ndwarshuis
/
phd_thesis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ENH almost proofread aim 1

This commit is contained in:
Nathan Dwarshuis 2021-08-04 14:13:06 -04:00
parent 240dbb7169
commit 4f51eca77c
3 changed files with 308 additions and 295 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
tables/luminex_panel.tex
View File

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

13
tex/references.bib
View File

@ -2660,6 +2660,19 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
publisher = {Massachusetts Medical Society}, 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: databaseType:bibtex;}
@Comment{jabref-meta: grouping: @Comment{jabref-meta: grouping:

589
tex/thesis.tex
View File

@ -199,12 +199,14 @@
\newacronym{zfn}{ZFN}{zinc-finger nuclease} \newacronym{zfn}{ZFN}{zinc-finger nuclease}
\newacronym{talen}{TALEN}{transcription activator-like effector nuclease} \newacronym{talen}{TALEN}{transcription activator-like effector nuclease}
\newacronym{qbd}{QbD}{quality-by-design} \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{qpcr}{qPCR}{quantitative polymerase chain reaction}
\newacronym{cstr}{CSTR}{continuously stirred tank bioreactor} \newacronym{cstr}{CSTR}{continuously stirred tank bioreactor}
\newacronym{esc}{ESC}{embryonic stem cell} \newacronym{esc}{ESC}{embryonic stem cell}
\newacronym{msc}{MSC}{mesenchymal stromal cells} \newacronym{msc}{MSC}{mesenchymal stromal cells}
\newacronym{scfv}{scFv}{single-chain fragment variable} \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 % SI units for uber nerds
@ -289,6 +291,7 @@
\newcommand{\catnum}[2]{(#1, #2)} \newcommand{\catnum}[2]{(#1, #2)}
\newcommand{\product}[3]{#1 \catnum{#2}{#3}} \newcommand{\product}[3]{#1 \catnum{#2}{#3}}
\newcommand{\thermo}{Thermo Fisher} \newcommand{\thermo}{Thermo Fisher}
\newcommand{\gehc}{GE Healthcare}
\newcommand{\sigald}{Sigma Aldrich} \newcommand{\sigald}{Sigma Aldrich}
\newcommand{\miltenyi}{Miltenyi Biotech} \newcommand{\miltenyi}{Miltenyi Biotech}
\newcommand{\bl}{Biolegend} \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 provide superior expansion and memory phenotype compared to state-of-the-art
bead-based T cell expansion technology. 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} \section{methods}
\subsection{dms functionalization}\label{sec:dms_fab} \subsection{DMS functionalization}\label{sec:dms_fab}
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -1294,24 +1283,23 @@ better retention of memory phenotype compared to current bead-based methods.
\label{fig:dms_flowchart} \label{fig:dms_flowchart}
\end{figure*} \end{figure*}
Gelatin microcarriers (\gls{cus} or \gls{cug}, GE Healthcare, DG-2001-OO and \product{\gls{cus}}{\gehc}{DG-2001-OO} or \product{\gls{cug}}{\gehc}{DG-0001-OO}
DG-0001-OO) were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and autoclaved. All
autoclaved. All subsequent steps were done aseptically, and all reactions were subsequent steps were done aseptically, and all reactions were carried out at
carried out at \SI{20}{\mg\per\ml} carriers at room temperature and agitated \SI{20}{\mg\per\ml} carriers at room temperature and agitated using an orbital
using an orbital shaker with a \SI{3}{\mm} orbit diameter. After autoclaving, shaker with a \SI{3}{\mm} orbit diameter. After autoclaving, the microcarriers
the microcarriers were washed using sterile \gls{pbs} three times in a 10:1 were washed using sterile \gls{pbs} three times in a 10:1 volume ratio.
volume ratio. \product{\Gls{snb}}{\thermo}{21217} was dissolved at \product{\Gls{snb}}{\thermo}{21217} was dissolved at approximately \SI{10}{\uM}
approximately \SI{10}{\uM} in sterile ultrapure water, and the true in sterile ultrapure water, and the true concentration was then determined using
concentration was then determined using the \gls{haba} assay (see below). the \gls{haba} assay (see below). \SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added
\SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added to carrier suspension and allowed to carrier suspension and allowed to react for \SI{60}{\minute} at
to react for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the \SI{700}{\rpm} of agitation. After the reaction, the amount of biotin remaining
reaction, the amount of biotin remaining in solution was quantified using the in solution was quantified using the \gls{haba} assay (see below). The carriers
\gls{haba} assay (see below). The carriers were then washed three times, which were then washed three times, which entailed adding sterile \gls{pbs} in a 10:1
entailed adding sterile \gls{pbs} in a 10:1 volumetric ratio, agitating at volumetric ratio, agitating at \SI{900}{\rpm} for \SI{10}{\minute}, adding up to
\SI{900}{\rpm} for \SI{10}{\minute}, adding up to a 15:1 volumetric ratio a 15:1 volumetric ratio (relative to reaction volume) of sterile \gls{pbs},
(relative to reaction volume) of sterile \gls{pbs}, centrifuging at centrifuging at \SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid
\SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid back down to the back down to the reaction volume.
reaction volume.
