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

ENH proofread most of aim 1

This commit is contained in:
Nathan Dwarshuis 2021-09-08 15:48:52 -04:00
parent 130edc364e
commit ab660e869d
1 changed files with 264 additions and 233 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

497
tex/thesis.tex
View File

@ -288,7 +288,10 @@
\DeclareSIUnit\IU{IU}
\DeclareSIUnit\rpm{RPM}
\DeclareSIUnit\carrier{carrier}
\DeclareSIUnit\gauge{gauge}
\DeclareSIUnit\dms{DMS}
\DeclareSIUnit\stp{STP}
\DeclareSIUnit\snb{SNB}
\DeclareSIUnit\cell{cells}
\DeclareSIUnit\ab{mAb}
\DeclareSIUnit\normal{N}
@ -370,6 +373,7 @@
\newcommand{\miltenyi}{Miltenyi Biotech}
\newcommand{\bl}{Biolegend}
\newcommand{\bd}{Becton Dickenson}
\newcommand{\pltread}{BioTek plate reader}
% the obligatory misc category
\newcommand{\inlinecode}{\texttt}
@ -1404,13 +1408,13 @@ novel considering the state-of-the-art technology for T cell manufacturing:
\section{Introduction}
The first aim was to develop a microcarrier system that mimics several key
aspects of the \invivo{} lymph node microenvironment. We compared compare this
system to state-of-the-art T cell activation technologies for both expansion
potential and memory cell formation. The governing hypothesis was that
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\footnote{adapted from \dmspaper{}}.
This aim was to develop a functionalized microcarrier system that mimics several
key aspects of the \invivo{} lymph node microenvironment. We compared compare
this system to state-of-the-art T cell activation technologies for both
expansion potential and memory cell formation. The governing hypothesis was that
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\footnote{adapted from \dmspaper{}}.
\section{Methods}
@ -1428,41 +1432,62 @@ bead-based T cell expansion technology\footnote{adapted from \dmspaper{}}.
\end{figure*}
\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.
were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} in a 15, 50, or 250
\si{\ml} conical tube. The mass of the tube with the \gls{pbs} and microcarriers
were recorded to the nearest millimeter (subsequently referred to here as
``reaction mass''). The tube was centrifuged for \SI{30}{\second} at
\SI{4500}{\gforce} to ensure all microcarriers were at the bottom of the tube.
The tube was then autoclaved using a \SI{15}{\minute} cycle at
\SI{121}{\degreeCelsius} and \SI{100}{\kPa\of{\gauge}}.
All subsequent steps were done aseptically, and all reactions were carried out
at \SI{20}{\mg\of{\carrier}\per\ml} 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. The volume after these washes was corrected by massing the tube and its
contents and adding or removing \gls{pbs} until the ``reaction mass'' was
reached.
\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.
the \gls{haba} assay (see below). \SI{2.5}{\nmol\of{\snb}\per\mg\of{\carrier}}
(unless otherwise noted) 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 attached to the microcarriers was determined indirectly by
measuring the biotin in solution via 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. The volume of the \gls{pbs} was
corrected by massing the tube and its contents and adding or removing \gls{pbs}
until the tube mass matched the ``reaction mass.''
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
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant
was taken for the \product{\gls{bca} assay}{\thermo}{23225}, and the carriers
were washed analogously to the previous wash step to remove the biotin, except
two washes were done and the agitation time was \SI{30}{\minute}. Biotinylated
To coat the microcarriers with \product{\gls{stp}}{Jackson
Immunoresearch}{016-000-114}, \SI{2}{\ug\of{\stp}\per\mg\of{\carrier}} was
added and allowed to react for \SI{60}{\minute} at \SI{700}{RPM} of agitation.
