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FIX acronyms and colagenase fig

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Nathan Dwarshuis 2021-09-09 16:14:04 -04:00
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@ -13,8 +13,6 @@
\usepackage{graphicx} \usepackage{graphicx}
\usepackage{subcaption} \usepackage{subcaption}
\usepackage{nth} \usepackage{nth}
\usepackage{hyperref} % must be before cleveref
\usepackage[capitalize]{cleveref}
\usepackage[version=4]{mhchem} \usepackage[version=4]{mhchem}
\usepackage{pgfgantt} \usepackage{pgfgantt}
\usepackage{setspace} \usepackage{setspace}
@ -22,6 +20,8 @@
\usepackage{tocloft} \usepackage{tocloft}
\usepackage{epigraph} \usepackage{epigraph}
\usepackage{threeparttable} \usepackage{threeparttable}
\usepackage{hyperref} % must be before cleveref
\usepackage[capitalize]{cleveref}
\hypersetup{ \hypersetup{
colorlinks=true, colorlinks=true,
@ -164,10 +164,6 @@
\newacronym{pdms}{PDMS}{polydimethylsiloxane} \newacronym{pdms}{PDMS}{polydimethylsiloxane}
\newacronym{dc}{DC}{dendritic cell} \newacronym{dc}{DC}{dendritic cell}
\newacronym{il}{IL}{interleukin} \newacronym{il}{IL}{interleukin}
\newacronym{il2}{IL2}{interleukin 2}
\newacronym{il15}{IL15}{interleukin 15}
\newacronym{il15r}{IL15R}{interleukin 15 receptor}
\newacronym{rhil2}{rhIL2}{recombinant human interleukin 2}
\newacronym{apc}{APC}{antigen presenting cell} \newacronym{apc}{APC}{antigen presenting cell}
\newacronym{mhc}{MHC}{major histocompatibility complex} \newacronym{mhc}{MHC}{major histocompatibility complex}
\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay} \newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
@ -215,6 +211,7 @@
\newacronym{moi}{MOI}{multiplicity of infection} \newacronym{moi}{MOI}{multiplicity of infection}
\newacronym{ifng}{IFN$\upgamma$}{interferon-$\upgamma$} \newacronym{ifng}{IFN$\upgamma$}{interferon-$\upgamma$}
\newacronym{tnfa}{TNF$\upalpha$}{tumor necrosis factor-$\upalpha$} \newacronym{tnfa}{TNF$\upalpha$}{tumor necrosis factor-$\upalpha$}
\newacronym{gmcsf}{GM-CSF}{granulocyte-macrophage colony stimulating factor}
\newacronym{sql}{SQL}{structured query language} \newacronym{sql}{SQL}{structured query language}
\newacronym{fcs}{FCS}{flow cytometry standard} \newacronym{fcs}{FCS}{flow cytometry standard}
\newacronym{ivis}{IVIS}{in vivo imaging system} \newacronym{ivis}{IVIS}{in vivo imaging system}
@ -357,9 +354,13 @@
% so I don't need to worry about abbreviating all the different interleukins % so I don't need to worry about abbreviating all the different interleukins
\newcommand{\il}[1]{\gls{il}-#1} \newcommand{\il}[1]{\gls{il}-#1}
\newcommand{\ilr}[1]{\gls{il}-#1R}
% ...and this one is just plain annoying
\newcommand{\ilXVra}[1]{\ilr{15}$\upalpha$}
% DOE stuff I don't feel like typing ad-nauseam % DOE stuff I don't feel like typing ad-nauseam
\newcommand{\pilII}{\gls{il2} concentration} \newcommand{\pilII}{\il{2} concentration}
\newcommand{\pdms}{\gls{dms} concentration} \newcommand{\pdms}{\gls{dms} concentration}
\newcommand{\pmab}{functional \gls{mab} surface density} \newcommand{\pmab}{functional \gls{mab} surface density}
\newcommand{\rmemh}{total \ptmemh{} cells} \newcommand{\rmemh}{total \ptmemh{} cells}
@ -621,7 +622,7 @@ tetramers (Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016, Piscopo2017,
present in the secondary lymphoid organs where T cells expand \invivo{}. present in the secondary lymphoid organs where T cells expand \invivo{}.
Typically, T cells are activated under close cell-cell contact, which allows for Typically, T cells are activated under close cell-cell contact, which allows for
efficient autocrine/paracrine signaling via growth-stimulating cytokines such as efficient autocrine/paracrine signaling via growth-stimulating cytokines such as
\gls{il2}. Additionally, the lymphoid tissues are comprised of \gls{ecm} \il{2}. Additionally, the lymphoid tissues are comprised of \gls{ecm}
components such as collagen and stromal cells, which provide signals to components such as collagen and stromal cells, which provide signals to
upregulate proliferation, cytokine production, and pro-survival upregulate proliferation, cytokine production, and pro-survival
pathways\cite{Gendron2003, Ohtani2008, Boisvert2007, Ben-Horin2004}. pathways\cite{Gendron2003, Ohtani2008, Boisvert2007, Ben-Horin2004}.
@ -645,18 +646,17 @@ discovering and validating novel \glspl{cqa} and \glspl{cpp} in the space of T
cell manufacturing are required to reproducibly manufacture these subtypes and cell manufacturing are required to reproducibly manufacture these subtypes and
ensure low-cost and safe products with maximal effectiveness in the clinic. ensure low-cost and safe products with maximal effectiveness in the clinic.
This dissertation describes a novel \acrlong{dms}-based method derived from This dissertation describes a novel \acrlong{dms}-based method for expanding T
porous microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} for cells using porous microcarriers functionalized with \acd{3} and \acd{28}
use in T cell expansion cultures. Microcarriers have historically been used \glspl{mab}. Microcarriers have historically been used in the bioprocess
throughout the bioprocess industry for adherent cultures such as \gls{cho} cells industry for adherent cultures such as \gls{cho} cells but not with suspension
but not with suspension cells such as T cells\cite{Heathman2015, Sart2011}. The cells such as T cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to
microcarriers chosen to make the \gls{dms} in this work have a microporous make the \gls{dms} in this work have a microporous structure that allows T cells
structure that allows T cells to grow inside and along the surface, providing to grow inside and along the surface, providing ample cell-cell contact for
ample cell-cell contact for enhanced autocrine and paracrine signaling. enhanced autocrine and paracrine signaling. Furthermore, the 3D surface of the
Furthermore, the 3D surface of the carriers provides a larger contact area for T carriers provides a larger contact area for T cells to interact with the
cells to interact with the \glspl{mab} relative to beads; this may better \glspl{mab} relative to beads; this may better emulate the large contact surface
emulate the large contact surface area that occurs between T cells and area that occurs between T cells and \glspl{dc}.
\glspl{dc}.
\section*{hypothesis} \section*{hypothesis}
@ -754,13 +754,12 @@ space of cell manufacturing, examples of \glspl{cqa} include markers on the
surface of cells and readouts from functional assays such as killing assays. In surface of cells and readouts from functional assays such as killing assays. In
general, these are poorly understood if they exist at all. general, these are poorly understood if they exist at all.
%% TODO IL2 use here is wonky \glspl{cpp} are parameters which may be tuned to control the outcome of process
\glspl{cpp} are parameters which may be tuned and varied to control the outcome and the quality of the final product. Examples include the type of media used
of process and the quality of the final product. Examples include the type of and the amount of \il{2} added. While these can be easy to control, the effect
media used and the amount of \il{2} added. While these can be easy to control, they have on the final outcome is generally unknown. Once \glspl{cpp} are known,
the effect they have on the final outcome is generally unknown. Once \glspl{cpp} they can be optimized to ensure that costs are minimized and potency of the
are known, they can be optimized to ensure that costs are minimized and potency cellular product is maximized.
of the cellular product is maximized.
