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ENH almost proofread aim 1

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tables/luminex_panel.tex
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@ -33,6 +33,5 @@
MIP-1$\upalpha$ & 10 & 2\\
MIP-1$\upbeta$ & 10 & 2\\
RANTES & 10 & 2\\
TGF$\upbeta$ & 1 & 3\\
\hline
\end{tabular}

13
tex/references.bib
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@ -2660,6 +2660,19 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
publisher = {Massachusetts Medical Society},
}
@Article{Waysbort2013,
author = {Nir Waysbort and Dor Russ and Benjamin M. Chain and Nir Friedman},
journal = {The Journal of Immunology},
title = {Coupled {IL}-2{\textendash}Dependent Extracellular Feedbacks Govern Two Distinct Consecutive Phases of {CD}4 T Cell Activation},
year = {2013},
month = {nov},
number = {12},
pages = {5822--5830},
volume = {191},
doi = {10.4049/jimmunol.1301575},
publisher = {The American Association of Immunologists},
}
@Comment{jabref-meta: databaseType:bibtex;}
@Comment{jabref-meta: grouping:

587
tex/thesis.tex
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@ -199,12 +199,14 @@
\newacronym{zfn}{ZFN}{zinc-finger nuclease}
\newacronym{talen}{TALEN}{transcription activator-like effector nuclease}
\newacronym{qbd}{QbD}{quality-by-design}
\newacronym{aws}{AWS}{amazon web services}
\newacronym{aws}{AWS}{Amazon Web Services}
\newacronym{qpcr}{qPCR}{quantitative polymerase chain reaction}
\newacronym{cstr}{CSTR}{continuously stirred tank bioreactor}
\newacronym{esc}{ESC}{embryonic stem cell}
\newacronym{msc}{MSC}{mesenchymal stromal cells}
\newacronym{scfv}{scFv}{single-chain fragment variable}
\newacronym{hepes}{HEPES}{4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid}
\newacronym{nhs}{NHS}{N-hydroxysulfosuccinimide}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SI units for uber nerds
@ -289,6 +291,7 @@
\newcommand{\catnum}[2]{(#1, #2)}
\newcommand{\product}[3]{#1 \catnum{#2}{#3}}
\newcommand{\thermo}{Thermo Fisher}
\newcommand{\gehc}{GE Healthcare}
\newcommand{\sigald}{Sigma Aldrich}
\newcommand{\miltenyi}{Miltenyi Biotech}
\newcommand{\bl}{Biolegend}
@ -1266,23 +1269,9 @@ microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
provide superior expansion and memory phenotype compared to state-of-the-art
bead-based T cell expansion technology.
% TODO this doesn't flow that well and is repetitive with what comes above
Microcarriers have been used throughout the bioprocess industry for adherent
cell cultures such as \gls{cho} cells and stem cells, as they are able to
achieve much greater surface area per unit volume than traditional 2D
cultures\cite{Heathman2015, Sart2011}. Adding adhesive \glspl{mab} to the
microcarriers will adapt them for suspension cell cultures such as T cells.
Consequently, the large macroporous structure will allow T cells to cluster more
closely, which in turn will enable better autocrine and paracrine signaling.
Specifically, two cytokines that are secreted by T cells, IL-2 and IL-15, are
known to drive expansion and memory phenotype respectively\cite{Buck2016}.
Therefore, the proposed microcarrier system should enable greater expansion and
better retention of memory phenotype compared to current bead-based methods.
\section{methods}
\subsection{dms functionalization}\label{sec:dms_fab}
\subsection{DMS functionalization}\label{sec:dms_fab}
\begin{figure*}[ht!]
\begingroup
@ -1294,24 +1283,23 @@ better retention of memory phenotype compared to current bead-based methods.
\label{fig:dms_flowchart}
\end{figure*}
Gelatin microcarriers (\gls{cus} or \gls{cug}, GE Healthcare, DG-2001-OO and
DG-0001-OO) were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and
autoclaved. All subsequent steps were done aseptically, and all reactions were
carried out at \SI{20}{\mg\per\ml} carriers at room temperature and agitated
using an orbital shaker with a \SI{3}{\mm} orbit diameter. After autoclaving,
the microcarriers were washed using sterile \gls{pbs} three times in a 10:1
volume ratio. \product{\Gls{snb}}{\thermo}{21217} was dissolved at
approximately \SI{10}{\uM} in sterile ultrapure water, and the true
concentration was then determined using the \gls{haba} assay (see below).
\SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added to carrier suspension and allowed
to react for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the
reaction, the amount of biotin remaining in solution was quantified using the
\gls{haba} assay (see below). The carriers were then washed three times, which
entailed adding sterile \gls{pbs} in a 10:1 volumetric ratio, agitating at
\SI{900}{\rpm} for \SI{10}{\minute}, adding up to a 15:1 volumetric ratio
(relative to reaction volume) of sterile \gls{pbs}, centrifuging at
\SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid back down to the
reaction volume.
\product{\gls{cus}}{\gehc}{DG-2001-OO} or \product{\gls{cug}}{\gehc}{DG-0001-OO}
were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and autoclaved. All
subsequent steps were done aseptically, and all reactions were carried out at
\SI{20}{\mg\per\ml} carriers at room temperature and agitated using an orbital
shaker with a \SI{3}{\mm} orbit diameter. After autoclaving, the microcarriers
were washed using sterile \gls{pbs} three times in a 10:1 volume ratio.
