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\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}

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  \Large{
    \textbf{
      Optimizing T Cell Manufacturing and Quality Using Functionalized
      Degradable Microscaffolds
    }
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    #1 \\
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\begin{document}

\begin{titlepage}
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    \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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\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-CD3 and CD28 \glspl{mab},
usually mounted on magnetic beads. This method fails to recapitulate many key
signals found \textit{in vivo} 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 \textit{in vivo}. 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.

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\tableofcontents

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\listoffigures

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\chapter{introduction}

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 anti-CD3 and anti-CD28 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 in vivo. 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 in vivo 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 in vivo. 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 anti-CD3 and anti-CD28 \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 in vivo 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*{overview}

Insert overview here

\section*{hypothesis}

Insert hypothesis here

\section*{specific aims}
\subsection*{aim 1}
\subsection*{aim 2}
\subsection*{aim 3}

\section*{outline}

\subsection*{Aim 1}

Aim 1

\subsection*{Aim 2}

Aim 2

\subsection*{Aim 3}

Aim 3

\chapter{background and significance}
\subsection*{background}
\subsection*{current T cell manufacturing technologies}

bla bla

\section*{strategies to optimize cell manufacturing}

bla bla

\subsection*{strategies to characterize cell manufacturing}

bla bla

\section{Innovation}

\chapter{aim 1}

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

\chapter{Aim 2}

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

\chapter{Aim 3}

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

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

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% 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}

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