ADD a bunch of background stuff
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@ -207,4 +207,24 @@
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\newblock \emph{\bibinfo{journal}{{BMC} Proceedings}}
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\newblock \emph{\bibinfo{journal}{{BMC} Proceedings}}
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\textbf{\bibinfo{volume}{5}} (\bibinfo{year}{2011}).
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\textbf{\bibinfo{volume}{5}} (\bibinfo{year}{2011}).
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\bibitem{Buck2016}
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\bibinfo{author}{Buck, M.~D.} \emph{et~al.}
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\newblock \bibinfo{title}{{Mitochondrial Dynamics Controls T Cell Fate through
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Metabolic Programming}}.
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\newblock \emph{\bibinfo{journal}{Cell}} \textbf{\bibinfo{volume}{166}},
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\bibinfo{pages}{114} (\bibinfo{year}{2016}).
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\bibitem{van_der_Windt_2012}
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\bibinfo{author}{van~der Windt, G.~J.} \emph{et~al.}
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\newblock \bibinfo{title}{Mitochondrial respiratory capacity is a critical
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regulator of {CD}8+ t cell memory development}.
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\newblock \emph{\bibinfo{journal}{Immunity}} \textbf{\bibinfo{volume}{36}},
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\bibinfo{pages}{68--78} (\bibinfo{year}{2012}).
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\bibitem{Spitzer2016}
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\bibinfo{author}{Spitzer, M.~H.} \& \bibinfo{author}{Nolan, G.~P.}
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\newblock \bibinfo{title}{Mass cytometry: Single cells, many features}.
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\newblock \emph{\bibinfo{journal}{Cell}} \textbf{\bibinfo{volume}{165}},
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\bibinfo{pages}{780--791} (\bibinfo{year}{2016}).
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\end{thebibliography}
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\end{thebibliography}
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174
tex/thesis.tex
174
tex/thesis.tex
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@ -54,6 +54,10 @@
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\newacronym{pdms}{PDMS}{polydimethylsiloxane}
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\newacronym{pdms}{PDMS}{polydimethylsiloxane}
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\newacronym{dc}{DC}{dendritic cell}
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\newacronym{dc}{DC}{dendritic cell}
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\newacronym{il2}{IL2}{interleukin 2}
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\newacronym{il2}{IL2}{interleukin 2}
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\newacronym{apc}{APC}{antigen presenting cell}
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\newacronym{mhc}{MHC}{major histocompatibility complex}
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\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
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\newacronym{nmr}{NMR}{nuclear magnetic resonance}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% my commands
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% my commands
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@ -257,6 +261,9 @@ quality in an industrial setting.
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\section*{overview}
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\section*{overview}
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% TODO this is basically the same as the first part of the backgound, I guess I
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% can just trim it down
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T cell-based immunotherapies have received great interest from clinicians and
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T cell-based immunotherapies have received great interest from clinicians and
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industry due to their potential to treat, and often cure, cancer and other
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industry due to their potential to treat, and often cure, cancer and other
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diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
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diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
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@ -405,20 +412,181 @@ conclusions in Chapter~\ref{conclusions}.
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\chapter{background and significance}\label{background}
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\chapter{background and significance}\label{background}
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\section*{background}
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\section*{background}
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% TODO break this apart into mfg tech and T cell phenotypes/quality
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% TODO consider adding a separate section on microcarriers and their use in
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% bioprocess
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% TODO add stuff about T cell licensing
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\subsection*{current T cell manufacturing technologies}
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\subsection*{current T cell manufacturing technologies}
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bla bla
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\Gls{car} T cell therapy has received great interest from both academia and
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industry due to its potential to treat cancer and other
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diseases\cite{Fesnak2016, Rosenberg2015}. In 2017, Novartis and Kite Pharma
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acquired FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
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two \gls{car} T cell therapies against B cell malignancies. Despite these
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successes, \gls{car} T cell therapies are constrained by an expensive and
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difficult-to-scale manufacturing process\cite{Roddie2019, Dwarshuis2017}.
