FIX toc capitalization (the wrong way)
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tex/thesis.tex
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tex/thesis.tex
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@ -19,6 +19,7 @@
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\usepackage{pgfgantt}
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\usepackage{setspace}
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\usepackage{listings}
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\usepackage{tocloft}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% my attempt to make MATLAB code look pretty
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@ -74,15 +75,25 @@
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\doublespacing{}
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\titleformat{\chapter}[block]{\filcenter\bfseries\large}
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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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\renewcommand*{\contentsname}{TABLE OF CONTENTS}
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\renewcommand{\listfigurename}{LIST OF FIGURES}
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\renewcommand{\listtablename}{LIST OF TABLES}
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\renewcommand{\cfttoctitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftaftertoctitle}{\hspace*{\fill}}
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\renewcommand{\cftlottitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftafterlottitle}{\hspace*{\fill}}
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\renewcommand{\cftloftitlefont}{\hspace*{\fill}\Large\bfseries}
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\renewcommand{\cftafterloftitle}{\hspace*{\fill}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% acronyms for the lazy
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%
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@ -420,17 +431,41 @@
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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*{acknowledgements}
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\addcontentsline{toc}{chapter}{acknowledgements}
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\tableofcontents
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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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\listoffigures
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\addcontentsline{toc}{chapter}{LIST OF FIGURES}
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\clearpage
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\listoftables
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\addcontentsline{toc}{chapter}{LIST OF TABLES}
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\clearpage
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\chapter*{LIST OF SYMBOLS AND ABBREVIATIONS}
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\addcontentsline{toc}{chapter}{LIST OF SYMBOLS AND ABBREVIATIONS}
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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*{summary}
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\addcontentsline{toc}{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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@ -458,30 +493,7 @@ method which can be utilized at scale for a clinical trial and beyond.
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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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\chapter{INTRODUCTION}
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\section*{overview}
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@ -605,11 +617,11 @@ forward. In \cref{aim1,aim2a,aim2b,aim3} we present the work pertaining to Aims
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conclusions as well as provide insights for how this work can be extended in the
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future.
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\chapter{background and significance}\label{background}
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\chapter{BACKGROUND AND INNOVATION}\label{background}
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\section*{background}
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\section{Background}
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\subsection{quality by design in cell manufacturing}
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\subsection{Quality by Design in Cell Manufacturing}
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The challenges for the cell manufacturing field are significant. Unlike other
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industries which manufacture inanimate products such as automobiles and
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@ -645,7 +657,7 @@ The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of
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this work are discussed further in \cref{sec:background_doe} and
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\cref{sec:background_quality}/\cref{sec:background_cqa} respectively.
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\subsection{T cells for immunotherapies}
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\subsection{T Cells for Immunotherapies}
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A variety of T cell therapies have been utilized with varying degrees of
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success, and we describe a few of the most prominent below. We should note that
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@ -716,7 +728,7 @@ date, there are almost 1000 clinical trials using \gls{car} T cells.
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% TODO there are other T cells like virus-specific T cells and gd T cells, not
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% that they matter...
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\subsection{scaling T cell expansion}
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\subsection{Scaling T Cell Expansion}
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In order to scale T cell therapies to meet clinical demands, automation and
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bioreactors will be necessary. To this end, there are several choices that have
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@ -761,7 +773,7 @@ Much work is still required in the space of bioreactor design for T cell
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manufacturing, but novel T cell expansion technologies such as that described in
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this work need to consider how it may be used at scale in such a system.
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\subsection{cell sources in T cell manufacturing}\label{sec:background_source}
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\subsection{Cell Sources in T Cell Manufacturing}\label{sec:background_source}
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T cells for cell manufacturing can be obtained broadly via two paradigms:
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autologous and allogeneic. The former involves obtaining T cells from a patient
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@ -807,7 +819,7 @@ very efficient. To date there are about 10 open clinical trials utilizing
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allogeneic T cell therapies edited with \gls{crispr} to reduce the likelihood of
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\gls{gvhd}.
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\subsection{overview of T cell quality}\label{sec:background_quality}
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\subsection{Overview of T Cell Quality}\label{sec:background_quality}
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T cells are highly heterogeneous and can exist in a variety of states and
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subtypes, many of which can be measured (at least indirectly) though biomarkers
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@ -864,7 +876,7 @@ using retro- or lentiviral vectors as their means of gene-editing must be tested
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for replication competent vectors\cite{Wang2013} and for contamination via
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bacteria or other pathogens.