To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson 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 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 step to remove excess \gls{stp}. They were washed once again in the cell culture
media to be used for the T cell expansion. 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 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 \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 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 \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}. 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, 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 extinction coefficient at \SI{500}{\nm} for \gls{haba}/avidin was assumed to be
\SI{34000}{\per\cm\per\molar}. \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 \product{\gls{bca} kit}{\thermo}{23227} according to the manufacturer’s
instructions, with the exception that the standard curve was made with known instructions, with the exception that the standard curve was made with known
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at 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 \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. \gls{elisa} standard curve.
Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was 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 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 carriers and allowed to react for \SI{20}{\minute} at room temperature under
constant agitation. The supernatant was quantified against a standard curve of 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 \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 adding \gls{fitcbt} to the \gls{stp}-coated \glspl{dms}, resuspending in
\SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet \SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
microscope}{Zeiss}{Z.1}. \Gls{mab} binding was verified visually by first microscope}{Zeiss}{Z.1}. Overall \gls{mab} binding was verified visually
staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001}, incubating for by first staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001},
\SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope. incubating for \SI{30}{\minute}, washing with \gls{pbs}, and imaging on a
confocal microscope.
\subsection{t cell culture}\label{sec:tcellculture} \subsection{t cell culture}\label{sec:tcellculture}
% TODO verify countess product number
Cryopreserved primary human T cells were either obtained as sorted Cryopreserved primary human T cells were either obtained as sorted
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from \product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection \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 in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
\product{OpTmizer}{\thermo}{A1048501} or \product{OpTmizer}{\thermo}{A1048501} or
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with \product{TexMACS}{\miltenyi}{170-076-307} supplemented with
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02}. Cell \SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02} unless
cultures were expanded for \SI{14}{\day} as counted from the time of initial otherwise noted. Cell cultures were expanded for \SI{14}{\day} as counted from
seeding and activation. Cell counts and viability were assessed using the time of initial seeding and activation. Cell counts and viability were
\product{trypan blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom assessed using \product{trypan blue}{\thermo}{T10282} or
Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo \product{\gls{aopi}}{Nexcelom Bioscience}{CS2-0106-5} and a \product{Countess
Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day} Automated Cell Counter}{Thermo Fisher}{Countess 3 FL}. Media was added to
depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose cultures every \SIrange{2}{3}{\day} depending on media color or a
threshold. Media glucose was measured using a \product{GlucCell glucose \SI{300}{\mg\per\deci\liter} minimum glucose threshold. Media glucose was
meter}{Chemglass}{CLS-1322-02}. measured using a \product{GlucCell glucose meter}{Chemglass}{CLS-1322-02}.
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul} Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{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. imaging on a spinning disk confocal microscope.
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson 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} \subsection{Quantifying cells on DMS interior}
% TODO add a product number to MTT (if I can find it) % 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 To visualize T cells on the interior of the \glspl{dms}, we stained them with
loosely attached cells were sampled as desired and filtered through a \gls{mtt}. \glspl{dms} with attached and loosely attached cells were sampled as
\SI{40}{\um} cell strainer. While still in the cell strainer, \glspl{dms} were desired and filtered through a \SI{40}{\um} cell strainer. While still in the
washed twice with \gls{pbs} and then dried by pulling liquid through the bottom cell strainer, \glspl{dms} were washed twice with \gls{pbs} and then dried by
of the cell strainer via a micropipette and dabbing with a KimWipe. \glspl{dms} pulling liquid through the bottom of the cell strainer via a micropipette and
were transferred to a 24 well plate with \SI{400}{\ul} media. \SI{40}{\ul} dabbing with a KimWipe. \glspl{dms} were transferred to a 24 well plate with
\gls{mtt} was added to each well and allowed to incubate for \SI{3}{\hour}, \SI{400}{\ul} media. \SI{40}{\ul} \gls{mtt} was added to each well and allowed
after which \glspl{dms} with cell were visualized via a brightfield microscope. 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 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. 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 instructions. Briefly, cells were transferred to flow tubes and washed twice
with \gls{pbs} by adding \SI{3}{\ml} to each tube, centrifuging for 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 \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, were analogously washed a third time with staining buffer (\SI{10}{\mM}
\SI{140}{\mM} NaCl, \SI{2.5}{\mM} CaCl\textsubscript{2}) and aspirated down to a \gls{hepes}, \SI{140}{\mM} NaCl, \SI{2.5}{\mM} \ce{CaCl2}) and aspirated down to
final volume of \SI{100}{\ul}. Cells were stained in this volume with 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} \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} \product{\gls{pi}}{\thermo}{P3566} and incubated for \SI{15}{\minute} at
in the dark. After incubation, \SI{400}{\ul} staining buffer was added to each \gls{rt} in the dark. After incubation, \SI{400}{\ul} staining buffer was added
tube. Each tube was then analyzed via flow cytometry. to each tube. Each tube was then analyzed via flow cytometry.
\subsection{quantification of Caspase-3/7} \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} \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 kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
supplemented with \product{5X diluent buffer}{\bl}{421203}, \product{\gls{tmb} supplemented with \product{\gls{tmb} substrate
substrate solution}{eBioscience}{00-4201-56}, and \SI{2}{\normal} solution}{eBioscience}{00-4201-56}, \product{5X diluent buffer}{\bl}{421203},
H\textsubscript{2}SO\textsubscript{4} stop solution made in house. Briefly, and \SI{2}{\normal} \ce{H2SO4} stop solution made in house. Briefly, cells were
cells were lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and the lysate
the lysate was quantified for protein using a \product{\gls{bca} was quantified for protein using a \product{\gls{bca} assay}{\thermo}{23225} as
assay}{\thermo}{23225} as directed. Standardized lysates were measured using directed. Standardized lysates were measured using the \gls{elisa} kit as
the \gls{elisa} kit as directed. directed.
\subsection{chemotaxis assay} \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 Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
were incubated with \num{1.5e5} target cells consisting of either \product{K562 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}. 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} 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}{ \product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar}
00-4505-51}, and \SI{1}{\ug\per\ml} \product{\acd{28}}{eBioscience}{302914} (all \product{monensin}{eBioscience}{ 00-4505-51}, and \SI{1}{\ug\per\ml}
functional grade \glspl{mab}) with \SI{250}{\ul} total volume. After \product{\acd{28}}{eBioscience}{302914} (all functional grade \glspl{mab}) with
\SI{4}{\hour} incubation at \SI{37}{\degreeCelsius}, cells were stained for CD3, \SI{250}{\ul} total volume. After \SI{4}{\hour} incubation at
CD4, and CD107a and analyzed on a BD LSR Fortessa. Readout was calculated as the \SI{37}{\degreeCelsius}, cells were stained for CD3, CD4, and CD107a and
percent \cdp{107a} cells of the total \cdp{8} fraction. 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}. \gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously
Briefly, cells were washed once and stained with \product{biotinylated described\cite{Zheng2012}. Briefly, cells were washed once and stained with
\gls{ptnl}}{\thermo}{29997}. After a subsequent wash, cells were stained with \product{biotinylated \gls{ptnl}}{\thermo}{29997}. After a subsequent wash,
\product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again, and analyzed on a cells were stained with \product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again,
BD Accuri. Readout was percent \gls{pe}+ cells as compared to secondary controls and analyzed on a \bd{} Accuri. Readout was percent \gls{pe}+ cells as compared
(\gls{pe}-\gls{stp} with no \gls{ptnl}). 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} \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 \product{DH5$\upalpha$ cells}{\thermo}{18265017} and incubated for
\SI{30}{\minute} on ice. The cell mixture was then heat-shocked at \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 \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 another \SI{2}{\minute}. \SI{950}{\ul} luria broth was added to the cells which
to the cells which were then centrifuged for \SI{1}{\hour} at \SI{225}{\rpm}. were then centrifuged for \SI{1}{\hour} at \SI{225}{\rpm}. \SI{20}{\ul} of the
\SI{20}{\ul} of the cell mixture was then spread over selection plates and cell mixture was then spread over selection plates and incubated overnight at
incubated overnight at \SI{37}{\degreeCelsius}. Colonies were selected the \SI{37}{\degreeCelsius}. Colonies were selected the following day and incubated
following day and incubated in \product{LB Broth}{TODO}{TODO} with in luria broth with \product{ampicillin}{\sigald{}}{A9518-5G} at
\product{ampicillin}{\sigald{}}{A9518-5G} at \SI{37}{\degreeCelsius} for \SI{37}{\degreeCelsius} for \SIrange{12}{16}{\hour} prior to using the
\SIrange{12}{16}{\hour} prior to using the \product{miniprep kit}{Qiagen}{27104} \product{miniprep kit}{Qiagen}{27104} as per the manufacturer's instructions to
as per the manufacturer's instructions to isolate the plasmid DNA. Transfer isolate the plasmid DNA. Transfer plasmid along with
plasmid along with \product{pMDLg/pRRE}{Addgene}{12251}, \product{pMDLg/pRRE}{Addgene}{12251}, \product{pRSV-Rev}{Addgene}{12253}, and
\product{pRSV-Rev}{Addgene}{12253}, and \product{pMD2.G}{Addgene}{12259} \product{pMD2.G}{Addgene}{12259} (generously provided by the Sloan lab at Emory
(generously provided by the Sloan lab at Emory University) in University) in \product{Opti-Mem}{\thermo}{31-985-070} with
\product{Opti-Mem}{\thermo}{31-985-070} with \product{lipfectamine \product{lipfectamine 2000}{\thermo}{11668019} were added dropwise to HEK 293T
2000}{\thermo}{11668019} were added dropwise to HEK 293T cells and incubated cells and incubated for \SI{6}{\hour}, after which all media was replaced with
for \SI{6}{\hour}, after which all media was replaced with fresh fresh media. fresh fresh media. After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was
After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was collected, pooled, and collected, pooled, and concentrated using a \product{Lenti-X
concentrated using a \product{Lenti-X concentrator}{Takara}{631231} prior to concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}.
