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FIX acronyms and colagenase fig

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@ -13,8 +13,6 @@
\usepackage{graphicx}
\usepackage{subcaption}
\usepackage{nth}
\usepackage{hyperref} % must be before cleveref
\usepackage[capitalize]{cleveref}
\usepackage[version=4]{mhchem}
\usepackage{pgfgantt}
\usepackage{setspace}
@ -22,6 +20,8 @@
\usepackage{tocloft}
\usepackage{epigraph}
\usepackage{threeparttable}
\usepackage{hyperref} % must be before cleveref
\usepackage[capitalize]{cleveref}
\hypersetup{
colorlinks=true,
@ -164,10 +164,6 @@
\newacronym{pdms}{PDMS}{polydimethylsiloxane}
\newacronym{dc}{DC}{dendritic cell}
\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{mhc}{MHC}{major histocompatibility complex}
\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
@ -215,6 +211,7 @@
\newacronym{moi}{MOI}{multiplicity of infection}
\newacronym{ifng}{IFN$\upgamma$}{interferon-$\upgamma$}
\newacronym{tnfa}{TNF$\upalpha$}{tumor necrosis factor-$\upalpha$}
\newacronym{gmcsf}{GM-CSF}{granulocyte-macrophage colony stimulating factor}
\newacronym{sql}{SQL}{structured query language}
\newacronym{fcs}{FCS}{flow cytometry standard}
\newacronym{ivis}{IVIS}{in vivo imaging system}
@ -357,9 +354,13 @@
% so I don't need to worry about abbreviating all the different interleukins
\newcommand{\il}[1]{\gls{il}-#1}
\newcommand{\ilr}[1]{\gls{il}-#1R}
% ...and this one is just plain annoying
\newcommand{\ilXVra}[1]{\ilr{15}$\upalpha$}
% DOE stuff I don't feel like typing ad-nauseam
\newcommand{\pilII}{\gls{il2} concentration}
\newcommand{\pilII}{\il{2} concentration}
\newcommand{\pdms}{\gls{dms} concentration}
\newcommand{\pmab}{functional \gls{mab} surface density}
\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{}.
Typically, T cells are activated under close cell-cell contact, which allows for
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
upregulate proliferation, cytokine production, and pro-survival
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
ensure low-cost and safe products with maximal effectiveness in the clinic.
This dissertation describes a novel \acrlong{dms}-based method derived from
porous microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} for
use in T cell expansion cultures. Microcarriers have historically been used
throughout the bioprocess industry for adherent cultures such as \gls{cho} cells
but not with suspension cells such as T cells\cite{Heathman2015, Sart2011}. The
microcarriers chosen to make the \gls{dms} in this work have a microporous
structure that allows T cells to grow inside and along the surface, providing
ample cell-cell contact for enhanced autocrine and paracrine signaling.
Furthermore, the 3D surface of the carriers provides a larger contact area for T
cells to interact with the \glspl{mab} relative to beads; this may better
emulate the large contact surface area that occurs between T cells and
\glspl{dc}.
This dissertation describes a novel \acrlong{dms}-based method for expanding T
cells using porous microcarriers functionalized with \acd{3} and \acd{28}
\glspl{mab}. Microcarriers have historically been used in the bioprocess
industry for adherent cultures such as \gls{cho} cells but not with suspension
cells such as T cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to
make the \gls{dms} in this work have a microporous structure that allows T cells
to grow inside and along the surface, providing ample cell-cell contact for
enhanced autocrine and paracrine signaling. Furthermore, the 3D surface of the
carriers provides a larger contact area for T cells to interact with the
\glspl{mab} relative to beads; this may better emulate the large contact surface
area that occurs between T cells and \glspl{dc}.
\section*{hypothesis}
@ -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
general, these are poorly understood if they exist at all.
%% TODO IL2 use here is wonky
\glspl{cpp} are parameters which may be tuned and varied to control the outcome
of process and the quality of the final product. Examples include the type of
media used and the amount of \il{2} added. While these can be easy to control,
the effect they have on the final outcome is generally unknown. Once \glspl{cpp}
are known, they can be optimized to ensure that costs are minimized and potency
of the cellular product is maximized.
\glspl{cpp} are parameters which may be tuned to control the outcome of process
and the quality of the final product. Examples include the type of media used
and the amount of \il{2} added. While these can be easy to control, the effect
they have on the final outcome is generally unknown. Once \glspl{cpp} are known,
they can be optimized to ensure that costs are minimized and potency of the
cellular product is maximized.