\product{\Gls{snb}}{\thermo}{21217} was dissolved at approximately \SI{10}{\uM}
in sterile ultrapure water, and the true concentration was then determined using
the \gls{haba} assay (see below). \SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added
to carrier suspension and allowed to react for \SI{60}{\minute} at
\SI{700}{\rpm} of agitation. After the reaction, the amount of biotin remaining
in solution was quantified using the \gls{haba} assay (see below). The carriers
were then washed three times, which entailed adding sterile \gls{pbs} in a 10:1
volumetric ratio, agitating at \SI{900}{\rpm} for \SI{10}{\minute}, adding up to
a 15:1 volumetric ratio (relative to reaction volume) of sterile \gls{pbs},
centrifuging at \SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid
back down to the reaction volume.
To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson
Immunoresearch}{016-000-114} was added and allowed to react for
@ -1332,13 +1320,19 @@ sampled to quantify remaining \gls{mab} concentration using an
step to remove excess \gls{stp}. They were washed once again in the cell culture
media to be used for the T cell expansion.
\begin{table}[!h] \centering
\caption{Properties of the microcarriers used}
\label{tab:carrier_props}
\input{../tables/carrier_properties.tex}
\end{table}
The concentration of the final \gls{dms} suspension was found by taking a
\SI{50}{\uL} sample, plating in a well, and imaging the entire well. The image
was then manually counted to obtain a concentration. Surface area for
\si{\ab\per\um\squared} was calculated using the properties for \gls{cus} and
\gls{cug} as given by the manufacturer {Table X}.
\gls{cug} as given by the manufacturer \cref{tab:carrier_props}.
\subsection{dms quality control assays}
\subsection{DMS quality control assays}
Biotin was quantified using the \product{\gls{haba} assay}{\sigald}{H2153-1VL}.
In the case of quantifying \gls{snb} prior to adding it to the microcarriers,
@ -1350,15 +1344,15 @@ Spectrophotometer using \product{\SI{70}{\ul} cuvettes}{BrandTech}{759200}. The
extinction coefficient at \SI{500}{\nm} for \gls{haba}/avidin was assumed to be
\SI{34000}{\per\cm\per\molar}.
\gls{stp} binding to the carriers was quantified indirectly using a
The \gls{stp} binding to the microcarriers was quantified indirectly using a
\product{\gls{bca} kit}{\thermo}{23227} according to the manufacturer’s
instructions, with the exception that the standard curve was made with known
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at
\SI{592}{\nm} was quantified using a Biotek plate reader.
\SI{592}{\nm} was quantified using a BioTek plate reader.
\Gls{mab} binding to the microcarriers was quantified indirectly using an
The \gls{mab} binding to the microcarriers was quantified indirectly using an
\gls{elisa} assay per the manufacturer’s instructions, with the exception that
the same antibodies used to coat the carriers were used as the standard for the
the same \glspl{mab} 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
@ -1366,18 +1360,18 @@ 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
\gls{fitcbt} 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 \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}}-\gls{fitc}}{\bl}{406001}, incubating for
\SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope.
microscope}{Zeiss}{Z.1}. Overall \gls{mab} binding was verified visually
by first 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}\label{sec:tcellculture}
% TODO verify countess product number
Cryopreserved primary human T cells were either obtained as sorted
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection
@ -1390,15 +1384,15 @@ otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
\product{OpTmizer}{\thermo}{A1048501} or
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02}. Cell
cultures were expanded for \SI{14}{\day} as counted from the time of initial
seeding and activation. Cell counts and viability were assessed using
\product{trypan blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom
Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo
Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day}
depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose
threshold. Media glucose was measured using a \product{GlucCell glucose
meter}{Chemglass}{CLS-1322-02}.
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02} unless
otherwise noted. Cell cultures were expanded for \SI{14}{\day} as counted from
the time of initial seeding and activation. Cell counts and viability were
assessed using \product{trypan blue}{\thermo}{T10282} or
\product{\gls{aopi}}{Nexcelom Bioscience}{CS2-0106-5} and a \product{Countess
Automated Cell Counter}{Thermo Fisher}{Countess 3 FL}. Media was added to
cultures every \SIrange{2}{3}{\day} depending on media color or a
\SI{300}{\mg\per\deci\liter} minimum glucose threshold. Media glucose was
measured using a \product{GlucCell glucose meter}{Chemglass}{CLS-1322-02}.
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
@ -1406,19 +1400,20 @@ Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
imaging on a spinning disk confocal microscope.
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
Wolf}{P/N 80192M} or a \product{6 well plate}{P/N 80240M}.
Wolf}{P/N 80192M} or a \product{6 well plate}{Wilson Wolf}{P/N 80240M}.
\subsection{Quantifying cells on DMS interior}
% TODO add a product number to MTT (if I can find it)
Cells were stained and visualized using \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 cell strainer, \glspl{dms} were
washed twice with \gls{pbs} and then dried by pulling liquid through the bottom
of the cell strainer via a micropipette and dabbing with a KimWipe. \glspl{dms}
were transferred to a 24 well plate with \SI{400}{\ul} media. \SI{40}{\ul}
\gls{mtt} was added to each well and allowed to incubate for \SI{3}{\hour},
after which \glspl{dms} with cell were visualized via a brightfield microscope.