storing at \SI{-80}{\degreeCelsius}.
\subsection{sulfo-NHS-biotin hydrolysis quantification} \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}. \cref{chem:snb_hydrolysis}.
% TODO make this look prettier
\begin{equation} \begin{equation}
\label{chem:snb_hydrolysis} \label{chem:snb_hydrolysis}
\ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH} \ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
\end{equation} \end{equation}
The hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb} was added Measuring the hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb}
to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well
analysis using a Biotech Plate Reader began immediately after prep, and readings plate. Kinetic analysis using a BioTek plate reader began immediately after
at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}. prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
\subsection{reaction kinetics quantification} \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 biotin-coated microcarriers, and supernatents were taken at fixed intervals and
quantified for \gls{stp} protein using the \gls{bca} assay. quantified for \gls{stp} protein using the \gls{bca} assay.
% TODO defend why the microcarriers were saturated with stp The geometric diffusivity of the microcarriers was determined using a
The effective diffusivity of the microcarriers was determined using a
pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere 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 with a fixed number of uniformly distributed `\gls{stp} binding sites' equal to
\gls{stp} molecules experimentally determined to bind to the microcarriers. the number of \gls{stp} molecules experimentally determined to bind to the
Because the reaction rate between biotin and \gls{stp} was so fast, we assumed microcarriers. Because the reaction rate between biotin and \gls{stp} is so fast
that the interface of free biotin shrunk as a function of \gls{stp} bound until (it is the strongest non-covalent bond in known existence), we assumed that the
the center of the microcarriers was reached. We also assumed that the pores in interface of free biotin shrunk as a function of \gls{stp} diffusing to the
the microcarriers were large enough that the interactions between the \gls{stp} unbound biotin interface until the center of the microcarriers was reached. We
and surfaces would be small, thus the apparent diffusivity could be represented also assumed that the pores in the microcarriers were large enough that the
as a fraction of the diffusion coefficient of \gls{stp} in water. This model was interactions between the \gls{stp} and surfaces would be small, thus the
given by \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}: 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 % TODO actually derive these equations, eg state the initial conditions and
% governing equation % governing equation
\begin{equation} \begin{equation}
\label{eqn:stp_diffusion_1} \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} \end{equation}
\begin{equation} \begin{equation}
\label{eqn:stp_diffusion_2} \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} \end{equation}
\noindent where \noindent where
\begin{itemize}[label={}] \begin{itemize}[label={}]
\item $D_{app}$ is the apparent diffusion rate which is equal to $D_{STP}\beta$ \item $D_{app}$ is the apparent diffusion rate of species $X$ which is equal to
\item $D_{STP}$ the diffusion rate of \gls{stp} (or \glspl{mab} for later $D\beta$
calculations) in water \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 \item $\beta$ a fractional parameter representing the tortuousity and void
fraction of the microcarriers (here called the `geometric diffusivity') 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 $t$ 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 $V$ is the volume of the bulk medium
\item $R$ is the average radius of the microcarriers \item $R$ is the average radius of the microcarriers
\item $n$ is the number of microcarriers in the reaction volume \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 \noindent where (in addition to the variables given already for
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}) \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2})
\begin{itemize}[label={}] \begin{itemize}[label={}]
\item $N_i$ is the radial flux of the species in question inside the \item $N_i$ is the radial flux of species $X$ inside the microcarriers
microcarriers \item $C_i$ is the concentration of species $X$ inside the microcarriers
\item $C_i$ is the concentration of the species in question inside the \item $C_{i,0}$ is the initial concentration of species $X$ inside
microcarriers
\item $C_{i,0}$ is the initial concentration of the species in question inside
the microcarriers (which is assumed to be the concentration in the bulk before the microcarriers (which is assumed to be the concentration in the bulk before
the wash volume is added) the wash volume is added)
\item $C_{b,0}$ is the initial bulk concentration of the species in question \item $C_{b,0}$ is the initial bulk concentration of species $X$ outside the
outside the microcarriers after the initial wash volume has been added microcarriers after the initial wash volume has been added
\item $C_{b,\infty}$ is the final bulk concentration of the species in \item $C_{b,\infty}$ is the final bulk concentration of species $X$ outside the
question outside the microcarriers microcarriers
\end{itemize} \end{itemize}
Note that in order to avoid solving a moving boundary value problem, the 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 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 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 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} 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 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{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 \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} 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} before washing analogously 3X with wash buffer. \SI{12.5}{\ul} detection
and \SI{25}{\ul} \gls{stppe} were sequentially added, incubated for \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. \SI{30}{\minute} and vortexed, and washed analogously to the sample step.
Finally, samples were resuspended in \SI{120}{\ul} reading buffer and analyzed 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, via a BioRad Bioplex 200 plate reader. An 8 point log\textsubscript{2} standard
and all samples were run with single replicates. curve was used, and all samples were run with single replicates.
Luminex data was preprocessed using R for inclusion in downstream analysis as Luminex data was preprocessed using R for inclusion in downstream analysis as
follows. Any cytokine level that was over-range (`OOR >' in output spreadsheet) 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 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. that were extrapolated from the standard curve were left unchanged.
\begin{table}[!h] \centering \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 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 \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 The data files to be aggregated included Microsoft Excel files which held
timeseries measurements for cell cultures (eg cell counts, viability, glucose, 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 example, flagging entries which had a reagent whose manufacturing date was after
the date the experiment started, which signifies a human input error). the date the experiment started, which signifies a human input error).
\subsection{statistical analysis}\label{sec:statistics} \subsection{statistical analysis}\label{sec:statistics}
For 1-way \gls{anova} analysis with Tukey multiple comparisons test, 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 For 2-way \gls{anova} analysis, the significance of main and interaction effects
was determined using the car library in R. 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 For least-squares linear regression, statistical significance was evaluated the
\inlinecode{lm} function in R. Stepwise regression models were obtained using \inlinecode{lm} function in R. All results with categorical variables are
the \inlinecode{stepAIC} function from the \inlinecode{MASS} package with
forward and reverse stepping. All results with categorical variables are
reported relative to baseline reference. Each linear regression was assessed for reported relative to baseline reference. Each linear regression was assessed for
validity using residual plots (to assess constant variance and independence validity using residual plots (to assess constant variance and independence
assumptions), QQplots and Shapiro-Wilk normality test (to assess normality 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} \label{fig:gating_strategy}
\end{figure*} \end{figure*}
% METHOD add flow cytometry
\begin{table}[!h] \centering \begin{table}[!h] \centering
\caption{\glspl{mab} used for flow cytometry} \caption{\glspl{mab} used for flow cytometry}
\label{tab:flow_mabs} \label{tab:flow_mabs}
\input{../tables/flow_mabs.tex} \input{../tables/flow_mabs.tex}
\end{table} \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} \section{results}
\subsection{DMSs can be fabricated in a controlled manner} \subsection{DMSs can be fabricated in a controlled manner}
Two types of gelatin-based microcariers, \gls{cus} and \gls{cug}, were % FIGURE flip the rows of this figure (right now the text is backward)
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)
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -1829,15 +1806,28 @@ together, we noted that the maximal \gls{mab} binding capacity occurred near
\label{fig:dms_coating} \label{fig:dms_coating}
\end{figure*} \end{figure*}
% TODO these caption titles suck Two types of gelatin-based microcariers, \gls{cus} and \gls{cug}, were
% TODO combine this DOE figure into one interaction plot covalently conjugated with varying concentration of \gls{snb} and then coated
\begin{table}[!h] \centering with \gls{stp} and \glspl{mab} to make \glspl{dms}. Aside from slight
\caption{Properties of the microcarriers used} differences in swelling ratio and crosslinking chemistry\cite{purcellmain} the
\label{tab:carrier_props} properties of \gls{cus} and \gls{cug} were the same (\cref{tab:carrier_props}).
\input{../tables/carrier_properties.tex} We chose to continue with the \gls{cus}-based \glspl{dms}, which showed higher
\end{table} overall \gls{stp} binding compared to \gls{cug}-based \glspl{dms}
(\cref{fig:cug_vs_cus}). We showed that by varying the concentration of
% TODO add chemical equation for which reactions I am describing here \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 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 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 critical to controlling the amount and \glspl{mab} and thus the amount of signal
the T cells receive downstream. the T cells receive downstream.
% TODO these caption titles suck
% TODO combine this DOE figure into one interaction plot
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \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 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, increase the number of deprotonated amines on the surface of the microcarrier,
it also increases the number of \ce{OH-} groups which can spontaneously 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 Furthermore, we observed that washing the microcarriers after autoclaving
increases the biotin binding rate (\cref{fig:dms_qc_washes}). While we did not 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. inflate the readout.