The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of
this work are discussed further in \cref{sec:background_doe} and
@ -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
from a patient, allowing the tumor-reactive lymphocytes to expand \exvivo{} from
within these fragments, and then administered these lymphocytes back to the
patient along with a high dose of \il{2}\cite{Rosenberg1988}. In particular,
patient along with a high dose of \\il{2}\cite{Rosenberg1988}. In particular,
\gls{til} therapy has shown robust results in treating
melanoma\cite{Rosenberg2011}, although \glspl{til} have been found in other
solid tumors such as gastointestinal, cervical, lung, and
@ -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
and Signal 2 without the need for antigen (which also means all T cells are
activated and not just a few specific clones). Additional signals may be
supplied in the form of cytokines (eg \il{2}, \il{7}, or \il{15}) or feeder
cells\cite{Forget2014}.
supplied in the form of cytokines (eg \il{2}, \il{7}, or \il{15}) or
feeder cells\cite{Forget2014}.
As this is a critical unit operation in the manufacturing of T cell therapies, a
number of commercial technologies exist to activate T cells\cite{Wang2016,
@ -1148,15 +1147,14 @@ memory T cells. Its role in the work of this dissertation is the subject of
further exploration in \cref{aim2b}.
Functionally, mice lacking the gene for either \il{15}\cite{Kennedy2000} or its
high affinity receptor \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
cytokine's importance in producing memory T cells for immunotherapies. T
cells themselves express \il{15} and all of its receptor
components\cite{MirandaCarus2005}. Additionally, blocking \il{15} itself or
\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}.
% ACRO fix the il2R and IL15R stuff
\il{15} has been puzzling historically because it shares almost the same pathway
as \il{2} yet has different functions\cite{Stonier2010, Osinalde2014, Giri1994,
Giri1995}. In particular, both cytokines bond with heterotrimeric receptors
@ -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
the \il{2} $\upalpha$ receptor (CD25) or the \il{15} $\upalpha$ chain
respectively, both of which have high affinity for their respective ligands. The
\il{2R$\upalpha$} chain itself does not have any signaling capacity, and
therefore all the signaling resulting from \il{2} is mediated thought the
$\upbeta$ and $\upgamma$ chains (namely via JAK1 and JAK3, which leads to STAT5
activation, which leads to T cell activation). \il{15R$\upalpha$} itself has
some innate signaling capacity, but this is poorly characterized in
lymphocytes\cite{Stonier2010}. Thus there is a significant overlap between the
functions of \il{2} and \il{15} due to their receptors sharing the $\upbeta$ and
$\upgamma$ chains, and perhaps the main driver of their differential functions
it the half life of each respective receptor\cite{Osinalde2014}.
\ilXVra{} chain itself does not have any signaling capacity, and therefore all
the signaling resulting from \il{2} is mediated thought the $\upbeta$ and
$\upgamma$ chains (namely via JAK1 and JAK3, which leads to STAT5 activation,
which leads to T cell activation). \ilXVra{} itself has some innate signaling
capacity, but this is poorly characterized in lymphocytes\cite{Stonier2010}.
Thus there is a significant overlap between the functions of \il{2} and \il{15}
due to their receptors sharing the $\upbeta$ and $\upgamma$ chains, and perhaps
the main driver of their differential functions it the half life of each
respective receptor\cite{Osinalde2014}.
Where \il{15} is unique is that many (or possibly most) of its functions derive
from being membrane-bound to its receptor\cite{Stonier2010}. Particularly,
\il{15R$\upalpha$} binds to soluble \il{15} which produces a complex that can
transmit signals to close neighboring cells (so called \textit{trans}
presentation). This has been demonstrated in adoptive cell models, where T cells
lacking \il{15R$\upalpha$} were able to generate memory T cells and proliferate
only when other cells were present which expressed
\il{15R$\upalpha$}\cite{Burkett2003, Schluns2004}. The implication of this
mechanism is that cells expression \il{15R$\upalpha$} either need to express
\il{15} themselves or be near other cells expressing \il{15}, and other cells in
proximity require the $\upbeta$ and $\upgamma$ chains to receive the signal. In
addition to \textit{trans} presentation, \il{15} may also work in a \textit{cis}
manner, where \il{15R$\upalpha$}/\il{15} complexes may bind to the $\upbeta$ and
$\upgamma$ chains on the same cell, assuming each subchain is expressed and
soluble \il{15} is available\cite{Olsen2007}. Finally, \il{15R$\upalpha$} itself
can exist in soluble form, which can bind to \il{15} and signal to cells which
are not adjacent to the source independent of the \textit{cis/trans} mechanisms
\ilXVra{} binds to soluble \il{15} which produces a complex that can transmit
signals to close neighboring cells (so called \textit{trans} presentation). This
has been demonstrated in adoptive cell models, where T cells lacking \ilXVra{}
were able to generate memory T cells and proliferate only when other cells were
present which expressed \ilXVra{} \cite{Burkett2003, Schluns2004}. The
implication of this mechanism is that cells expression \ilXVra{} either need to
express \il{15} themselves or be near other cells expressing \il{15}, and other
cells in proximity require the $\upbeta$ and $\upgamma$ chains to receive the
signal. In addition to \textit{trans} presentation, \il{15} may also work in a
\textit{cis} manner, where \ilXVra{}/\il{15} complexes may bind to the $\upbeta$
and $\upgamma$ chains on the same cell, assuming each subchain is expressed and
soluble \il{15} is available\cite{Olsen2007}. Finally, \ilXVra{} itself can
exist in soluble form, which can bind to \il{15} and signal to cells which are
not adjacent to the source independent of the \textit{cis/trans} mechanisms
already described\cite{Budagian2004}.
