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\makeglossaries{}
\newacronym{act}{ACT}{adoptive cell therapies}
\newacronym{car}{CAR}{chimeric antigen receptor}
\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
\newacronym{ecm}{ECM}{extracellular matrix}
\newacronym{cqa}{CQA}{critical quality attribute}
\newacronym{cpp}{CPP}{critical process parameter}
\newacronym{dms}{DMS}{degradable microscaffold}
\newacronym{doe}{DOE}{design of experiments}
\newacronym{gmp}{GMP}{Good Manufacturing Practices}
\newacronym{cho}{CHO}{Chinese hamster ovary}
\newacronym{all}{ALL}{acute lymphoblastic leukemia}
\newacronym{pdms}{PDMS}{polydimethylsiloxane}
\newacronym{dc}{DC}{dendritic cell}
\newacronym{il2}{IL2}{interleukin 2}
\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}
\newacronym{nmr}{NMR}{nuclear magnetic resonance}
\newacronym{haba}{HABA}{4-hydroxyazobenene-2-carboxylic-acid}
\newacronym{pbs}{PBS}{phosphate buffered saline}
\newacronym{bca}{BCA}{bicinchoninic acid assay}
\newacronym{bsa}{BSA}{bovine serum albumin}
\newacronym{stp}{STP}{streptavidin}
\newacronym{stppe}{STP-PE}{streptavidin-phycoerythrin}
\newacronym{snb}{SNB}{sulfo-nhs-biotin}
\newacronym{cug}{CuG}{Cultispher G}
\newacronym{cus}{CuS}{Cultispher S}
\newacronym{pbmc}{PBMC}{peripheral blood mononuclear cells}
\newacronym{macs}{MACS}{magnetic activated cell sorting}
\newacronym{aopi}{AO/PI}{acridine orange/propidium iodide}
\newacronym{igg}{IgG}{immunoglobulin G}
\newacronym{pe}{PE}{phycoerythrin}
\newacronym{ptnl}{PTN-L}{Protein L}
\newacronym{af647}{AF647}{Alexa Fluor 647}
\newacronym{anova}{ANOVA}{analysis of variance}
\newacronym{crispr}{CRISPR}{clustered regularly interspaced short
  palindromic repeats}

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\DeclareSIUnit\IU{IU}
\DeclareSIUnit\rpm{RPM}
\DeclareSIUnit\dms{DMS}
\DeclareSIUnit\cell{cells}
\DeclareSIUnit\ab{mAb}
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  \Large{
    \textbf{
      Optimizing T Cell Manufacturing and Quality Using Functionalized
      Degradable Microscaffolds
    }
  }
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    #1 \\
    #2 \\
    \textit{#3}
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\newcommand{\miltenyi}{Miltenyi Biotech}
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\begin{document}

\begin{titlepage}
  \begin{mytitlepage}
    \mytitle{}

    \vfill

    \Large{
      A Dissertation \\
      Presented to \\
      The Academic Faculty \\

      \vspace{1.5em}

      by

      \vspace{1.5em}

      Nathan John Dwarshuis, B.S. \\

      \vfill

      In Partial Fulfillment \\
      of the Requirements for the Degree \\
      Doctor of Philosophy in Biomedical Engineering in the \\
      Wallace H. Coulter Department of Biomedical Engineering

      \vfill

      Georgia Institute of Technology and Emory University \\
      August 2021

      \vfill

      COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
    }
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\end{titlepage}

\onecolumn \pagenumbering{roman}
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\begin{mytitlepage}
  \mytitle{}
\end{mytitlepage}

\vfill

\large{
  \noindent
  Committee Members

  \begin{multicols}{2}
    \begin{singlespace}

      \mycommitteemember{Dr.\ Krishnendu\ Roy\ (Advisor)}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Madhav\ Dhodapkar}
      {Department of Hematology and Medical Oncology}
      {Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Melissa\ Kemp}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \columnbreak{}
      \null{}
      \vfill

      \mycommitteemember{Dr.\ Wilbur\ Lam}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

      \vspace{1.5em}

      \mycommitteemember{Dr.\ Sakis\ Mantalaris}
      {Department of Biomedical Engineering}
      {Georgia Institute of Technology and Emory University}

    \end{singlespace}
  \end{multicols}

  \vspace{1.5em}

  \hfill Date Approved:
}

\clearpage

\chapter*{acknowledgements}
\addcontentsline{toc}{chapter}{acknowledgements} 

Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational.

\clearpage

\chapter*{summary}
\addcontentsline{toc}{chapter}{summary} 

\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
manufacturing large numbers of high quality cells remains challenging. Currently
approved T cell expansion technologies involve \anti-cd{3} and \anti-cd{28}
\glspl{mab}, usually mounted on magnetic beads. This method fails to
recapitulate many key signals found \invivo{} and is also heavily licensed by a
few companies, limiting its long-term usefulness to manufactures and clinicians.
Furthermore, we understand that highly potent T cells are generally
less-differentiated subtypes such as central memory and stem memory T cells.
Despite this understanding, little has been done to optimize T cell expansion
for generating these subtypes, including measurement and feedback control
strategies that are necessary for any modern manufacturing process.