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\subsection*{T cell activation methods}\label{sec:background_activation}
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\subsection{T cell Activation Methods}\label{sec:background_activation}
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In order for T cells to be expanded \exvivo{} they must be activated with a
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stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \Invivo{},
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@ -929,7 +941,7 @@ recapitulate the cellular membrane, large interfacial contact area,
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None have been demonstrated to demonstrably expand high quality T cells as
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outlined in \cref{sec:background_quality}.
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\subsection{microcarriers in bioprocessing}
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\subsection{Microcarriers in Bioprocessing}
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In this work, we explored microcarriers as the basis for an alternative to the
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methods described in \cref{sec:background_activation}.
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@ -999,7 +1011,7 @@ FDA fast-track-approved combination retinal pigment epithelial cell product
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(Spheramine, Titan Pharmaceuticals)\cite{purcellmain}. This regulatory history
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will aid in clinical translation.
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\subsection*{integrins and T cell signaling}
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\subsection{Integrins and T Cell Signaling}
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Because the microcarriers used in this work are derived from collagen, one key
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question is how these collagen domains may interact with the T cells during
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@ -1035,7 +1047,7 @@ stimulated in the presence of collagen I\cite{Boisvert2007}.
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% with fibronectin and has been shown to lead to higher IL2 production (Iwata et
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% al 2000).
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\subsection*{the role of IL15 in memory T cell proliferation}
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\subsection{The Role of IL15 in Memory T Cell Proliferation}
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\il{15} is a cytokine that is involved with the proliferation and homeostasis of
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memory T cells. Its role in the work of this dissertation is the subject of
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@ -1087,7 +1099,7 @@ a soluble form, which can bind to \il{15} and signal to cells which are not
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adjacent to the source independent of the \textit{cis/trans} mechanisms already
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described\cite{Budagian2004}.
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\subsection*{overview of design of experiments}\label{sec:background_doe}
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\subsection{Overview of Design of Experiments}\label{sec:background_doe}
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The \gls{dms} system described in this dissertation has a number of parameters
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that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal
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@ -1165,7 +1177,7 @@ influence the directions for future work. To this end, the types of \glspl{doe}
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we generally used were fractional factorial designs with three levels, which
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enable the estimation of both main effects and second order quadratic effects.
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\subsection*{identification and standardization of CPPs and
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\subsection{Identification and Standardization of CPPs and
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CQAs}\label{sec:background_cqa}
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% BACKGROUND at least attempt to show that there is alot of work in the space
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@ -1267,9 +1279,9 @@ novel considering the state-of-the-art technology for T cell manufacturing:
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effect on cell phenotype.
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\end{itemize}
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\chapter{aim 1}\label{aim1}
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\chapter{AIM 1}\label{aim1}
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\section{introduction}
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\section{Introduction}
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The first aim was to develop a microcarrier system that mimics several key
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aspects of the \invivo{} lymph node microenvironment. We compared compare this
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@ -1279,9 +1291,9 @@ microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
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provide superior expansion and memory phenotype compared to state-of-the-art
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bead-based T cell expansion technology.
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\section{methods}
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\section{Methods}
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\subsection{DMS functionalization}\label{sec:dms_fab}
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\subsection{DMS Functionalization}\label{sec:dms_fab}
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\begin{figure*}[ht!]
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\begingroup
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@ -1342,7 +1354,7 @@ was then manually counted to obtain a concentration. Surface area for
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\si{\ab\per\um\squared} was calculated using the properties for \gls{cus} and
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\gls{cug} as given by the manufacturer \cref{tab:carrier_props}.
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\subsection{DMS quality control assays}
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\subsection{DMS Quality Control Assays}
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Biotin was quantified using the \product{\gls{haba} assay}{\sigald}{H2153-1VL}.
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In the case of quantifying \gls{snb} prior to adding it to the microcarriers,
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@ -1380,7 +1392,7 @@ by first staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001},
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incubating for \SI{30}{\minute}, washing with \gls{pbs}, and imaging on a
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confocal microscope.
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\subsection{t cell culture}\label{sec:tcellculture}
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\subsection{T Cell Culture}\label{sec:tcellculture}
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Cryopreserved primary human T cells were either obtained as sorted
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\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
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@ -1412,7 +1424,7 @@ imaging on a spinning disk confocal microscope.
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In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
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Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}.
