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tables/causal_inference_control.tex
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@ -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$^{***}$ \\

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tables/causal_inference_treat.tex
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@ -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 \\

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tex/thesis.tex
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@ -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}