The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of 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
@ -778,7 +777,7 @@ One of the first successful T cell-based immunotherapies against cancer is
\glspl{til}\cite{Rosenberg2015}. This method works by excising tumor fragments \glspl{til}\cite{Rosenberg2015}. This method works by excising tumor fragments
from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{} from from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{} from
within these fragments, and then administered these lymphocytes back to the within these fragments, and then administered these lymphocytes back to the
patient along with a high dose of \il{2}\cite{Rosenberg1988}. In particular, patient along with a high dose of \\il{2}\cite{Rosenberg1988}. In particular,
\gls{til} therapy has shown robust results in treating \gls{til} therapy has shown robust results in treating
melanoma\cite{Rosenberg2011}, although \glspl{til} have been found in other melanoma\cite{Rosenberg2011}, although \glspl{til} have been found in other
solid tumors such as gastointestinal, cervical, lung, and solid tumors such as gastointestinal, cervical, lung, and
@ -1004,8 +1003,8 @@ There are many ways to activate T cells \invitro{}, but the simplest and most
common is to use \glspl{mab} that cross-link CD3 and CD28, which supply Signal 1 common is to use \glspl{mab} that cross-link CD3 and CD28, which supply Signal 1
and Signal 2 without the need for antigen (which also means all T cells are and Signal 2 without the need for antigen (which also means all T cells are
activated and not just a few specific clones). Additional signals may be activated and not just a few specific clones). Additional signals may be
supplied in the form of cytokines (eg \il{2}, \il{7}, or \il{15}) or feeder supplied in the form of cytokines (eg \il{2}, \il{7}, or \il{15}) or
cells\cite{Forget2014}. feeder cells\cite{Forget2014}.
As this is a critical unit operation in the manufacturing of T cell therapies, a As this is a critical unit operation in the manufacturing of T cell therapies, a
number of commercial technologies exist to activate T cells\cite{Wang2016, number of commercial technologies exist to activate T cells\cite{Wang2016,
@ -1148,15 +1147,14 @@ memory T cells. Its role in the work of this dissertation is the subject of
further exploration in \cref{aim2b}. further exploration in \cref{aim2b}.
Functionally, mice lacking the gene for either \il{15}\cite{Kennedy2000} or its Functionally, mice lacking the gene for either \il{15}\cite{Kennedy2000} or its
high affinity receptor \il{15R$\upalpha$}\cite{Lodolce1998} are generally high affinity receptor \ilXVra{}\cite{Lodolce1998} are generally
healthy but show a deficit in memory CD8 T cells, thus underscoring this healthy but show a deficit in memory CD8 T cells, thus underscoring this
cytokine's importance in producing memory T cells for immunotherapies. T cytokine's importance in producing memory T cells for immunotherapies. T
cells themselves express \il{15} and all of its receptor cells themselves express \il{15} and all of its receptor
components\cite{MirandaCarus2005}. Additionally, blocking \il{15} itself or components\cite{MirandaCarus2005}. Additionally, blocking \il{15} itself or
\il{15R$\upalpha$} \invitro{} has been shown to inhibit homeostatic \ilXVra{} \invitro{} has been shown to inhibit homeostatic
proliferation of resting human T cells\cite{MirandaCarus2005}. proliferation of resting human T cells\cite{MirandaCarus2005}.
% ACRO fix the il2R and IL15R stuff
\il{15} has been puzzling historically because it shares almost the same pathway \il{15} has been puzzling historically because it shares almost the same pathway
as \il{2} yet has different functions\cite{Stonier2010, Osinalde2014, Giri1994, as \il{2} yet has different functions\cite{Stonier2010, Osinalde2014, Giri1994,
Giri1995}. In particular, both cytokines bond with heterotrimeric receptors Giri1995}. In particular, both cytokines bond with heterotrimeric receptors
@ -1164,33 +1162,32 @@ which share the common $\upgamma$ subchain (CD132) as well as the \il{2}
$\upbeta$ receptor (CD122). The difference is the third subchain which is either $\upbeta$ receptor (CD122). The difference is the third subchain which is either
the \il{2} $\upalpha$ receptor (CD25) or the \il{15} $\upalpha$ chain the \il{2} $\upalpha$ receptor (CD25) or the \il{15} $\upalpha$ chain
respectively, both of which have high affinity for their respective ligands. The respectively, both of which have high affinity for their respective ligands. The
\il{2R$\upalpha$} chain itself does not have any signaling capacity, and \ilXVra{} chain itself does not have any signaling capacity, and therefore all
therefore all the signaling resulting from \il{2} is mediated thought the the signaling resulting from \il{2} is mediated thought the $\upbeta$ and
$\upbeta$ and $\upgamma$ chains (namely via JAK1 and JAK3, which leads to STAT5 $\upgamma$ chains (namely via JAK1 and JAK3, which leads to STAT5 activation,
activation, which leads to T cell activation). \il{15R$\upalpha$} itself has which leads to T cell activation). \ilXVra{} itself has some innate signaling
some innate signaling capacity, but this is poorly characterized in capacity, but this is poorly characterized in lymphocytes\cite{Stonier2010}.
lymphocytes\cite{Stonier2010}. Thus there is a significant overlap between the Thus there is a significant overlap between the functions of \il{2} and \il{15}
functions of \il{2} and \il{15} due to their receptors sharing the $\upbeta$ and due to their receptors sharing the $\upbeta$ and $\upgamma$ chains, and perhaps
$\upgamma$ chains, and perhaps the main driver of their differential functions the main driver of their differential functions it the half life of each
it the half life of each respective receptor\cite{Osinalde2014}. respective receptor\cite{Osinalde2014}.
Where \il{15} is unique is that many (or possibly most) of its functions derive Where \il{15} is unique is that many (or possibly most) of its functions derive
from being membrane-bound to its receptor\cite{Stonier2010}. Particularly, from being membrane-bound to its receptor\cite{Stonier2010}. Particularly,
\il{15R$\upalpha$} binds to soluble \il{15} which produces a complex that can \ilXVra{} binds to soluble \il{15} which produces a complex that can transmit
transmit signals to close neighboring cells (so called \textit{trans} signals to close neighboring cells (so called \textit{trans} presentation). This
presentation). This has been demonstrated in adoptive cell models, where T cells has been demonstrated in adoptive cell models, where T cells lacking \ilXVra{}
lacking \il{15R$\upalpha$} were able to generate memory T cells and proliferate were able to generate memory T cells and proliferate only when other cells were
only when other cells were present which expressed present which expressed \ilXVra{} \cite{Burkett2003, Schluns2004}. The
\il{15R$\upalpha$}\cite{Burkett2003, Schluns2004}. The implication of this implication of this mechanism is that cells expression \ilXVra{} either need to
mechanism is that cells expression \il{15R$\upalpha$} either need to express express \il{15} themselves or be near other cells expressing \il{15}, and other
\il{15} themselves or be near other cells expressing \il{15}, and other cells in cells in proximity require the $\upbeta$ and $\upgamma$ chains to receive the
proximity require the $\upbeta$ and $\upgamma$ chains to receive the signal. In signal. In addition to \textit{trans} presentation, \il{15} may also work in a
addition to \textit{trans} presentation, \il{15} may also work in a \textit{cis} \textit{cis} manner, where \ilXVra{}/\il{15} complexes may bind to the $\upbeta$
manner, where \il{15R$\upalpha$}/\il{15} complexes may bind to the $\upbeta$ and and $\upgamma$ chains on the same cell, assuming each subchain is expressed and
$\upgamma$ chains on the same cell, assuming each subchain is expressed and soluble \il{15} is available\cite{Olsen2007}. Finally, \ilXVra{} itself can
soluble \il{15} is available\cite{Olsen2007}. Finally, \il{15R$\upalpha$} itself exist in soluble form, which can bind to \il{15} and signal to cells which are
can exist in soluble form, which can bind to \il{15} and signal to cells which not adjacent to the source independent of the \textit{cis/trans} mechanisms
are not adjacent to the source independent of the \textit{cis/trans} mechanisms
already described\cite{Budagian2004}. already described\cite{Budagian2004}.
\subsection{Overview of Design of Experiments}\label{sec:background_doe} \subsection{Overview of Design of Experiments}\label{sec:background_doe}
@ -1492,7 +1489,7 @@ attachment using an \product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}.