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\subsection{Quantifying cells on DMS interior}
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\subsection{Quantifying Cells on DMS Interior}
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% TODO add a product number to MTT (if I can find it)
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To visualize T cells on the interior of the \glspl{dms}, we stained them with
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@ -1432,7 +1444,7 @@ Cells were then transferred to a tube containing \SI{400}{\ul} at
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\SI{45}{\minute} at \SI{37}{\degreeCelsius}, after which cells were counted as
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already described in \cref{sec:tcellculture}.
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\subsection{quantification of apoptosis using Annexin-V}
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\subsection{Quantification of Apoptosis Using Annexin-V}
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Apoptosis was quantified using \gls{anv} according to the manufacturer's
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instructions. Briefly, cells were transferred to flow tubes and washed twice
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@ -1446,7 +1458,7 @@ a final volume of \SI{100}{\ul}. Cells were stained in this volume with
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\gls{rt} in the dark. After incubation, \SI{400}{\ul} staining buffer was added
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to each tube. Each tube was then analyzed via flow cytometry.
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\subsection{quantification of Caspase-3/7}
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\subsection{Quantification of Caspase-3/7}
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\Gls{cas37} was quantified using \product{CellEvent dye}{\thermo}{C10723}
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according the manufacturer's instructions. Briefly, a 2X (\SI{8}{\mM}) working
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@ -1454,7 +1466,7 @@ solution of CellEvent dye was added to \SI{100}{\ul} cell suspension (at least
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\num{5e4} cells) and incubated at \SI{37}{\degreeCelsius} for \SI{30}{\minute}.
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After incubation, cells were immediately analyzed via flow cytometry.
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\subsection{quantification of BCL-2}
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\subsection{Quantification of BCL-2}
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\Gls{bcl2} was quantified using an \product{Human Total Bcl-2 DuoSet \gls{elisa}
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kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
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@ -1466,7 +1478,7 @@ was quantified for protein using a \product{\gls{bca} assay}{\thermo}{23225} as
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directed. Standardized lysates were measured using the \gls{elisa} kit as
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directed.
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\subsection{chemotaxis assay}
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\subsection{Chemotaxis Assay}
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% TODO not sure about the transwell product number
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Migratory function was assayed using a transwell chemotaxis assay as previously
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@ -1479,7 +1491,7 @@ transwell was quantified for total cells using \product{countbright
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beads}{\thermo}{C36950}. The final readout was normalized using the
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\SI{0}{\ng\per\mL} concentration as background.
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\subsection{degranulation assay}
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\subsection{Degranulation Assay}
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Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
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assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
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analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent
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\cdp{107a} cells of the total \cdp{8} fraction.
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\subsection{CAR expression}
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\subsection{CAR Expression}
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\gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously
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described\cite{Zheng2012}. Briefly, cells were washed once and stained with
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@ -1509,7 +1521,7 @@ to secondary controls (\gls{pe}-\gls{stp} with no \gls{ptnl}).
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was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute}
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prior to analyzing on a \bd{} Accuri
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\subsection{car plasmid and lentiviral transduction}
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\subsection{CAR Plasmid and Lentiviral Transduction}
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The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$
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promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
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collected, pooled, and concentrated using a \product{Lenti-X
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concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}.
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\subsection{sulfo-NHS-biotin hydrolysis quantification}
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\subsection{Sulfo-NHS-Biotin Hydrolysis Quantification}
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The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by
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\cref{chem:snb_hydrolysis}.
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plate. Kinetic analysis using a BioTek plate reader began immediately after
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prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
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\subsection{reaction kinetics quantification}
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\subsection{Reaction Kinetics Quantification}
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The diffusion of \gls{stp} into biotin-coated microcarriers was determined
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experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of
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\input{../tables/luminex_panel.tex}
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\end{table}
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\subsection{data aggregation and meta-analysis}
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\subsection{Data Aggregation and Meta-Analysis}
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In order to perform meta-analysis on all experimental data generate using the
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\gls{dms} system, we developed a program to curate and aggregate the raw
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the date the experiment started, which signifies a human input error).
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\subsection{statistical analysis}\label{sec:statistics}
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\subsection{Statistical Analysis}\label{sec:statistics}
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For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
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significance was assessed using the \inlinecode{stat\_compare\_means} function
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context of pure error). Statistical significance was evaluated at $\upalpha$ =
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0.05.
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\subsection{flow cytometry}\label{sec:flow_cytometry}
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\subsection{Flow Cytometry}\label{sec:flow_cytometry}
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\begin{figure*}[ht!]
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\begingroup
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@ -1784,9 +1796,9 @@ All \glspl{mab} used for flow cytometry are shown in \cref{tab:flow_mabs}. Other
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reagents for specialized assays such as degranulation are described in their
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respective sections. Cells were gated according to \cref{fig:gating_strategy}.
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\section{results}
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\section{Results}
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\subsection{DMSs can be fabricated in a controlled manner}
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\subsection{DMSs Can be Fabricated in a Controlled Manner}
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% FIGURE flip the rows of this figure (right now the text is backward)
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\begin{figure*}[ht!]
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@ -1948,7 +1960,7 @@ water prior to adding it to the microcarrier suspension (which itself is in
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\label{fig:dms_kinetics}
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\end{figure*}
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\subsection{reaction kinetics for coating the DMSs}
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\subsection{Reaction Kinetics for Coating the DMSs}
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We investigated the reaction kinetics of all three coating steps (accompanying
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MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the
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changing the cell surface and feeding strategy for the T cells, and any one of
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these ‘mediating variables’ might actually be the cause of the responses.
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\section{discussion}
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\section{Discussion}
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% DISCUSSION this is fluffy
|
||||
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
|
||||
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
|
||||
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
|
||||
that warrant further study.
|
||||
|
||||
\section{methods}
|
||||
\section{Methods}
|
||||
|
||||
\subsection{study design}
|
||||
\subsection{Study Design}
|
||||
|
||||
\begin{figure*}[ht!]
|
||||
\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
|
||||
\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
|
||||
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
|
||||
performed using \gls{aopi}.
|
||||
|
||||
\subsection{flow cytometry}
|
||||
\subsection{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
|
||||
\cref{sec:luminex_analysis}.
|
||||
|
||||
\subsection{NMR metabolomics}
|
||||
\subsection{NMR Metabolomics}
|
||||
|
||||
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
|
||||
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris\footnote{all
|
||||
|
@ -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
|
||||
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
|
||||
\cref{sec:statistics}.
|
||||
|
||||
Seven \gls{ml} techniques were implemented to predict three responses related to
|
||||
the memory phenotype of the cultured T cells under different process parameters
|
||||
conditions (i.e. \rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods
|
||||
the memory phenotype of the cultured T cells under different process
|
||||
conditions (\rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods
|
||||
executed were \gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso}, \gls{plsr},
|
||||
\gls{svm}, and DataModeler’s \gls{sr}\footnote{\gls{sr} was performed by Theresa
|
||||
Kotanchek at Evolved Analytics, \gls{rf}, \gls{gbm}, \gls{cif}, \gls{plsr},
|
||||
|
@ -2972,7 +2984,7 @@ model with \gls{loocv} tuned parameters.
|
|||
% Table M2 shows the parameter values evaluated per model
|
||||
% at the final stages of results reporting.
|
||||
|
||||
\subsection{consensus analysis}
|
||||
\subsection{Consensus Analysis}
|
||||
|
||||
Consensus analysis of the relevant variables extracted from each machine
|
||||
learning model was done to identify consistent predictive features of quality at
|
||||
|
@ -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
|
||||
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
|
||||
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
|
||||
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
|
||||
% 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
|
||||
model.
|
||||
|
||||
\subsection{Modeling with artificial intelligence methods reveals potential
|
||||
\subsection{Modeling With Artificial Intelligence Methods Reveals Potential
|
||||
CQAs}
|
||||
|
||||
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
|
||||
\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
|
||||
on day 6 (S6), or various combindation of each for all seven \gls{ml}
|
||||
techniques}
|
||||
|
@ -3291,16 +3304,16 @@ data were collected in plates) (\cref{fig:grex_luminex}).
|
|||
\gls{sr} models achieved the highest predictive performance
|
||||
($R^2$>\SI{93}{\percent}) when using multi-omics predictors for all endpoint
|
||||
responses (\cref{tab:mod_results}). \gls{sr} achieved $R^2$>\SI{98}{\percent}
|
||||
while \gls{gbm} tree-based ensembles showed \gls{loocv} $R^2$ >
|
||||
\SI{95}{\percent} for \rmemh{} and \rmemk{} responses. Similarly, \gls{lasso},
|
||||
\gls{plsr}, and \gls{svm} methods showed consistently high \gls{loocv},
|
||||
(\SI{92.9}{\percent}, \SI{99.7}{\percent}, and \SI{90.5}{\percent}
|
||||
respectively), to predict the \rratio{}. Yet, about \SI{10}{\percent} reduction
|
||||
in \gls{loocv}, \SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with
|
||||
these three methods. Lastly, \gls{sr} and \gls{plsr} achieved
|
||||
$R^2$>\SI{90}{\percent} while other \gls{ml} methods exhibited exceedingly
|
||||
variable \gls{loocv} (\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for
|
||||
\gls{lasso}) for \rmemk{}.
|
||||
while \gls{gbm} ensembles showed \gls{loocv} $R^2$ > \SI{95}{\percent} for
|
||||
\rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, \gls{plsr}, and
|
||||
\gls{svm} methods showed consistently high \gls{loocv}, (\SI{92.9}{\percent},
|
||||
\SI{99.7}{\percent}, and \SI{90.5}{\percent} respectively), to predict the
|
||||
\rratio{}. Yet, about \SI{10}{\percent} reduction in \gls{loocv},
|
||||
\SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with these three
|
||||
methods. Lastly, \gls{sr} and \gls{plsr} achieved $R^2$>\SI{90}{\percent} while
|
||||
other \gls{ml} methods exhibited exceedingly variable \gls{loocv}
|
||||
(\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for \gls{lasso}) for
|
||||
\rmemk{}.
|
||||
|
||||
\begin{figure*}[ht!]
|
||||
\begingroup
|
||||
|
@ -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
|
||||
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
|
||||
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
|
||||
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,
|
||||
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
|
||||
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
|
||||
% 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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
analyzing via a \bd{} Accuri flow cytometer.
|
||||
|
||||
\subsection{IL15 blocking experiments}
|
||||
\subsection{IL15 Blocking Experiments}
|
||||
|
||||
To block the \gls{il15r}, we supplemented T cell
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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.
|
||||
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
|
||||
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
|
||||
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,
|
||||
% 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
|
||||
% 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
|
||||
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}.
|
||||
|
||||
|
||||
\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 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
|
||||
endpoint criteria using carbon dioxide asphyxiation.
|
||||
|
||||
\subsection{statistics}
|
||||
\subsection{Statistics}
|
||||
|
||||
For the \invivo{} model, the survival curves were created and statistically
|
||||
analyzed using GraphPad Prism using the Mantel-Cox test to assess significance
|
||||
between survival groups.
|
||||
|
||||
\section{results}
|
||||
\section{Results}
|
||||
|
||||
\begin{figure*}[ht!]
|
||||
\begingroup
|
||||
|
@ -4107,8 +4120,8 @@ between survival groups.
|
|||
\input{../tables/mouse_dose_car.tex}
|
||||
\end{table}
|
||||
|
||||
\subsection{DMS-expanded T cells show greater anti-tumor activity \invivo{}
|
||||
compared to beads}
|
||||
\subsection{DMS-expanded T Cells Show Greater Anti-Tumor Activity \invivo{}
|
||||
Compared to Beads}
|
||||
|
||||
% FIGURE put growth first in this figure
|
||||
\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
|
||||
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
|
||||
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}
|
||||
\end{figure*}
|
||||
|
||||
\section{discussion}
|
||||
\section{Discussion}
|
||||
|
||||
\begin{figure*}[ht!]
|
||||
\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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
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
|
||||
animal-origin concerns.
|
||||
|
||||
\subsection{mechanistic investigation}
|
||||
\subsection{Mechanistic Investigation}
|
||||
|
||||
Despite the improved outcomes in terms of expansion and phenotype relative to
|
||||
beads, we don't have a good understanding of why they \gls{dms} platform works
|
||||
|
@ -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,
|
||||
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
|
||||
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
|
||||
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.
|
||||
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
|
||||
(\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
|
||||
\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
|
||||
|
||||
\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
|
||||
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}.
|
||||
|
||||
\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
|
||||
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}
|
||||
|
||||
\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:
|
||||
|
||||
|
@ -4656,7 +4669,7 @@ Complete output from this code is shown below:
|
|||
|
||||
\input{../code/washing_out.tex}
|
||||
|
||||
\chapter{references}
|
||||
\chapter{REFERENCES}
|
||||
\renewcommand{\chapter}[2]{} % noop the original bib section header
|
||||
|
||||
\bibliography{references}
|
||||
|
|
Loading…
Reference in New Issue