ADD a bunch of text for aim 1

tables/causal_inference_control.tex

@@ -5,7 +5,7 @@
 \hline \\[-1.8ex] 
 %  & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
 % \cline{2-4} 
-\\[-1.8ex] & log(Fold Change) & \ptmem~\% & $\Updelta$CD4+ \% \\ 
+\\[-1.8ex] & log(Fold Change) & \ptmemp{} & \dpthp{} \\ 
 \hline \\[-1.8ex] 
  Activation Method [DMS] & 0.890$^{***}$ & 0.027$^{**}$ & 0.089$^{***}$ \\ 
   Bioreactor [Grex] & $-$1.712$^{***}$ & $-$0.464$^{***}$ & 0.333$^{***}$ \\ 

tables/causal_inference_treat.tex

@@ -5,7 +5,7 @@
 \hline \\[-1.8ex] 
  & \multicolumn{3}{c}{\textit{Dependent variable:}} \\ 
 \cline{2-4} 
-\\[-1.8ex] & log(Fold Change) & \ptmem~\% & $\Updelta$CD4+ \% \\ 
+\\[-1.8ex] & log(Fold Change) & \ptmemp{} & \dpthp{} \\ 
 \\[-1.8ex] & (1) & (2) & (3)\\ 
 \hline \\[-1.8ex] 
  Activation Method [DMS] & 0.903$^{***}$ & 0.048$^{*}$ & 0.011 \\ 

tex/thesis.tex

@@ -1,5 +1,7 @@
 % \documentclass[twocolumn]{article}
 \documentclass{report}
+% TODO I want to keep figures in each subsection, which this doesn't do
+\usepackage[section]{placeins}
 \usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry}
 \usepackage{siunitx}
 \usepackage{multicol}
@@ -70,11 +72,15 @@
 \newacronym{aopi}{AO/PI}{acridine orange/propidium iodide}
 \newacronym{igg}{IgG}{immunoglobulin G}
 \newacronym{pe}{PE}{phycoerythrin}
+\newacronym{fitc}{FITC}{Fluorescein}
+\newacronym{fitcbt}{FITC-BT}{Fluorescein-biotin}
 \newacronym{ptnl}{PTN-L}{Protein L}
 \newacronym{af647}{AF647}{Alexa Fluor 647}
 \newacronym{anova}{ANOVA}{analysis of variance}
 \newacronym{crispr}{CRISPR}{clustered regularly interspaced short
   palindromic repeats}
+\newacronym{mtt}{MTT}{3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide}
+\newacronym{bmi}{BMI}{body mass index}
 
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % SI units for uber nerds
@@ -118,16 +124,22 @@
 
 \newcommand{\cd}[1]{CD{#1}}
 \newcommand{\anti}[1]{anti-{#1}}
+\newcommand{\antih}[1]{anti-human {#1}}
+\newcommand{\antim}[1]{anti-mouse {#1}}
 \newcommand{\acd}[1]{\anti{\cd{#1}}}
+\newcommand{\ahcd}[1]{\antih{\cd{#1}}}
+\newcommand{\amcd}[1]{\antim{\cd{#1}}}
 \newcommand{\pos}[1]{#1+}
 \newcommand{\cdp}[1]{\pos{\cd{#1}}}
 \newcommand{\cdn}[1]{\cd{#1}-}
 \newcommand{\ptmem}{\cdp{62L}\pos{CCR7}}
+\newcommand{\ptmemp}{\ptmem{}~\si{\percent}}
 \newcommand{\pth}{\cdp{4}}
+\newcommand{\pthp}{\pth{}~\si{\percent}}
 \newcommand{\ptk}{\cdp{8}}
 \newcommand{\ptmemh}{\pth\ptmem}
 \newcommand{\ptmemk}{\ptk\ptmem}
-\newcommand{\dpth}{$\Updelta$\cdp{4}}
+\newcommand{\dpthp}{$\Updelta$\pthp{}}
 
 \newcommand{\catnum}[2]{(#1, #2)}
 \newcommand{\product}[3]{#1 \catnum{#2}{#3}}
@@ -757,17 +769,17 @@ the same antibodies used to coat the carriers were used as the standard for the
 \gls{elisa} standard curve.
 
 Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
-quantified indirectly using \product{FITC-biotin}{\thermo}{B10570}.
+quantified indirectly using \product{\gls{fitcbt}}{\thermo}{B10570}.
-Briefly, \SI{400}{\pmol\per\ml} FITC-biotin were added to \gls{stp}-coated
+Briefly, \SI{400}{\pmol\per\ml} \gls{fitcbt} were added to \gls{stp}-coated
 carriers and allowed to react for \SI{20}{\minute} at room temperature under
 constant agitation. The supernatant was quantified against a standard curve of
-FITC-biotin using a Biotek plate reader.
+\gls{fitcbt} using a Biotek plate reader.
 