Fully functionalized \glspl{dms} were washed in sterile \gls{pbs} analogous to Fully functionalized \glspl{dms} were washed in sterile \gls{pbs} analogous to
the previous washing step to remove excess \gls{stp}. the previous washing step to remove excess \gls{stp}.
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Microcarrier properties} \caption{Microcarrier properties}
\label{tab:carrier_props} \label{tab:carrier_props}
\input{../tables/carrier_properties.tex} \input{../tables/carrier_properties.tex}
@ -1561,15 +1558,16 @@ otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
\product{OpTmizer}{\thermo}{A1048501} or \product{OpTmizer}{\thermo}{A1048501} or
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with \product{TexMACS}{\miltenyi}{170-076-307} supplemented with
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02} unless \SIrange{100}{400}{\IU\per\ml} \product{recombinant human
otherwise noted. Cell cultures were expanded for \SI{14}{\day} as counted from \il{2}}{Peprotech}{200-02} unless otherwise noted. Cell cultures were expanded
the time of initial seeding and activation. Cell counts and viability were for \SI{14}{\day} as counted from the time of initial seeding and activation.
assessed using \product{trypan blue}{\thermo}{T10282} or Cell counts and viability were assessed using \product{trypan
\product{\gls{aopi}}{Nexcelom Bioscience}{CS2-0106-5} and a \product{Countess blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom
Automated Cell Counter}{Thermo Fisher}{Countess 3 FL}. Media was added to Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo
cultures every \SIrange{2}{3}{\day} depending on media color or a Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day}
\SI{300}{\mg\per\deci\liter} minimum glucose threshold. Media glucose was depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose
measured using a \product{GlucCell glucose meter}{Chemglass}{CLS-1322-02}. threshold. Media glucose was measured using a \product{GlucCell glucose
meter}{Chemglass}{CLS-1322-02}.
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul} Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul} \product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
@ -1870,7 +1868,7 @@ was set to the maximum value of the standard curve for that cytokine. Any value
that was under-range (`OOR <' in output spreadsheet) was set to zero. All values that was under-range (`OOR <' in output spreadsheet) was set to zero. All values
that were extrapolated from the standard curve were left unchanged. that were extrapolated from the standard curve were left unchanged.
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Luminex panel} \caption{Luminex panel}
\label{tab:luminex_panel} \label{tab:luminex_panel}
\input{../tables/luminex_panel.tex} \input{../tables/luminex_panel.tex}
@ -1884,7 +1882,7 @@ datafiles into a \gls{sql} database (\cref{sec:appendix_meta}).
The data files to be aggregated included Microsoft Excel files which held The data files to be aggregated included Microsoft Excel files which held
timeseries measurements for cell cultures (eg cell counts, viability, glucose, timeseries measurements for cell cultures (eg cell counts, viability, glucose,
\gls{il2} added, media added, and media removed), \gls{fcs} files for cellular \il{2} added, media added, and media removed), \gls{fcs} files for cellular
phenotypes, and FlowJo files which held gating parameters and statistics based phenotypes, and FlowJo files which held gating parameters and statistics based
on the \gls{fcs} files. Additional information which was held in electronic lab on the \gls{fcs} files. Additional information which was held in electronic lab
notebooks (eg OneNote files) was not easily parsable, and thus this data was notebooks (eg OneNote files) was not easily parsable, and thus this data was
@ -1930,7 +1928,7 @@ context of pure error). Significance was evaluated at $\upalpha$ = 0.05.
\label{fig:gating_strategy} \label{fig:gating_strategy}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Antibodies used for flow cytometry} \caption{Antibodies used for flow cytometry}
\label{tab:flow_mabs} \label{tab:flow_mabs}
\input{../tables/flow_mabs.tex} \input{../tables/flow_mabs.tex}
@ -2244,9 +2242,9 @@ traditional beads, and significantly greater expansion after \SI{12}{\day} of
culture (\cref{fig:dms_expansion_bead}). We also observed no T cell expansion culture (\cref{fig:dms_expansion_bead}). We also observed no T cell expansion
using \glspl{dms} coated with an isotype control mAb compared to \glspl{dms} using \glspl{dms} coated with an isotype control mAb compared to \glspl{dms}
coated with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}), coated with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}),
confirming specificity of the expansion method. Given that \il{2} does not lead confirming specificity of the expansion method. Given that \il{2} does not
to expansion on its own, we know that the expansion of the T cells shown here is lead to expansion on its own, we know that the expansion of the T cells shown
due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}. here is due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2314,7 +2312,7 @@ by lowering apoptosis.
\label{fig:dms_inside} \label{fig:dms_inside}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for fraction of cells in \acrshortpl{dms} at day 14} \caption{Regression for fraction of cells in \acrshortpl{dms} at day 14}
\label{tab:inside_regression} \label{tab:inside_regression}
\input{../tables/inside_fraction_regression.tex} \input{../tables/inside_fraction_regression.tex}
@ -2473,7 +2471,7 @@ showing that migration was likely independent of \gls{car} transduction.
transduced or untransduced T cells stained with \gls{ptnl}.} transduced or untransduced T cells stained with \gls{ptnl}.}
All data is from T cells expanded for \SI{14}{\day}. All data is from T cells expanded for \SI{14}{\day}.
} }
\label{fig:car_production} \label{fig:car_cd19}
\end{figure*} \end{figure*}
\begin{figure*}[ht!] \begin{figure*}[ht!]
@ -2494,7 +2492,7 @@ showing that migration was likely independent of \gls{car} transduction.
\subcap{fig:car_degran_migration}{Endpoint plot for transmigration assay \subcap{fig:car_degran_migration}{Endpoint plot for transmigration assay
with \gls{anova}.} All data is from T cells expanded for \SI{14}{\day}. with \gls{anova}.} All data is from T cells expanded for \SI{14}{\day}.
} }
\label{fig:car_production} \label{fig:car_degran}
\end{figure*} \end{figure*}
In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms} In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms}
@ -2579,8 +2577,8 @@ We also quantified the cytokines released during the \gls{grex} expansion using
Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
released higher concentrations of cytokines compared to beads released higher concentrations of cytokines compared to beads
(\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory (\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory
cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This demonstrates that cytokines such as \gls{gmcsf}, \gls{ifng}, and \gls{tnfa}. This demonstrates
\glspl{dms} could lead to more robust activation. that \glspl{dms} could lead to more robust activation.
Taken together, these data suggest that \gls{dms} also lead to robust expansion Taken together, these data suggest that \gls{dms} also lead to robust expansion
in \gls{grex} bioreactors, although more optimization may be necessary to in \gls{grex} bioreactors, although more optimization may be necessary to
@ -2603,7 +2601,6 @@ seen in tissue-culture plates.
\label{fig:nonstick} \label{fig:nonstick}
\end{figure*} \end{figure*}
% DISCUSSION alude to this figure
We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
and could be detected on the final T cell product. This test is important for and could be detected on the final T cell product. This test is important for
clinical translation as any residual \glspl{mab} on T cells injected into the clinical translation as any residual \glspl{mab} on T cells injected into the
@ -2634,8 +2631,8 @@ included in this dataset. Obviously the principle treatment parameter was
either beads or \glspl{dms}. We also included ``bioreactor'' which was a either beads or \glspl{dms}. We also included ``bioreactor'' which was a
categorical variable for growing the T cells in a \gls{grex} bioreactor or categorical variable for growing the T cells in a \gls{grex} bioreactor or
polystyrene plates, ``feed criteria'' which represented the criteria used to polystyrene plates, ``feed criteria'' which represented the criteria used to
feed the cells (media color or a glucose meter), ``IL2 Feed Conc.'' as a feed the cells (media color or a glucose meter), ``\il{2} Feed Conc.'' as a
continuous parameter for the concentration of IL2 added each feed cycle, and continuous parameter for the concentration of \il{2} added each feed cycle, and
``CD19-CAR Transduced'' representing if the cells were lentivirally transduced ``CD19-CAR Transduced'' representing if the cells were lentivirally transduced
or not. Unfortunately, many of these parameters correlated with each other or not. Unfortunately, many of these parameters correlated with each other
despite the large size of our dataset, so the only two parameters for which despite the large size of our dataset, so the only two parameters for which
@ -2653,28 +2650,28 @@ In addition to these treatment parameters, we also included covariates to
improve the precision of our model. Among these were donor parameters including improve the precision of our model. Among these were donor parameters including
age, \gls{bmi}, demographic, and gender, as well as the initial viability and age, \gls{bmi}, demographic, and gender, as well as the initial viability and
CD4:CD8 ratio of the cryopreserved cell lots used in the experiments CD4:CD8 ratio of the cryopreserved cell lots used in the experiments
(\cref{tab:meta_donors}). We also included the age (in days) of IL2, growth (\cref{tab:meta_donors}). We also included the age (in days) of \il{2}, growth
media, and thaw media; for IL2 this was the time elapsed since reconstitution, media, and thaw media; for \il{2} this was the time elapsed since
and for the others it was the elapsed time since the manufacturing date reconstitution, and for the others it was the elapsed time since the
according to the vendor. Each experiment was performed by one of three manufacturing date according to the vendor. Each experiment was performed by one
operators, so this was included as a three-level categorical parameter. Lastly, of three operators, so this was included as a three-level categorical parameter.