Lastly, we asked what the effect on reaction pH had on spontaneous degradation 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 of \gls{snb} while in solution (\cref{chem:snb_hydrolysis}). If the \gls{snb}
minutes of preparation, then it is important to carefully control the timing significantly degrades within minutes of preparation, then it is important to
between \gls{snb} solution preparation and addition to the microcarriers. We carefully control the timing between \gls{snb} solution preparation and addition
found that in the presence of \gls{di} water, \gls{snb} is extremely stable to the microcarriers. We found that in the presence of \gls{di} water, \gls{snb}
(\cref{fig:dms_snb_decay_curves}) where it decays rapidly in the presence of is extremely stable (\cref{fig:dms_snb_decay_curves}) where it decays rapidly in
\gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water curve actually the presence of \gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water
decreases slightly, possibly due to \gls{snb} absorbing to the plate surface. curve actually decreases slightly, possibly due to \gls{snb} absorbing to the
\gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but the lack of plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but
hydrolysis in \gls{di} water can be explained by the fact that biotin itself is the lack of hydrolysis in \gls{di} water can be explained by the fact that
acidic, and thus the reaction is self-inhibitory in an unbuffered and neutral pH biotin itself is acidic, and thus the reaction is self-inhibitory in an
system. Because we dissolve our \gls{snb} in \gls{di} water prior to adding it unbuffered and neutral pH system. Because we dissolve our \gls{snb} in \gls{di}
to the microcarrier suspension (which itself is in \gls{pbs}) this result water prior to adding it to the microcarrier suspension (which itself is in
indicated that hydrolysis is not of concern when adding \gls{snb} within \gls{pbs}) this result indicated that hydrolysis is not of concern when adding
minutes. \gls{snb} within minutes.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -1946,47 +1938,53 @@ minutes.
\label{fig:dms_kinetics} \label{fig:dms_kinetics}
\end{figure*} \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 We investigated the reaction kinetics of all three coating steps (accompanying
multiple batches of \SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt} MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the
with \gls{snb} in parallel and sacrificially analyzed each at varying timepoints reaction kinetics of the biotin binding step, we reacted multiple batches of
using the \gls{haba} assay. This was performed at two different concentrations. \SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt} with \gls{snb} in
We observed that for either concentration, the reaction was over 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 \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 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 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 for biotin attached can be set to \SI{30}{\minute}\footnote{we actually used
\SI{60}{\minute} for most of the runs as outlined in methods, which shouldn't \SI{60}{\minute} as outlined in methods, which shouldn't make any difference
make any difference except save for being excessive according to this result}. 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 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 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 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 using the \gls{bca} assay to indirectly quantify the amount of attached
\gls{stp} mass. We found this reaction took approximately \SI{30}{\minute} \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 (\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} this experimental binding data to compute the geometric diffusivity of the
binding reaction. Using the diffusion rate of the \gls{stp} 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 (\SI{6.2e-7}{\cm\squared\per\second}), we then calculated the geometric
diffusivity of the microcarriers to be 0.190 (see diffusivity of the microcarriers to be 0.190 (see
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}). \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}).
% RESULT state how I calculated the number of mab/surface area Using this geometric diffusivity and the known diffusion coefficient of a
Using this effective diffusivity and the known diffusion coefficient of a \gls{mab} protein in water, we calculated the binding of \glspl{mab} per time
\gls{mab} protein in water, we calculated predict the binding of \glspl{mab} per onto the microcarriers (this obviously assumes that the effectively diffusivity
time onto the microcarriers (this obviously assumes that the effectively is independent of the protein used, which should be reasonable given that the
diffusivity is independent of the protein used, which should be reasonable given pores of the microcarriers are huge compared to the proteins, and we don't
that the pores of the microcarriers are huge compared to the proteins, and we expect any significant reaction between the protein and the microcarrier surface
don't expect any significant reaction between the protein and the microcarrier save for the \gls{stp}-biotin binding reaction). Once again, we used the maximum
surface save for the \gls{stp}-biotin binding reaction). According to this number of \glspl{mab} observed to determine the number of `binding sites' for
model, the \gls{mab} binding reaction should be complete within \SI{75}{\minute} \glspl{mab} on the microcarriers, which should correspond to the upper-bound for
under the conditions used for our protocol the reaction time (\cref{fig:mab_coating}). According to this model, the
(\cref{fig:dms_mab_per_time})\footnote{We actually used \SI{60}{\minute} as \gls{mab} binding reaction should be complete within \SI{75}{\minute} under the
describe in the method section as this model was not updated with new conditions used for our protocol (\cref{fig:dms_mab_per_time})\footnote{We
parameters until recently; however, we should point out that even at actually used \SI{60}{\minute} as describe in the method section as this model
\SI{60}{\minute} the reaction appears to be >\SI{95}{\percent} complete}. 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 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 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 steps were \SI{30}{\minute}, which is a significant margin of safety (albeit
one that could be optimized). 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} \subsection{DMSs can efficiently expand T cells compared to beads}
% FIGURE make sure the day on these is correct
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2038,10 +2038,11 @@ one that could be optimized).