 \Gls{stp} binding was verified after the \gls{stp}-binding step visually by
-adding biotin-FITC to the \gls{stp}-coated \glspl{dms}, resuspending in
+adding \gls{fitcbt} to the \gls{stp}-coated \glspl{dms}, resuspending in
 \SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
  microscope}{Zeiss}{Z.1}. \Gls{mab} binding was verified visually by first
-staining with \product{\anti{gls{igg}}-FITC}{\bl}{406001}, incubating for
+staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001}, incubating for
 \SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope.
 
 \subsection{t cell culture}
@@ -888,6 +900,34 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
 
 \subsection{DMSs can be fabricated in a controlled manner}
 
+Two types of gelatin-based microcariers, \gls{cus} and \gls{cug}, were
+covalently conjugated with varying concentration of \gls{snb} and then coated
+with \gls{stp} and \glspl{mab} to make \glspl{dms}. Aside from slight
+differences in swelling ratio and crosslinking chemistry {\#}[Purcell
+documentation], the properties of \gls{cus} and \gls{cug} were the same
+(\cref{tab:carrier_props}). We chose to continue with the \gls{cus}-based
+\glspl{dms}, which showed higher overall \gls{stp} binding compared to
+\gls{cug}-based \glspl{dms} (\cref{fig:cug_vs_cus}). We showed that by varying
+the concentration of \gls{snb}, we were able to precisely control the amount of
+attached biotin (\cref{fig:biotin_coating}), mass of attached \gls{stp}
+(\cref{fig:stp_coating}), and mass of attached \glspl{mab}
+(\cref{fig:mab_coating}). Furthermore, we showed that the microcarriers were
+evenly coated with \gls{stp} on the surface and throughout the interior as
+evidenced by the presence of biotin-binding sites occupied with \gls{stp}-\gls{fitc}
+on the microcarrier surfaces after the \gls{stp}-coating step
+(\cref{fig:stp_carrier_fitc}). Finally, we confirmed that biotinylated
+\glspl{mab} were bound to the \glspl{dms} by staining either \gls{stp} or
+\gls{stp} and \gls{mab}-coated carriers with \antim{\gls{igg}-\gls{fitc}} and imaging
+on a confocal microscope (\cref{fig:mab_carrier_fitc}). Taking this together, we
+noted that the maximal \gls{mab} binding capacity occurred near \SI{50}{\nmol}
+biotin input (which corresponded to \SI{2.5}{\nmol\per\mg\of{\dms}}) thus we
+used this in subsequent processes.
+
+% TODO add paragraph explaining the qc stuff
+
+% TODO add paragraph explaining the reaction kinetics stuff
+
+% TODO flip the rows of this figure (right now the text is backward)
 \begin{figure*}[ht!]
   \begingroup
 
@@ -903,7 +943,7 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \caption[\gls{dms} Coating]
   {\gls{dms} functionalization results.
     \subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
-      treated with biotin-FITC and imaged using a lightsheet microscope.}
+      treated with \gls{fitcbt} and imaged using a lightsheet microscope.}
     \subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
       \glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
       lightsheet microscope.} \subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
@@ -949,12 +989,6 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \label{fig:dms_flowchart}
 \end{figure*}
 
-\begin{table}[!h] \centering
-  \caption{Properties of the microcarriers used}
-  \label{tab:carrier_props}
-  \input{../tables/carrier_properties.tex}
-\end{table}
-
 \begin{figure*}[ht!]
   \begingroup
 
@@ -964,17 +998,32 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \phantomsubcaption\label{fig:dms_stp_per_time}
 
   \endgroup
-  \caption[\gls{dms} Reaction timing]
+  \caption[\gls{dms} Reaction kinetics]
   {Reaction kinetics for microcarrier functionalization.
     \subcap{fig:dms_biotin_rxn_mass}{Biotin mass bound per time}
     \subcap{fig:dms_biotin_rxn_frac}{Fraction of input biotin bound per time}
     \subcap{fig:dms_stp_per_time}{\Gls{stp} bound per time.}
   }
-  \label{fig:dms_flowchart}
+  \label{fig:dms_kinetics}
 \end{figure*}
 
 \subsection{DMSs can efficiently expand T cells compared to beads}
 
+% TODO add other subfigures here
+We next sought to determine how our \glspl{dms} could expand T cells compared to
+state-of-the-art methods used in industry. All bead expansions were performed as
+per the manufacturer’s protocol, with the exception that the starting cell
+densities were matched between the beads and carriers to
+~\SI{2.5e6}{\cell\per\ml}. Throughout the culture we observed that T cells in
+\gls{dms} culture grew in tight clumps on the surface of the \glspl{dms} as well
+as inside the pores of the \glspl{dms}
+(\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore, we observed that
+the \glspl{dms} conferred greater expansion compared to traditional beads, and
+significantly greater expansion after \SI{12}{\day} of culture {Figure X}. We
+also observed no T cell expansion using \glspl{dms} coated with an isotype
+control mAb compared to \glspl{dms} coated with \acd{3}/\acd{28} \glspl{mab}
+{Figure X}, confirming specificity of the expansion method.
+
 % TODO make sure the day on these is correct
 \begin{figure*}[ht!]
   \begingroup
@@ -995,6 +1044,21 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \label{fig:dms_cells}
 \end{figure*}
 
+% TODO add a regression table to quantify this better
+% TODO state the CI of what are inside the carriers
+We also asked how many cells were inside the \glspl{dms} vs. free-floating in
+suspension and/or loosely attached to the surface. We qualitatively verified the
+presence of cells inside the \glspl{dms} using a \gls{mtt} stain to opaquely
+mark cells and enable visualization on a brightfield microscope
+(\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different densities and
+expanding for \SI{14}{\day}, we filtered the \glspl{dms} out of the cell
+suspension and digested them using dispase to free any cells attached on the
+inner surface. We observed that approximately \SI{15}{\percent} of the total
+cells after \SI{14}{\day} were on the interior surface of the \glspl{dms}
+(\cref{fig:dms_inside_regression}).
+
+%, and this did not significantly change with initial seeding density (Supplemental Table 1).
+
 % TODO add this to the methods section
 % TODO double check the timing of this experiment (it might not be day 14)
 % TODO provide the regression results and coefficients from this
@@ -1008,9 +1072,9 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \endgroup
   \caption[A subset of T cells grow in interior of \glspl{dms}]
   {A percentage of T cells grow in the interior of \glspl{dms}.
-    \subcap{fig:dms_inside_bf}{T cells stained dark with MTT after growing on
+    \subcap{fig:dms_inside_bf}{T cells stained dark with \gls{mtt} after
-      either coated or uncoated \glspl{dms} for 14 days visualized with 
+      growing on either coated or uncoated \glspl{dms} for 14 days visualized
-      brightfield microscope.}
+      with brightfield microscope.}
     \subcap{fig:dms_inside_regression}{Linear regression performed on T cell
       percentages harvested on the interior of the \glspl{dms} vs the initial
       starting cell density.}
@@ -1018,9 +1082,31 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \label{fig:dms_inside}
 \end{figure*}
 
-
 \subsection{DMSs lead to greater expansion and memory and CD4+ phenotypes}
 
+After observing differences in expansion, we further hypothesized that the
+\gls{dms} cultures could lead to a different T cell phenotype. In particular, we
+were interested in the formation of naïve and memory T cells, as these represent
+a subset with higher replicative potential and therefore improved clinical
+prognosis\cite{Gattinoni2011, Wang2018}. We measured naïve and memory T cell
+frequency staining for CCR7 and CD62L (both of which are present on lower
+differentiated T cells such as naïve, central memory, and stem memory
+cells\cite{Gattinoni2012}). Using three donors, we noted again \glspl{dms}
+produced more T cells over a \SI{14}{\day} expansion than beads, with
+significant differences in number appearing as early after \SI{5}{\day}
+(\cref{fig:dms_exp_fold_change}). Furthermore, we noted that \glspl{dms}
+produced more memory/naïve cells after \SI{14}{\day} when compared to beads for
+all donors (\cref{fig:dms_exp_mem,fig:dms_exp_cd4}) showing that the \gls{dms}
+platform is able to selectively expand potent, early differentiation T cells.
+
+Of additional interest was the preservation of the CD4+ compartment. In healthy
+donor samples (such as those used here), the typical CD4:CD8 ratio is 2:1. We
+noted that \glspl{dms} produced more CD4+ T cells than bead cultures as well as
+naïve/memory, showing that the \gls{dms} platform can selectively expand CD4 T
+cells to a greater degree than beads (Figure 2c). The trends held true when
+observing the CD4+ and CD8+ fractions of the naïve/memory subset (CD62L+CCR7+)
+(\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
+
 \begin{figure*}[ht!]
   \begingroup
 
@@ -1048,6 +1134,8 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
   \label{fig:dms_exp}
 \end{figure*}
 
+% TODO add a paragraph for this figure
+
 % TODO this figure has weird proportions
 \begin{figure*}[ht!]
   \begingroup
@@ -1069,6 +1157,39 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
 
 \subsection*{DMSs can be used to produce functional CAR T cells}
 
+After optimizing for naïve/memory and CD4 yield, we sought to determine if the
+\glspl{dms} were compatible with lentiviral transduction protocols used to
+generate \gls{car} T cells27,28. We added a \SI{24}{\hour} transduction step on
+day 1 of the \SI{14}{\day} expansion to insert an anti-CD19 \gls{car}29 and
+subsequently measured the surface expression of the \gls{car} on day 14
+\cref{fig:car_production_flow_pl,fig:car_production_endpoint_pl}. We noted that
+there was robust \gls{car} expression in over \SI{25}{\percent} of expanded T
+cells, and there was no observable difference in \gls{car} expression between
+beads and \glspl{dms}.
+
+We also verified the functionality of expanded \gls{car} T cells using a
+degranulation assay\cite{Zheng2012}. Briefly, T cells were cocultured with
+target cells (either wild-type K562 or CD19-expressing K562 cells) for
+\SI{4}{\hour}, after which the culture was analyzed via flow cytometry for the
+appearance of CD107a on CD8+ T cells. CD107a is found on the inner-surface of
+cytotoxic granules and will emerge on the surface after cytotoxic T cells are
+activated and degranulate. Indeed, we observed degranulation in T cells expanded
+with both beads and \glspl{dms}, although not to an observably different degree
+\cref{fig:car_production_flow_degran,fig:car_production_endpoint_degran}. Taken
+together, these results indicated that the \glspl{dms} provide similar
+transduction efficiency compared to beads.
+
+We also verified that expanded T cells were migratory using a chemotaxis assay
+for CCL21; since \glspl{dms} produced a larger percentage of naïve and memory T
+cells (which have CCR7, the receptor for CCL21) we would expect higher migration
+in \gls{dms}-expanded cells vs.\ their bead counterparts. Indeed, we noted a
+significantly higher migration percentage for T cells grown using \glspl{dms}
+versus beads (\cref{fig:car_production_migration}). Interestingly, there also
+appeared to be a decrease in CCL21 migration between transduced and untransduced
+T cells expanded using beads, but this interaction effect was only weakly
+significant (p = 0.068). No such effect was seen for \gls{dms}-expanded T cells,
+showing that migration was likely independent of \gls{car} transduction.
+
 \begin{figure*}[ht!]
   \begingroup
 
@@ -1098,6 +1219,14 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
 
 \subsection{DMSs do not leave antibodies attached to cell product}
 
+We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
+and could be detected on the final T cell product. This test is important for
+clinical translation as any residual \glspl{mab} on T cells injected into the
+patient could elicit an undesirable \antim{\gls{igg}} immune response. We did
+not detect the presence of either \ahcd{3} or \ahcd{28} \glspl{mab} (both of
+which were \gls{igg}) on the final T cell product after \SI{14}{\day} of
+expansion (\cref{fig:nonstick}).
+
 \begin{figure*}[ht!]
   \begingroup
 
@@ -1115,6 +1244,90 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
 \subsection{DMSs consistently outperform bead-based expansion compared to
 beads in a variety of conditions}
 
+n order to establish the robustness of our method, we combined all experiments
+performed in our lab using beads or \glspl{dms} and combined them into one
+dataset. Since each experiment was performed using slightly different process
+conditions, we hypothesized that performing causal inference on such a dataset
+would not only indicate if the \glspl{dms} indeed led to better results under a
+variety of conditions, but would also indicate other process parameters that
+influence the outcome. The dataset was curated by compiling all experiments and
+filtering those that ended at day 14 and including flow cytometry results for
+the \ptmem{} and \pth{} populations. We further filtered our data to only
+include those experiments where the surface density of the CD3 and CD28
+\gls{mab} were held constant (since some of our experiments varied these on the
+\glspl{dms}). This ultimately resulted in a dataset with 162 runs spanning 15
+experiments between early 2017 and early 2021.
+
+% TODO add some correlation analysis to this
+
+Since the aim of the analysis was to perform causal inference, we determined 6
+possible treatment variables which we controlled when designing the experiments
+included in this dataset. Obviously the principle treatment parameter was
+‘activation method’ which represented the effect of activating T cells with
+either beads or our DMS method. We also included ‘bioreactor’ which was a
+categorical for growing the T cells in a Grex bioreactor vs polystyrene plates,
+‘feed criteria’ which represented the criteria used to feed the cells (using
+media color or a glucose meter), ‘IL2 Feed Conc’ as a continuous parameter for
+the concentration of IL2 added each feed cycle, and ‘CD19-CAR Transduced’
+representing if the cells were lentivirally transduced or not. Unfortunately,
+many of these parameters correlated with each other highly despite the large
+size of our dataset, so the only two parameters for which causal relationships
+could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
+note that these were not the only set of theoretical treatment parameters that
+we could have used. For example, media feed rate is an important process
+parameter, but this was dependent on the feeding criteria and the growth rate of
+the cells, which in turn is determined by activation method. Therefore, ‘media
+feed rate’ (or similar) is a ‘post-treatment parameter’ and would have violated
+the backdoor criteria and severely biased our estimates of the treatment
+parameters themselves.
+
+In addition to these treatment parameters, we also included covariates to
+improve the precision of our model. Among these were donor parameters including
+age, \gls{bmi}, demographic, and gender, as well as the initial viability and
+CD4/CD8 ratio of the cryopreserved cell lots used in the experiments. We also
+included the age of key reagents such as IL2, media, and the anti-aggregate
+media used to thaw the T cells prior to activation. Each experiment was
+performed by one of three operators, so this was included as a three-level
+categorical parameter. Lastly, some of our experiments were sampled
+longitudinally, so we included a boolean categorical to represented this
+modification as removing conditioned media as the cell are expanding could
+disrupt signaling pathways.
+
+% TODO the real reason we log-transformed was because box-cox and residual plots
+We first asked what the effect of each of our treatment parameters was on the
+responses of interest, which were fold change of the cells, the \ptmemp{}, and
+\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
+performed a linear regression using activation method and bioreactor as
+predictors (the only treatments that were shown to be balanced)
+(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the
+exponential nature of T cell growth. We observe that the treatments are
+significant in all cases except for the \dpthp{}; however, we also observe that
+relatively little of the variability is explained by these simple models ($R^2$
+between 0.17 and 0.44).
+
+% TODO add the regression diagnostics to this
+We then included all covariates and unbalanced treatment parameters and
+performed linear regression again
+(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
+controlling for additional noise, the models explained much more variability
+($R^2$ between 0.76 and 0.87) and had relatively constant variance and small
+deviations for normality as per the assumptions of regression analysis {Figure
+  X}. Furthermore, the coefficient for activation method in the case of fold
+change changed very little but still remained quite high (note the
+log-transformation) with \SI{143}{\percent} increase in fold change compared to
+beads. Furthermore, the coefficient for \ptmemp{} dropped to about
+\SI{2.7}{\percent} different and almost became non-significant at $\upalpha$ =
+0.05, and the \dpthp{} response increased to almost a \SI{9}{\percent} difference
+and became highly significant. Looking at the bioreactor treatment, we see that
+using the bioreactor in the case of fold change and \ptmemp{} is actually harmful
+to the response, while at the same time it seems to increase the \dpthp{}
+response. We should note that this parameter merely represents whether or not
+the choice was made experimentally to use a bioreactor or not; it does not
+indicate why the bioreactor helped or hurt a certain response. For example,
+using a Grex entails changing the cell surface and feeding strategy for the T
+cells, and any one of these ‘mediating variables’ might actually be the cause of
+the responses.
+
 % TODO these tables have extra crap in them that I don't need to show
 \begin{table}[!h] \centering
   \caption{Causal Inference on treatment variables only}
@@ -1141,14 +1354,14 @@ beads in a variety of conditions}
   {\glspl{dms} exhibit superior performance compared to beads controlling for
     many experimental and process conditions. Effect sizes for
     \subcap{fig:metaanalysis_fx_exp}{fold change},
-    \subcap{fig:metaanalysis_fx_mem}{\ptmem{} cells}, and
+    \subcap{fig:metaanalysis_fx_mem}{\ptmemp{}}, and
-    \subcap{fig:metaanalysis_fx_cd4}{\dpth{} cells}. The dotted line represents
+    \subcap{fig:metaanalysis_fx_cd4}{\dpthp{}}. The dotted line represents
     the mean of the bead population. The red and blue dots represent the effect
     size of using \gls{dms} instead of beads only considering treatment
     variables (\cref{tab:ci_treat}) or treatment and control variables
     (\cref{tab:ci_controlled}) respectively.
   }
-  \label{fig:nonstick}
+  \label{fig:metaanalysis_fx}
 \end{figure*}
 
 \section{discussion}