some of our experiments were sampled longitudinally, so we included a boolean Lastly, some of our experiments were sampled longitudinally, so we included a
categorical to represented this modification as removing conditioned media as boolean categorical to represented this modification as removing conditioned
the cell are expanding could disrupt signaling pathways. media as the cell are expanding could disrupt signaling pathways.
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Causal inference on treatment variables} \caption{Causal inference on treatment variables}
\label{tab:ci_treat} \label{tab:ci_treat}
\input{../tables/causal_inference_treat.tex} \input{../tables/causal_inference_treat.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Causal inference on all variables} \caption{Causal inference on all variables}
\label{tab:ci_controlled} \label{tab:ci_controlled}
\input{../tables/causal_inference_control.tex} \input{../tables/causal_inference_control.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Causal inference on all variables (single donor)} \caption{Causal inference on all variables (single donor)}
\label{tab:ci_single} \label{tab:ci_single}
\input{../tables/causal_inference_single.tex} \input{../tables/causal_inference_single.tex}
@ -2745,7 +2742,6 @@ extreme in other donors.
\section{Discussion} \section{Discussion}
% DISCUSSION this is fluffy
We have developed a method for activating T cells which leads to superior We have developed a method for activating T cells which leads to superior
expansion with higher number of naïve/memory and CD4+ T cells compared to expansion with higher number of naïve/memory and CD4+ T cells compared to
state-of-the-art microbead technology (\cref{fig:dms_exp}). Other groups have state-of-the-art microbead technology (\cref{fig:dms_exp}). Other groups have
@ -2828,34 +2824,6 @@ cytokine concentrations, feed rates, and other measurements which may perturb
cell cultures, as this will be the foundation of modern process control cell cultures, as this will be the foundation of modern process control
necessary to have a fully-automated manufacturing system. necessary to have a fully-automated manufacturing system.
% It is important to note that all T cell cultures in this study were performed up
% to 14 days. Others have demonstrated that potent memory T cells may be obtained
% simply by culturing T cells as little as 5 days using traditional
% beads\cite{Ghassemi2018}. It is unknown if the naïve/memory phenotype of our DMS
% system could be further improved by reducing the culture time, but we can
% hypothesize that similar results would be observed given the lower number of
% doublings in a 5 day culture. We should also note that we investigated one
% subtype (\ptmem{}) in this study. Future work will focus on other memory
% subtypes such as tissue resident memory and stem memory T cells, as well as the
% impact of using the DMS system on the generation of these subtypes.
% DISCUSSION this sounds sketchy
% Another advantage is that the DMS system appears to induce a faster growth rate
% of T cells given the same IL2 concentration compared to beads (Supplemental
% Figure 8) along with retaining naïve and memory phenotype. This has benefits in
% multiple contexts. Firstly, some patients have small starting T cell populations
% (such as infants or those who are severely lymphodepleted), and thus require
% more population doublings to reach a usable dose. Our data suggests the time to
% reach this dose would be reduced, easing scheduling a reducing cost. Secondly,
% the allogeneic T cell model would greatly benefit from a system that could
% create large numbers of T cells with naïve and memory phenotype. In contrast to
% the autologous model which is currently used for Kymriah and Yescarta,
% allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
% across many doses for multiple patients\cite{Harrison2019}. Since it is
% economically advantageous to grow as many T cells as possible in one batch in
% the allogeneic model (reduced start up and harvesting costs, fewer required cell
% donations), the DMSs offer an advantage over current technology.
The \gls{dms} system could be used as a drop in replacement for beads in many of The \gls{dms} system could be used as a drop in replacement for beads in many of
current allogeneic therapies. Indeed, given its higher potential for expansion current allogeneic therapies. Indeed, given its higher potential for expansion
(\cref{fig:dms_exp,tab:ci_controlled}), it may work in cases where the beads (\cref{fig:dms_exp,tab:ci_controlled}), it may work in cases where the beads
@ -3145,11 +3113,11 @@ depicted using a Venn diagram from the \inlinecode{venn} R package.
\subsection{DMSs Grow T Cells With Lower IL2 Concentrations} \subsection{DMSs Grow T Cells With Lower IL2 Concentrations}
Prior to the main experiments in this aim, we assessed the effect of lowering Prior to the main experiments in this aim, we assessed the effect of lowering
the \gls{il2} concentration on the T cells grown with either bead or \gls{dms}. the \il{2} concentration on the T cells grown with either bead or \gls{dms}.
One of our hypotheses for the \gls{dms} system was that higher cell density One of our hypotheses for the \gls{dms} system was that higher cell density
would enhance cross-talk between T cells. Since \gls{il2} is secreted by would enhance cross-talk between T cells. Since \il{2} is secreted by
activated T cells themselves, T cells in the \gls{dms} system may need less or activated T cells themselves, T cells in the \gls{dms} system may need less or
no \gls{il2} if this is true. no \il{2} if this is true.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -3162,40 +3130,40 @@ no \gls{il2} if this is true.
\endgroup \endgroup
\caption[T Cells Grown at Varying IL2 Concentrations] \caption[T Cells Grown at Varying IL2 Concentrations]
{\glspl{dms} grow T cells effectively at lower IL2 concentrations. {\glspl{dms} grow T cells effectively at lower \il{2} concentrations.
\subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown \subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown
with either bead or \glspl{dms} using varying IL2 concentrations.} with either bead or \glspl{dms} using varying \il{2} concentrations.}
Day 14 counts of either \subcap{fig:il2_mod_total}{total cells} or Day 14 counts of either \subcap{fig:il2_mod_total}{total cells} or
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \gls{il2} \subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \il{2}
concentration. concentration.
\subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated \subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated
populations at day 14 of culture for each \gls{il2} concentration.} populations at day 14 of culture for each \il{2} concentration.}
} }
\label{fig:il2_mod} \label{fig:il2_mod}
\end{figure*} \end{figure*}
We varied the concentration of \gls{il2} from \SIrange{0}{100}{\IU\per\ml} and We varied the concentration of \il{2} from \SIrange{0}{100}{\IU\per\ml} and
expanded T cells as described in \cref{sec:tcellculture}. T cells grown with expanded T cells as described in \cref{sec:tcellculture}. T cells grown with
either method expanded robustly as \gls{il2} concentration was increased either method expanded robustly as \il{2} concentration was increased
(\cref{fig:il2_mod_timecourse}). Surprisingly, neither the bead or the \gls{dms} (\cref{fig:il2_mod_timecourse}). Surprisingly, neither the bead or the \gls{dms}
group expanded at all with \SI{0}{\IU\per\ml} \gls{il2}. When examining the group expanded at all with \SI{0}{\IU\per\ml} \il{2}. When examining the
endpoint fold change after \SI{14}{\day}, we observed that the difference endpoint fold change after \SI{14}{\day}, we observed that the difference
between the bead and \gls{dms} appears to be greater at lower \gls{il2} between the bead and \gls{dms} appears to be greater at lower \il{2}
concentrations (\cref{fig:il2_mod_total}). Furthermore, the same trend can be concentrations (\cref{fig:il2_mod_total}). Furthermore, the same trend can be
seen when only examining the \ptmem{} cell expansion at day 14 seen when only examining the \ptmem{} cell expansion at day 14
(\cref{fig:il2_mod_mem}). In this case, the \ptmemp{} of the T cells seemed to (\cref{fig:il2_mod_mem}). In this case, the \ptmemp{} of the T cells seemed to
be relatively close at higher \gls{il2} concentrations, but separated further at be relatively close at higher \il{2} concentrations, but separated further at
lower concentrations (\cref{fig:il2_mod_flow}) lower concentrations (\cref{fig:il2_mod_flow})
Taken together, these data do not support the hypothesis that the \gls{dms} Taken together, these data do not support the hypothesis that the \gls{dms}
system does not need \gls{il2} at all; however, it appears to have a modest system does not need \il{2} at all; however, it appears to have a modest
advantage at lower \gls{il2} concentrations compared to beads. For this reason, advantage at lower \il{2} 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 \il{2} concentrations starting
at \SI{10}{\IU\per\ml} in the remainder of this aim. at \SI{10}{\IU\per\ml} in the remainder of this aim.