\subsection{Overview of Design of Experiments}\label{sec:background_doe}
@ -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
the previous washing step to remove excess \gls{stp}.
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Microcarrier properties}
\label{tab:carrier_props}
\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
\product{OpTmizer}{\thermo}{A1048501} or
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02} unless
otherwise noted. Cell cultures were expanded for \SI{14}{\day} as counted from
the time of initial seeding and activation. Cell counts and viability were
assessed using \product{trypan blue}{\thermo}{T10282} or
\product{\gls{aopi}}{Nexcelom Bioscience}{CS2-0106-5} and a \product{Countess
Automated Cell Counter}{Thermo Fisher}{Countess 3 FL}. Media was added to
cultures every \SIrange{2}{3}{\day} depending on media color or a
\SI{300}{\mg\per\deci\liter} minimum glucose threshold. Media glucose was
measured using a \product{GlucCell glucose meter}{Chemglass}{CLS-1322-02}.
\SIrange{100}{400}{\IU\per\ml} \product{recombinant human
\il{2}}{Peprotech}{200-02} unless otherwise noted. Cell cultures were expanded
for \SI{14}{\day} as counted from the time of initial seeding and activation.
Cell counts and viability were assessed using \product{trypan
blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom
Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo
Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day}
depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose
threshold. Media glucose was measured using a \product{GlucCell glucose
meter}{Chemglass}{CLS-1322-02}.
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
@ -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 were extrapolated from the standard curve were left unchanged.
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Luminex panel}
\label{tab:luminex_panel}
\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
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
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
@ -1930,7 +1928,7 @@ context of pure error). Significance was evaluated at $\upalpha$ = 0.05.
\label{fig:gating_strategy}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Antibodies used for flow cytometry}
\label{tab:flow_mabs}
\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
using \glspl{dms} coated with an isotype control mAb compared to \glspl{dms}
coated with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}),
confirming specificity of the expansion method. Given that \il{2} does not lead
to expansion on its own, we know that the expansion of the T cells shown here is
due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
confirming specificity of the expansion method. Given that \il{2} does not
lead to expansion on its own, we know that the expansion of the T cells shown
here is due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
\begin{figure*}[ht!]
\begingroup
@ -2314,7 +2312,7 @@ by lowering apoptosis.
\label{fig:dms_inside}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for fraction of cells in \acrshortpl{dms} at day 14}
\label{tab:inside_regression}
\input{../tables/inside_fraction_regression.tex}
@ -2473,7 +2471,7 @@ showing that migration was likely independent of \gls{car} transduction.
transduced or untransduced T cells stained with \gls{ptnl}.}
All data is from T cells expanded for \SI{14}{\day}.
}
\label{fig:car_production}
\label{fig:car_cd19}
\end{figure*}
\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
with \gls{anova}.} All data is from T cells expanded for \SI{14}{\day}.
}
\label{fig:car_production}
\label{fig:car_degran}
\end{figure*}
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
released higher concentrations of cytokines compared to beads
(\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory
cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This demonstrates that
\glspl{dms} could lead to more robust activation.
cytokines such as \gls{gmcsf}, \gls{ifng}, and \gls{tnfa}. This demonstrates
that \glspl{dms} could lead to more robust activation.
Taken together, these data suggest that \gls{dms} also lead to robust expansion
in \gls{grex} bioreactors, although more optimization may be necessary to
@ -2603,7 +2601,6 @@ seen in tissue-culture plates.
