ADD experiments for finding the mechanism
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@ -2742,6 +2742,32 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
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publisher = {Elsevier {BV}},
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publisher = {Elsevier {BV}},
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}
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}
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@Article{Wang1984,
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author = {James C. Wang},
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journal = {Journal of Materials Science},
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title = {Young{\textquotesingle}s modulus of porous materials},
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year = {1984},
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month = {mar},
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number = {3},
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pages = {801--808},
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volume = {19},
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doi = {10.1007/bf00540451},
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publisher = {Springer Science and Business Media {LLC}},
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}
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@Article{Ju2017,
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author = {Lining Ju and Cheng Zhu},
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journal = {Biophysical Journal},
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title = {Benchmarks of Biomembrane Force Probe Spring Constant Models},
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year = {2017},
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month = {dec},
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number = {12},
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pages = {2842--2845},
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volume = {113},
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doi = {10.1016/j.bpj.2017.10.013},
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publisher = {Elsevier {BV}},
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}
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@Comment{jabref-meta: databaseType:bibtex;}
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@Comment{jabref-meta: databaseType:bibtex;}
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@Comment{jabref-meta: grouping:
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@Comment{jabref-meta: grouping:
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100
tex/thesis.tex
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tex/thesis.tex
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@ -95,7 +95,7 @@
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{\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase}
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{\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase}
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\titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap}
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\titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap}
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\titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap}
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\titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap}
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\titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap}
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\titleformat{\subsubsection}[runin]{\bfseries}{}{0pt}{\titlecap}
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\setlist[description]{font=$\bullet$~\textbf\normalfont}
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\setlist[description]{font=$\bullet$~\textbf\normalfont}
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@ -4574,16 +4574,94 @@ animal-origin concerns.
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Despite the improved outcomes in terms of expansion and phenotype relative to
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Despite the improved outcomes in terms of expansion and phenotype relative to
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beads, we don't have a good understanding of why they \gls{dms} platform works
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beads, we don't have a good understanding of why they \gls{dms} platform works
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as well as it does. Several broad areas remain to be investigated, including the
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as well as it does. The following are several plausible hypotheses and a
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role of the increased cytokine output (including \il{15} which was explored to
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proposed experiment for testing them:
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some extent in this work), the role of cells on the interior of the \gls{dms}
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relative to those outside the \gls{dms}, and the role of the physical surface
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\subsubsection{Cytokine Cross-talk}
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properties of the \gls{dms} (including the morphology and the stiffness). One
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plausible hypothesis to be tested is that the bumpy microcarrier surface is more
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As hypothesized in the beginning of this work, the \gls{dms} may derive their
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like that of an \gls{apc}, which enhances immunological synapse formation and
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advantage through increased cytokine cross-talk. While this work found that
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thus activation. Another related hypothesis is that the signal strength is
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blocking \il{15} did not lead to differences in outcome, other cytokines could
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lower than the beads, which leads to increased proliferation, less exhaustion,
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be explored in a similar vein.
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and by extension more memory.
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An efficient test of this hypothesis would be to simply incubate T cells grown
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with either bead or \glspl{dms} with a cocktail of \glspl{mab} each feeding
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cycle that target the cytokines seen in \cref{fig:doe_luminex}, assuming that at
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least a few of the targeted cytokines will cause a difference. The experiment
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should be sized appropriately such that the second order interaction effect can
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be resolved (that is, the effect of adding the cocktail conditional on the
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activation method). In these terms, we hypothesize that the growth and phenotype
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will be more similar between the beads and \glspl{dms} when the cocktail is
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added, while the \gls{dms} will have better expansion and phenotype when the
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cocktail is not added. If this experiment shows any effects, the cytokines
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responsible can be resolved by testing individually (or in small pools).
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One caveat with this approach is that it assumes that the \gls{mab} cocktail
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will completely quench their target cytokines between each feed cycle. This assumption
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can be tested by running luminex with each cocktail addition. If a given
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cytokine is undetectable, this indicates that the blocking \gls{mab} completely
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quenched all target cytokine at the time of addition and in the time between
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feeding cycles.
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\subsubsection{Interior cell phenotype}
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Unlike the beads, the \glspl{dms} have interior and exterior surfaces. We
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demonstrated that some T cell expand on the interior of the \glspl{dms}, and is
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plausible that these cells are phenotypically different than those growing on
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the exterior or completely detached from the microcarriers, and that this leads
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to an asymmetric cytokine cross-talk which accounts for the population-level
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differences seen in comparison to the beads.
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Experimentally, the first step involves separating the \glspl{dms} from the
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loosely or non-adhered T cells and digesting the \glspl{dms} wth \gls{cold}
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(concentrations of \SI{10}{\ug\per\ml} will completely the \glspl{dms} within
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\SIrange{30}{45}{\min}) isolate the interior T cells. Unfortunately, only
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\SIrange{10}{20}{\percent} of all cells will be on the interior, so the interior
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group may only have cells on the order of \si{1e3} to \si{1e4} for analysis. A
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good first pass experiment would be to analyze both populations with a T cell
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differentiation/activation state flow panel first (since flow cytometry is
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relatively cheap and doesn't require a large number of cells) to simply
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establish if the two groups are different phenotypes or are in a different state
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of activation. From there, more in-depth analysis using \gls{cytof} or another
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high-dimensionality method may be used to evaluate differential cytokine
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expression.
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\subsubsection{Antibody Surface Density}
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While our \gls{doe} experiments showed a relationship between activating
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\gls{mab} density and number of cells, we don't know how the \gls{mab} surface
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density of the \gls{dms} compares to that of the beads. In all likelihood, the
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\gls{mab} density on the \gls{dms} surface is lower (given the number of total
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binding sites on \gls{stp} and the number of \glspl{mab} that actually bind)
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which may lead to differences in performance\cite{Lozza2008}.
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% TODO make sure this actually is "below"
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Before attempting this experiment, it will be vital to improve the \gls{dms}
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manufacturing process such that \gls{mab} binding is predictable and
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reproducible (see below). Once this is established, we can then determine the
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amount of \glspl{mab} that bind to the beads, which could be performed much like
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the \gls{mab} binding step is quantified in the \gls{dms} process (eg with
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ELISA, \cref{fig:dms_flowchart}). Knowing this, we can vary the
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\gls{mab} surface density for both the bead and the \glspl{dms} using a dummy
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\gls{mab} as done previously with the \gls{doe} experiments in \cref{aim2a}.
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Using varying surface densities that are matched per-area between the beads and
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\glspl{dms} we can then activate T cells and assess their growth/phenotype as a
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function of surface density and the presentation method.
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\subsubsection{Surface Stiffness}
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The beads and \gls{dms} are composed of different materials: iron/polymer in the
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former case and cross-linked gelatin in the latter. These materials likely have
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different stiffnesses, and stiffness could play a role in T cell
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activation\cite{Lambert2017}.
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This hypotheses will be difficult to test directly, so it is advised to
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eliminate other hypothesis before proceeding here. Direct testing could be
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performed using a force probe to determine the Young's modulus of each
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material\cite{Ju2017}. Since the microcarriers are porous and the cells will be
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interacting with the bulk material itself, the void fraction and pore size will
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need to be taken into account to find the bulk material properties of the
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cross-linked gelatin\cite{Wang1984}.
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\subsection{Additional Ligands and Signals on the DMSs}
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\subsection{Additional Ligands and Signals on the DMSs}
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