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Of critical concern, state-of-the-art manufacturing techniques focus only on
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Signal 1 and Signal 2-based activation via anti-CD3 and anti-CD28 \glspl{mab},
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typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
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nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
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tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
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strategies overlook many of the signaling components present in the secondary
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lymphoid organs where T cells normally expand. Typically, T cells are activated
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under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
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peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
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signals. These close quarters allow for efficient autocrine/paracrine signaling
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among the expanding T cells, which secrete IL2 and other cytokines to assist
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their own growth. Additionally, the lymphoid tissues are comprised of \gls{ecm}
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components such as collagen, which provide signals to upregulate proliferation,
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cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
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Boisvert2007, Ben-Horin2004}.
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A variety of solutions have been proposed to make the T cell expansion process
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more physiological. One strategy is to use modified feeder cell cultures to
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provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
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While this has the theoretical capacity to mimic several key components of the
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lymph node, it is hard to reproduce on a large scale due to the complexity and
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inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
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Others have proposed biomaterials-based solutions to circumvent this problem,
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including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
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Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
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beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
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beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
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large interfacial contact area, 3D-structure, or soft surfaces T cells normally
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experience \textit{in vivo}. While these have been shown to provide superior
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expansion compared to traditional microbeads, no method has been able to show
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preferential expansion of functional memory and CD4 T cell populations.
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Generally, T cells with a lower differentiation state such as memory cells have
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been shown to provide superior anti-tumor potency, presumably due to their
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higher potential to replicate, migrate, and engraft, leading to a long-term,
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durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
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Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
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cytokine release properties and ability to resist exhaustion\cite{Wang2018,
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Yang2017}, and no method exists to preferentially expand the CD4 population
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compared to state-of-the-art systems.
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Here we propose a method using microcarriers functionalized with anti-CD3 and
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anti-CD28 \glspl{mab} for use in T cell expansion cultures. Microcarriers have
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historically been used throughout the bioprocess industry for adherent cultures
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such as stem cells and \gls{cho} cells, but not with suspension cells such as T
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cells\cite{Heathman2015, Sart2011}. The carriers have a macroporous structure
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that allows T cells to grow inside and along the surface, providing ample
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cell-cell contact for enhanced autocrine and paracrine signaling. Furthermore,
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the carriers are composed of gelatin, which is a collagen derivative and
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therefore has adhesion domains that are also present within the lymph nodes.
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Finally, the 3D surface of the carriers provides a larger contact area for T
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cells to interact with the \glspl{mab} relative to beads; this may better
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emulate the large contact surface area that occurs between T cells and
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\glspl{dc}.
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\subsection*{strategies to optimize cell manufacturing}
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\subsection*{strategies to optimize cell manufacturing}
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bla bla
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The \gls{dms} system has a number of parameters that can be optimized, and a
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\gls{doe} is an ideal framework to test multiple parameters simultaneously. The
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goal of \gls{doe} is to answer a data-driven question with the least number of
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resources. It was developed in many non-biological industries throughout the
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\nth{20} century such as the automotive and semiconductor industries where
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engineers needed to minimize downtime and resource consumption on full-scale
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production lines.
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% TODO add a bit more about the math of a DOE here
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\Glspl{doe} served three purposes in this dissertation. First, we used them as
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screening tools, which allowed us to test many input parameters and filter out
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the few that likely have the greatest effect on the response. Second, they were
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used to make a robust response surface model to predict optimums using
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relatively few resources, especially compared to full factorial or
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one-factor-at-a-time approaches. Third, we used \glspl{doe} to discover novel
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effects and interactions that generated hypotheses that could influence the
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directions for future work.
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\subsection*{strategies to characterize cell manufacturing}
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\subsection*{strategies to characterize cell manufacturing}
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bla bla
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A number of multiomics strategies exist which can generate rich datasets for T
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cells. We will consider several multiomics strategies within this proposal:
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\begin{description}
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\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
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many bulk (not single cell) cytokine concentrations simultaneously
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in a media sample. Since this only requires media (as opposed to
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destructively measuring cells) we will use this as a longitudinal
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measurement.
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\item[Metabolomics:] It is well known that T cells of different
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lineages have different metabolic profiles; for instance memory T
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cells have larger aerobic capacity and fatty acid
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oxidation\cite{Buck2016, van_der_Windt_2012}. We will interrogate
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key metabolic species using \gls{nmr} in collaboration with the
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Edison Lab at the University of Georgia. This will be both a
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longitudinal assay using media samples (since some metabolites may
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be expelled from cells that are indicative of their phenotype) and
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at endpoint where we will lyse the cells and interogate their entire
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metabolome.
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\item[Flow and Mass Cytometry:] Flow cytometry using fluorophores has been used
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extensively for immune cell analysis, but has a practical limit of
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approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
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traditional flow cytometry except that it uses heavy-metal \gls{mab}
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conjugates, which has a practical limit of over 50 markers. This will be
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useful in determining precise subpopulations and phenotypes that may be
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influencing responses, especially when one considers that many cell types can
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be defined by more than one marker combination. We will perform this at
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endpoint. While mass cytometry is less practical than simple flow cytometers
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such as the BD Accuri, we may find that only a few markers are required to
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accurately predict performance, and thus this could easily translate to
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industry using relatively cost-effective equipment.
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\end{description}
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% TODO add a computational section
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% TODO add a section explaining causal inference since this is a big part of
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% the end of aim 1
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\section{Innovation}
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\section{Innovation}
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\subsection{Innovation}
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Several aspects of this work are novel considering the state-of-the-art
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technology for T cell manufacturing:
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\begin{itemize}
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\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
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bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
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the licenses for these techniques is controlled by only a few companies
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(Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
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is more expensive to implement and therefore hinders companies from entering
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the rapidly growing T cell manufacturing arena. Providing an alternative as we
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are doing will add more options, increase competition among both raw material
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and T cell manufacturers, and consequently drive down cell therapy market
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prices and increase innovation throughout the industry.
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\item This is the first technology for T cell immunotherapies that selectively
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expands memory T cell populations with greater efficiency relative to
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bead-based expansion Others have demonstrated methods that can achieve greater
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expansion of T cells, but not necessarily specific populations that are known
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to be potent.
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\item We propose to optimize our systems using \gls{doe} methodology, which is a
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strategy commonly used in other industries and disciplines but has yet to gain
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wide usage in the development of cell therapies. \Glspl{doe} are advantageous
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as they allow the inspection of multiple parameters simultaneously, allowing
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efficient and comprehensive analysis of the system vs a one-factor-at-a-time
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approach. We believe this method is highly relevant to the development of cell
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therapies, not only for process optimization but also hypotheses generation.
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Of further note, most \textit{in vivo} experiments are not done using a
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\gls{doe}-based approach; however, a \gls{doe} is perfectly natural for a
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large mouse study where one naturally desires to use as few animals as
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possible.
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\item The \gls{dms} system is be compatible with static bioreactors such as the
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G-Rex which has been adopted throughout the cell therapy industry. Thus this
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technology can be easily incorporated into existing cell therapy process that
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are performed at scale.
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\item We analyzed our system using a multiomics approach, which will enable the
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discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
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has been applied to T cells previously, it has not been done in the context of
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a large \gls{doe}-based model. This approach is aware of the whole design
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space, and thus enables greater understanding of process parameters and their
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effect on cell phenotype.
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\end{itemize}
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\chapter{aim 1}\label{aim1}
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\chapter{aim 1}\label{aim1}
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\section{introduction}
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\section{introduction}
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