\label{fig:nonstick}
\end{figure*}
% DISCUSSION alude to this figure
We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
and could be detected on the final T cell product. This test is important for
clinical translation as any residual \glspl{mab} on T cells injected into the
@ -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
categorical variable for growing the T cells in a \gls{grex} bioreactor or
polystyrene plates, ``feed criteria'' which represented the criteria used to
feed the cells (media color or a glucose meter), ``IL2 Feed Conc.'' as a
continuous parameter for the concentration of IL2 added each feed cycle, and
feed the cells (media color or a glucose meter), ``\il{2} Feed Conc.'' as a
continuous parameter for the concentration of \il{2} added each feed cycle, and
``CD19-CAR Transduced'' representing if the cells were lentivirally transduced
or not. Unfortunately, many of these parameters correlated with each other
despite the large size of our dataset, so the only two parameters for which
@ -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
age, \gls{bmi}, demographic, and gender, as well as the initial viability and
CD4:CD8 ratio of the cryopreserved cell lots used in the experiments
(\cref{tab:meta_donors}). We also included the age (in days) of IL2, growth
media, and thaw media; for IL2 this was the time elapsed since reconstitution,
and for the others it was the elapsed time since the manufacturing date
according to the vendor. Each experiment was performed by one of three
operators, so this was included as a three-level categorical parameter. Lastly,
some of our experiments were sampled longitudinally, so we included a boolean
categorical to represented this modification as removing conditioned media as
the cell are expanding could disrupt signaling pathways.
(\cref{tab:meta_donors}). We also included the age (in days) of \il{2}, growth
media, and thaw media; for \il{2} this was the time elapsed since
reconstitution, and for the others it was the elapsed time since the
manufacturing date according to the vendor. Each experiment was performed by one
of three operators, so this was included as a three-level categorical parameter.
Lastly, some of our experiments were sampled longitudinally, so we included a
boolean categorical to represented this modification as removing conditioned
media as the cell are expanding could disrupt signaling pathways.
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Causal inference on treatment variables}
\label{tab:ci_treat}
\input{../tables/causal_inference_treat.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Causal inference on all variables}
\label{tab:ci_controlled}
\input{../tables/causal_inference_control.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Causal inference on all variables (single donor)}
\label{tab:ci_single}
\input{../tables/causal_inference_single.tex}
@ -2745,7 +2742,6 @@ extreme in other donors.
\section{Discussion}
% DISCUSSION this is fluffy
We have developed a method for activating T cells which leads to superior
expansion with higher number of naïve/memory and CD4+ T cells compared to
state-of-the-art microbead technology (\cref{fig:dms_exp}). Other groups have
@ -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
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
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
@ -3145,11 +3113,11 @@ depicted using a Venn diagram from the \inlinecode{venn} R package.
\subsection{DMSs Grow T Cells With Lower IL2 Concentrations}
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
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
no \gls{il2} if this is true.
no \il{2} if this is true.
\begin{figure*}[ht!]
\begingroup
@ -3162,40 +3130,40 @@ no \gls{il2} if this is true.
\endgroup
\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
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
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \gls{il2}
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \il{2}
concentration.
\subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated
populations at day 14 of culture for each \gls{il2} concentration.}
populations at day 14 of culture for each \il{2} concentration.}
}
\label{fig:il2_mod}
\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
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}
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
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
seen when only examining the \ptmem{} cell expansion at day 14
(\cref{fig:il2_mod_mem}). In this case, the \ptmemp{} of the T cells seemed to
be relatively close at higher \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})
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
advantage at lower \gls{il2} concentrations compared to beads. For this reason,
we decided to investigate the lower range of \gls{il2} concentrations starting
system does not need \il{2} at all; however, it appears to have a modest
advantage at lower \il{2} concentrations compared to beads. For this reason,
we decided to investigate the lower range of \il{2} concentrations starting
at \SI{10}{\IU\per\ml} in the remainder of this aim.
\subsection{DOE Shows Optimal Conditions for Potent T Cells}
\begin{table}[!h]
\begin{table}[!ht]
\centering
\begin{threeparttable}
\caption{DOE Runs}
@ -3263,42 +3231,41 @@ were shown in \cref{tab:doe_runs}.
\endgroup
\caption[T Cell Optimization Through \acrshortpl{doe}]
{\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
\subcap{fig:doe_responses_mem}{total \ptmem{} T cells},
\subcap{fig:doe_responses_cd4}{total \pth{} T cells},
\subcap{fig:doe_responses_mem4}{total \ptmemh{} T cells}, and
\subcap{fig:doe_responses_ratio}{ratio of CD4 and CD8 T cells in the
\ptmem{} compartment}. Each point represents one run.
}
\ptmem{} compartment}. Each point represents one run. }
\label{fig:doe_responses}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for total \ptmem{} cells (first order)}
\label{tab:doe_mem1.tex}
\input{../tables/doe_mem1.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for total \ptmem{} cells (third order)}
\label{tab:doe_mem2.tex}
\input{../tables/doe_mem2.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for total \pth{} cells}
\label{tab:doe_cd4.tex}
\input{../tables/doe_cd4.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for total \ptmemh{} cells}
\label{tab:doe_mem4.tex}
\input{../tables/doe_mem4.tex}
\end{table}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for \ptmem{} CD4:CD8 ratio}
\label{tab: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
(\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
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
@ -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
collected in plates) (\cref{fig:grex_luminex}).
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption[Machine Learning Model Results]
{Results for \gls{ml} modeling using process parameters (PP) with
only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome
@ -3445,11 +3409,12 @@ other \gls{ml} methods exhibited exceedingly variable \gls{loocv}
\end{figure*}
The top-performing technique, \gls{sr}, showed that the median aggregated
predictions for \rmemh{} \rmemk{} increases when IL2 concentration, IL15, and
IL2R increase while IL17a decreases in conjunction with other features. These
patterns combined with low values of \pdms{} and GM-CSF uniquely characterized
maximum \rmemk{}. Meanwhile, higher glycine but lower IL13 in combination with
others showed maximum \rmemh{} predictions (\cref{fig:sr_omics}).
predictions for \rmemh{} \rmemk{} increases when \il{2} concentration, \il{15},
and \ilr{2} increase while \il{17a} decreases in conjunction with other
features. These patterns combined with low values of \pdms{} and \gls{gmcsf}
uniquely characterized maximum \rmemk{}. Meanwhile, higher glycine but lower
\il{13} in combination with others showed maximum \rmemh{} predictions
(\cref{fig:sr_omics}).
\begin{figure*}[ht!]
\begingroup
@ -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}
methods for predicting \rratio{} when considering features with the highest
importance scores across models (\cref{fig:mod_flower_48r}). Other features,
IL2R, IL4, IL17a, and \pdms{}, were commonly selected in $\ge$ 5 \gls{ml}
methods (\cref{fig:mod_flower_48r}). When restricting the models only to include
metabolome, formate was the sole predictor shared by all.
\ilr{2}, \il{4}, \il{17a}, and \pdms{}, were commonly selected in $\ge$ 5
\gls{ml} methods (\cref{fig:mod_flower_48r}). When restricting the models only
to include metabolome, formate was the sole predictor shared by all.
When performing similar analysis on \rmemh{}, no species for either secretome or
metabolome was shared by all models (\cref{fig:mod_flower_cd4}). These models
also had worse fits compared to those for \rratio{} (\cref{tab:mod_results}).
For the secretome, IL4, IL17a, and IL2R were agreed upon by $\ge$ 5 models. For
the metabolome, formate once again was shared by $\ge$ 5 models as well as
lactate.
For the secretome, \il{4}, \il{17a}, and \ilr{2} were agreed upon by $\ge$ 5
models. For the metabolome, formate once again was shared by $\ge$ 5 models as
well as lactate.
\begin{figure*}[ht!]
\begingroup
@ -3497,28 +3462,26 @@ lactate.
\endgroup
\caption[NMR Day 4 Correlations]
{\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_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
visualizing connections in all cases was 0.8.
\subcap{fig:nmr_cors_matrix}{The correlation matrix for all predictive
features and the total \ptmemh{} response.}
}
\subcap{fig:nmr_cors_matrix}{Correlation matrix for all features and total
\ptmemh{} yield.} }
\label{fig:nmr_cors}
\end{figure*}
We also asked if day 4 \gls{nmr} features could predict \ptmemh{}; these models
generally fit well despite being 2 days earlier in the process
(\cref{fig:nmr_cors})\footnote{for anyone wondering why we don't have the
matching secretome data for day 4, blame UPS for losing our samples}. Lactate
and formate correlated with each other and with \rmemh{}. Furthermore, lactate
positively correlated with \pdms{} and negatively correlated with glucose
(\cref{fig:nmr_cors_lactate}). Formate also had the same correlation patterns
(\cref{fig:nmr_cors_formate}). Glucose was only negatively correlated with
formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest
that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked.
(\cref{fig:nmr_cors}). Lactate and formate correlated with each other and
\rmemh{}. Furthermore, lactate positively correlated with \pdms{} and negatively
correlated with glucose (\cref{fig:nmr_cors_lactate}). Formate also had the same
correlation patterns (\cref{fig:nmr_cors_formate}). Glucose was only negatively
correlated with formate and lactate (\cref{fig:nmr_cors_glucose}). Together,
these data suggest that lactate, formate, \pdms{}, and \rmemh{} are
fundamentally linked.
\section{Discussion}
@ -3545,53 +3508,55 @@ maximizing model validity for model-based decision making, optimizing processing
parameters to maximize yield, and developing emulators for online optimization
and human understanding\cite{Kotancheka}.
An in-depth characterization of potential \gls{dms} based T cell \glspl{cqa}
An in-depth characterization of potential \gls{dms}-based T cell \glspl{cqa}
includes a list of cytokine and \gls{nmr} features from media samples that are
crucial in many aspects of T cell fate decisions and effector functions of
immune cells. Cytokine features slightly improved prediction and dominated the
ranking of important features and variable combinations when modeling together
with \gls{nmr} media analysis and process parameters (\cref{fig:mod_flower}).
crucial to fate and effector functions of immune cells. Cytokine features
slightly improved prediction and dominated the ranking of important features and
variable combinations when modeling together with \gls{nmr} media analysis and
process parameters (\cref{fig:mod_flower}).
Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and
IL15 were biologically assessed in terms of their known functions and activities
associated with T cells. T helper cells secrete more cytokines than T cytotoxic
cells, as per their main functions, and activated T cells secrete more cytokines
than resting T cells. It is possible that some cytokines simply reflect the
\rratio{} and the activation degree by proxy proliferation. However, the exact
ratio of expected cytokine abundance is less clear and depends on the subtypes
present, thus examination of each relevant cytokine is needed.
Predictive cytokine features such as \gls{tnfa}, \ilr{2}, \il{4}, \il{17a},
\il{13}, and \il{15} were biologically assessed in terms of their known
functions and activities associated with T cells. T helper cells secrete more
cytokines than T cytotoxic cells, as per their main functions, and activated T
cells secrete more cytokines than resting T cells. It is possible that some
cytokines simply reflect the \rratio{} and the activation degree by proxy
proliferation. However, the exact ratio of expected cytokine abundance is less
clear and depends on the subtypes present, thus examination of each relevant
cytokine is needed.
IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was more abundant
than IL2 in solution, this might reduce the overall effect of IL2, which could
be further investigated by blocking IL2R with an antibody. In T cells, TNF can
increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may
also induce apoptosis depending on concentration and alter the CD4:CD8
ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound
form, this may either increase or decrease CD4:CD8 ratio and/or memory T cells
depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since
only soluble TNF was measured, membrane TNF is needed to understand its impact
on both CD4:CD8 ratio and memory T cells. Furthermore, IL13 is known to be
critical for \gls{th2} response and therefore could be secreted if there are
significant \glspl{th2} already present in the starting
population\cite{Wong2011}. This cytokine has limited signaling in T cells and is
thought to be more of an effector than a differentiation
cytokine\cite{Junttila2018}. It might be emerging here due to an initially large
number of \glspl{th2} or because \glspl{th2} were preferentially expanded;
indeed, IL4, also found important, is the canonical cytokine that induces
\gls{th2} differentiation (\cref{fig:mod_flower}). The role of these cytokines
could be investigated by quantifying \glspl{th1}, \glspl{th2}, or \glspl{th17}
both in the starting population and longitudinally. Similar to IL13, IL17 is an
effector cytokine produced by \glspl{th17}\cite{Amatya2017} thus may reflect the
number of \glspl{th17} in the population. GM-CSF has been linked with activated
T cells, specifically \glspl{th17}, but it is not clear if this cytokine is
inducing differential expansion of CD8+ T cells or if it is simply a covariate
with another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15
has been shown to be essential for memory signaling and effective in skewing
\gls{car} T cells toward \glspl{tscm} when using membrane-bound IL15Ra and
IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to
induce large numbers of memory T cells by functioning in an autocrine/paracrine
manner and could be explored by blocking either the cytokine or its receptor.
\ilr{2} is secreted by activated T cells and binds to \il{2}, acting as a sink
to dampen its effect on T cells\cite{Witkowska2005}. Since \ilr{2} was more
abundant than \il{2} in solution, this might reduce the overall effect of
\il{2}, which could be further investigated by blocking \ilr{2} with an
antibody. In T cells, \gls{tnfa} can increase \ilr{2}, proliferation, and
cytokine production\cite{Mehta2018}. It may also induce apoptosis depending on
concentration and alter the CD4:CD8 ratio\cite{Vudattu2005}. Given that TNF has
both a soluble and membrane-bound form, this may either increase or decrease
CD4:CD8 ratio and/or memory T cells depending on the ratio of the membrane to
soluble TNF\cite{Mehta2018}. Since only soluble \gls{tnfa} was measured,
membrane \gls{tnfa} is needed to understand its impact on both CD4:CD8 ratio and
memory T cells. Furthermore, \il{13} is known to be critical for \gls{th2}
response and therefore could be secreted if there are significant \glspl{th2}
already present in the starting population\cite{Wong2011}. This cytokine has
limited signaling in T cells and is thought to be more of an effector than a
differentiation cytokine\cite{Junttila2018}. It might be emerging here due to an
initially large number of \glspl{th2} or because \glspl{th2} were preferentially
expanded; indeed, \il{4}, also found important, is the canonical cytokine that
induces \gls{th2} differentiation (\cref{fig:mod_flower}). The role of these
cytokines could be investigated by quantifying \glspl{th1}, \glspl{th2}, or
\glspl{th17} both in the starting population and longitudinally. Similar to
\il{13}, \il{17} is an effector cytokine produced by
\glspl{th17}\cite{Amatya2017} thus may reflect the number of \glspl{th17} in the
population. \gls{gmcsf} has been linked with activated T cells, specifically
\glspl{th17}, but it is not clear if this cytokine is inducing differential
expansion of CD8+ T cells or if it is simply a covariate with another cytokine
inducing this expansion\cite{Becher2016}. Finally, \il{15} has been shown to be
essential for memory signaling and effective in skewing \gls{car} T cells toward
\glspl{tscm} when using membrane-bound \ilXVra{} and \ilr{15}\cite{Hurton2016}.
Its high predictive behavior goes with its ability to induce large numbers of
memory T cells by functioning in an autocrine/paracrine manner and could be
explored by blocking either the cytokine or its receptor.
Moreover, many predictive metabolites found here are consistent with metabolic
activity associated with T cell activation and differentiation, yet it is not
@ -3715,16 +3680,16 @@ analyzing via a \bd{} Accuri flow cytometer.
\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
\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
cells were grown as otherwise described in \cref{sec:tcellculture} with the
exception that volumes were split by $\frac{1}{3}$ to keep the culture volume
constant and minimize the amount of \gls{mab} required.
To block soluble \gls{il15}, we supplemented analogously with
\product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
To block soluble \il{15}, we supplemented analogously with
\product{\anti{\il{15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
control}{\bl}{B236633}.
\section{Results}
@ -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
specifically targets collagen proteins. Since our \glspl{dms} are composed of
porcine-derived collagen, this enzyme should target the \gls{dms} while sparing
the cells along with any markers we wish to analyze. We tested this specific
hypothesis using either \gls{colb}, \gls{cold} or \gls{hbss}, and stained the
cells using a typical marker panel to assess if any of the markers were cleaved
off by the enzyme which would bias our final readout. The marker histograms in
the \gls{cold} group were similar to that of the buffer group, while the
\gls{colb} group visibly lowered CD62L and CD4, indicating partial enzymatic
cleavage (\cref{fig:collagenase_fx}). Based on this result, we used \gls{cold}
moving forward.
the cells along with any markers we wish to analyze. We tested this hypothesis
using \gls{colb}, \gls{cold} or \gls{hbss}, and then analyzed the cells via flow
cytometry to assess if the enzymes would cleave off markers of interest. The
histograms in the \gls{cold} group were similar to that of the buffer group,
while the \gls{colb} group visibly lowered CD62L and CD4, indicating partial
enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we used
\gls{cold} moving forward.
\begin{figure*}[ht!]
\begingroup
@ -3754,7 +3718,7 @@ moving forward.
\endgroup
\caption[Effects of Collagenase Treatment on T cells]
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
stained for various surface markers and analyzing via flow cytometry.}
stained for various surface markers and analyzed via flow cytometry.}
\label{fig:collagenase_fx}
\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
cells were \cdp{8} cells. When analyzing the same data using \gls{tsne}, we
observed a higher fraction of CD27 and lower fraction of CD45RO in the
\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the
CD27+CD45RO- population, we see there is higher density in the \textit{removed}
group, indicating more of this population (\cref{fig:spade_tsne_stem}).
Together, these data indicate that removing \glspl{dms} at lower timepoints
leads to higher expansion, lower \pthp{}, and higher fraction of
lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} does the
inverse.
\textit{removed} group (\cref{fig:spade_tsne_all}). Manually gating on the
CD27+CD45RO- population more cells with this phenotype in the \textit{removed}
group (\cref{fig:spade_tsne_stem}). Together, these data indicate that removing
\glspl{dms} at lower timepoints leads to higher expansion, lower \pthp{}, and
higher fraction of lower differentiated T cells such as \gls{tscm}, and adding
\gls{dms} does the inverse.
\subsection{Blocking Integrin Does Not Alter Expansion or Phenotype}
@ -3900,7 +3863,7 @@ cells through \gls{a2b1} and \gls{a2b2}, causing them to grow better in the
\label{fig:integrin_1}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_1}}
\label{tab:integrin_1_reg}
\input{../tables/integrin_1_reg.tex}
@ -3938,7 +3901,7 @@ significantly different between any of the groups
\label{fig:integrin_2}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Regression for day 14 phenotype shown in \cref{fig:integrin_2}}
\label{tab:integrin_2_reg}
\input{../tables/integrin_2_reg.tex}
@ -3959,14 +3922,14 @@ is not due to signaling through \gls{a2b1} or \gls{a2b2}.
\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
extend in \gls{dms} compared to bead cultures (\cref{fig:doe_luminex}). One of
our driving hypotheses in designing the \gls{dms} system was that the higher
cell density would lead to greater local signaling. Since we observed higher
\ptmemp{} across many conditions, we hypothesized that \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
\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.
\begin{figure*}[ht!]
@ -3980,7 +3943,7 @@ density.
\endgroup
\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}.
Longitudinal measurements of
\subcap{fig:il15_1_fc}{fold change} and
@ -3992,7 +3955,7 @@ density.
\label{fig:il15_1}
\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
\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. There was no difference in the
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
\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}.
Longitudinal measurements of
\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}
\end{figure*}
We next tried blocking soluble \gls{il15} itself using either a \gls{mab} or an
\gls{igg} isotype control. Anti-\gls{il15} or \gls{igg} isotype control was
We next tried blocking soluble \il{15} itself using either a \gls{mab} or an
\gls{igg} isotype control. Anti-\il{15} or \gls{igg} isotype control was
added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in
excess of the \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,
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
gains its advantages via the \gls{il15} pathway.
gains its advantages via the \il{15} pathway.
\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}.
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
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
@ -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
\gls{mab} was already very expensive in the concentrations employed for our
experiment). Fourth, blocking the soluble protein may not have worked because
\il{15} may have been secreted and immediately captured via \il{15R$\upalpha$}
either by the cell that secreted it or by a neighboring cell.
\il{15} may have been secreted and immediately captured via \ilXVra{} either by
the cell that secreted it or by a neighboring cell.
Regardless of whether or not \il{15} is important for the overall mechanism that
differentiates the \glspl{dms} from the beads, adding \il{15} or its receptor
@ -4191,7 +4154,7 @@ using the Mantel-Cox test to assess significance between survival groups.
\label{fig:mouse_dosing_overview}
\end{figure*}
\begin{table}[!h] \centering
\begin{table}[!ht] \centering
\caption{Cells injected for \acrshort{car} T cell \invivo{} dose study}
\label{tab:mouse_dosing_results}
\input{../tables/mouse_dose_car.tex}
@ -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
differentiation\cite{Gattinoni2012, Lozza2008, Lanzavecchia2005, Corse2011}. We
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
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
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
expansion and quality of T cells which may be utilized. The simplest next step
is to simply vary the ratio of \acd{3} and \acd{28} signal. Another obvious
example is to attach \il{15}/\il{15R$\upalpha$} complexes to the surface to
mimic \textit{trans} presentation from other cell types\cite{Stonier2010}. Other
example is to attach \il{15}/\ilXVra{} complexes to the surface to mimic
\textit{trans} presentation from other cell types\cite{Stonier2010}. Other
adhesion ligands or peptides such as GFOGER could be used to stimulate T cells
and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral
delivery systems could theoretically be attached to the \gls{dms}, greatly
@ -4867,7 +4830,7 @@ The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
\chapter{META ANALYSIS DONORS}\label{sec:appendix_donors}
\begin{table}[!h]
\begin{table}[!ht]
\caption{Donors used in meta-analysis}
\begin{subtable}[t]{\textwidth} \centering
\caption{characteristics}