewhere in the
model. model.
\subsection{Modeling with artificial intelligence methods reveals potential \subsection{Modeling With Artificial Intelligence Methods Reveals Potential
CQAs} CQAs}
Due to the heterogeneity of the multivariate data collected and knowing that no Due to the heterogeneity of the multivariate data collected and knowing that no
@ -3280,7 +3292,8 @@ data were collected in plates) (\cref{fig:grex_luminex}).
% TABLE this table looks like crap, break it up into smaller tables % TABLE this table looks like crap, break it up into smaller tables
\begin{table}[!h] \centering \begin{table}[!h] \centering
\caption{Results for data-driven modeling using process parameters (PP) with \caption[Results for data-driven modeling]
{Results for data-driven modeling using process parameters (PP) with
only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome 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} on day 6 (S6), or various combindation of each for all seven \gls{ml}
techniques} techniques}
@ -3291,16 +3304,16 @@ data were collected in plates) (\cref{fig:grex_luminex}).
\gls{sr} models achieved the highest predictive performance \gls{sr} models achieved the highest predictive performance
($R^2$>\SI{93}{\percent}) when using multi-omics predictors for all endpoint ($R^2$>\SI{93}{\percent}) when using multi-omics predictors for all endpoint
responses (\cref{tab:mod_results}). \gls{sr} achieved $R^2$>\SI{98}{\percent} responses (\cref{tab:mod_results}). \gls{sr} achieved $R^2$>\SI{98}{\percent}
while \gls{gbm} tree-based ensembles showed \gls{loocv} $R^2$ > while \gls{gbm} ensembles showed \gls{loocv} $R^2$ > \SI{95}{\percent} for
\SI{95}{\percent} for \rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, \rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, \gls{plsr}, and
\gls{plsr}, and \gls{svm} methods showed consistently high \gls{loocv}, \gls{svm} methods showed consistently high \gls{loocv}, (\SI{92.9}{\percent},
(\SI{92.9}{\percent}, \SI{99.7}{\percent}, and \SI{90.5}{\percent} \SI{99.7}{\percent}, and \SI{90.5}{\percent} respectively), to predict the
respectively), to predict the \rratio{}. Yet, about \SI{10}{\percent} reduction \rratio{}. Yet, about \SI{10}{\percent} reduction in \gls{loocv},
in \gls{loocv}, \SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with \SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with these three
these three methods. Lastly, \gls{sr} and \gls{plsr} achieved methods. Lastly, \gls{sr} and \gls{plsr} achieved $R^2$>\SI{90}{\percent} while
$R^2$>\SI{90}{\percent} while other \gls{ml} methods exhibited exceedingly other \gls{ml} methods exhibited exceedingly variable \gls{loocv}
variable \gls{loocv} (\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for (\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for \gls{lasso}) for
\gls{lasso}) for \rmemk{}. \rmemk{}.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -3395,7 +3408,7 @@ positively correlate with \pdms{} and negatively correlate with glucose
formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest
that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked. that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked.
\section{discussion} \section{Discussion}
\gls{cpp} modeling and understanding are critical to new product development and \gls{cpp} modeling and understanding are critical to new product development and
in cell therapy development, it can have life-saving implications. The in cell therapy development, it can have life-saving implications. The
@ -3531,9 +3544,9 @@ More definitive conclusions of metabolic activity across the expanding cell
population can be addressed by a closed system, ideally with on-line process population can be addressed by a closed system, ideally with on-line process
sensors and controls for formate, lactate, along with ethanol and glucose. sensors and controls for formate, lactate, along with ethanol and glucose.
\chapter{aim 2b}\label{aim2b} \chapter{AIM 2B}\label{aim2b}
\section{introduction} \section{Introduction}
The purpose of this sub-aim was to further explore the \gls{dms} platform, 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 specifically for mechanisms and pathways that could be the basis for additional
@ -3543,9 +3556,9 @@ normal operating conditions at which it was used up until this point either
through temporal modulation of activation signal or by blocking pathways of through temporal modulation of activation signal or by blocking pathways of
interest using \glspl{mab}. interest using \glspl{mab}.
\section{methods} \section{Methods}
\subsection{DMSs temporal modulation} \subsection{DMSs Temporal Modulation}
% METHOD The concentration for the surface marker cleavage experiment was much % METHOD The concentration for the surface marker cleavage experiment was much
% higher, if that matters % higher, if that matters
@ -3563,7 +3576,7 @@ Adding \glspl{dms} was relatively much simpler; the number of \gls{dms} used per
area on day 0 was scaled up by 3 on day 4 to match the change from a 96 well 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. plate to a 24 well plate, effectively producing a constant activation signal.
\subsection{mass cytometry and clustering analysis} \subsection{Mass Wytometry and Clustering Analysis}
T cells were stained using a \product{34 \gls{cytof} marker T cells were stained using a \product{34 \gls{cytof} marker
panel}{Fluidigm}{201322} and \product{cisplatin}{Fluidigm}{201064} which were panel}{Fluidigm}{201322} and \product{cisplatin}{Fluidigm}{201064} which were
@ -3578,7 +3591,7 @@ calculation neighborhood size of 5 and local density approximation factor of
\SI{1}{\percent}\cite{Qiu2017}. All markers in the \gls{cytof} panel were used \SI{1}{\percent}\cite{Qiu2017}. All markers in the \gls{cytof} panel were used
in the analysis in the analysis
\subsection{integrin blocking experiments} \subsection{Integrin Blocking Experiments}
To block \gls{a2b1} and \gls{a2b2}, active T cell cultures with \gls{dms} were To block \gls{a2b1} and \gls{a2b2}, active T cell cultures with \gls{dms} were
supplemented with \product{\anti{\gls{a2b1}}}{\sigald}{MAB1973Z} and supplemented with \product{\anti{\gls{a2b1}}}{\sigald}{MAB1973Z} and
@ -3591,7 +3604,7 @@ 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 \product{\anti{\gls{a2b2}}-\gls{fitc}}{\bl}{359305} on day 6 of culture and
analyzing via a \bd{} Accuri flow cytometer. analyzing via a \bd{} Accuri flow cytometer.
\subsection{IL15 blocking experiments} \subsection{IL15 Blocking Experiments}
To block the \gls{il15r}, we supplemented T cell To block the \gls{il15r}, we supplemented T cell
cultures activated with \gls{dms} with either cultures activated with \gls{dms} with either
@ -3605,9 +3618,9 @@ To block soluble \gls{il15}, we supplemented analogously with
\product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype \product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
control}{\bl}{B236633}. control}{\bl}{B236633}.
\section{results} \section{Results}
\subsection{adding or removing DMSs alters expansion and phenotype} \subsection{Adding or Removing DMSs Alters Expansion and Phenotype}
We hypothesized that adding or removing \gls{dms} in the middle of an active 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 culture would alter the activation signal and hence the growth trajectory and
@ -3745,7 +3758,7 @@ leads to potentially higher expansion, lower \pthp{}, and higher fraction of
lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to
do the inverse. do the inverse.
\subsection{blocking integrin binding does not alter expansion or phenotype} \subsection{Blocking Integrin Binding Does not Alter Expansion or Phenotype}
One of the reasons the \gls{dms} platform might perform better than the beads is 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 the fact that they are composed of gelatin, which is a collagen derivative. The
@ -3835,7 +3848,7 @@ CD4, or CD8) were statistically different between groups
Taken together, these data suggest that the advantage of the \gls{dms} platform Taken together, these data suggest that the advantage of the \gls{dms} platform
is not due to signaling through \gls{a2b1} or \gls{a2b2}. is not due to signaling through \gls{a2b1} or \gls{a2b2}.
\subsection{blocking IL15 signaling does not alter expansion or phenotype} \subsection{Blocking IL15 Signaling does not Alter Expansion or Phenotype}
\gls{il15} is a cytokine responsible for memory T cell survival and maintenance. \gls{il15} is a cytokine responsible for memory T cell survival and maintenance.
Furthermore, we observed in other experiments that it is secreted to a much Furthermore, we observed in other experiments that it is secreted to a much
@ -3921,7 +3934,7 @@ blocking \gls{il15} led to no difference in growth or phenotype.
In summary, this data did not support the hypothesis that the \gls{dms} platform In summary, this data did not support the hypothesis that the \gls{dms} platform
gains its advantages via the \gls{il15} pathway. gains its advantages via the \gls{il15} pathway.
\section{discussion} \section{Discussion}
This work provides insight for how the \gls{dms} operates and may be optimized This work provides insight for how the \gls{dms} operates and may be optimized
further. The data showing increased \pthp{} when \glspl{dms} are added and the further. The data showing increased \pthp{} when \glspl{dms} are added and the
@ -4030,9 +4043,9 @@ the early work with \il{15} in mice\cite{Lodolce1998}.
% cell density in the DMS cultures would lead to more of these trans interactions, % cell density in the DMS cultures would lead to more of these trans interactions,
% and therefore upregulate the IL15 pathway and lead to more memory T cells. % and therefore upregulate the IL15 pathway and lead to more memory T cells.
\chapter{aim 3}\label{aim3} \chapter{AIM 3}\label{aim3}
\section{introduction} \section{Introduction}
% DO NOT COMMENT OUT THIS LINE: the real purpose of this aim was to appease % 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 % Nature Biotech because they think that animal models are necessary for good
@ -4048,16 +4061,16 @@ 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 which has been shown to produce lower-differentiated T cells with higher
potency\cite{Ghassemi2018}. potency\cite{Ghassemi2018}.
\section{methods} \section{Methods}
\subsection{CD19-CAR T cell generation} \subsection{CD19-CAR T Cell Generation}
\subsection{T cell culture} \subsection{T Cell Culture}
T cells were grown as described in \cref{sec:tcellculture}. T cells were grown as described in \cref{sec:tcellculture}.
\subsection{\invivo{} therapeutic efficacy in NSG mice model} \subsection{\Invivo{} Therapeutic Efficacy in NSG Mice Model}
% METHOD describe how the luciferase cells were generated (eg the kwong lab) % METHOD describe how the luciferase cells were generated (eg the kwong lab)
% METHOD use actual product numbers for mice % METHOD use actual product numbers for mice
@ -4081,13 +4094,13 @@ determined by \gls{iacuc} euthanasia criteria (hunched back, paralysis,
blindness, lethargy, and weight loss). Mice were euthanized according to these blindness, lethargy, and weight loss). Mice were euthanized according to these
endpoint criteria using carbon dioxide asphyxiation. endpoint criteria using carbon dioxide asphyxiation.
\subsection{statistics} \subsection{Statistics}
For the \invivo{} model, the survival curves were created and statistically For the \invivo{} model, the survival curves were created and statistically
analyzed using GraphPad Prism using the Mantel-Cox test to assess significance analyzed using GraphPad Prism using the Mantel-Cox test to assess significance
between survival groups. between survival groups.
\section{results} \section{Results}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -4107,8 +4120,8 @@ between survival groups.
\input{../tables/mouse_dose_car.tex} \input{../tables/mouse_dose_car.tex}
\end{table} \end{table}
\subsection{DMS-expanded T cells show greater anti-tumor activity \invivo{} \subsection{DMS-expanded T Cells Show Greater Anti-Tumor Activity \invivo{}
compared to beads} Compared to Beads}
% FIGURE put growth first in this figure % FIGURE put growth first in this figure
\begin{figure*}[ht!] \begin{figure*}[ht!]
@ -4218,7 +4231,7 @@ expansion in the case of \gls{dms}
due to the memory phenotype given that it was actually slightly higher in the due to the memory phenotype given that it was actually slightly higher in the
case of beads (\cref{fig:mouse_dosing_qc_mem}). case of beads (\cref{fig:mouse_dosing_qc_mem}).
\subsection{Beads and DMSs perform similarly at earlier timepoints} \subsection{Beads and DMSs Perform Similarly at Earlier Timepoints}
We then asked how T cells harvested using either beads or \gls{dms} performed We then asked how T cells harvested using either beads or \gls{dms} performed
when harvested at earlier timepoints\cite{Ghassemi2018}. We performed the same when harvested at earlier timepoints\cite{Ghassemi2018}. We performed the same
@ -4318,7 +4331,7 @@ other groups in regard to the final tumor burden.
\label{fig:mouse_timecourse_ivis} \label{fig:mouse_timecourse_ivis}
\end{figure*} \end{figure*}
\section{discussion} \section{Discussion}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -4402,9 +4415,9 @@ the \ptcarp{} of the final product. Followup experiments would need to be
performed to determine the precise phenotype responsible for these responses in performed to determine the precise phenotype responsible for these responses in
our hands. our hands.
\chapter{conclusions and future work}\label{conclusions} \chapter{CONCLUSIONS AND FUTURE WORK}\label{conclusions}
\section{conclusions} \section{Conclusions}
This dissertation describes the development of a novel T cell expansion This dissertation describes the development of a novel T cell expansion
platform, including the fabrication, quality control, and biological validation platform, including the fabrication, quality control, and biological validation
@ -4513,12 +4526,12 @@ since these T cell immunotherapies are activated and expanded with either
soluble \glspl{mab} or bead-immobilized \glspl{mab}, our system will likely soluble \glspl{mab} or bead-immobilized \glspl{mab}, our system will likely
serve as a drop-in substitution to provide these benefits. serve as a drop-in substitution to provide these benefits.
\section{future directions} \section{Future Directions}
There are several important next steps to perform with this work, many of which 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: will be relevent to using this technology in a clinical trial:
\subsection{Translation to GMP process} \subsection{Translation to GMP Process}
While this work was done with translatability and \gls{qc} in mind, an important While this work was done with translatability and \gls{qc} in mind, an important
feature that is missing from the process currently is the use of \gls{gmp} feature that is missing from the process currently is the use of \gls{gmp}
@ -4542,7 +4555,7 @@ 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 replaced. The \gls{snb} is a synthetic small molecule and thus does not have any
animal-origin concerns. animal-origin concerns.
\subsection{mechanistic investigation} \subsection{Mechanistic Investigation}
Despite the improved outcomes in terms of expansion and phenotype relative to Despite the improved outcomes in terms of expansion and phenotype relative to
beads, we don't have a good understanding of why they \gls{dms} platform works beads, we don't have a good understanding of why they \gls{dms} platform works
@ -4557,7 +4570,7 @@ thus activation. Another related hypothesis is that the signal strength is
lower than the beads, which leads to increased proliferation, less exhaustion, lower than the beads, which leads to increased proliferation, less exhaustion,
and by extension more memory. and by extension more memory.
\subsection{additional ligands and signals on the DMSs} \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 In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated
on the surface of the \gls{dms}. The chemistry used for the \gls{dms} is very on the surface of the \gls{dms}. The chemistry used for the \gls{dms} is very
@ -4574,7 +4587,7 @@ and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral
delivery systems could theoretically be attached to the \gls{dms}, greatly delivery systems could theoretically be attached to the \gls{dms}, greatly
simplifying the transduction step. simplifying the transduction step.
\subsection{assessing performance using unhealthy donors} \subsection{Assessing Performance Using Unhealthy Donors}
All the work presented in this dissertation was performed using healthy 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 This was mostly due to the fact that it was much easier to obtain healthy donor
@ -4586,7 +4599,7 @@ expansion technology given that even in healthy donors, we observed the
\gls{dms} platform to work where the beads failed \gls{dms} platform to work where the beads failed
(\cref{fig:dms_exp_fold_change}). (\cref{fig:dms_exp_fold_change}).
\subsection{translation to bioreactors} \subsection{Translation to Bioreactors}
In this work we performed some preliminary experiments demonstrating that the In this work we performed some preliminary experiments demonstrating that the
\gls{dms} platform can work in a Grex bioreactor. While an important first step, \gls{dms} platform can work in a Grex bioreactor. While an important first step,
@ -4610,7 +4623,7 @@ additional adhesion ligands to make the T cells attach more strongly).
\clearpage \clearpage
\appendix \appendix
\chapter{meta analysis database code}\label{sec:appendix_meta} \chapter{META ANALYSIS DATABASE CODE}\label{sec:appendix_meta}
The code used to aggregate all experimental data was written primarily in 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 Python, with a subprocess running R in a Docker container to handle the flow
@ -4631,7 +4644,7 @@ hosted using \gls{aws} using their proprietary Aurora implementation.
The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}. The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
\chapter{binding kinetics code}\label{sec:appendix_binding} \chapter{BINDING KINETICS CODE}\label{sec:appendix_binding}
The \gls{stp} binding kinetic profile was fit and calculated using the following The \gls{stp} binding kinetic profile was fit and calculated using the following
MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to
@ -4646,7 +4659,7 @@ reflect the \gls{mab} coating process.
\lstinputlisting{../code/diffusion_mab.m} \lstinputlisting{../code/diffusion_mab.m}
\chapter{washing kinetics code}\label{sec:appendix_washing} \chapter{WASHING KINETICS CODE}\label{sec:appendix_washing}
The wash steps for the \gls{dms} were modeled using the following code: The wash steps for the \gls{dms} were modeled using the following code:
@ -4656,7 +4669,7 @@ Complete output from this code is shown below:
\input{../code/washing_out.tex} \input{../code/washing_out.tex}
\chapter{references} \chapter{REFERENCES}
\renewcommand{\chapter}[2]{} % noop the original bib section header \renewcommand{\chapter}[2]{} % noop the original bib section header
\bibliography{references} \bibliography{references}