\subsection{DOE Shows Optimal Conditions for Potent T Cells} \subsection{DOE Shows Optimal Conditions for Potent T Cells}
\begin{table}[!h] \begin{table}[!ht]
\centering \centering
\begin{threeparttable} \begin{threeparttable}
\caption{DOE Runs} \caption{DOE Runs}
@ -3263,42 +3231,41 @@ were shown in \cref{tab:doe_runs}.
\endgroup \endgroup
\caption[T Cell Optimization Through \acrshortpl{doe}] \caption[T Cell Optimization Through \acrshortpl{doe}]
{\gls{doe} methodology reveals optimal conditions for expanding T cell {\gls{doe} methodology reveals optimal conditions for expanding T cell
subsets. Responses vs IL2 concentration, \gls{dms} concentration, and subsets. Responses vs \il{2} concentration, \gls{dms} concentration, and
functional \gls{mab} percentage are shown for functional \gls{mab} percentage are shown for
\subcap{fig:doe_responses_mem}{total \ptmem{} T cells}, \subcap{fig:doe_responses_mem}{total \ptmem{} T cells},
\subcap{fig:doe_responses_cd4}{total \pth{} T cells}, \subcap{fig:doe_responses_cd4}{total \pth{} T cells},
\subcap{fig:doe_responses_mem4}{total \ptmemh{} T cells}, and \subcap{fig:doe_responses_mem4}{total \ptmemh{} T cells}, and
\subcap{fig:doe_responses_ratio}{ratio of CD4 and CD8 T cells in the \subcap{fig:doe_responses_ratio}{ratio of CD4 and CD8 T cells in the
\ptmem{} compartment}. Each point represents one run. \ptmem{} compartment}. Each point represents one run. }
}
\label{fig:doe_responses} \label{fig:doe_responses}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for total \ptmem{} cells (first order)} \caption{Regression for total \ptmem{} cells (first order)}
\label{tab:doe_mem1.tex} \label{tab:doe_mem1.tex}
\input{../tables/doe_mem1.tex} \input{../tables/doe_mem1.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for total \ptmem{} cells (third order)} \caption{Regression for total \ptmem{} cells (third order)}
\label{tab:doe_mem2.tex} \label{tab:doe_mem2.tex}
\input{../tables/doe_mem2.tex} \input{../tables/doe_mem2.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for total \pth{} cells} \caption{Regression for total \pth{} cells}
\label{tab:doe_cd4.tex} \label{tab:doe_cd4.tex}
\input{../tables/doe_cd4.tex} \input{../tables/doe_cd4.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for total \ptmemh{} cells} \caption{Regression for total \ptmemh{} cells}
\label{tab:doe_mem4.tex} \label{tab:doe_mem4.tex}
\input{../tables/doe_mem4.tex} \input{../tables/doe_mem4.tex}
\end{table} \end{table}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for \ptmem{} CD4:CD8 ratio} \caption{Regression for \ptmem{} CD4:CD8 ratio}
\label{tab:doe_ratio.tex} \label{tab:doe_ratio.tex}
\input{../tables/doe_ratio.tex} \input{../tables/doe_ratio.tex}
@ -3318,9 +3285,6 @@ confidence to the location of this second order feature. The remainder of the
responses showed mostly linear relationships in all parameter cases responses showed mostly linear relationships in all parameter cases
(\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}). (\cref{fig:doe_responses_cd4,fig:doe_responses_mem4,fig:doe_responses_ratio}).
% RESULT it seems arbitrary that I went straight to a third order model, the real
% reason is because it seemed weird that a second order model didn't find
% anything to be significant
We performed linear regression on the three input parameters as well as a binary We performed linear regression on the three input parameters as well as a binary
parameter representing if a given run came from the first or second \gls{doe} parameter representing if a given run came from the first or second \gls{doe}
(called ``dataset''). Starting with the total \ptmem{} cells response, we fit a (called ``dataset''). Starting with the total \ptmem{} cells response, we fit a
@ -3408,7 +3372,7 @@ that a set of bead based runs which were run in parallel, in agreement with the
luminex data obtained previously in the \gls{grex} system (these data were luminex data obtained previously in the \gls{grex} system (these data were
collected in plates) (\cref{fig:grex_luminex}). collected in plates) (\cref{fig:grex_luminex}).
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption[Machine Learning Model Results] \caption[Machine Learning Model Results]
{Results for \gls{ml} modeling using process parameters (PP) with {Results for \gls{ml} modeling using process parameters (PP) with
only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome
@ -3445,11 +3409,12 @@ other \gls{ml} methods exhibited exceedingly variable \gls{loocv}
\end{figure*} \end{figure*}
The top-performing technique, \gls{sr}, showed that the median aggregated The top-performing technique, \gls{sr}, showed that the median aggregated
predictions for \rmemh{} \rmemk{} increases when IL2 concentration, IL15, and predictions for \rmemh{} \rmemk{} increases when \il{2} concentration, \il{15},
IL2R increase while IL17a decreases in conjunction with other features. These and \ilr{2} increase while \il{17a} decreases in conjunction with other
patterns combined with low values of \pdms{} and GM-CSF uniquely characterized features. These patterns combined with low values of \pdms{} and \gls{gmcsf}
maximum \rmemk{}. Meanwhile, higher glycine but lower IL13 in combination with uniquely characterized maximum \rmemk{}. Meanwhile, higher glycine but lower
others showed maximum \rmemh{} predictions (\cref{fig:sr_omics}). \il{13} in combination with others showed maximum \rmemh{} predictions
(\cref{fig:sr_omics}).
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -3474,16 +3439,16 @@ Selecting \gls{cpp} and \glspl{cqa} candidates consistently for T cell memory is
desired. Here, \gls{tnfa} was found in consensus across all seven \gls{ml} desired. Here, \gls{tnfa} was found in consensus across all seven \gls{ml}
methods for predicting \rratio{} when considering features with the highest methods for predicting \rratio{} when considering features with the highest
importance scores across models (\cref{fig:mod_flower_48r}). Other features, importance scores across models (\cref{fig:mod_flower_48r}). Other features,
IL2R, IL4, IL17a, and \pdms{}, were commonly selected in $\ge$ 5 \gls{ml} \ilr{2}, \il{4}, \il{17a}, and \pdms{}, were commonly selected in $\ge$ 5
methods (\cref{fig:mod_flower_48r}). When restricting the models only to include \gls{ml} methods (\cref{fig:mod_flower_48r}). When restricting the models only
metabolome, formate was the sole predictor shared by all. to include metabolome, formate was the sole predictor shared by all.
When performing similar analysis on \rmemh{}, no species for either secretome or When performing similar analysis on \rmemh{}, no species for either secretome or
metabolome was shared by all models (\cref{fig:mod_flower_cd4}). These models metabolome was shared by all models (\cref{fig:mod_flower_cd4}). These models
also had worse fits compared to those for \rratio{} (\cref{tab:mod_results}). also had worse fits compared to those for \rratio{} (\cref{tab:mod_results}).
For the secretome, IL4, IL17a, and IL2R were agreed upon by $\ge$ 5 models. For For the secretome, \il{4}, \il{17a}, and \ilr{2} were agreed upon by $\ge$ 5
the metabolome, formate once again was shared by $\ge$ 5 models as well as models. For the metabolome, formate once again was shared by $\ge$ 5 models as
lactate. well as lactate.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -3497,28 +3462,26 @@ lactate.
\endgroup \endgroup
\caption[NMR Day 4 Correlations] \caption[NMR Day 4 Correlations]
{\gls{nmr} features at day 4 are strongly correlated with each other and the {\gls{nmr} features at day 4 are strongly correlated with each other and the
response variables. Highly correlated relationships are shown for response. Highly correlated relationships are shown for
\subcap{fig:nmr_cors_lactate}{lactate}, \subcap{fig:nmr_cors_lactate}{lactate},
\subcap{fig:nmr_cors_formate}{formate}, and \subcap{fig:nmr_cors_formate}{formate}, and
\subcap{fig:nmr_cors_glucose}{glucose}. Blue and blue connections indicate \subcap{fig:nmr_cors_glucose}{glucose}. Blue and red connections indicate
positive and negative correlations respectively. The threshold for positive and negative correlations respectively. The threshold for
visualizing connections in all cases was 0.8. visualizing connections in all cases was 0.8.
\subcap{fig:nmr_cors_matrix}{The correlation matrix for all predictive \subcap{fig:nmr_cors_matrix}{Correlation matrix for all features and total
features and the total \ptmemh{} response.} \ptmemh{} yield.} }
}
\label{fig:nmr_cors} \label{fig:nmr_cors}
\end{figure*} \end{figure*}
We also asked if day 4 \gls{nmr} features could predict \ptmemh{}; these models We also asked if day 4 \gls{nmr} features could predict \ptmemh{}; these models
generally fit well despite being 2 days earlier in the process generally fit well despite being 2 days earlier in the process
(\cref{fig:nmr_cors})\footnote{for anyone wondering why we don't have the (\cref{fig:nmr_cors}). Lactate and formate correlated with each other and
matching secretome data for day 4, blame UPS for losing our samples}. Lactate \rmemh{}. Furthermore, lactate positively correlated with \pdms{} and negatively
and formate correlated with each other and with \rmemh{}. Furthermore, lactate correlated with glucose (\cref{fig:nmr_cors_lactate}). Formate also had the same
positively correlated with \pdms{} and negatively correlated with glucose correlation patterns (\cref{fig:nmr_cors_formate}). Glucose was only negatively
(\cref{fig:nmr_cors_lactate}). Formate also had the same correlation patterns correlated with formate and lactate (\cref{fig:nmr_cors_glucose}). Together,
(\cref{fig:nmr_cors_formate}). Glucose was only negatively correlated with these data suggest that lactate, formate, \pdms{}, and \rmemh{} are
formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest fundamentally linked.
that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked.
\section{Discussion} \section{Discussion}
@ -3545,53 +3508,55 @@ maximizing model validity for model-based decision making, optimizing processing
parameters to maximize yield, and developing emulators for online optimization parameters to maximize yield, and developing emulators for online optimization
and human understanding\cite{Kotancheka}. and human understanding\cite{Kotancheka}.
An in-depth characterization of potential \gls{dms} based T cell \glspl{cqa} An in-depth characterization of potential \gls{dms}-based T cell \glspl{cqa}
includes a list of cytokine and \gls{nmr} features from media samples that are includes a list of cytokine and \gls{nmr} features from media samples that are
crucial in many aspects of T cell fate decisions and effector functions of crucial to fate and effector functions of immune cells. Cytokine features
immune cells. Cytokine features slightly improved prediction and dominated the slightly improved prediction and dominated the ranking of important features and
ranking of important features and variable combinations when modeling together variable combinations when modeling together with \gls{nmr} media analysis and
with \gls{nmr} media analysis and process parameters (\cref{fig:mod_flower}). process parameters (\cref{fig:mod_flower}).
Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and Predictive cytokine features such as \gls{tnfa}, \ilr{2}, \il{4}, \il{17a},
IL15 were biologically assessed in terms of their known functions and activities \il{13}, and \il{15} were biologically assessed in terms of their known
associated with T cells. T helper cells secrete more cytokines than T cytotoxic functions and activities associated with T cells. T helper cells secrete more
cells, as per their main functions, and activated T cells secrete more cytokines cytokines than T cytotoxic cells, as per their main functions, and activated T
than resting T cells. It is possible that some cytokines simply reflect the cells secrete more cytokines than resting T cells. It is possible that some
\rratio{} and the activation degree by proxy proliferation. However, the exact cytokines simply reflect the \rratio{} and the activation degree by proxy
ratio of expected cytokine abundance is less clear and depends on the subtypes proliferation. However, the exact ratio of expected cytokine abundance is less
present, thus examination of each relevant cytokine is needed. clear and depends on the subtypes present, thus examination of each relevant
cytokine is needed.
IL2R is secreted by activated T cells and binds to IL2, acting as a sink to \ilr{2} is secreted by activated T cells and binds to \il{2}, acting as a sink
dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was more abundant to dampen its effect on T cells\cite{Witkowska2005}. Since \ilr{2} was more
than IL2 in solution, this might reduce the overall effect of IL2, which could abundant than \il{2} in solution, this might reduce the overall effect of
be further investigated by blocking IL2R with an antibody. In T cells, TNF can \il{2}, which could be further investigated by blocking \ilr{2} with an
increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may antibody. In T cells, \gls{tnfa} can increase \ilr{2}, proliferation, and
also induce apoptosis depending on concentration and alter the CD4:CD8 cytokine production\cite{Mehta2018}. It may also induce apoptosis depending on
ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound concentration and alter the CD4:CD8 ratio\cite{Vudattu2005}. Given that TNF has
form, this may either increase or decrease CD4:CD8 ratio and/or memory T cells both a soluble and membrane-bound form, this may either increase or decrease
depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since CD4:CD8 ratio and/or memory T cells depending on the ratio of the membrane to
only soluble TNF was measured, membrane TNF is needed to understand its impact soluble TNF\cite{Mehta2018}. Since only soluble \gls{tnfa} was measured,
on both CD4:CD8 ratio and memory T cells. Furthermore, IL13 is known to be membrane \gls{tnfa} is needed to understand its impact on both CD4:CD8 ratio and
critical for \gls{th2} response and therefore could be secreted if there are memory T cells. Furthermore, \il{13} is known to be critical for \gls{th2}
significant \glspl{th2} already present in the starting response and therefore could be secreted if there are significant \glspl{th2}
population\cite{Wong2011}. This cytokine has limited signaling in T cells and is already present in the starting population\cite{Wong2011}. This cytokine has
thought to be more of an effector than a differentiation limited signaling in T cells and is thought to be more of an effector than a
cytokine\cite{Junttila2018}. It might be emerging here due to an initially large differentiation cytokine\cite{Junttila2018}. It might be emerging here due to an
number of \glspl{th2} or because \glspl{th2} were preferentially expanded; initially large number of \glspl{th2} or because \glspl{th2} were preferentially
indeed, IL4, also found important, is the canonical cytokine that induces expanded; indeed, \il{4}, also found important, is the canonical cytokine that
\gls{th2} differentiation (\cref{fig:mod_flower}). The role of these cytokines induces \gls{th2} differentiation (\cref{fig:mod_flower}). The role of these
could be investigated by quantifying \glspl{th1}, \glspl{th2}, or \glspl{th17} cytokines could be investigated by quantifying \glspl{th1}, \glspl{th2}, or
both in the starting population and longitudinally. Similar to IL13, IL17 is an \glspl{th17} both in the starting population and longitudinally. Similar to
effector cytokine produced by \glspl{th17}\cite{Amatya2017} thus may reflect the \il{13}, \il{17} is an effector cytokine produced by
number of \glspl{th17} in the population. GM-CSF has been linked with activated \glspl{th17}\cite{Amatya2017} thus may reflect the number of \glspl{th17} in the
T cells, specifically \glspl{th17}, but it is not clear if this cytokine is population. \gls{gmcsf} has been linked with activated T cells, specifically
inducing differential expansion of CD8+ T cells or if it is simply a covariate \glspl{th17}, but it is not clear if this cytokine is inducing differential
with another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15 expansion of CD8+ T cells or if it is simply a covariate with another cytokine
has been shown to be essential for memory signaling and effective in skewing inducing this expansion\cite{Becher2016}. Finally, \il{15} has been shown to be
\gls{car} T cells toward \glspl{tscm} when using membrane-bound IL15Ra and essential for memory signaling and effective in skewing \gls{car} T cells toward
IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to \glspl{tscm} when using membrane-bound \ilXVra{} and \ilr{15}\cite{Hurton2016}.
induce large numbers of memory T cells by functioning in an autocrine/paracrine Its high predictive behavior goes with its ability to induce large numbers of
manner and could be explored by blocking either the cytokine or its receptor. memory T cells by functioning in an autocrine/paracrine manner and could be
explored by blocking either the cytokine or its receptor.
Moreover, many predictive metabolites found here are consistent with metabolic Moreover, many predictive metabolites found here are consistent with metabolic
activity associated with T cell activation and differentiation, yet it is not activity associated with T cell activation and differentiation, yet it is not
@ -3715,16 +3680,16 @@ 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 \ilXVra{}, we supplemented T cell
cultures activated with \gls{dms} with either cultures activated with \gls{dms} with either
\product{\anti{\gls{il15r}}}{RnD}{AF247} or \product{\gls{igg} isotype \product{\anti{\ilXVra{}}}{RnD}{AF247} or \product{\gls{igg} isotype
control}{RnD}{AB-108-C} at the indicated timepoints and concentrations. T control}{RnD}{AB-108-C} at the indicated timepoints and concentrations. T
cells were grown as otherwise described in \cref{sec:tcellculture} with the cells were grown as otherwise described in \cref{sec:tcellculture} with the
exception that volumes were split by $\frac{1}{3}$ to keep the culture volume exception that volumes were split by $\frac{1}{3}$ to keep the culture volume
constant and minimize the amount of \gls{mab} required. constant and minimize the amount of \gls{mab} required.
To block soluble \gls{il15}, we supplemented analogously with To block soluble \il{15}, we supplemented analogously with
\product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype \product{\anti{\il{15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
control}{\bl}{B236633}. control}{\bl}{B236633}.
\section{Results} \section{Results}
@ -3737,14 +3702,13 @@ phenotype of T cells. While adding \glspl{dms} was simple, the easiest way to
remove \glspl{dms} was to use enzymatic digestion. Collagenase is an enzyme that remove \glspl{dms} was to use enzymatic digestion. Collagenase is an enzyme that
specifically targets collagen proteins. Since our \glspl{dms} are composed of specifically targets collagen proteins. Since our \glspl{dms} are composed of
porcine-derived collagen, this enzyme should target the \gls{dms} while sparing porcine-derived collagen, this enzyme should target the \gls{dms} while sparing
the cells along with any markers we wish to analyze. We tested this specific the cells along with any markers we wish to analyze. We tested this hypothesis
hypothesis using either \gls{colb}, \gls{cold} or \gls{hbss}, and stained the using \gls{colb}, \gls{cold} or \gls{hbss}, and then analyzed the cells via flow
cells using a typical marker panel to assess if any of the markers were cleaved cytometry to assess if the enzymes would cleave off markers of interest. The
off by the enzyme which would bias our final readout. The marker histograms in histograms in the \gls{cold} group were similar to that of the buffer group,
the \gls{cold} group were similar to that of the buffer group, while the while the \gls{colb} group visibly lowered CD62L and CD4, indicating partial
\gls{colb} group visibly lowered CD62L and CD4, indicating partial enzymatic enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we used
cleavage (\cref{fig:collagenase_fx}). Based on this result, we used \gls{cold} \gls{cold} moving forward.
moving forward.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -3754,7 +3718,7 @@ moving forward.
\endgroup \endgroup
\caption[Effects of Collagenase Treatment on T cells] \caption[Effects of Collagenase Treatment on T cells]
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then {T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
stained for various surface markers and analyzing via flow cytometry.} stained for various surface markers and analyzed via flow cytometry.}
\label{fig:collagenase_fx} \label{fig:collagenase_fx}
\end{figure*} \end{figure*}
@ -3859,13 +3823,12 @@ much higher fraction of \gls{tscm} cells compared to the \textit{no change}
group, which had more ``transitory \gls{tscm} cells.'' The majority of these group, which had more ``transitory \gls{tscm} cells.'' The majority of these
cells were \cdp{8} cells. When analyzing the same data using \gls{tsne}, we cells were \cdp{8} cells. When analyzing the same data using \gls{tsne}, we
observed a higher fraction of CD27 and lower fraction of CD45RO in the observed a higher fraction of CD27 and lower fraction of CD45RO in the
\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the \textit{removed} group (\cref{fig:spade_tsne_all}). Manually gating on the
CD27+CD45RO- population, we see there is higher density in the \textit{removed} CD27+CD45RO- population more cells with this phenotype in the \textit{removed}
group, indicating more of this population (\cref{fig:spade_tsne_stem}). group (\cref{fig:spade_tsne_stem}). Together, these data indicate that removing
Together, these data indicate that removing \glspl{dms} at lower timepoints \glspl{dms} at lower timepoints leads to higher expansion, lower \pthp{}, and
leads to higher expansion, lower \pthp{}, and higher fraction of higher fraction of lower differentiated T cells such as \gls{tscm}, and adding
lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} does the \gls{dms} does the inverse.
inverse.
\subsection{Blocking Integrin Does Not Alter Expansion or Phenotype} \subsection{Blocking Integrin Does Not Alter Expansion or Phenotype}
@ -3900,7 +3863,7 @@ cells through \gls{a2b1} and \gls{a2b2}, causing them to grow better in the
\label{fig:integrin_1} \label{fig:integrin_1}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_1}} \caption{Regression for day 14 phenotype shown in \cref{fig:integrin_1}}
\label{tab:integrin_1_reg} \label{tab:integrin_1_reg}
\input{../tables/integrin_1_reg.tex} \input{../tables/integrin_1_reg.tex}
@ -3938,7 +3901,7 @@ significantly different between any of the groups
\label{fig:integrin_2} \label{fig:integrin_2}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_2}} \caption{Regression for day 14 phenotype shown in \cref{fig:integrin_2}}
\label{tab:integrin_2_reg} \label{tab:integrin_2_reg}
\input{../tables/integrin_2_reg.tex} \input{../tables/integrin_2_reg.tex}
@ -3959,14 +3922,14 @@ is not due to signaling through \gls{a2b1} or \gls{a2b2}.
\subsection{Blocking IL15 Does Not Alter Expansion or Phenotype} \subsection{Blocking IL15 Does Not Alter Expansion or Phenotype}
\gls{il15} is a cytokine responsible for memory T cell survival and maintenance. \il{15} is a cytokine responsible for memory T cell survival and maintenance.
Furthermore, previous experiments showed that it is secreted to a much greater Furthermore, previous experiments showed that it is secreted to a much greater
extend in \gls{dms} compared to bead cultures (\cref{fig:doe_luminex}). One of extend in \gls{dms} compared to bead cultures (\cref{fig:doe_luminex}). One of
our driving hypotheses in designing the \gls{dms} system was that the higher our driving hypotheses in designing the \gls{dms} system was that the higher
cell density would lead to greater local signaling. Since we observed higher cell density would lead to greater local signaling. Since we observed higher
\ptmemp{} across many conditions, we hypothesized that \gls{il15} may be \ptmemp{} across many conditions, we hypothesized that \il{15} may be
responsible for this, and further that the unique \textit{cis/trans} activity of responsible for this, and further that the unique \textit{cis/trans} activity of
\gls{il15} may be more active in the \gls{dms} system due to higher cell \il{15} may be more active in the \gls{dms} system due to higher cell
density. density.
\begin{figure*}[ht!] \begin{figure*}[ht!]
@ -3980,7 +3943,7 @@ density.
\endgroup \endgroup
\caption[IL15 Blocking I] \caption[IL15 Blocking I]
{Blocking IL15Ra does not lead to differences in memory or growth. {Blocking \ilXVra{} does not lead to differences in memory or growth.
\subcap{fig:il15_1_overview}{Experimental overview}. \subcap{fig:il15_1_overview}{Experimental overview}.
Longitudinal measurements of Longitudinal measurements of
\subcap{fig:il15_1_fc}{fold change} and \subcap{fig:il15_1_fc}{fold change} and
@ -3992,7 +3955,7 @@ density.
\label{fig:il15_1} \label{fig:il15_1}
\end{figure*} \end{figure*}
We first tested this hypothesis by blocking \gls{il15r} with either a specific We first tested this hypothesis by blocking \ilXVra{} with either a specific
\gls{mab} or an \gls{igg} isotype control at \gls{mab} or an \gls{igg} isotype control at
\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. There was no difference in the \SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. There was no difference in the
expansion rate of blocked or unblocked cells (this experiment also had expansion rate of blocked or unblocked cells (this experiment also had
@ -4015,7 +3978,7 @@ the markers, and by extension showing no difference in phenotype
\endgroup \endgroup
\caption[IL15 Blocking II] \caption[IL15 Blocking II]
{Blocking soluble IL15 does not lead to differences in memory or growth. {Blocking soluble \il{15} does not lead to differences in memory or growth.
\subcap{fig:il15_2_overview}{Experimental overview}. \subcap{fig:il15_2_overview}{Experimental overview}.
Longitudinal measurements of Longitudinal measurements of
\subcap{fig:il15_2_fc}{fold change} and \subcap{fig:il15_2_fc}{fold change} and
@ -4027,16 +3990,16 @@ the markers, and by extension showing no difference in phenotype
\label{fig:il15_2} \label{fig:il15_2}
\end{figure*} \end{figure*}
We next tried blocking soluble \gls{il15} itself using either a \gls{mab} or an We next tried blocking soluble \il{15} itself using either a \gls{mab} or an
\gls{igg} isotype control. Anti-\gls{il15} or \gls{igg} isotype control was \gls{igg} isotype control. Anti-\il{15} or \gls{igg} isotype control was
added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in
excess of the \gls{il15} concentration seen in past experiments by over excess of the \il{15} concentration seen in past experiments by over
\num{20000} times. Similarly, there was no difference between fold change, \num{20000} times. Similarly, there was no difference between fold change,
viability, or marker histograms between any of these markers, showing that viability, or marker histograms between any of these markers, showing that
blocking \gls{il15} led to no difference in growth or phenotype. blocking \il{15} led to no difference in growth or phenotype.
In summary, this data did not support the hypothesis that the \gls{dms} platform 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 \il{15} pathway.
\section{Discussion} \section{Discussion}
@ -4091,7 +4054,7 @@ and survival, and thus adding them along with with the \glspl{mab} could enhance
T cell expansion\cite{Aoudjit2000, Gendron2003, Boisvert2007}. T cell expansion\cite{Aoudjit2000, Gendron2003, Boisvert2007}.
We also failed to uphold our hypothesis that the \gls{dms} system gains its We also failed to uphold our hypothesis that the \gls{dms} system gains its
advantage via \gls{il15} signaling. There could be multiple reasons for why advantage via \il{15} signaling. There could be multiple reasons for why
blocking either \il{15} itself or its receptor would not influence the response blocking either \il{15} itself or its receptor would not influence the response
at all. First, it could be that \il{15} is not important in our system, which is at all. First, it could be that \il{15} is not important in our system, which is
not likely given the importance of \il{15} in T cells expansion and particularly not likely given the importance of \il{15} in T cells expansion and particularly
@ -4108,8 +4071,8 @@ degree). The way to test this would be to simply titrate increasing
concentrations of \gls{mab} (which we did not do in our case because the concentrations of \gls{mab} (which we did not do in our case because the
\gls{mab} was already very expensive in the concentrations employed for our \gls{mab} was already very expensive in the concentrations employed for our
experiment). Fourth, blocking the soluble protein may not have worked because experiment). Fourth, blocking the soluble protein may not have worked because
\il{15} may have been secreted and immediately captured via \il{15R$\upalpha$} \il{15} may have been secreted and immediately captured via \ilXVra{} either by
either by the cell that secreted it or by a neighboring cell. the cell that secreted it or by a neighboring cell.
Regardless of whether or not \il{15} is important for the overall mechanism that Regardless of whether or not \il{15} is important for the overall mechanism that
differentiates the \glspl{dms} from the beads, adding \il{15} or its receptor differentiates the \glspl{dms} from the beads, adding \il{15} or its receptor
@ -4191,7 +4154,7 @@ using the Mantel-Cox test to assess significance between survival groups.
\label{fig:mouse_dosing_overview} \label{fig:mouse_dosing_overview}
\end{figure*} \end{figure*}
\begin{table}[!h] \centering \begin{table}[!ht] \centering
\caption{Cells injected for \acrshort{car} T cell \invivo{} dose study} \caption{Cells injected for \acrshort{car} T cell \invivo{} dose study}
\label{tab:mouse_dosing_results} \label{tab:mouse_dosing_results}
\input{../tables/mouse_dose_car.tex} \input{../tables/mouse_dose_car.tex}
@ -4551,9 +4514,9 @@ expansion rate. This agrees with other data we obtained in \cref{aim2a} and with
what others have generally reported about signal strength and T cell what others have generally reported about signal strength and T cell
differentiation\cite{Gattinoni2012, Lozza2008, Lanzavecchia2005, Corse2011}. We differentiation\cite{Gattinoni2012, Lozza2008, Lanzavecchia2005, Corse2011}. We
did not find any mechanistic relationship between either integrin signaling or did not find any mechanistic relationship between either integrin signaling or
\gls{il15} signaling. In the case of the former, it may be more likely that the \il{15} signaling. In the case of the former, it may be more likely that the
\glspl{dms} surfaces are saturated to the point of sterically hindering any \glspl{dms} surfaces are saturated to the point of sterically hindering any
integrin interactions with the collagen surface. In the case of \gls{il15}, more integrin interactions with the collagen surface. In the case of \il{15}, more
experiments likely need to be done in order to plausibly rule out this mechanism experiments likely need to be done in order to plausibly rule out this mechanism
and/or determine if it is involved at all. and/or determine if it is involved at all.
@ -4800,8 +4763,8 @@ ligands (in addition to integrin-binding domains and \il{15} complexes as
described at the end of \cref{aim2b}) that could have profound effects on the described at the end of \cref{aim2b}) that could have profound effects on the
expansion and quality of T cells which may be utilized. The simplest next step expansion and quality of T cells which may be utilized. The simplest next step
is to simply vary the ratio of \acd{3} and \acd{28} signal. Another obvious is to simply vary the ratio of \acd{3} and \acd{28} signal. Another obvious
example is to attach \il{15}/\il{15R$\upalpha$} complexes to the surface to example is to attach \il{15}/\ilXVra{} complexes to the surface to mimic
mimic \textit{trans} presentation from other cell types\cite{Stonier2010}. Other \textit{trans} presentation from other cell types\cite{Stonier2010}. Other
adhesion ligands or peptides such as GFOGER could be used to stimulate T cells adhesion ligands or peptides such as GFOGER could be used to stimulate T cells
and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral 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
@ -4867,7 +4830,7 @@ The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
\chapter{META ANALYSIS DONORS}\label{sec:appendix_donors} \chapter{META ANALYSIS DONORS}\label{sec:appendix_donors}
\begin{table}[!h] \begin{table}[!ht]
\caption{Donors used in meta-analysis} \caption{Donors used in meta-analysis}
\begin{subtable}[t]{\textwidth} \centering \begin{subtable}[t]{\textwidth} \centering
\caption{characteristics} \caption{characteristics}