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
cell strainer, \glspl{dms} were washed twice with \gls{pbs} and then dried by
pulling liquid through the bottom of the cell strainer via a micropipette and
dabbing with a KimWipe. \glspl{dms} were transferred to a 24 well plate with
\SI{400}{\ul} media. \SI{40}{\ul} \gls{mtt} was added to each well and allowed
to incubate for \SI{3}{\hour}, after which \glspl{dms} with cell were visualized
via a brightfield microscope.
To quantify cells on the interior of \glspl{dms}, cells and \glspl{dms} were
isolated analogously to those for the \gls{mtt} stain up until the drying step.
@ -1433,13 +1428,13 @@ Apoptosis was quantified using \gls{anv} according to the manufacturer's
instructions. Briefly, cells were transferred to flow tubes and washed twice
with \gls{pbs} by adding \SI{3}{\ml} to each tube, centrifuging for
\SI{400}{\gforce}, and aspirating the liquid down to \SI{200}{\ul}. The cells
were analogously washed a third time with staining buffer (\SI{10}{\mM} HEPES,
\SI{140}{\mM} NaCl, \SI{2.5}{\mM} CaCl\textsubscript{2}) and aspirated down to a
final volume of \SI{100}{\ul}. Cells were stained in this volume with
were analogously washed a third time with staining buffer (\SI{10}{\mM}
\gls{hepes}, \SI{140}{\mM} NaCl, \SI{2.5}{\mM} \ce{CaCl2}) and aspirated down to
a final volume of \SI{100}{\ul}. Cells were stained in this volume with
\SI{1}{\ul} \product{\gls{anv}-\gls{fitc}}{\bl}{640906} and \SI{5}{\ul}
\product{\gls{pi}}{\thermo}{P3566} and incubated for \SI{15}{\minute} at gls{rt}
in the dark. After incubation, \SI{400}{\ul} staining buffer was added to each
tube. Each tube was then analyzed via flow cytometry.
\product{\gls{pi}}{\thermo}{P3566} and incubated for \SI{15}{\minute} at
\gls{rt} in the dark. After incubation, \SI{400}{\ul} staining buffer was added
to each tube. Each tube was then analyzed via flow cytometry.
\subsection{quantification of Caspase-3/7}
@ -1453,13 +1448,13 @@ After incubation, cells were immediately analyzed via flow cytometry.
\Gls{bcl2} was quantified using an \product{Human Total Bcl-2 DuoSet \gls{elisa}
kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
supplemented with \product{5X diluent buffer}{\bl}{421203}, \product{\gls{tmb}
substrate solution}{eBioscience}{00-4201-56}, and \SI{2}{\normal}
H\textsubscript{2}SO\textsubscript{4} stop solution made in house. Briefly,
cells were lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and
the lysate was quantified for protein using a \product{\gls{bca}
assay}{\thermo}{23225} as directed. Standardized lysates were measured using
the \gls{elisa} kit as directed.
supplemented with \product{\gls{tmb} substrate
solution}{eBioscience}{00-4201-56}, \product{5X diluent buffer}{\bl}{421203},
and \SI{2}{\normal} \ce{H2SO4} stop solution made in house. Briefly, cells were
lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and the lysate
was quantified for protein using a \product{\gls{bca} assay}{\thermo}{23225} as
directed. Standardized lysates were measured using the \gls{elisa} kit as
directed.
\subsection{chemotaxis assay}
@ -1479,24 +1474,30 @@ transwell was quantified for total cells using \product{countbright
Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
were incubated with \num{1.5e5} target cells consisting of either \product{K562
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}.
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar} \product{monensin}{eBioscience}{
00-4505-51}, and \SI{1}{\ug\per\ml} \product{\acd{28}}{eBioscience}{302914} (all
functional grade \glspl{mab}) with \SI{250}{\ul} total volume. After
\SI{4}{\hour} incubation at \SI{37}{\degreeCelsius}, cells were stained for CD3,
CD4, and CD107a and analyzed on a BD LSR Fortessa. Readout was calculated as the
percent \cdp{107a} cells of the total \cdp{8} fraction.
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar}
\product{monensin}{eBioscience}{ 00-4505-51}, and \SI{1}{\ug\per\ml}
\product{\acd{28}}{eBioscience}{302914} (all functional grade \glspl{mab}) with
\SI{250}{\ul} total volume. After \SI{4}{\hour} incubation at
\SI{37}{\degreeCelsius}, cells were stained for CD3, CD4, and CD107a and
analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent
\cdp{107a} cells of the total \cdp{8} fraction.
\subsection{car expression}
\subsection{CAR expression}
\gls{car} expression was quantified as previously described\cite{Zheng2012}.
Briefly, cells were washed once and stained with \product{biotinylated
\gls{ptnl}}{\thermo}{29997}. After a subsequent wash, cells were stained with
\product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again, and analyzed on a
BD Accuri. Readout was percent \gls{pe}+ cells as compared to secondary controls
(\gls{pe}-\gls{stp} with no \gls{ptnl}).
\gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously
described\cite{Zheng2012}. Briefly, cells were washed once and stained with
\product{biotinylated \gls{ptnl}}{\thermo}{29997}. After a subsequent wash,
cells were stained with \product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again,
and analyzed on a \bd{} Accuri. Readout was percent \gls{pe}+ cells as compared
to secondary controls (\gls{pe}-\gls{stp} with no \gls{ptnl}).
\gls{car} expression of the \anti{\gls{bcma}} \gls{car} was quantified using a
\product{\gls{fitc}-labeled \gls{bcma} protein}{Acro}{Bca-hf254}. \SI{100}{\ng}
was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute}
prior to analyzing on a \bd{} Accuri
\subsection{car plasmid and lentiviral transduction}
@ -1527,39 +1528,37 @@ Kochenderfer at the NIH)\cite{Lam2020} was added to \SI{50}{\ul}
\product{DH5$\upalpha$ cells}{\thermo}{18265017} and incubated for
\SI{30}{\minute} on ice. The cell mixture was then heat-shocked at
\SI{42}{\degreeCelsius} for \SI{20}{\minute} before being placed on ice for
another \SI{2}{\minute}. \SI{950}{\ul} \product{LB Broth}{TODO}{TODO} was added
to the cells which were then centrifuged for \SI{1}{\hour} at \SI{225}{\rpm}.
\SI{20}{\ul} of the cell mixture was then spread over selection plates and
incubated overnight at \SI{37}{\degreeCelsius}. Colonies were selected the
following day and incubated in \product{LB Broth}{TODO}{TODO} with
\product{ampicillin}{\sigald{}}{A9518-5G} at \SI{37}{\degreeCelsius} for
\SIrange{12}{16}{\hour} prior to using the \product{miniprep kit}{Qiagen}{27104}
as per the manufacturer's instructions to isolate the plasmid DNA. Transfer
plasmid along with \product{pMDLg/pRRE}{Addgene}{12251},
\product{pRSV-Rev}{Addgene}{12253}, and \product{pMD2.G}{Addgene}{12259}
(generously provided by the Sloan lab at Emory University) in
\product{Opti-Mem}{\thermo}{31-985-070} with \product{lipfectamine
2000}{\thermo}{11668019} were added dropwise to HEK 293T cells and incubated
for \SI{6}{\hour}, after which all media was replaced with fresh fresh media.
After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was collected, pooled, and
concentrated using a \product{Lenti-X concentrator}{Takara}{631231} prior to
storing at \SI{-80}{\degreeCelsius}.
another \SI{2}{\minute}. \SI{950}{\ul} luria broth was added to the cells which
were then centrifuged for \SI{1}{\hour} at \SI{225}{\rpm}. \SI{20}{\ul} of the
cell mixture was then spread over selection plates and incubated overnight at
\SI{37}{\degreeCelsius}. Colonies were selected the following day and incubated
in luria broth with \product{ampicillin}{\sigald{}}{A9518-5G} at
\SI{37}{\degreeCelsius} for \SIrange{12}{16}{\hour} prior to using the
\product{miniprep kit}{Qiagen}{27104} as per the manufacturer's instructions to
isolate the plasmid DNA. Transfer plasmid along with
\product{pMDLg/pRRE}{Addgene}{12251}, \product{pRSV-Rev}{Addgene}{12253}, and
\product{pMD2.G}{Addgene}{12259} (generously provided by the Sloan lab at Emory
University) in \product{Opti-Mem}{\thermo}{31-985-070} with
\product{lipfectamine 2000}{\thermo}{11668019} were added dropwise to HEK 293T
cells and incubated for \SI{6}{\hour}, after which all media was replaced with
fresh fresh media. After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was
collected, pooled, and concentrated using a \product{Lenti-X
concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}.
\subsection{sulfo-NHS-biotin hydrolysis quantification}
The equation for hydrolysis of \gls{snb} was assumed to follow
The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by
\cref{chem:snb_hydrolysis}.
% TODO make this look prettier
\begin{equation}
\label{chem:snb_hydrolysis}
\ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
\end{equation}
The hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb} was added
to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic
analysis using a Biotech Plate Reader began immediately after prep, and readings
at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
Measuring the hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb}
was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well
plate. Kinetic analysis using a BioTek plate reader began immediately after
prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
\subsection{reaction kinetics quantification}
@ -1568,41 +1567,44 @@ experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of
biotin-coated microcarriers, and supernatents were taken at fixed intervals and
quantified for \gls{stp} protein using the \gls{bca} assay.
% TODO defend why the microcarriers were saturated with stp
The effective diffusivity of the microcarriers was determined using a
The geometric diffusivity of the microcarriers was determined using a
pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere
with a fixed number of `\gls{stp} binding sites' equal to the number of
\gls{stp} molecules experimentally determined to bind to the microcarriers.
Because the reaction rate between biotin and \gls{stp} was so fast, we assumed
that the interface of free biotin shrunk as a function of \gls{stp} bound until
the center of the microcarriers was reached. We also assumed that the pores in
the microcarriers were large enough that the interactions between the \gls{stp}
and surfaces would be small, thus the apparent diffusivity could be represented
as a fraction of the diffusion coefficient of \gls{stp} in water. This model was
given by \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
with a fixed number of uniformly distributed `\gls{stp} binding sites' equal to
the number of \gls{stp} molecules experimentally determined to bind to the
microcarriers. Because the reaction rate between biotin and \gls{stp} is so fast
(it is the strongest non-covalent bond in known existence), we assumed that the
interface of free biotin shrunk as a function of \gls{stp} diffusing to the
unbound biotin interface until the center of the microcarriers was reached. We
also assumed that the pores in the microcarriers were large enough that the
interactions between the \gls{stp} and surfaces would be small, thus the
geometric diffusivity could be represented as a fraction of the diffusion
coefficient of \gls{stp} in water. This model was given by
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
% TODO actually derive these equations, eg state the initial conditions and
% governing equation
\begin{equation}
\label{eqn:stp_diffusion_1}
\frac{dr}{dt} = \frac{-D_{app}C}{Br(1-r/R)}
\frac{dr}{dt} = \frac{-D_{app}C_b}{Br(1-r/R)}
\end{equation}
\begin{equation}
\label{eqn:stp_diffusion_2}
\frac{dC}{dt} = \frac{-4 \pi n D_{app} C}{V(1/r-1/R)}
\frac{dC_b}{dt} = \frac{-4 \pi n D_{app} C_b}{V(1/r-1/R)}
\end{equation}
\noindent where
\begin{itemize}[label={}]
\item $D_{app}$ is the apparent diffusion rate which is equal to $D_{STP}\beta$
\item $D_{STP}$ the diffusion rate of \gls{stp} (or \glspl{mab} for later
calculations) in water
\item $D_{app}$ is the apparent diffusion rate of species $X$ which is equal to
$D\beta$
\item $D$ the diffusion rate of species $X$ in water at room temperature
(where $X$ is \gls{stp} in this example and \glspl{mab} later in this section)
\item $\beta$ a fractional parameter representing the tortuousity and void
fraction of the microcarriers (here called the `geometric diffusivity')
\item $r$ is the interfatial radius of the unbound biotin within a microcarrier
\item $r$ is the interfatial radius of the unbound binding sites for species $X$
within a microcarrier
\item $t$ is the reaction time
\item $C$ is the concentration of \gls{stp} in the bulk solution
\item $C_b$ is the concentration of species $X$ in the bulk solution
\item $V$ is the volume of the bulk medium
\item $R$ is the average radius of the microcarriers
\item $n$ is the number of microcarriers in the reaction volume
@ -1646,17 +1648,15 @@ partial differential equation and boundary conditions:
\noindent where (in addition to the variables given already for
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2})
\begin{itemize}[label={}]
\item $N_i$ is the radial flux of the species in question inside the
microcarriers
\item $C_i$ is the concentration of the species in question inside the
microcarriers
\item $C_{i,0}$ is the initial concentration of the species in question inside
\item $N_i$ is the radial flux of species $X$ inside the microcarriers
\item $C_i$ is the concentration of species $X$ inside the microcarriers
\item $C_{i,0}$ is the initial concentration of species $X$ inside
the microcarriers (which is assumed to be the concentration in the bulk before
the wash volume is added)
\item $C_{b,0}$ is the initial bulk concentration of the species in question
outside the microcarriers after the initial wash volume has been added
\item $C_{b,\infty}$ is the final bulk concentration of the species in
question outside the microcarriers
\item $C_{b,0}$ is the initial bulk concentration of species $X$ outside the
microcarriers after the initial wash volume has been added
\item $C_{b,\infty}$ is the final bulk concentration of species $X$ outside the
microcarriers
\end{itemize}
Note that in order to avoid solving a moving boundary value problem, the
@ -1669,7 +1669,7 @@ concentration should change little.
The same diffusion coefficients were used in determining the kinetics of the
washing steps, and \SI{5.0e-6}{\cm\squared\per\second}\cite{Niether2020} was
used as the diffusion coefficient for free biotin (which should be the only
species left in solution after all the \gls{snb} has hydrolyzed).
reactive species left in solution after all the \gls{snb} has hydrolyzed).
All diffusion coefficients were taken to be valid at \gls{rt} and in \gls{di}
water, which is a safe assumption given that our reaction medium was 1X
@ -1689,17 +1689,17 @@ thawed at \gls{rt} and vortexed to ensure homogeneity. To run the plate,
\SI{25}{\ul} of magnetic beads were added to the plate and washed 3X using
\SI{300}{\ul} of wash buffer. \SI{25}{\ul} of samples or standard were added to
the plate and incubated for \SI{120}{\minute} at \SI{850}{\rpm} at \gls{rt}
before washing analogously 3X with wash. \SI{12.5}{\ul} detection \glspl{mab}
and \SI{25}{\ul} \gls{stppe} were sequentially added, incubated for
before washing analogously 3X with wash buffer. \SI{12.5}{\ul} detection
\glspl{mab} and \SI{25}{\ul} \gls{stppe} were sequentially added, incubated for
\SI{30}{\minute} and vortexed, and washed analogously to the sample step.
Finally, samples were resuspended in \SI{120}{\ul} reading buffer and analyzed
via a BioRad Bioplex 200 plate reader. An 8 point log2 standard curve was used,
and all samples were run with single replicates.
via a BioRad Bioplex 200 plate reader. An 8 point log\textsubscript{2} standard
curve was used, and all samples were run with single replicates.
Luminex data was preprocessed using R for inclusion in downstream analysis as
follows. Any cytokine level that was over-range (`OOR >' in output spreadsheet)
was set to the maximum value of the standard curve for that cytokine. Any value
that was under-range (`OOR <l in output spreadsheet) was set to zero. All values
that was under-range (`OOR <' in output spreadsheet) was set to zero. All values
that were extrapolated from the standard curve were left unchanged.
\begin{table}[!h] \centering
@ -1712,7 +1712,7 @@ that were extrapolated from the standard curve were left unchanged.
In order to perform meta-analysis on all experimental data generate using the
\gls{dms} system, we developed a program to curate and aggregate the raw
datafiles into a \gls{sql} database (\cref{fig:meta_overview}).
datafiles into a \gls{sql} database (\cref{sec:appendix_meta}).
The data files to be aggregated included Microsoft Excel files which held
timeseries measurements for cell cultures (eg cell counts, viability, glucose,
@ -1732,6 +1732,7 @@ This program included checks to ensure the integrity of source data (for
example, flagging entries which had a reagent whose manufacturing date was after
the date the experiment started, which signifies a human input error).
\subsection{statistical analysis}\label{sec:statistics}
For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
@ -1740,11 +1741,8 @@ with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R.
For 2-way \gls{anova} analysis, the significance of main and interaction effects
was determined using the car library in R.
% TODO not all of this stuff applied to my regressions
For least-squares linear regression, statistical significance was evaluated the
\inlinecode{lm} function in R. Stepwise regression models were obtained using
the \inlinecode{stepAIC} function from the \inlinecode{MASS} package with
forward and reverse stepping. All results with categorical variables are
\inlinecode{lm} function in R. All results with categorical variables are
reported relative to baseline reference. Each linear regression was assessed for
validity using residual plots (to assess constant variance and independence
assumptions), QQplots and Shapiro-Wilk normality test (to assess normality
@ -1766,42 +1764,21 @@ context of pure error). Statistical significance was evaluated at $\upalpha$ =
\label{fig:gating_strategy}
\end{figure*}
% METHOD add flow cytometry
\begin{table}[!h] \centering
\caption{\glspl{mab} used for flow cytometry}
\label{tab:flow_mabs}
\input{../tables/flow_mabs.tex}
\end{table}
All \glspl{mab} used for flow cytometry are shown in \cref{tab:flow_mabs}. Other
reagents for specialized assays such as degranulation are described in their
respective sections. Cells were gated according to \cref{fig:gating_strategy}.
\section{results}
\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\cite{purcellmain} 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 flip the rows of this figure (right now the text is backward)
% FIGURE flip the rows of this figure (right now the text is backward)
\begin{figure*}[ht!]
\begingroup
@ -1829,15 +1806,28 @@ together, we noted that the maximal \gls{mab} binding capacity occurred near
\label{fig:dms_coating}
\end{figure*}
% TODO these caption titles suck
% TODO combine this DOE figure into one interaction plot
\begin{table}[!h] \centering
\caption{Properties of the microcarriers used}
\label{tab:carrier_props}
\input{../tables/carrier_properties.tex}
\end{table}
% TODO add chemical equation for which reactions I am describing here
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\cite{purcellmain} 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{fitcbt} 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}/\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.
We then asked how sensitive the \gls{dms} manufacturing process was to a variety
of variables. In particular, we focused on the biotin-binding step, since it
@ -1846,6 +1836,8 @@ 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
\begin{figure*}[ht!]
\begingroup
@ -1895,7 +1887,7 @@ We also observed that the reaction pH does not influence the amount of biotin
attached (\cref{fig:dms_qc_ph}), which indicates that while higher pH might
increase the number of deprotonated amines on the surface of the microcarrier,
it also increases the number of \ce{OH-} groups which can spontaneously
hydrolyze the \gls{snb} in solution.
hydrolyze the \gls{snb} in solution (\cref{chem:snb_hydrolysis}).
Furthermore, we observed that washing the microcarriers after autoclaving
increases the biotin binding rate (\cref{fig:dms_qc_washes}). While we did not
@ -1907,20 +1899,20 @@ lightly-suspended peptides/protein fragments are also measured and therefore
inflate the readout.
Lastly, we asked what the effect on reaction pH had on spontaneous degradation
of \gls{snb} while in solution. If the \gls{snb} significantly degrades within
minutes of preparation, then it is important to carefully control the timing
between \gls{snb} solution preparation and addition to the microcarriers. We
found that in the presence of \gls{di} water, \gls{snb} is extremely stable
(\cref{fig:dms_snb_decay_curves}) where it decays rapidly in the presence of
\gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water curve actually
decreases slightly, possibly due to \gls{snb} absorbing to the plate surface.
\gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but the lack of
hydrolysis in \gls{di} water can be explained by the fact that biotin itself is
acidic, and thus the reaction is self-inhibitory in an unbuffered and neutral pH
system. Because we dissolve our \gls{snb} in \gls{di} water prior to adding it
to the microcarrier suspension (which itself is in \gls{pbs}) this result
indicated that hydrolysis is not of concern when adding \gls{snb} within
minutes.
of \gls{snb} while in solution (\cref{chem:snb_hydrolysis}). If the \gls{snb}
significantly degrades within minutes of preparation, then it is important to
carefully control the timing between \gls{snb} solution preparation and addition
to the microcarriers. We found that in the presence of \gls{di} water, \gls{snb}
is extremely stable (\cref{fig:dms_snb_decay_curves}) where it decays rapidly in
the presence of \gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water
curve actually decreases slightly, possibly due to \gls{snb} absorbing to the
plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but
the lack of hydrolysis in \gls{di} water can be explained by the fact that
biotin itself is acidic, and thus the reaction is self-inhibitory in an
unbuffered and neutral pH system. Because we dissolve our \gls{snb} in \gls{di}
water prior to adding it to the microcarrier suspension (which itself is in
\gls{pbs}) this result indicated that hydrolysis is not of concern when adding
\gls{snb} within minutes.
\begin{figure*}[ht!]
\begingroup
@ -1946,47 +1938,53 @@ minutes.
\label{fig:dms_kinetics}
\end{figure*}
We also investigated the reaction kinetics of all three coating steps.
\subsection{reaction kinetics for coating the DMSs}
To quantify the reaction kinetics of the biotin binding step, we reacted
multiple batches of \SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt}
with \gls{snb} in parallel and sacrificially analyzed each at varying timepoints
using the \gls{haba} assay. This was performed at two different concentrations.
We observed that for either concentration, the reaction was over in
We investigated the reaction kinetics of all three coating steps (accompanying
MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the
reaction kinetics of the biotin binding step, we reacted multiple batches of
\SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt} with \gls{snb} in
parallel and sacrificially analyzed each at varying timepoints using the
\gls{haba} assay. This was performed at two different concentrations. We
observed that for either concentration, the reaction was over in
\SIrange{20}{30}{\minute} (\cref{fig:dms_biotin_rxn_mass}). Furthermore, when
put in terms of fraction of input \gls{snb}, we observed that the curves are
almost identical (\cref{fig:dms_biotin_rxn_frac}). Given this, the reaction step
for biotin attached was set to \SI{30}{\minute}\footnote{we actually used
\SI{60}{\minute} for most of the runs as outlined in methods, which shouldn't
make any difference except save for being excessive according to this result}.
for biotin attached can be set to \SI{30}{\minute}\footnote{we actually used
\SI{60}{\minute} as outlined in methods, which shouldn't make any difference
except save for being excessive according to this result}.
% RESULT state how we calculated the number of stp/site
Next, we quantified the amount of \gls{stp} reacted with the surface of the
biotin-coated microcarriers. Different batches of biotin-coated \glspl{dms} were
coated with \SI{40}{\ug\per\ml} \gls{stp} and sampled at intermediate timepoints
using the \gls{bca} assay to indirectly quantify the amount of attached
\gls{stp} mass. We found this reaction took approximately \SI{30}{\minute}
(\cref{fig:dms_stp_per_time}). Assuming a quasi-steady-state paradigm, we used
this experimental binding data to fit a continuous model for the \gls{stp}
binding reaction. Using the diffusion rate of the \gls{stp}
this experimental binding data to compute the geometric diffusivity of the
microcarriers and fit a continuous model for the \gls{stp} binding reaction. We
computed the number of `binding sites' using the maximum mass observed to bind
to the \gls{dms}, which should describe the upper-bound for reaction time
(\cref{fig:stp_coating}). Using the diffusion rate of the \gls{stp}
(\SI{6.2e-7}{\cm\squared\per\second}), we then calculated the geometric
diffusivity of the microcarriers to be 0.190 (see
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}).
% RESULT state how I calculated the number of mab/surface area
Using this effective diffusivity and the known diffusion coefficient of a
\gls{mab} protein in water, we calculated predict the binding of \glspl{mab} per
time onto the microcarriers (this obviously assumes that the effectively
diffusivity is independent of the protein used, which should be reasonable given
that the pores of the microcarriers are huge compared to the proteins, and we
don't expect any significant reaction between the protein and the microcarrier
surface save for the \gls{stp}-biotin binding reaction). According to this
model, the \gls{mab} binding reaction should be complete within \SI{75}{\minute}
under the conditions used for our protocol
(\cref{fig:dms_mab_per_time})\footnote{We actually used \SI{60}{\minute} as
describe in the method section as this model was not updated with new
parameters until recently; however, we should point out that even at
\SI{60}{\minute} the reaction appears to be >\SI{95}{\percent} complete}.
Using this geometric diffusivity and the known diffusion coefficient of a
\gls{mab} protein in water, we calculated the binding of \glspl{mab} per time
onto the microcarriers (this obviously assumes that the effectively diffusivity
is independent of the protein used, which should be reasonable given that the
pores of the microcarriers are huge compared to the proteins, and we don't
expect any significant reaction between the protein and the microcarrier surface
save for the \gls{stp}-biotin binding reaction). Once again, we used the maximum
number of \glspl{mab} observed to determine the number of `binding sites' for
\glspl{mab} on the microcarriers, which should correspond to the upper-bound for
the reaction time (\cref{fig:mab_coating}). According to this model, the
\gls{mab} binding reaction should be complete within \SI{75}{\minute} under the
conditions used for our protocol (\cref{fig:dms_mab_per_time})\footnote{We
actually used \SI{60}{\minute} as describe in the method section as this model
was not updated with new parameters until recently; however, we should point
out that even at \SI{60}{\minute} the reaction appears to be
>\SI{95}{\percent} complete}.
Finally, we calculated the number of wash steps needed to remove the reagents
between each step, including the time for each wash which required the geometric
@ -2024,9 +2022,11 @@ should not that the washing time for both the \gls{stp} and \gls{mab} coating
steps were \SI{30}{\minute}, which is a significant margin of safety (albeit
one that could be optimized).
MATLAB code and output for all the wash step calculations are given in
\cref{sec:appendix_washing}.
\subsection{DMSs can efficiently expand T cells compared to beads}
% FIGURE make sure the day on these is correct
\begin{figure*}[ht!]
\begingroup
@ -2038,10 +2038,11 @@ one that could be optimized).
\caption[T cells growing on \glspl{dms}]
{Cells grow in tight clusters in and around functionalized \gls{dms}.
\subcap{fig:dms_cells_phase}{Phase-contrast image of T cells growing on
\glspl{dms} on day 7}
\glspl{dms}}
\subcap{fig:dms_cells_fluor}{Confocal images of T cells in varying z-planes
growing on \glspl{dms} on day 9. \Glspl{dms} were stained using
\gls{stppe} (red) and T cells were stained using \acd{45}-\gls{af647}.}
Images are from day 7 of culture.
}
\label{fig:dms_cells}
\end{figure*}
@ -2202,28 +2203,26 @@ harvested after \SI{14}{\day}) (\cref{tab:inside_regression}).
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.
were interested in the formation of \glspl{tn}, \gls{tscm}, and \glspl{tcm} as
these represent a subset with higher capacity to replicate and therefore
improved clinical prognosis\cite{Gattinoni2011, Wang2018}. We measured the
frequency of these subtypes by staining for CCR7 and CD62L. Using three donor
lots, 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 (\ptmem{})
(\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
cells to a greater degree than beads \cref{fig:dms_exp_cd4}. The trends held
true when observing the CD4+ and CD8+ fractions of the naïve/memory subset
(\ptmem{}) (\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
% FIGURE this figure has weird proportions
% FIGURE this figure was not produced with the same donors as the figure above,
% which is really confusing
\begin{figure*}[ht!]
@ -2249,7 +2248,7 @@ experiments\footnote{these results were not always consistent, see the
metaanalysis at the end of this aim for an in-depth quantification of this
observation} that the fraction of \ptmem{} and \pth{} T cells was higher in
the \gls{dms} groups compared to the bead groups (\cref{fig:dms_phenotype}).
This result was seen for multiple donors. We should not that in the case of
This result was seen for multiple donors. We should note that in the case of
\pthp{}, the donors we used had an initial \pthp{} that was much higher (healthy
donors generally have a CD4:CD8 ratio of 2:1), so the proper interpretation of
this is that the \pthp{} decreases less over the culture period with the
@ -2267,12 +2266,13 @@ technology.
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
generate \gls{car} T cells\cite{Tumaini2013, Lamers2014}. We added a
\SI{24}{\hour} transduction step on day 1 of the \SI{14}{\day} expansion to
insert an anti-CD19 \gls{car}\cite{Milone2009} 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
@ -2283,8 +2283,8 @@ 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
(\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
@ -2298,6 +2298,7 @@ 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.
% FIGURE break this up to give the text more flexibility
\begin{figure*}[ht!]
\begingroup
@ -2391,8 +2392,8 @@ we did not move the T cells to a larger bioreactor as they grew in contrast with
our plate cultures. This means that the cells had higher growth area
constraints, which may have nullified any advantage to the expansion that we
seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
area could mean higher signaling and higher differentiation rate to effector T
cells, which was why the \ptmemp{} was so low compared to other data
area could mean higher signaling and higher differentiation rate to
\glspl{teff}, which was why the \ptmemp{} was so low compared to other data
(\cref{fig:dms_phenotype_mem}).
\begin{figure*}[ht!]
@ -2466,7 +2467,7 @@ 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
either beads or our \gls{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
@ -2477,11 +2478,11 @@ 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.
parameter, but in our experiments 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
@ -2548,43 +2549,43 @@ 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.
($R^2$ 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 a \SI{2.7}{\percent} increase and
almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
increased to almost a \SI{9}{\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.
\section{discussion}
% DISCUSSION this is fluffy
We have developed a T cell expansion system that recapitulates key features of
the in vivo lymph node microenvironment using DMSs functionalized with
activating mAbs. This strategy provided superior expansion with higher number of
naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology
(Figure 2). Other groups have used biomaterials approaches to mimic the \invivo{}
microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013};
however, to our knowledge this is the first system that specifically drives
naïve/memory and CD4+ T cell formation in a scalable, potentially
bioreactor-compatible manufacturing process.
% DISCUSSION assuage krish by showing that in the isotype control fig that IL2
% doesn't activation T cells: https://www.jimmunol.org/content/jimmunol/191/12/5822.full.pdf
We have developed a T cell expansion shows superior expansion with higher number
of naïve/memory and CD4+ T cells compared to state-of-the-art microbead
technology (\cref{fig:dms_exp}). Other groups have used biomaterials approaches
to mimic the \invivo{} microenvironment\cite{Cheung2018, Rio2018, Delalat2017,
Lambert2017, Matic2013}; however, to our knowledge this is the first system
that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
potentially bioreactor-compatible manufacturing process. Given that the
isotype-control \glspl{mab} does not lead to expansion and that \il{2} does not
lead to expansion on its own (\cref{fig:dms_expansion_isotype}), we know that
the expansion of the T cells shown here is due to the \acd{3} and \acd{28}
\glspl{mab}\cite{Waysbort2013}.
Memory and naïve T cells have been shown to be important clinically. Compared to
effectors, they have a higher proliferative capacity and are able to engraft for
months; thus they are able to provide long-term immunity with smaller
\glspl{teff}, they have a higher proliferative capacity and are able to engraft
for months; thus they are able to provide long-term immunity with smaller
doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
led to greater survival both in mouse tumor models and human
patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical