diff --git a/figures/dms_coating.svg b/figures/dms_coating.svg
index d92f48c..500ae76 100644
--- a/figures/dms_coating.svg
+++ b/figures/dms_coating.svg
@@ -67,6 +67,90 @@
d="M 38.628906,5.480469 H 114 V 74 H 38.628906 Z m 0,0"
id="path373044" />
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a.
+ sodipodi:role="line">e.
STP-Coated
mAb-Coated
200 μm
Uncoated
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STP-Coated
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diff --git a/tex/thesis.tex b/tex/thesis.tex
index 1c4e6f7..1e3b00f 100644
--- a/tex/thesis.tex
+++ b/tex/thesis.tex
@@ -781,9 +781,6 @@ glioblastoma, neuroblastoma, and prostate cancer, breast cancer, non-small-cell
lung cancer, and others\cite{Rosenberg2015, Wang2014, Fesnak2016, Guo2016}. To
date, there are almost 1000 clinical trials using \gls{car} T cells.
-% TODO there are other T cells like virus-specific T cells and gd T cells, not
-% that they matter...
-
\subsection{Scaling T Cell Expansion}
In order to scale T cell therapies to meet clinical demands, automation and
@@ -1098,11 +1095,6 @@ against Fas-mediated apoptosis in the presence of collagen I\cite{Aoudjit2000,
production \invitro{} when T cells derived from human \glspl{pbmc} are
stimulated in the presence of collagen I\cite{Boisvert2007}.
-% TODO there are other receptors I could name here that were not explored Other
-% integrins that interact with the environment include a4b1, which interacts
-% with fibronectin and has been shown to lead to higher IL2 production (Iwata et
-% al 2000).
-
\subsection{The Role of IL15 in Memory T Cell Proliferation}
\il{15} is a cytokine that is involved with the proliferation and homeostasis of
@@ -1514,7 +1506,6 @@ In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
\subsection{Quantifying Cells on DMS Interior}
-% TODO add a product number to MTT (if I can find it)
To visualize T cells on the interior of the \glspl{dms}, we stained them with
\gls{mtt}. \glspl{dms} with attached and loosely attached cells were sampled as
desired and filtered through a \SI{40}{\um} cell strainer. While still in the
@@ -1568,7 +1559,6 @@ directed.
\subsection{Chemotaxis Assay}
-% TODO not sure about the transwell product number
Migratory function was assayed using a transwell chemotaxis assay as previously
described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
@@ -1887,31 +1877,33 @@ respective sections. Cells were gated according to \cref{fig:gating_strategy}.
\subsection{DMSs Can be Fabricated in a Controlled Manner}
-% FIGURE flip the rows of this figure (right now the text is backward)
\begin{figure*}[ht!]
\begingroup
\includegraphics{../figures/dms_coating.png}
- \phantomsubcaption\label{fig:stp_carrier_fitc}
- \phantomsubcaption\label{fig:mab_carrier_fitc}
\phantomsubcaption\label{fig:cug_vs_cus}
\phantomsubcaption\label{fig:biotin_coating}
\phantomsubcaption\label{fig:stp_coating}
\phantomsubcaption\label{fig:mab_coating}
+ \phantomsubcaption\label{fig:stp_carrier_fitc}
+ \phantomsubcaption\label{fig:mab_carrier_fitc}
\endgroup
\caption[\gls{dms} Coating]
{\gls{dms} functionalization results.
+ \subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
+ density on either \gls{cus} or \gls{cug} microcarriers. Surface density
+ was estimated using the properties in~\cref{tab:carrier_props}}.
+ Total binding curve of \subcap{fig:biotin_coating}{biotin},
+ \subcap{fig:stp_coating}{\gls{stp}}, and
+ \subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added for
+ batches manufactured on different dates.
\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
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
- density on either \gls{cus} or \gls{cug} microcarriers. Surface density
- was estimated using the properties in~\cref{tab:carrier_props}} Total
- binding curve of \subcap{fig:biotin_coating}{biotin},
- \subcap{fig:stp_coating}{\gls{stp}}, and
- \subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added. }
+ lightsheet microscope.}
+ }
\label{fig:dms_coating}
\end{figure*}
@@ -1945,8 +1937,8 @@ appeared that the \gls{mab} binding was quadratically related to biotin binding
critical to controlling the amount and \glspl{mab} and thus the amount of signal
the T cells receive downstream.
-% TODO these caption titles suck
-% TODO combine this DOE figure into one interaction plot
+% FIGURE these caption titles suck
+% FIGURE combine this DOE figure into one interaction plot
\begin{figure*}[ht!]
\begingroup
@@ -2566,11 +2558,9 @@ 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
+\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16
experiments between early 2017 and early 2021.
-% FIGURE 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
@@ -2646,7 +2636,6 @@ disrupt signaling pathways.
\label{fig:metaanalysis_fx}
\end{figure*}
-% 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
@@ -2658,29 +2647,25 @@ 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).
-% RESULT 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{131}{\percent} increase in fold change compared to beads. Furthermore,
-the coefficient for \ptmemp{} dropped to a \SI{3.5}{\percent} increase and
-almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
-increased to a \SI{7.4}{\percent} increase 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.
+($R^2$ between 0.76 and 0.87). 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{131}{\percent} increase in fold
+change compared to beads. Furthermore, the coefficient for \ptmemp{} dropped to
+a \SI{3.5}{\percent} increase and almost became non-significant at $\upalpha$ =
+0.05, and the \dpthp{} response increased to a \SI{7.4}{\percent} increase 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.
Finally, we stratified on the most common donor (vendor ID 338 from Astarte
Biotech) as this was responsible for almost half the data (80 runs) and repeated
@@ -2951,22 +2936,17 @@ dimensional spectra were referenced, water/end regions removed, and normalized
with the PQN algorithm\cite{Dieterle2006} using an in-house MATLAB (The
MathWorks, Inc.) toolbox.
-% TODO add the supplemental figure alluded to here?
To reduce the total number of spectral features from approximately 250 peaks and
enrich for those that would be most useful for statistical modeling, a
variance-based feature selection was performed within MATLAB. For each digitized
point on the spectrum, the variance was calculated across all experimental
samples and plotted. Clearly-resolved features corresponding to peaks in the
variance spectrum were manually binned and integrated to obtain quantitative
-feature intensities across all samples.
-% (Supp.Fig.S24).
-In addition to highly variable features, several other clearly resolved and
-easily identifiable features were selected (glucose, \gls{bcaa} region, etc).
-Some features were later discovered to belong to the same metabolite but were
-included in further analysis.
+feature intensities across all samples. In addition to highly variable features,
+several other clearly resolved and easily identifiable features were selected
+(glucose, \gls{bcaa} region, etc). Some features were later discovered to belong
+to the same metabolite but were included in further analysis.
-% I think this is the right source? it seems wrong in the manuscript but this
-% source at least talks about an optimization score
Two-dimensional spectra collected on pooled samples were uploaded to COLMARm web
server, where \gls{hsqc} peaks were automatically matched to database peaks.
\gls{hsqc} matches were manually reviewed with additional 2D and proton spectra
@@ -2975,16 +2955,6 @@ the levels of spectral data supporting the match as previously
described\cite{Dashti2017}. Annotated metabolites were matched to previously
selected features used for statistical analysis.
-% I'm pretty sure this isn't relevant
-% Using the list of annotated metabolites obtained above, an approximation of a
-% representative experimental spectrum was generated using the GISSMO mixture
-% simulation tool.39,40 With the simulated mixture of compounds, generated at 600
-% MHz to match the experimental data, a new simulation was generated at 80 MHz to
-% match the field strength of commercially available benchtop NMR spectrometers.
-% The GISSMO tool allows visualization of signals contributed from each individual
-% compound as well as the mixture, which allows annotation of features in the
-% mixture belonging to specific compounds.
-
Several low abundance features selected for analysis did not have database
matches and were not annotated. Statistical total correlation spectroscopy41
suggested that some of these unknown features belonged to the same molecules
@@ -3084,10 +3054,6 @@ the \gls{svm} regression models evaluating the cost parameter value with best
\gls{loocv}. Prediction performance was measured for all models using the final
model with \gls{loocv} tuned parameters.
-% TODO do I need this?
-% Table M2 shows the parameter values evaluated per model
-% at the final stages of results reporting.
-
\subsection{Consensus Analysis}
Consensus analysis of the relevant variables extracted from each machine
@@ -3615,21 +3581,6 @@ for proliferation following activation\cite{Cao2014}. It may be that glycolytic
cells dominate in the culture at the early time points used for prediction, and
higher lactate reflects more cells.
-% TODO not sure how much I should include here since I didn't do this analysis
-% AT ALL
-% Ethanol patterns are difficult to interpret since its production in mammalian
-% cells is still poorly understood31. Fresh media analysis indicates ethanol
-% presence in the media used, possibly utilized as a carrier solvent for certain
-% formula components. However, this does not explain the high variability and
-% trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
-% chemical, variation could be introduced by sample handling throughout the
-% analysis process. Nonetheless, it is also possible that ethanol excreted into
-% media over time, impacting processes regulating redox and reactive oxygen
-% species which have previously been shown to be crucial in T cell signaling and
-% differentiation32.
-
-% this looks fine since it is just parroting sources, just need to paraphrase a
-% little
Metabolites that consistently decreased over time are consistent with the
primary carbon source (glucose) and essential amino acids (\gls{bcaa},
histidine) that must be continually consumed by proliferating cells. Moreover,
@@ -4008,7 +3959,6 @@ density.
\label{fig:il15_1}
\end{figure*}
-% FIGURE just gate these as normal because this looks sketchy
We first tested this hypothesis by blocking \gls{il15r} with either a specific
\gls{mab} or an \gls{igg} isotype control at
\SI{5}{\ug\per\ml}\cite{MirandaCarus2005}. We observed no difference in the
@@ -4555,7 +4505,6 @@ including highly potent \ptmem{} and \pth{} T cells, and produces higher
percentages of both. If commercialized, this would be a compelling asset the T
cell manufacturing industry.
-% TODO double check the numbers at the end
In \cref{aim1}, we develop the \gls{dms} platform and verified its efficacy
\invitro{}. Importantly, this included \gls{qc} steps at every critical step of
the fabrication process to ensure that the \gls{dms} can be made within a
@@ -4567,8 +4516,8 @@ reputable vendors, and they have a regulatory history in human cell therapies
that will aid in clinical translation\cite{purcellmain}. Both these will help
in translatability. On average, we demonstrated that the \gls{dms} outperforms
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
-\SI{9.8}{\percent} controlling for donor, operator, and a variety of process
+expansion, \ptmemp{}, and \pthp{} by \SI{131}{\percent}, \SI{3.5}{\percent}, and
+\SI{7.4}{\percent} controlling for donor, operator, and a variety of process
conditions.
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)
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}
manufacturing process such that \gls{mab} binding is predictable and
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
function of surface density and the presentation method.
-\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}.
-
-% TODO this might warrant a better figure
+% FIGURE this might warrant a better figure
\subsection{Reducing Ligand Variance}
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.
\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}
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
hosted using \gls{aws} using their proprietary Aurora implementation.
-% TODO explain what the colors mean
+% FIGURE explain what the colors mean
\begin{figure*}[ht!]
\begingroup