ADD spade gating figure
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@ -2723,8 +2723,6 @@ T cells were stained using a \product{34 \gls{cytof} marker
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used according to the manufacturer’s instructions. \numrange{2e6}{3e6} stained
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used according to the manufacturer’s instructions. \numrange{2e6}{3e6} stained
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cells per group were analyzed on a Fluidigm Helios.
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cells per group were analyzed on a Fluidigm Helios.
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% FIGURE add the spade gating diagram from the paper
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Unbiased cell clusters were obtained using \gls{spade} analysis by pooling three
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Unbiased cell clusters were obtained using \gls{spade} analysis by pooling three
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representative \gls{fcs} files and running the \gls{spade} pipeline with k-means
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representative \gls{fcs} files and running the \gls{spade} pipeline with k-means
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clustering (k = 100), arcsinh transformation with cofactor 5, density
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clustering (k = 100), arcsinh transformation with cofactor 5, density
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@ -2829,36 +2827,18 @@ are fundamentally altered by changing the number of \glspl{dms} temporally.
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\label{fig:add_rem}
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\label{fig:add_rem}
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\end{figure*}
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\end{figure*}
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We next asked what the effect of removing the \glspl{dms} would have on other
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phenotypes, specifically \gls{tcm} and \gls{tscm} cells. To this end we stained
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cells using a 34-marker mass cytometry panel and analyzed them using a Fluidigm
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Helios. After pooling the \gls{fcs} file events from each group and analyzing
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them via \gls{spade} we see that there is a strong bifurcation of CD4 and CD8 T
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cells. We also observe that among CD27, CD45RA, and CD45RO (markers commonly
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used to identify \gls{tcm} and \gls{tscm} subtypes) we see clear `metaclusters'
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composed of individual \gls{spade} clusters which are high for that marker
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(\cref{fig:spade_msts}). We then gated each of these metaclusters according to
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their marker levels and assigned them to one of three phenotypes for both the
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CD4 and CD8 compartments: \gls{tcm} (high CD45RO, low CD45RA, high CD27),
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\gls{tscm} (low CD45RO, high CD45RA, high CD27), and `transitory' \gls{tscm}
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cells (mid CD45RO, mid CD45RA, high CD27). Together these represent low
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differentiated cells which should be highly potent as anti-tumor therapies.
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When quantifying the number of cells from each experimental group in these
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\begin{figure*}[ht!]
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phenotypes, we clearly see that the number of lower differentiated cells is much
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\begingroup
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higher in the \textit{no change} or \textit{removed} groups compared to the
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\textit{added} group (\cref{fig:spade_quant}). Furthermore, the \textit{removed}
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\includegraphics{../figures/spade_gates.png}
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group had a much higher fraction of \gls{tscm} cells compared to the \textit{no
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change} group, which had more `transitory \gls{tscm} cells'. The majority of
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\endgroup
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these cells were \cdp{8} cells. When analyzing the same data using \gls{tsne},
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\caption[SPADE Gating Strategy]
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we observe a higher fraction of CD27 and lower fraction of CD45RO in the the
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{Gating strategy for quantifying early-differentiated T cells via
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\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the
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\gls{spade}.}
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CD27+CD45RO- population, we see there is higher density in the \textit{removed}
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\label{fig:spade_gates}
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group, indicating more of this population (\cref{fig:spade_tsne_stem}).
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\end{figure*}
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Together, these data indicate that removing \glspl{dms} at lower timepoints
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leads to potentially higher expansion, lower \pthp{}, and higher fraction of
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lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to
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do the inverse.
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% TODO this needs some better annotations
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% TODO this needs some better annotations
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% TODO put the quant graph before the tsne stuff
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% TODO put the quant graph before the tsne stuff
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@ -2890,6 +2870,38 @@ do the inverse.
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\label{fig:spade}
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\label{fig:spade}
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\end{figure*}
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\end{figure*}
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We next asked what the effect of removing the \glspl{dms} would have on other
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||||||
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phenotypes, specifically \gls{tcm} and \gls{tscm} cells. To this end we stained
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|
cells using a 34-marker mass cytometry panel and analyzed them using a Fluidigm
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||||||
|
Helios. After pooling the \gls{fcs} file events from each group and analyzing
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||||||
|
them via \gls{spade} we see that there is a strong bifurcation of CD4 and CD8 T
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||||||
|
cells. We also observe that among CD27, CD45RA, and CD45RO (markers commonly
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||||||
|
used to identify \gls{tcm} and \gls{tscm} subtypes) we see clear `metaclusters'
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|
composed of individual \gls{spade} clusters which are high for that marker
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(\cref{fig:spade_msts,fig:spade_gates}). We then gated each of these
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metaclusters according to their marker levels and assigned them to one of three
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phenotypes for both the CD4 and CD8 compartments: \gls{tcm} (high CD45RO, low
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CD45RA, high CD27), \gls{tscm} (low CD45RO, high CD45RA, high CD27), and
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`transitory' \gls{tscm} cells (mid CD45RO, mid CD45RA, high CD27). Together
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these represent low differentiated cells which should be highly potent as
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anti-tumor therapies.
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When quantifying the number of cells from each experimental group in these
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phenotypes, we clearly see that the number of lower differentiated cells is much
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higher in the \textit{no change} or \textit{removed} groups compared to the
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\textit{added} group (\cref{fig:spade_quant}). Furthermore, the \textit{removed}
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group had a much higher fraction of \gls{tscm} cells compared to the \textit{no
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change} group, which had more `transitory \gls{tscm} cells'. The majority of
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these cells were \cdp{8} cells. When analyzing the same data using \gls{tsne},
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we observe a higher fraction of CD27 and lower fraction of CD45RO in the the
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\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the
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|
CD27+CD45RO- population, we see there is higher density in the \textit{removed}
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group, indicating more of this population (\cref{fig:spade_tsne_stem}).
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Together, these data indicate that removing \glspl{dms} at lower timepoints
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leads to potentially higher expansion, lower \pthp{}, and higher fraction of
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lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to
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do the inverse.
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\subsection{blocking integrin binding does not alter expansion or phenotype}
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\subsection{blocking integrin binding does not alter expansion or phenotype}
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% BACKGROUND add background into why integrins are important
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% BACKGROUND add background into why integrins are important
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