The goal of this thesis was to develop a microcarrier-based \gls{dms} T cell
expansion system as well as determine biologically-meaningful \glspl{cqa} and
\glspl{cpp} that could be used to optimize for highly-potent T cells. In Aim 1,
we develop and characterized the \gls{dms} system, including quality control
steps. We also demonstrate the feasiblity of expanding highly-potent memory and
CD4+ T cells, and showing compatibility with existing \gls{car} transduction
methods. In aim 2, we use \gls{doe} methodology to optimize the \gls{dms}
platform, and develop a computational pipeline to identify and model the effect
of measurable \glspl{cqa} and \glspl{cpp} on the final product. In aim 3, we
demonstrate the effectiveness of the \gls{dms} platform \invivo{}. This
thesis lays the groundwork for a novel T cell expansion method which can be used
in a clinical setting, and also provides a path toward optimizing for product
quality in an industrial setting.

\clearpage

\tableofcontents

\clearpage

\listoffigures

\clearpage

\listoftables

\clearpage

% \twocolumn
\chapter*{acronyms}
\addcontentsline{toc}{chapter}{acronyms}

\printglossary[type=\acronymtype]

\clearpage
\pagenumbering{arabic}

\clearpage

\chapter{introduction}

\section*{overview}

% TODO this is basically the same as the first part of the backgound, I guess I
% can just trim it down

T cell-based immunotherapies have received great interest from clinicians and
industry due to their potential to treat, and often cure, cancer and other
diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
received FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
two genetically-modified \gls{car} T cell therapies against B cell malignancies.
Despite these successes, \gls{car} T cell therapies are constrained by an
expensive and difficult-to-scale manufacturing process with little control on
cell quality and phenotype3,4. State-of-the-art T cell manufacturing techniques
focus on \acd{3} and \acd{28} activation and expansion, typically
presented on superparamagnetic, iron-based microbeads (Invitrogen Dynabead,
Miltenyi MACS beads), on nanobeads (Miltenyi TransACT), or in soluble tetramers
(Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016, Piscopo2017, Bashour2015}.
These strategies overlook many of the signaling components 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}
components such as collagen, which provide signals to upregulate proliferation,
cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
  Boisvert2007, Ben-Horin2004}. We hypothesized that culture conditions that
better mimic these \invivo{} expansion conditions of T cells, can significantly
improve the quality and quantity of manufactured T cells and provide better
control on the resulting T cell phenotype.

% TODO mention the Cloudz stuff that's in my presentation

A variety of solutions have been proposed to make the T cell expansion process
more physiological. One strategy is to use modified feeder cell cultures to
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
While this has the theoretical capacity to mimic many components of the lymph
node, it is hard to reproduce on a large scale due to the complexity and
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
Others have proposed biomaterials-based solutions to circumvent this problem,
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
experience \invivo{}. While these have been shown to provide superior expansion
compared to traditional microbeads, none of these methods has been able to show
preferential expansion of functional naïve/memory and CD4 T cell populations.
Generally, T cells with a lower differentiation state such as naïve and memory
cells have been shown to provide superior anti-tumor potency, presumably due to
their higher potential to replicate, migrate, and engraft, leading to a
long-term, durable response\cite{Xu2014, Fraietta2018, Gattinoni2011,
  Gattinoni2012}. Likewise, CD4 T cells are similarly important to anti-tumor
potency due to their cytokine release properties and ability to resist
exhaustion\cite{Wang2018, Yang2017}. Therefore, methods to increase naïve/memory
and CD4 T cells in the final product are needed, a critical consideration being
ease of translation to industry and ability to interface with scalable systems
such as bioreactors.

% TODO probably need to address some of the modeling stuff here as well

This thesis describes a novel degradable microscaffold-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 stem cells and
\gls{cho} cells, but not with suspension cells such as T
cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to make the DMSs in
this study have a microporous structure that allows T cells to grow inside and
along the surface, providing ample cell-cell contact for enhanced autocrine and
paracrine signaling. Furthermore, the carriers are composed of gelatin, which is
a collagen derivative and therefore has adhesion domains that are also present
within the lymph nodes. Finally, the 3D surface of the carriers provides a
larger contact area for T cells to interact with the \glspl{mab} relative to
beads; this may better emulate the large contact surface area that occurs
between T cells and \glspl{dc}. These microcarriers are readily available in
over 30 countries and are used in an FDA fast-track-approved combination retinal
pigment epithelial cell product (Spheramine, Titan Pharmaceuticals) {\#}[Purcell
documentation]. This regulatory history will aid in clinical translation. We
show that compared to traditional microbeads, \gls{dms}-expanded T cells not
only provide superior expansion, but consistently provide a higher frequency of
naïve/memory and CD4 T cells (CCR7+CD62L+) across multiple donors. We also
demonstrate functional cytotoxicity using a CD19 \gls{car} and a superior
performance, even at a lower \gls{car} T cell dose, of \gls{dms}-expanded
\gls{car}-T cells \invivo{} in a mouse xenograft model of human B cell
\gls{all}. Our results indicate that \glspl{dms} provide a robust and scalable
platform for manufacturing therapeutic T cells with higher naïve/memory
phenotype and more balanced CD4+ T cell content.

\section*{hypothesis}

The hypothesis of this dissertation was that using \glspl{dms} created from
off-the-shelf microcarriers and coated with activating \glspl{mab} would lead to
higher quantity and quality T cells as compared to state-of-the-art bead-based
expansion. The objective of this dissertation was to develop this platform, test
its effectiveness both \invivo{} and \invivo{}, and develop computational
pipelines that could be used in a manufacturing environment.

\section*{specific aims}

The specific aims of this dissertation are outlined in
\cref{fig:graphical_overview}.

\begin{figure*}[ht!]
  \begingroup

  \includegraphics[width=\textwidth]{example-image-a}

  \endgroup
  \caption[Project Overview]{High-level workflow.}
  \label{fig:graphical_overview}
\end{figure*}

\subsection*{aim 1: develop and optimize a novel T cell expansion process that
  mimics key components of the lymph nodes}

% TODO this might be easier to break apart in separate aims

In this first aim, we demonstrated the process for manufacturing \glspl{dms},
including quality control steps that are necessary for translation of this
platform into a scalable manufacturing setting. We also demonstrate that the
\gls{dms} platform leads to higher overall expansion of T cells and higher
overall fractions of potent memory and CD4+ subtypes desired for T cell
therapies. Finally, we demonstrate \invitro{} that the \gls{dms} platform can be
used to generate functional \gls{car} T cells targeted toward CD19.

\subsection*{aim 2: develop methods to control and predict T cell quality}

For this second aim, we investigated methods to identify and control \glspl{cqa}
and glspl{cpp} for manufacturing T cells using the \gls{dms} platform. This was
accomplished through two sub-aims:

\begin{itemize}
  \item[A --] Develop computational methods to control and predict T cell
    expansion and quality
  \item[B --] Perturb \gls{dms} expansion to identify additional mechanistic
    controls for expansion and quality
\end{itemize}

\subsection*{aim 3: confirm potency of T cells from novel T cell expansion
  process using \invivo{} xenograft mouse model}

In this final aim, we demonstrate the effectiveness of \gls{dms}-expanded T
cells compared to state-of-the-art beads using \invivo{} mouse models for
\gls{all}.

\section*{outline}

In Chapter~\ref{background}, we provide additional background on the current
state of T cell manufacturing and how the work in this dissertation moves the
field forward. In Chapters~\ref{aim1},~\ref{aim2}, and~\ref{aim3} we present the
work pertaining to Aims 1, 2, and 3 respectively. Finally, we present our final
conclusions in Chapter~\ref{conclusions}.

\chapter{background and significance}\label{background}
\section*{background}

% TODO break this apart into mfg tech and T cell phenotypes/quality
% TODO consider adding a separate section on microcarriers and their use in
% bioprocess
% TODO add stuff about T cell licensing
\subsection*{current T cell manufacturing technologies}

\Gls{car} T cell therapy has received great interest from both academia and
industry due to its potential to treat cancer and other
diseases\cite{Fesnak2016, Rosenberg2015}. In 2017, Novartis and Kite Pharma
acquired FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
two \gls{car} T cell therapies against B cell malignancies. Despite these
successes, \gls{car} T cell therapies are constrained by an expensive and
difficult-to-scale manufacturing process\cite{Roddie2019, Dwarshuis2017}.

Of critical concern, state-of-the-art manufacturing techniques focus only on
Signal 1 and Signal 2-based activation via \acd{3} and \acd{28} \glspl{mab},
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
strategies overlook many of the signaling components present in the secondary
lymphoid organs where T cells normally expand. Typically, T cells are activated
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
signals. These close quarters allow for efficient autocrine/paracrine signaling
among the expanding T cells, which secrete gls{il2} and other cytokines to
assist their own growth. Additionally, the lymphoid tissues are comprised of
\gls{ecm} components such as collagen, which provide signals to upregulate
proliferation, cytokine production, and pro-survival pathways\cite{Gendron2003,
  Ohtani2008, Boisvert2007, Ben-Horin2004}.

A variety of solutions have been proposed to make the T cell expansion process
more physiological. One strategy is to use modified feeder cell cultures to
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
While this has the theoretical capacity to mimic several key components of the
lymph node, it is hard to reproduce on a large scale due to the complexity and
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
Others have proposed biomaterials-based solutions to circumvent this problem,
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
experience \textit{in vivo}. While these have been shown to provide superior
expansion compared to traditional microbeads, no method has been able to show
preferential expansion of functional memory and CD4 T cell populations.
Generally, T cells with a lower differentiation state such as memory cells have
been shown to provide superior anti-tumor potency, presumably due to their
higher potential to replicate, migrate, and engraft, leading to a long-term,
durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
cytokine release properties and ability to resist exhaustion\cite{Wang2018,
  Yang2017}, and no method exists to preferentially expand the CD4 population
compared to state-of-the-art systems.

Here we propose a method using microcarriers functionalized with \acd{3} and
\acd{28} \glspl{mab} for use in T cell expansion cultures. Microcarriers have
historically been used throughout the bioprocess industry for adherent cultures
such as stem cells and \gls{cho} cells, but not with suspension cells such as T
cells\cite{Heathman2015, Sart2011}. The carriers have a macroporous structure
that allows T cells to grow inside and along the surface, providing ample
cell-cell contact for enhanced autocrine and paracrine signaling. Furthermore,
the carriers are composed of gelatin, which is a collagen derivative and
therefore has adhesion domains that are also present within the lymph nodes.
Finally, the 3D surface of the carriers provides a larger contact area for T
cells to interact with the \glspl{mab} relative to beads; this may better
emulate the large contact surface area that occurs between T cells and
\glspl{dc}.

\subsection*{strategies to optimize cell manufacturing}

The \gls{dms} system has a number of parameters that can be optimized, and a
\gls{doe} is an ideal framework to test multiple parameters simultaneously. The
goal of \gls{doe} is to answer a data-driven question with the least number of
resources. It was developed in many non-biological industries throughout the
\nth{20} century such as the automotive and semiconductor industries where
engineers needed to minimize downtime and resource consumption on full-scale
production lines.

% TODO add a bit more about the math of a DOE here

\Glspl{doe} served three purposes in this dissertation. First, we used them as
screening tools, which allowed us to test many input parameters and filter out
the few that likely have the greatest effect on the response. Second, they were
used to make a robust response surface model to predict optimums using
relatively few resources, especially compared to full factorial or
one-factor-at-a-time approaches. Third, we used \glspl{doe} to discover novel
effects and interactions that generated hypotheses that could influence the
directions for future work.

\subsection*{strategies to characterize cell manufacturing}

A number of multiomics strategies exist which can generate rich datasets for T
cells. We will consider several multiomics strategies within this proposal:

\begin{description}
\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
  many bulk (not single cell) cytokine concentrations simultaneously
  in a media sample. Since this only requires media (as opposed to
  destructively measuring cells) we will use this as a longitudinal
  measurement.
\item[Metabolomics:] It is well known that T cells of different
  lineages have different metabolic profiles; for instance memory T
  cells have larger aerobic capacity and fatty acid
  oxidation\cite{Buck2016, van_der_Windt_2012}. We will interrogate
  key metabolic species using \gls{nmr} in collaboration with the
  Edison Lab at the University of Georgia. This will be both a
  longitudinal assay using media samples (since some metabolites may
  be expelled from cells that are indicative of their phenotype) and
  at endpoint where we will lyse the cells and interogate their entire
  metabolome.
\item[Flow and Mass Cytometry:] Flow cytometry using fluorophores has been used
  extensively for immune cell analysis, but has a practical limit of
  approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
  traditional flow cytometry except that it uses heavy-metal \gls{mab}
  conjugates, which has a practical limit of over 50 markers. This will be
  useful in determining precise subpopulations and phenotypes that may be
  influencing responses, especially when one considers that many cell types can
  be defined by more than one marker combination. We will perform this at
  endpoint. While mass cytometry is less practical than simple flow cytometers
  such as the BD Accuri, we may find that only a few markers are required to
  accurately predict performance, and thus this could easily translate to
  industry using relatively cost-effective equipment.
\end{description}

% TODO add a computational section

% TODO add a section explaining causal inference since this is a big part of
% the end of aim 1

\section{Innovation}

\subsection{Innovation}

Several aspects of this work are novel considering the state-of-the-art
technology for T cell manufacturing:

\begin{itemize}
\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
  bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
  the licenses for these techniques is controlled by only a few companies
  (Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
  is more expensive to implement and therefore hinders companies from entering
  the rapidly growing T cell manufacturing arena. Providing an alternative as we
  are doing will add more options, increase competition among both raw material
  and T cell manufacturers, and consequently drive down cell therapy market
  prices and increase innovation throughout the industry.
\item This is the first technology for T cell immunotherapies that selectively
  expands memory T cell populations with greater efficiency relative to
  bead-based expansion Others have demonstrated methods that can achieve greater
  expansion of T cells, but not necessarily specific populations that are known
  to be potent.
\item We propose to optimize our systems using \gls{doe} methodology, which is a
  strategy commonly used in other industries and disciplines but has yet to gain
  wide usage in the development of cell therapies. \Glspl{doe} are advantageous
  as they allow the inspection of multiple parameters simultaneously, allowing
  efficient and comprehensive analysis of the system vs a one-factor-at-a-time
  approach. We believe this method is highly relevant to the development of cell
  therapies, not only for process optimization but also hypotheses generation.
  Of further note, most \textit{in vivo} experiments are not done using a
  \gls{doe}-based approach; however, a \gls{doe} is perfectly natural for a
  large mouse study where one naturally desires to use as few animals as
  possible.
\item The \gls{dms} system is be compatible with static bioreactors such as the
  G-Rex which has been adopted throughout the cell therapy industry. Thus this
  technology can be easily incorporated into existing cell therapy process that
  are performed at scale.
\item We analyzed our system using a multiomics approach, which will enable the
  discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
  has been applied to T cells previously, it has not been done in the context of
  a large \gls{doe}-based model. This approach is aware of the whole design
  space, and thus enables greater understanding of process parameters and their
  effect on cell phenotype.
\end{itemize}

\chapter{aim 1}\label{aim1}

\section{introduction}

The first aim was to develop a microcarrier system that mimics several key
aspects of the \invivo{} lymph node microenvironment. We compared compare this
system to state-of-the-art T cell activation technologies for both expansion
potential and memory cell formation. The governing hypothesis was that
microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
provide superior expansion and memory phenotype compared to state-of-the-art
bead-based T cell expansion technology.

% TODO this doesn't flow that well and is repetitive with what comes above

Microcarriers have been used throughout the bioprocess industry for adherent
cell cultures such as \gls{cho} cells and stem cells, as they are able to
achieve much greater surface area per unit volume than traditional 2D
cultures\cite{Heathman2015, Sart2011}. Adding adhesive \glspl{mab} to the
microcarriers will adapt them for suspension cell cultures such as T cells.
Consequently, the large macroporous structure will allow T cells to cluster more
closely, which in turn will enable better autocrine and paracrine signaling.
Specifically, two cytokines that are secreted by T cells, IL-2 and IL-15, are
known to drive expansion and memory phenotype respectively\cite{Buck2016}.
Therefore, the proposed microcarrier system should enable greater expansion and
better retention of memory phenotype compared to current bead-based methods.

\section{methods}
\subsection{dms functionalization}

\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_flowchart.png}

  \endgroup
  \caption[\gls{dms} Flowchart]{Overview of \gls{dms} manufacturing process.}
  \label{fig:dms_flowchart}
\end{figure*}

Gelatin microcarriers (\gls{cus} or \gls{cug}, GE Healthcare, DG-2001-OO and
DG-0001-OO) were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and
autoclaved. All subsequent steps were done aseptically, and all reactions were
carried out at \SI{20}{\mg\per\ml} carriers at room temperature and agitated
using an orbital shaker with a \SI{3}{\mm} orbit diameter. After autoclaving,
the microcarriers were washed using sterile \gls{pbs} three times in a 10:1
volume ratio. \product{\Gls{snb}}{\thermo}{21217} was dissolved at
approximately \SI{10}{\micro\molar} in sterile ultrapure water, and the true
concentration was then determined using the \gls{haba} assay (see below).
\SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added to carrier suspension and allowed
to react for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the
reaction, the amount of biotin remaining in solution was quantified using the
\gls{haba} assay (see below). The carriers were then washed three times, which
entailed adding sterile \gls{pbs} in a 10:1 volumetric ratio, agitating at
\SI{900}{\rpm} for \SI{10}{\minute}, adding up to a 15:1 volumetric ratio
(relative to reaction volume) of sterile \gls{pbs}, centrifuging at
\SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid back down to the
reaction volume.

To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson
  Immunoresearch}{ 016-000-114} was added and allowed to react for
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant
was taken for the \gls{bca} assay, and the carriers were washed analogously to
the previous wash step to remove the biotin, except two washes were done and the
agitation time was \SI{30}{\minute}. Biotinylated \glspl{mab} against human CD3
\catnum{\bl}{317320} and CD28 \catnum{\bl}{302904} were combined in a 1:1 mass
ratio and added to the carriers at \SI{0.2}{\ug\of{\ab}\per\mg\of{\dms}}. Along
with the \glspl{mab}, sterile \product{\gls{bsa}}{Sigma}{A9576} was added to a
final concentration of \SI{2}{\percent} in order to prevent non-specific binding
of the antibodies to the reaction tubes. \glspl{mab} were allowed to bind to the
carriers for \SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding,
supernatants were sampled to quantify remaining \gls{mab} concentration using an
\product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}. Fully functionalized
\glspl{dms} were washed in sterile \gls{pbs} analogous to the previous washing
step to remove excess \gls{stp}. They were washed once again in the cell culture
media to be used for the T cell expansion.

The concentration of the final \gls{dms} suspension was found by taking a
\SI{50}{\uL} sample, plating in a well, and imaging the entire well. The image
was then manually counted to obtain a concentration. Surface area for
\si{\ab\per\um\squared} was calculated using the properties for \gls{cus} and
\gls{cug} as given by the manufacturer {Table X}.

%TODO this bit belongs in the next aim
% In the case of the \gls{doe} experiment where
% variable mAb surface density was utilized, the anti-CD3/anti-CD28 mAb mixture
% was further combined with a biotinylated isotype control to reduce the overall
% fraction of targeted mAbs (for example the 60\% mAb surface density corresponded
% to 3 mass parts anti-CD3, 3 mass parts anti-CD8, and 4 mass parts isotype
% control).

\subsection{dms quality control assays}

Biotin was quantified using the \product{\gls{haba} assay}{Sigma}{H2153-1VL}. In
the case of quantifying \gls{snb} prior to adding it to the microcarriers, the
sample volume was quenched in a 1:1 volumetric ratio with \SI{1}{\molar} NaOH
and allowed to react for \SI{1}{\minute} in order to prevent the reactive ester
linkages from binding to the avidin proteins in the \gls{haba}/avidin premix.
All quantifications of \gls{haba} were performed on an Eppendorf D30
Spectrophotometer using \product{\SI{70}{\ul} cuvettes}{BrandTech}{759200}. The
extinction coefficient at \SI{500}{\nm} for \gls{haba}/avidin was assumed to be
\SI{34000}{\per\cm\per\molar}.

\gls{stp} binding to the carriers was quantified indirectly using a
\product{\gls{bca} kit }{\thermo}{23227} according to the manufacturer’s
instructions, with the exception that the standard curve was made with known
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at
\SI{592}{\nm} was quantified using a Biotek plate reader.

\Gls{mab} binding to the microcarriers was quantified indirectly using an
\gls{elisa} assay per the manufacturer’s instructions, with the exception that
the same antibodies used to coat the carriers were used as the standard for the
\gls{elisa} standard curve.

Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
quantified indirectly using \product{FITC-biotin}{\thermo}{B10570}.
Briefly, \SI{400}{\pmol\per\ml} FITC-biotin were added to \gls{stp}-coated
carriers and allowed to react for \SI{20}{\minute} at room temperature under
constant agitation. The supernatant was quantified against a standard curve of
FITC-biotin using a Biotek plate reader.

\Gls{stp} binding was verified after the \gls{stp}-binding step visually by
adding biotin-FITC to the \gls{stp}-coated \glspl{dms}, resuspending in
\SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
 microscope}{Zeiss}{Z.1}. \Gls{mab} binding was verified visually by first
staining with \product{\anti{gls{igg}}-FITC}{\bl}{406001}, incubating for
\SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope.

\subsection{t cell culture}

% TODO verify countess product number
Cryopreserved primary human T cells were either obtained as sorted
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection
\product{\cdp{3} \gls{macs} kit}{\miltenyi}{130-096-535}. T cells were activated
using \glspl{dms} or \product{\SI{3.5}{\um} CD3/CD28 magnetic
  beads}{\miltenyi}{130-091-441}. In the case of beads, T cells were activated
at the manufacturer recommended cell:bead ratio of 2:1. In the case of
\glspl{dms}, cells were activated using \SI{2500}{\dms\per\cm\squared} unless
otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
\product{OpTmizer}{\thermo}{A1048501} or
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02}. Cell
cultures were expanded for \SI{14}{\day} as counted from the time of initial
seeding and activation. Cell counts and viability were assessed using
\product{trypan blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom
  Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo
  Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day}
depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose
threshold. Media glucose was measured using a \product{GlucCell glucose
  meter}{Chemglass}{CLS-1322-02}.

% TODO this belongs in aim 2
% In order to remove \glspl{dms} from
% culture, collagenase D (Sigma Aldrich) was sterile filtered in culture media and
% added to a final concentration of \SI{50}{\ug\per\ml} during media addition.

Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
\product{\acd{45}-\gls{af647}}{\bl}{368538}, incubating for \SI{1}{\hour}, and
imaging on a spinning disk confocal microscope.

\subsection{chemotaxis assay}

% TODO not sure about the transwell product number
Migratory function was assayed using a transwell chemotaxis assay as previously
described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
basolateral chamber loaded with \SI{600}{\ul} media and 0, 250, or
\SI{1000}{\ng\per\mL} \product{CCL21}{Peprotech}{250-13}. The plate was
incubated for \SI{4}{\hour} after loading, and the basolateral chamber of each
transwell was quantified for total cells using \product{countbright
  beads}{\thermo}{C36950}. The final readout was normalized using the
\SI{0}{\ng\per\mL} concentration as background.

\subsection{degranulation assay}

Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
were incubated with \num{1.5e5} target cells consisting of either \product{K562
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}.
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar} \product{monensin}{eBioscience}{
00-4505-51}, and \SI{1}{\ug\per\ml} \product{\acd{28}}{eBioscience}{302914} (all
functional grade \glspl{mab}) with \SI{250}{\ul} total volume. After
\SI{4}{\hour} incubation at \SI{37}{\degreeCelsius}, cells were stained for CD3,
CD4, and CD107a and analyzed on a BD LSR Fortessa. Readout was calculated as the
percent \cdp{107a} cells of the total \cdp{8} fraction.

\subsection{car expression}

\gls{car} expression was quantified as previously described\cite{Zheng2012}.
Briefly, cells were washed once and stained with \product{biotinylated
  \gls{ptnl}}{\thermo}{29997}. After a subsequent wash, cells were stained with
\product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again, and analyzed on a
BD Accuri. Readout was percent \gls{pe}+ cells as compared to secondary controls
(\gls{pe}-\gls{stp} with no \gls{ptnl}).

\subsection{car plasmid and lentiviral transduction}

The anti-CD19-CD8-CD137-CD3z \gls{car} with the EF1$\upalpha$
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core.
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the
Cincinnati Children's Hospital Medical Center Viral Vector Core. To transduce
primary human T cells, \product{retronectin}{Takara}{T100A} was coated onto
non-TC treated 96 well plates and used to immobilize lentiviral vector particles
according to the manufacturer's instructions. Briefly, retronectin solution was
adsorbed overnight at \SI{4}{\degreeCelsius} and blocked the next day using
\gls{bsa}. Prior to transduction, lentiviral supernatant was spinoculated at
\SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}. T cells were
activated in 96 well plates using beads or \glspl{dms} for \SI{24}{\hour}, and
then cells and beads/\glspl{dms} were transferred onto lentiviral vector coated
plates and incubated for another \SI{24}{\hour}. Cells and beads/\glspl{dms}
were removed from the retronectin plates using vigorous pipetting and
transferred to another 96 well plate wherein expansion continued.

\subsection{statistical analysis}

For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
significance was assessed using the \inlinecode{stat\_compare\_means} function
with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R.
For 2-way \gls{anova} analysis, the significance of main and interaction effects
was determined using the car library in R.

% TODO not all of this stuff applied to my regressions
For least-squares linear regression, statistical significance was evaluated the
\inlinecode{lm} function in R. Stepwise regression models were obtained using
the \inlinecode{stepAIC} function from the \inlinecode{MASS} package with
forward and reverse stepping. All results with categorical variables are
reported relative to baseline reference. Each linear regression was assessed for
validity using residual plots (to assess constant variance and independence
assumptions), QQplots and Shapiro-Wilk normality test (to assess normality
assumptions), Box-Cox plots (to assess need for power transformations), and
lack-of-fit tests where replicates were present (to assess model fit in the
context of pure error). Statistical significance was evaluated at $\upalpha$ =
0.05.

% TODO add meta-analysis section

\section{results}

\subsection{DMSs can be fabricated in a controlled manner}

\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_coating.png}
  \phantomsubcaption\label{fig:stp_carrier_fitc}
  \phantomsubcaption\label{fig:mab_carrier_fitc}
  \phantomsubcaption\label{fig:cug_vs_cus}
  \phantomsubcaption\label{fig:biotin_coating}
  \phantomsubcaption\label{fig:stp_coating}
  \phantomsubcaption\label{fig:mab_coating}

  \endgroup
  \caption[\gls{dms} Coating]
  {\gls{dms} functionalization results.
    \subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
      treated with biotin-FITC and imaged using a lightsheet microscope.}
    \subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
      \glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
      lightsheet microscope.} \subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
      density on either \gls{cus} or \gls{cug} microcarriers. Surface density
      was estimated using the properties in~\cref{tab:carrier_props}} Total
    binding curve of \subcap{fig:biotin_coating}{biotin},
    \subcap{fig:stp_coating}{\gls{stp}}, and
    \subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added. }
  \label{fig:dms_flowchart}
\end{figure*}

% TODO these caption titles suck
% TODO combine this DOE figure into one interaction plot
\begin{table}[!h] \centering
  \caption{Properties of the microcarriers used}
  \label{tab:carrier_props}
  \input{../tables/carrier_properties.tex}
\end{table}

\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_qc.png}
  \phantomsubcaption\label{fig:dms_qc_doe}
  \phantomsubcaption\label{fig:dms_qc_ph}
  \phantomsubcaption\label{fig:dms_qc_washes}
  \phantomsubcaption\label{fig:dms_snb_decay_curves}

  \endgroup
  \caption[\gls{dms} Quality Control]
  {\gls{dms} quality control investigation and development
    \subcap{fig:dms_qc_doe}{\gls{doe} investigating the effect of initial mass
      of microcarriers, reaction temperature, and biotin concentration on
      biotin attachment.}
    \subcap{fig:dms_qc_ph}{Effect of reaction ph on biotin attachment.}
    \subcap{fig:dms_qc_washes}{effect of post-autoclave washing of the
      microcarriers on biotin attachment.}
    \subcap{fig:dms_snb_decay_curves}{Hydrolysis curves of \gls{snb} in
      \gls{pbs} of differing pH.}
    All statistical tests where p-values are noted are given by two-tailed t
    tests.
  }
  \label{fig:dms_flowchart}
\end{figure*}

\begin{table}[!h] \centering
  \caption{Properties of the microcarriers used}
  \label{tab:carrier_props}
  \input{../tables/carrier_properties.tex}
\end{table}

\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_timing.png}
  \phantomsubcaption\label{fig:dms_biotin_rxn_mass}
  \phantomsubcaption\label{fig:dms_biotin_rxn_frac}
  \phantomsubcaption\label{fig:dms_stp_per_time}

  \endgroup
  \caption[\gls{dms} Reaction timing]
  {Reaction kinetics for microcarrier functionalization.
    \subcap{fig:dms_biotin_rxn_mass}{Biotin mass bound per time}
    \subcap{fig:dms_biotin_rxn_frac}{Fraction of input biotin bound per time}
    \subcap{fig:dms_stp_per_time}{\Gls{stp} bound per time.}
  }
  \label{fig:dms_flowchart}
\end{figure*}

\subsection{DMSs can efficiently expand T cells compared to beads}

% TODO make sure the day on these is correct
\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/cells_on_dms.png}
  \phantomsubcaption\label{fig:dms_cells_phase}
  \phantomsubcaption\label{fig:dms_cells_fluor}

  \endgroup
  \caption[T cells growing on \glspl{dms}]
  {Cells grow in tight clusters in and around functionalized \gls{dms}.
    \subcap{fig:dms_cells_phase}{Phase-contrast image of T cells growing on
      \glspl{dms} on day 7}
    \subcap{fig:dms_cells_fluor}{Confocal images of T cells in varying z-planes
      growing on \glspl{dms} on day 9. \Glspl{dms} were stained using
      \gls{stppe} (red) and T cells were stained using \acd{45}-\gls{af647}.}
  }
  \label{fig:dms_cells}
\end{figure*}

% TODO add this to the methods section
% TODO double check the timing of this experiment (it might not be day 14)
% TODO provide the regression results and coefficients from this
\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_inside.png}
  \phantomsubcaption\label{fig:dms_inside_bf}
  \phantomsubcaption\label{fig:dms_inside_regression}

  \endgroup
  \caption[A subset of T cells grow in interior of \glspl{dms}]
  {A percentage of T cells grow in the interior of \glspl{dms}.
    \subcap{fig:dms_inside_bf}{T cells stained dark with MTT after growing on
      either coated or uncoated \glspl{dms} for 14 days visualized with 
      brightfield microscope.}
    \subcap{fig:dms_inside_regression}{Linear regression performed on T cell
      percentages harvested on the interior of the \glspl{dms} vs the initial
      starting cell density.}
  }
  \label{fig:dms_inside}
\end{figure*}


\subsection{DMSs lead to greater expansion and memory and CD4+ phenotypes}

\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_vs_bead_expansion.png}
  \phantomsubcaption\label{fig:dms_exp_fold_change}
  \phantomsubcaption\label{fig:dms_exp_mem}
  \phantomsubcaption\label{fig:dms_exp_cd4}
  \phantomsubcaption\label{fig:dms_exp_mem4}
  \phantomsubcaption\label{fig:dms_exp_mem8}

  \endgroup
  \caption[\gls{dms} vs bead expansion]
  {\gls{dms} lead to superior expansion of T cells compared to beads across
    multiple donors.
    \subcap{fig:dms_exp_fold_change}{Longitudinal fold change of T cells grown
      using either \glspl{dms} or beads. Significance was evaulated using t
      tests at each timepoint}
    Fold change of subpopulations expanded using either \gls{dms} or beads at
    day 14, including 
    \subcap{fig:dms_exp_mem}{\ptmem{} cells},
    \subcap{fig:dms_exp_cd4}{\pth{} cells},
    \subcap{fig:dms_exp_mem4}{\ptmemh{} cells}, and
    \subcap{fig:dms_exp_mem8}{\ptmemk{} cells}. \sigkey{}
  }
  \label{fig:dms_exp}
\end{figure*}

% TODO this figure has weird proportions
\begin{figure*}[ht!]
  \begingroup

  \includegraphics{../figures/dms_phenotypes.png}
  \phantomsubcaption\label{fig:dms_phenotype_mem}
  \phantomsubcaption\label{fig:dms_phenotype_cd4}

  \endgroup
  \caption[Representative flow plots of \ptmem{} and \pth{} T cells]
  {Representative flow plots of \ptmem{} and \pth{} T cells at day 14 of
    expansion using either beads or \glspl{dms}. For three representative donor
    samples, phenotypes are shown for \subcap{fig:dms_phenotype_mem}{\ptmem{}}
    and \subcap{fig:dms_phenotype_cd4}{\pth}. Each population was also gated on
    \cdp{3} T cells.
  }
  \label{fig:dms_phenotype}
\end{figure*}

\subsection*{DMSs can be used to produce functional CAR T cells}

% protein L + degranulation + migration

\subsection{DMSs do not leave antibodies attached to cell product}

% non-leaching figure

\subsection{DMSs consistently outperform bead-based expansion compared to
beads in a variety of conditions}

% TODO these tables have extra crap in them that I don't need to show
\begin{table}[!h] \centering
  \caption{Causal Inference on treatment variables only}
  \label{tab:ci_treat}
  \input{../tables/causal_inference_treat.tex}
\end{table}

\begin{table}[!h] \centering
  \caption{Causal Inference on treatment variables and control variables}
  \label{tab:ci_controlled}
  \input{../tables/causal_inference_control.tex}
\end{table}

% meta-analysis regression effect sizes

\section{discussion}

\chapter{aim 2}\label{aim2}

\section{introduction}
\section{methods}
\section{results}
\section{discussion}

\chapter{aim 3}\label{aim3}

\section{introduction}
\section{methods}
\section{results}
\section{discussion}

\chapter{conclusions and future work}\label{conclusions}
\section{conclusions}
\section{future work}

\onecolumn
\clearpage

% TODO some people put appendices here....not sure if I need to

\chapter{References}
\renewcommand{\section}[2]{} % noop the original bib section header

\bibliography{references}

\bibliographystyle{naturemag}

\end{document}
