From 9f990e5da090adc3c4bd9631a1c9c48bfb16599b Mon Sep 17 00:00:00 2001 From: ndwarshuis Date: Sun, 25 Jul 2021 22:25:23 -0400 Subject: [PATCH] ADD a bunch of text for aim 1 --- tables/causal_inference_control.tex | 2 +- tables/causal_inference_treat.tex | 2 +- tex/thesis.tex | 257 +++++++++++++++++++++++++--- 3 files changed, 237 insertions(+), 24 deletions(-) diff --git a/tables/causal_inference_control.tex b/tables/causal_inference_control.tex index 0cdc1a2..717faca 100644 --- a/tables/causal_inference_control.tex +++ b/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$^{***}$ \\ diff --git a/tables/causal_inference_treat.tex b/tables/causal_inference_treat.tex index 6c5668e..b2c34b0 100644 --- a/tables/causal_inference_treat.tex +++ b/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 \\ diff --git a/tex/thesis.tex b/tex/thesis.tex index 29ef6aa..3b0572b 100644 --- a/tex/thesis.tex +++ b/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}. -Briefly, \SI{400}{\pmol\per\ml} FITC-biotin were added to \gls{stp}-coated +quantified indirectly using \product{\gls{fitcbt}}{\thermo}{B10570}. +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 - either coated or uncoated \glspl{dms} for 14 days visualized with - brightfield microscope.} + \subcap{fig:dms_inside_bf}{T cells stained dark with \gls{mtt} after + growing on either coated or uncoated \glspl{dms} for 14 days visualized + 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_cd4}{\dpth{} cells}. The dotted line represents + \subcap{fig:metaanalysis_fx_mem}{\ptmemp{}}, and + \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}