\caption[T cells growing on \glspl{dms}] \caption[T cells growing on \glspl{dms}]
{Cells grow in tight clusters in and around functionalized \gls{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 \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 \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 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}.} \gls{stppe} (red) and T cells were stained using \acd{45}-\gls{af647}.}
Images are from day 7 of culture.
} }
\label{fig:dms_cells} \label{fig:dms_cells}
\end{figure*} \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 After observing differences in expansion, we further hypothesized that the
\gls{dms} cultures could lead to a different T cell phenotype. In particular, we \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 were interested in the formation of \glspl{tn}, \gls{tscm}, and \glspl{tcm} as
a subset with higher replicative potential and therefore improved clinical these represent a subset with higher capacity to replicate and therefore
prognosis\cite{Gattinoni2011, Wang2018}. We measured naïve and memory T cell improved clinical prognosis\cite{Gattinoni2011, Wang2018}. We measured the
frequency staining for CCR7 and CD62L (both of which are present on lower frequency of these subtypes by staining for CCR7 and CD62L. Using three donor
differentiated T cells such as naïve, central memory, and stem memory lots, we noted again \glspl{dms} produced more T cells over a \SI{14}{\day}
cells\cite{Gattinoni2012}). Using three donors, we noted again \glspl{dms} expansion than beads, with significant differences in number appearing as early
produced more T cells over a \SI{14}{\day} expansion than beads, with after \SI{5}{\day} (\cref{fig:dms_exp_fold_change}). Furthermore, we noted that
significant differences in number appearing as early after \SI{5}{\day} \glspl{dms} produced more memory/naïve cells after \SI{14}{\day} when compared
(\cref{fig:dms_exp_fold_change}). Furthermore, we noted that \glspl{dms} to beads for all donors (\cref{fig:dms_exp_mem,fig:dms_exp_cd4}) showing that
produced more memory/naïve cells after \SI{14}{\day} when compared to beads for the \gls{dms} platform is able to selectively expand potent, early
all donors (\cref{fig:dms_exp_mem,fig:dms_exp_cd4}) showing that the \gls{dms} differentiation T cells.
platform is able to selectively expand potent, early differentiation T cells.
Of additional interest was the preservation of the CD4+ compartment. In healthy 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 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 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 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 cells to a greater degree than beads \cref{fig:dms_exp_cd4}. The trends held
observing the CD4+ and CD8+ fractions of the naïve/memory subset (\ptmem{}) true when observing the CD4+ and CD8+ fractions of the naïve/memory subset
(\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}). (\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, % FIGURE this figure was not produced with the same donors as the figure above,
% which is really confusing % which is really confusing
\begin{figure*}[ht!] \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 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 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}). 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 \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 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 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 After optimizing for naïve/memory and CD4 yield, we sought to determine if the
\glspl{dms} were compatible with lentiviral transduction protocols used to \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 generate \gls{car} T cells\cite{Tumaini2013, Lamers2014}. We added a
day 1 of the \SI{14}{\day} expansion to insert an anti-CD19 \gls{car}29 and \SI{24}{\hour} transduction step on day 1 of the \SI{14}{\day} expansion to
subsequently measured the surface expression of the \gls{car} on day 14 insert an anti-CD19 \gls{car}\cite{Milone2009} and subsequently measured the
\cref{fig:car_production_flow_pl,fig:car_production_endpoint_pl}. We noted that surface expression of the \gls{car} on day 14
there was robust \gls{car} expression in over \SI{25}{\percent} of expanded T (\cref{fig:car_production_flow_pl,fig:car_production_endpoint_pl}). We noted
cells, and there was no observable difference in \gls{car} expression between 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}. beads and \glspl{dms}.
We also verified the functionality of expanded \gls{car} T cells using a 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 cytotoxic granules and will emerge on the surface after cytotoxic T cells are
activated and degranulate. Indeed, we observed degranulation in T cells expanded activated and degranulate. Indeed, we observed degranulation in T cells expanded
with both beads and \glspl{dms}, although not to an observably different degree 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 (\cref{fig:car_production_flow_degran,fig:car_production_endpoint_degran}).
together, these results indicated that the \glspl{dms} provide similar Taken together, these results indicated that the \glspl{dms} provide similar
transduction efficiency compared to beads. transduction efficiency compared to beads.
We also verified that expanded T cells were migratory using a chemotaxis assay 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, 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. showing that migration was likely independent of \gls{car} transduction.
% FIGURE break this up to give the text more flexibility
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \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 our plate cultures. This means that the cells had higher growth area
constraints, which may have nullified any advantage to the expansion that we constraints, which may have nullified any advantage to the expansion that we
seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
area could mean higher signaling and higher differentiation rate to effector T area could mean higher signaling and higher differentiation rate to
cells, which was why the \ptmemp{} was so low compared to other data \glspl{teff}, which was why the \ptmemp{} was so low compared to other data
(\cref{fig:dms_phenotype_mem}). (\cref{fig:dms_phenotype_mem}).
\begin{figure*}[ht!] \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 possible treatment variables which we controlled when designing the experiments
included in this dataset. Obviously the principle treatment parameter was included in this dataset. Obviously the principle treatment parameter was
‘activation method’ which represented the effect of activating T cells with ‘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, 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 ‘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 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 could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
note that these were not the only set of theoretical treatment parameters that 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 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 parameter, but in our experiments this was dependent on the feeding criteria and
the cells, which in turn is determined by activation method. Therefore, ‘media the growth rate of the cells, which in turn is determined by activation method.
feed rate’ (or similar) is a ‘post-treatment parameter’ and would have violated Therefore, ‘media feed rate’ (or similar) is a ‘post-treatment parameter’ and
the backdoor criteria and severely biased our estimates of the treatment would have violated the backdoor criteria and severely biased our estimates of
parameters themselves. the treatment parameters themselves.
In addition to these treatment parameters, we also included covariates to In addition to these treatment parameters, we also included covariates to
improve the precision of our model. Among these were donor parameters including 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 performed linear regression again
(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after (\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
controlling for additional noise, the models explained much more variability 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 ($R^2$ between 0.76 and 0.87).
deviations for normality as per the assumptions of regression analysis {Figure % and had relatively constant variance and small
X}. Furthermore, the coefficient for activation method in the case of fold % deviations for normality as per the assumptions of regression analysis {Figure
change changed very little but still remained quite high (note the % X}.
log-transformation) with \SI{143}{\percent} increase in fold change compared to Furthermore, the coefficient for activation method in the case of fold change
beads. Furthermore, the coefficient for \ptmemp{} dropped to about changed very little but still remained quite high (note the log-transformation)
\SI{2.7}{\percent} different and almost became non-significant at $\upalpha$ = with \SI{143}{\percent} increase in fold change compared to beads. Furthermore,
0.05, and the \dpthp{} response increased to almost a \SI{9}{\percent} difference the coefficient for \ptmemp{} dropped to a \SI{2.7}{\percent} increase and
and became highly significant. Looking at the bioreactor treatment, we see that almost became non-significant at $\upalpha$ = 0.05, and the \dpthp{} response
using the bioreactor in the case of fold change and \ptmemp{} is actually harmful increased to almost a \SI{9}{\percent} increase and became highly significant.
to the response, while at the same time it seems to increase the \dpthp{} Looking at the bioreactor treatment, we see that using the bioreactor in the
response. We should note that this parameter merely represents whether or not case of fold change and \ptmemp{} is actually harmful to the response, while at
the choice was made experimentally to use a bioreactor or not; it does not the same time it seems to increase the \dpthp{} response. We should note that
indicate why the bioreactor helped or hurt a certain response. For example, this parameter merely represents whether or not the choice was made
using a Grex entails changing the cell surface and feeding strategy for the T experimentally to use a bioreactor or not; it does not indicate why the
cells, and any one of these ‘mediating variables’ might actually be the cause of bioreactor helped or hurt a certain response. For example, using a Grex entails
the responses. 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} \section{discussion}
% DISCUSSION this is fluffy % DISCUSSION this is fluffy
We have developed a T cell expansion system that recapitulates key features of We have developed a T cell expansion shows superior expansion with higher number
the in vivo lymph node microenvironment using DMSs functionalized with of naïve/memory and CD4+ T cells compared to state-of-the-art microbead
activating mAbs. This strategy provided superior expansion with higher number of technology (\cref{fig:dms_exp}). Other groups have used biomaterials approaches
naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology to mimic the \invivo{} microenvironment\cite{Cheung2018, Rio2018, Delalat2017,
(Figure 2). Other groups have used biomaterials approaches to mimic the \invivo{} Lambert2017, Matic2013}; however, to our knowledge this is the first system
microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013}; that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
however, to our knowledge this is the first system that specifically drives potentially bioreactor-compatible manufacturing process. Given that the
naïve/memory and CD4+ T cell formation in a scalable, potentially isotype-control \glspl{mab} does not lead to expansion and that \il{2} does not
bioreactor-compatible manufacturing process. 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}
% DISCUSSION assuage krish by showing that in the isotype control fig that IL2 \glspl{mab}\cite{Waysbort2013}.
% doesn't activation T cells: https://www.jimmunol.org/content/jimmunol/191/12/5822.full.pdf
Memory and naïve T cells have been shown to be important clinically. Compared to 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 \glspl{teff}, they have a higher proliferative capacity and are able to engraft
months; thus they are able to provide long-term immunity with smaller for months; thus they are able to provide long-term immunity with smaller
doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
led to greater survival both in mouse tumor models and human led to greater survival both in mouse tumor models and human
patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical