FIX rows of dms coating figure and ADD extra coating data
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tex/thesis.tex
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tex/thesis.tex
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@ -781,9 +781,6 @@ glioblastoma, neuroblastoma, and prostate cancer, breast cancer, non-small-cell
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lung cancer, and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To
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lung cancer, and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To
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date, there are almost 1000 clinical trials using \gls{car} T cells.
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date, there are almost 1000 clinical trials using \gls{car} T cells.
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% TODO there are other T cells like virus-specific T cells and gd T cells, not
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% that they matter...
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\subsection{Scaling T Cell Expansion}
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\subsection{Scaling T Cell Expansion}
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In order to scale T cell therapies to meet clinical demands, automation and
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In order to scale T cell therapies to meet clinical demands, automation and
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@ -1098,11 +1095,6 @@ against Fas-mediated apoptosis in the presence of collagen I\cite{Aoudjit2000,
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production \invitro{} when T cells derived from human \glspl{pbmc} are
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production \invitro{} when T cells derived from human \glspl{pbmc} are
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stimulated in the presence of collagen I\cite{Boisvert2007}.
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stimulated in the presence of collagen I\cite{Boisvert2007}.
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% TODO there are other receptors I could name here that were not explored Other
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% integrins that interact with the environment include a4b1, which interacts
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% with fibronectin and has been shown to lead to higher IL2 production (Iwata et
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% al 2000).
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\subsection{The Role of IL15 in Memory T Cell Proliferation}
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\subsection{The Role of IL15 in Memory T Cell Proliferation}
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\il{15} is a cytokine that is involved with the proliferation and homeostasis of
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\il{15} is a cytokine that is involved with the proliferation and homeostasis of
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@ -1514,7 +1506,6 @@ In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
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\subsection{Quantifying Cells on DMS Interior}
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\subsection{Quantifying Cells on DMS Interior}
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% TODO add a product number to MTT (if I can find it)
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To visualize T cells on the interior of the \glspl{dms}, we stained them with
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To visualize T cells on the interior of the \glspl{dms}, we stained them with
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\gls{mtt}. \glspl{dms} with attached and loosely attached cells were sampled as
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\gls{mtt}. \glspl{dms} with attached and loosely attached cells were sampled as
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desired and filtered through a \SI{40}{\um} cell strainer. While still in the
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desired and filtered through a \SI{40}{\um} cell strainer. While still in the
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@ -1568,7 +1559,6 @@ directed.
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\subsection{Chemotaxis Assay}
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\subsection{Chemotaxis Assay}
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% TODO not sure about the transwell product number
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Migratory function was assayed using a transwell chemotaxis assay as previously
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Migratory function was assayed using a transwell chemotaxis assay as previously
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described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
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described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
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\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
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\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
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@ -1887,31 +1877,33 @@ respective sections. Cells were gated according to \cref{fig:gating_strategy}.
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\subsection{DMSs Can be Fabricated in a Controlled Manner}
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\subsection{DMSs Can be Fabricated in a Controlled Manner}
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% FIGURE flip the rows of this figure (right now the text is backward)
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\begin{figure*}[ht!]
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\begin{figure*}[ht!]
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\begingroup
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\begingroup
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\includegraphics{../figures/dms_coating.png}
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\includegraphics{../figures/dms_coating.png}
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\phantomsubcaption\label{fig:stp_carrier_fitc}
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\phantomsubcaption\label{fig:mab_carrier_fitc}
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\phantomsubcaption\label{fig:cug_vs_cus}
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\phantomsubcaption\label{fig:cug_vs_cus}
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\phantomsubcaption\label{fig:biotin_coating}
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\phantomsubcaption\label{fig:biotin_coating}
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\phantomsubcaption\label{fig:stp_coating}
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\phantomsubcaption\label{fig:stp_coating}
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\phantomsubcaption\label{fig:mab_coating}
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\phantomsubcaption\label{fig:mab_coating}
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\phantomsubcaption\label{fig:stp_carrier_fitc}
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\phantomsubcaption\label{fig:mab_carrier_fitc}
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\endgroup
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\endgroup
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\caption[\gls{dms} Coating]
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\caption[\gls{dms} Coating]
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{\gls{dms} functionalization results.
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{\gls{dms} functionalization results.
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\subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
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density on either \gls{cus} or \gls{cug} microcarriers. Surface density
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was estimated using the properties in~\cref{tab:carrier_props}}.
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Total binding curve of \subcap{fig:biotin_coating}{biotin},
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\subcap{fig:stp_coating}{\gls{stp}}, and
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\subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added for
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batches manufactured on different dates.
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\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
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\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
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treated with \gls{fitcbt} and imaged using a lightsheet microscope.}
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treated with \gls{fitcbt} and imaged using a lightsheet microscope.}
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\subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
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\subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
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\glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
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\glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
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lightsheet microscope.} \subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
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lightsheet microscope.}
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density on either \gls{cus} or \gls{cug} microcarriers. Surface density
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}
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was estimated using the properties in~\cref{tab:carrier_props}} Total
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binding curve of \subcap{fig:biotin_coating}{biotin},
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\subcap{fig:stp_coating}{\gls{stp}}, and
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\subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added. }
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\label{fig:dms_coating}
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\label{fig:dms_coating}
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\end{figure*}
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\end{figure*}
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@ -1945,8 +1937,8 @@ appeared that the \gls{mab} binding was quadratically related to biotin binding
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critical to controlling the amount and \glspl{mab} and thus the amount of signal
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critical to controlling the amount and \glspl{mab} and thus the amount of signal
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the T cells receive downstream.
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the T cells receive downstream.
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% TODO these caption titles suck
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% FIGURE these caption titles suck
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% TODO combine this DOE figure into one interaction plot
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% FIGURE combine this DOE figure into one interaction plot
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\begin{figure*}[ht!]
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\begin{figure*}[ht!]
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\begingroup
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\begingroup
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@ -2566,11 +2558,9 @@ filtering those that ended at day 14 and including flow cytometry results for
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the \ptmem{} and \pth{} populations. We further filtered our data to only
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the \ptmem{} and \pth{} populations. We further filtered our data to only
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include those experiments where the surface density of the CD3 and CD28
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include those experiments where the surface density of the CD3 and CD28
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\gls{mab} were held constant (since some of our experiments varied these on the
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\gls{mab} were held constant (since some of our experiments varied these on the
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\glspl{dms}). This ultimately resulted in a dataset with 162 runs spanning 15
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\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16
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experiments between early 2017 and early 2021.
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experiments between early 2017 and early 2021.
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% FIGURE add some correlation analysis to this
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Since the aim of the analysis was to perform causal inference, we determined 6
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Since the aim of the analysis was to perform causal inference, we determined 6
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possible treatment variables which we controlled when designing the experiments
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possible treatment variables which we controlled when designing the experiments
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included in this dataset. Obviously the principle treatment parameter was
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included in this dataset. Obviously the principle treatment parameter was
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@ -2646,7 +2636,6 @@ disrupt signaling pathways.
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\label{fig:metaanalysis_fx}
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\label{fig:metaanalysis_fx}
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\end{figure*}
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\end{figure*}
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% TODO the real reason we log-transformed was because box-cox and residual plots
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We first asked what the effect of each of our treatment parameters was on the
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We first asked what the effect of each of our treatment parameters was on the
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responses of interest, which were fold change of the cells, the \ptmemp{}, and
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responses of interest, which were fold change of the cells, the \ptmemp{}, and
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\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
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\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
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@ -2658,29 +2647,25 @@ significant in all cases except for the \dpthp{}; however, we also observe that
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relatively little of the variability is explained by these simple models ($R^2$
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relatively little of the variability is explained by these simple models ($R^2$
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between 0.17 and 0.44).
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between 0.17 and 0.44).
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% RESULT add the regression diagnostics to this
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We then included all covariates and unbalanced treatment parameters and
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We then included all covariates and unbalanced treatment parameters and
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performed linear regression again
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performed linear regression again
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(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
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(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
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controlling for additional noise, the models explained much more variability
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controlling for additional noise, the models explained much more variability
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($R^2$ between 0.76 and 0.87).
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($R^2$ between 0.76 and 0.87). Furthermore, the coefficient for activation
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% and had relatively constant variance and small
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method in the case of fold change changed very little but still remained quite
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% deviations for normality as per the assumptions of regression analysis {Figure
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high (note the log-transformation) with \SI{131}{\percent} increase in fold
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% X}.
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change compared to beads. Furthermore, the coefficient for \ptmemp{} dropped to
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Furthermore, the coefficient for activation method in the case of fold change
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a \SI{3.5}{\percent} increase and almost became non-significant at $\upalpha$ =
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changed very little but still remained quite high (note the log-transformation)
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0.05, and the \dpthp{} response increased to a \SI{7.4}{\percent} increase and
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with \SI{131}{\percent} increase in fold change compared to beads. Furthermore,
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became highly significant. Looking at the bioreactor treatment, we see that
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the coefficient for \ptmemp{} dropped to a \SI{3.5}{\percent} increase and
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using the bioreactor in the case of fold change and \ptmemp{} is actually
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almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
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harmful to the response, while at the same time it seems to increase the
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increased to a \SI{7.4}{\percent} increase and became highly significant.
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\dpthp{} response. We should note that this parameter merely represents whether
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Looking at the bioreactor treatment, we see that using the bioreactor in the
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or not the choice was made experimentally to use a bioreactor or not; it does
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case of fold change and \ptmemp{} is actually harmful to the response, while at
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not indicate why the bioreactor helped or hurt a certain response. For example,
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the same time it seems to increase the \dpthp{} response. We should note that
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using a Grex entails changing the cell surface and feeding strategy for the T
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this parameter merely represents whether or not the choice was made
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cells, and any one of these ‘mediating variables’ might actually be the cause of
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experimentally to use a bioreactor or not; it does not indicate why the
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the responses.
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bioreactor helped or hurt a certain response. For example, using a Grex entails
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changing the cell surface and feeding strategy for the T cells, and any one of
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these ‘mediating variables’ might actually be the cause of the responses.
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Finally, we stratified on the most common donor (vendor ID 338 from Astarte
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Finally, we stratified on the most common donor (vendor ID 338 from Astarte
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Biotech) as this was responsible for almost half the data (80 runs) and repeated
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Biotech) as this was responsible for almost half the data (80 runs) and repeated
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@ -2951,22 +2936,17 @@ dimensional spectra were referenced, water/end regions removed, and normalized
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with the PQN algorithm\cite{Dieterle2006} using an in-house MATLAB (The
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with the PQN algorithm\cite{Dieterle2006} using an in-house MATLAB (The
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MathWorks, Inc.) toolbox.
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MathWorks, Inc.) toolbox.
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% TODO add the supplemental figure alluded to here?
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To reduce the total number of spectral features from approximately 250 peaks and
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To reduce the total number of spectral features from approximately 250 peaks and
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enrich for those that would be most useful for statistical modeling, a
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enrich for those that would be most useful for statistical modeling, a
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variance-based feature selection was performed within MATLAB. For each digitized
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variance-based feature selection was performed within MATLAB. For each digitized
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point on the spectrum, the variance was calculated across all experimental
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point on the spectrum, the variance was calculated across all experimental
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samples and plotted. Clearly-resolved features corresponding to peaks in the
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samples and plotted. Clearly-resolved features corresponding to peaks in the
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variance spectrum were manually binned and integrated to obtain quantitative
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variance spectrum were manually binned and integrated to obtain quantitative
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feature intensities across all samples.
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feature intensities across all samples. In addition to highly variable features,
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% (Supp.Fig.S24).
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several other clearly resolved and easily identifiable features were selected
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In addition to highly variable features, several other clearly resolved and
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(glucose, \gls{bcaa} region, etc). Some features were later discovered to belong
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easily identifiable features were selected (glucose, \gls{bcaa} region, etc).
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to the same metabolite but were included in further analysis.
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Some features were later discovered to belong to the same metabolite but were
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included in further analysis.
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% I think this is the right source? it seems wrong in the manuscript but this
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% source at least talks about an optimization score
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Two-dimensional spectra collected on pooled samples were uploaded to COLMARm web
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Two-dimensional spectra collected on pooled samples were uploaded to COLMARm web
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server, where \gls{hsqc} peaks were automatically matched to database peaks.
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server, where \gls{hsqc} peaks were automatically matched to database peaks.
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\gls{hsqc} matches were manually reviewed with additional 2D and proton spectra
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\gls{hsqc} matches were manually reviewed with additional 2D and proton spectra
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@ -2975,16 +2955,6 @@ the levels of spectral data supporting the match as previously
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described\cite{Dashti2017}. Annotated metabolites were matched to previously
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described\cite{Dashti2017}. Annotated metabolites were matched to previously
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selected features used for statistical analysis.
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selected features used for statistical analysis.
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% I'm pretty sure this isn't relevant
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% Using the list of annotated metabolites obtained above, an approximation of a
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% representative experimental spectrum was generated using the GISSMO mixture
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% simulation tool.39,40 With the simulated mixture of compounds, generated at 600
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% MHz to match the experimental data, a new simulation was generated at 80 MHz to
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% match the field strength of commercially available benchtop NMR spectrometers.
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% The GISSMO tool allows visualization of signals contributed from each individual
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% compound as well as the mixture, which allows annotation of features in the
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% mixture belonging to specific compounds.
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Several low abundance features selected for analysis did not have database
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Several low abundance features selected for analysis did not have database
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matches and were not annotated. Statistical total correlation spectroscopy41
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matches and were not annotated. Statistical total correlation spectroscopy41
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suggested that some of these unknown features belonged to the same molecules
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suggested that some of these unknown features belonged to the same molecules
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@ -3084,10 +3054,6 @@ the \gls{svm} regression models evaluating the cost parameter value with best
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\gls{loocv}. Prediction performance was measured for all models using the final
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\gls{loocv}. Prediction performance was measured for all models using the final
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model with \gls{loocv} tuned parameters.
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model with \gls{loocv} tuned parameters.
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% TODO do I need this?
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% Table M2 shows the parameter values evaluated per model
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% at the final stages of results reporting.
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\subsection{Consensus Analysis}
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\subsection{Consensus Analysis}
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Consensus analysis of the relevant variables extracted from each machine
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Consensus analysis of the relevant variables extracted from each machine
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@ -3615,21 +3581,6 @@ for proliferation following activation\cite{Cao2014}. It may be that glycolytic
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cells dominate in the culture at the early time points used for prediction, and
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cells dominate in the culture at the early time points used for prediction, and
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higher lactate reflects more cells.
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higher lactate reflects more cells.
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% TODO not sure how much I should include here since I didn't do this analysis
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% AT ALL
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% Ethanol patterns are difficult to interpret since its production in mammalian
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% cells is still poorly understood31. Fresh media analysis indicates ethanol
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% presence in the media used, possibly utilized as a carrier solvent for certain
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% formula components. However, this does not explain the high variability and
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% trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
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% chemical, variation could be introduced by sample handling throughout the
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% analysis process. Nonetheless, it is also possible that ethanol excreted into
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% media over time, impacting processes regulating redox and reactive oxygen
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% species which have previously been shown to be crucial in T cell signaling and
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% differentiation32.
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% this looks fine since it is just parroting sources, just need to paraphrase a
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% little
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Metabolites that consistently decreased over time are consistent with the
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Metabolites that consistently decreased over time are consistent with the
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primary carbon source (glucose) and essential amino acids (\gls{bcaa},
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primary carbon source (glucose) and essential amino acids (\gls{bcaa},
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histidine) that must be continually consumed by proliferating cells. Moreover,
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histidine) that must be continually consumed by proliferating cells. Moreover,
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@ -4008,7 +3959,6 @@ density.
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\label{fig:il15_1}
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\label{fig:il15_1}
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\end{figure*}
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\end{figure*}
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% FIGURE just gate these as normal because this looks sketchy
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We first tested this hypothesis by blocking \gls{il15r} with either a specific
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We first tested this hypothesis by blocking \gls{il15r} with either a specific
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\gls{mab} or an \gls{igg} isotype control at
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\gls{mab} or an \gls{igg} isotype control at
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\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. We observed no difference in the
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\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. We observed no difference in the
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@ -4555,7 +4505,6 @@ including highly potent \ptmem{} and \pth{} T cells, and produces higher
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percentages of both. If commercialized, this would be a compelling asset the T
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percentages of both. If commercialized, this would be a compelling asset the T
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cell manufacturing industry.
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cell manufacturing industry.
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% TODO double check the numbers at the end
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In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
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In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
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\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
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\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
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the fabrication process to ensure that the \gls{dms} can be made within a
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the fabrication process to ensure that the \gls{dms} can be made within a
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@ -4567,8 +4516,8 @@ reputable vendors, and they have a regulatory history in human cell therapies
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that will aid in clinical translation\cite{purcellmain}. Both these will help
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that will aid in clinical translation\cite{purcellmain}. Both these will help
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in translatability. On average, we demonstrated that the \gls{dms} outperforms
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in translatability. On average, we demonstrated that the \gls{dms} outperforms
|
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state-of-the-art bead-based T cell expansion technology in terms of total fold
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state-of-the-art bead-based T cell expansion technology in terms of total fold
|
||||||
expansion, \ptmemp{}, and \pthp{} by \SI{143}{\percent}, \SI{2.5}{\percent}, and
|
expansion, \ptmemp{}, and \pthp{} by \SI{131}{\percent}, \SI{3.5}{\percent}, and
|
||||||
\SI{9.8}{\percent} controlling for donor, operator, and a variety of process
|
\SI{7.4}{\percent} controlling for donor, operator, and a variety of process
|
||||||
conditions.
|
conditions.
|
||||||
|
|
||||||
In addition to larger numbers of potent T cells, other advantages of our
|
In addition to larger numbers of potent T cells, other advantages of our
|
||||||
|
@ -4744,7 +4693,6 @@ density of the \gls{dms} compares to that of the beads. In all likelihood, the
|
||||||
binding sites on \gls{stp} and the number of \glspl{mab} that actually bind)
|
binding sites on \gls{stp} and the number of \glspl{mab} that actually bind)
|
||||||
which may lead to differences in performance\cite{Lozza2008}.
|
which may lead to differences in performance\cite{Lozza2008}.
|
||||||
|
|
||||||
% TODO make sure this actually is "below"
|
|
||||||
Before attempting this experiment, it will be vital to improve the \gls{dms}
|
Before attempting this experiment, it will be vital to improve the \gls{dms}
|
||||||
manufacturing process such that \gls{mab} binding is predictable and
|
manufacturing process such that \gls{mab} binding is predictable and
|
||||||
reproducible (see below). Once this is established, we can then determine the
|
reproducible (see below). Once this is established, we can then determine the
|
||||||
|
@ -4757,22 +4705,7 @@ Using varying surface densities that are matched per-area between the beads and
|
||||||
\glspl{dms} we can then activate T cells and assess their growth/phenotype as a
|
\glspl{dms} we can then activate T cells and assess their growth/phenotype as a
|
||||||
function of surface density and the presentation method.
|
function of surface density and the presentation method.
|
||||||
|
|
||||||
\subsubsection{Surface Stiffness}
|
% FIGURE this might warrant a better figure
|
||||||
|
|
||||||
The beads and \gls{dms} are composed of different materials: iron/polymer in the
|
|
||||||
former case and cross-linked gelatin in the latter. These materials likely have
|
|
||||||
different stiffnesses, and stiffness could play a role in T cell
|
|
||||||
activation\cite{Lambert2017}.
|
|
||||||
|
|
||||||
This hypotheses will be difficult to test directly, so it is advised to
|
|
||||||
eliminate other hypothesis before proceeding here. Direct testing could be
|
|
||||||
performed using a force probe to determine the Young's modulus of each
|
|
||||||
material\cite{Ju2017}. Since the microcarriers are porous and the cells will be
|
|
||||||
interacting with the bulk material itself, the void fraction and pore size will
|
|
||||||
need to be taken into account to find the bulk material properties of the
|
|
||||||
cross-linked gelatin\cite{Wang1984}.
|
|
||||||
|
|
||||||
% TODO this might warrant a better figure
|
|
||||||
\subsection{Reducing Ligand Variance}
|
\subsection{Reducing Ligand Variance}
|
||||||
|
|
||||||
While we have robust quality control steps to quantify each step of the
|
While we have robust quality control steps to quantify each step of the
|
||||||
|
@ -4856,6 +4789,22 @@ potential mitigation strategies:
|
||||||
due to its automated nature.
|
due to its automated nature.
|
||||||
\end{description}
|
\end{description}
|
||||||
|
|
||||||
|
|
||||||
|
\subsubsection{Surface Stiffness}
|
||||||
|
|
||||||
|
The beads and \gls{dms} are composed of different materials: iron/polymer in the
|
||||||
|
former case and cross-linked gelatin in the latter. These materials likely have
|
||||||
|
different stiffnesses, and stiffness could play a role in T cell
|
||||||
|
activation\cite{Lambert2017}.
|
||||||
|
|
||||||
|
This hypotheses will be difficult to test directly, so it is advised to
|
||||||
|
eliminate other hypothesis before proceeding here. Direct testing could be
|
||||||
|
performed using a force probe to determine the Young's modulus of each
|
||||||
|
material\cite{Ju2017}. Since the microcarriers are porous and the cells will be
|
||||||
|
interacting with the bulk material itself, the void fraction and pore size will
|
||||||
|
need to be taken into account to find the bulk material properties of the
|
||||||
|
cross-linked gelatin\cite{Wang1984}.
|
||||||
|
|
||||||
\subsection{Additional Ligands and Signals on the DMSs}
|
\subsection{Additional Ligands and Signals on the DMSs}
|
||||||
|
|
||||||
In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated
|
In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated
|
||||||
|
@ -4916,7 +4865,7 @@ Python, with a subprocess running R in a Docker container to handle the flow
|
||||||
cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
|
cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
|
||||||
hosted using \gls{aws} using their proprietary Aurora implementation.
|
hosted using \gls{aws} using their proprietary Aurora implementation.
|
||||||
|
|
||||||
% TODO explain what the colors mean
|
% FIGURE explain what the colors mean
|
||||||
\begin{figure*}[ht!]
|
\begin{figure*}[ht!]
|
||||||
\begingroup
|
\begingroup
|
||||||
|
|
||||||
|
|
Loading…
Reference in New Issue