After the reaction, \SI{400}{\ul} supernatant (regardless of tube size) was
taken for the \product{\gls{bca} assay}{\thermo}{23225} in order to indirectly
quantify \gls{stp} attachment. Prior to the assay, the supernatent was filtered
through a \SI{40}{\um} cell strainer to remove any stray microcarriers, which
could increase the \gls{bca} readout as the assay is protein-agnostic and each
microcarrier is approximately \SI{1}{\ug}. The carriers were washed analogously
to the previous wash step to remove biotin, except two wash cycles were used,
the agitation time was \SI{30}{\minute}, and the first cycle had an extra
\SI{400}{\ul} \gls{pbs} to make up for the volume removed for the \gls{bca}
assay.
To coat with \glspl{mab}, sterile \product{\gls{bsa}}{\sigald}{A9576} was first
added to a final concentration of \SI{2}{\percent} in order to prevent
non-specific binding of the \glspl{mab} to the reaction tubes. Biotinylated
\glspl{mab} against human CD3 \catnum{\bl}{317320} and CD28 \catnum{\bl}{302904}
were combined in a 1:1 mass ratio and added to the carriers at
\SI{0.2}{\ug\of{\ab}\per\mg\of{\dms}}. Along with the \glspl{mab}, sterile
\product{\gls{bsa}}{\sigald}{A9576} was added to a final concentration of
\SI{2}{\percent} in order to prevent non-specific binding of the antibodies to
the reaction tubes. \glspl{mab} were allowed to bind to the carriers for
\SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding, supernatants were
sampled to quantify remaining \gls{mab} concentration using an
\product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}. Fully functionalized
\glspl{dms} were washed in sterile \gls{pbs} analogous to the previous washing
step to remove excess \gls{stp}. They were washed once again in the cell culture
media to be used for the T cell expansion.
\SI{0.2}{\ug\of{\ab}\per\mg\of{\carrier}}. \glspl{mab} were allowed to bind to
the carriers for \SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding,
\SI{400}{\ul} supernatant was sampled to indirectly quantify \gls{mab}
attachment using an \product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}.
Fully functionalized \glspl{dms} were washed in sterile \gls{pbs} analogous to
the previous washing step to remove excess \gls{stp}.
\begin{table}[!h] \centering
\caption{Microcarrier properties}
@ -1470,7 +1495,9 @@ media to be used for the T cell expansion.
\input{../tables/carrier_properties.tex}
\end{table}
The concentration of the final \gls{dms} suspension was found by taking a
Finished \glspl{dms} were washed once again in the cell culture media (analogous
to previous washing steps) to be used for the T cell expansion. 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
@ -1486,25 +1513,28 @@ ester linkages from binding to the avidin proteins in the \gls{haba}/avidin
premix. All quantifications of \gls{haba} were performed on an Eppendorf D30
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}.
\SI{34000}{\per\cm\per\molar}\footnote{\SI{500}{\nm} is normally used for the
\gls{haba} assay, but the spectrophotometer to which we had access only had
\SI{490}{\nm} as the closest wavelength; the extinction coefficient should
change little}.
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 \pltread{}.
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 \glspl{mab} used to coat the carriers were used as the standard for the
\gls{elisa} standard curve.
\gls{elisa} standard curve. This assay was quantified using a \pltread{}.
Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
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 \pltread{}.
\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
@ -1523,7 +1553,7 @@ Cryopreserved primary human T cells were either obtained as sorted
using \glspl{dms} or \product{\SI{3.5}{\um} CD3/CD28 magnetic
beads}{\miltenyi}{130-091-441}. In the case of beads, T cells were activated
at the manufacturer recommended cell:bead ratio of 2:1. In the case of
\glspl{dms}, cells were activated using \SI{2500}{\dms\per\cm\squared} unless
\glspl{dms}, cells were activated using \SI{1500}{\dms\per\cm\squared} unless
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
@ -1616,9 +1646,9 @@ 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
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}
wild type cells}{ATCC}{CCL-243} or \cdp{19} 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
@ -1643,23 +1673,24 @@ prior to analyzing on a \bd{} Accuri
\subsection{CAR Plasmid and Lentiviral Transduction}
The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$
The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} sequence with the EF1$\upalpha$
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core.
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the
Cincinnati Children's Hospital Medical Center Viral Vector Core. RNA titer was
calculated using a \product{Lenti-X \gls{qpcr} titer kit}{Takara}{631235}. To
transduce primary human T cells, \product{retronectin}{Takara}{T100A} was coated
onto non-TC treated 96 well plates and used to immobilize lentiviral vector
particles according to the manufacturer's instructions. Briefly, retronectin
solution was adsorbed overnight at \SI{4}{\degreeCelsius} and blocked the next
day using \gls{bsa}. Prior to transduction, lentiviral supernatant was
spinoculated at \SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}.
T cells were activated in 96 well plates using beads or \glspl{dms} for
\SI{24}{\hour}, and then cells and beads/\glspl{dms} were transferred onto
lentiviral vector coated plates and incubated for another \SI{24}{\hour}. Cells
and beads/\glspl{dms} were removed from the retronectin plates using vigorous
pipetting and transferred to another 96 well plate wherein expansion continued.
\product{FUGW transfer plasmid}{Addgene}{14883} kindly provided by the Emory
Viral Vector Core. Lentiviral vectors were synthesized by the Emory Viral Vector
Core or the Cincinnati Children's Hospital Medical Center Viral Vector Core. RNA
titer was calculated using a \product{Lenti-X \gls{qpcr} titer
kit}{Takara}{631235}. To transduce primary human T cells,
\product{retronectin}{Takara}{T100A} was coated onto non-TC treated 96 well
plates and used to immobilize lentiviral vector particles according to the
manufacturer's instructions. Briefly, retronectin solution was adsorbed
overnight at \SI{4}{\degreeCelsius} and blocked the next day using \gls{bsa}.
Prior to transduction, lentiviral supernatant was spinoculated at
\SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}. T cells were
activated in 96 well plates using beads or \glspl{dms} for \SI{24}{\hour}, and
then cells and beads/\glspl{dms} were transferred onto lentiviral vector coated
plates and incubated for another \SI{24}{\hour}. Cells and beads/\glspl{dms}
were removed from the retronectin plates using vigorous pipetting and
transferred to another 96 well plate wherein expansion continued.
% METHOD fill in missing product numbers
\gls{bcma} \gls{car} lentiviral vector was synthesized in house as
@ -1697,19 +1728,36 @@ The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by
\ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
\end{equation}
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}.
Measuring the hydrolysis of \gls{snb} was performed spectroscopically as the
extinction coefficient of \ce{NHS-} is well-known. \gls{snb} was added to either
\gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic analysis
using a \pltread{} began immediately after prep, and readings at \SI{260}{\nm}
were taken every minute for \SI{2}{\hour}. The extinction coefficient of
\ce{NHS-} at \SI{260}{\nm} was assumed to be \SI{8600}{\per\cm\per\molar}.
\subsection{Reaction Kinetics Quantification}
The diffusion of \gls{stp} into biotin-coated microcarriers was determined
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.
The reaction kinetics of \gls{stp} attaching to biotin-coated microcarriers was
determined 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 as
described above.
The geometric diffusivity of the microcarriers was determined using a
To model diffusion in the microcarriers, we assumed that its pores were large
enough that the interactions between the \gls{stp} and surfaces would be small.
This means that the apparent, macroscropic diffusion of a given species within
the microcarriers would only depend on the aqueous diffusion coefficient of
\gls{stp} and a fractional factor (the ``geometric diffusivity'') representing
the additional path length an \gls{stp} molecule would take in the microcarriers
due to the tortuousity and void fraction of its pore network. This is given in
\cref{eqn:stp_diffusion_3}.
\begin{equation}
\label{eqn:stp_diffusion_3}
\gls{sym:appdiff}=\gls{sym:diff} \gls{sym:geodiff}
\end{equation}
This 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 uniformly distributed ``receptors'' equal to the number
of \gls{stp} molecules (here called ``ligands'') experimentally determined to
@ -1717,11 +1765,8 @@ 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 unbound receptors (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,eqn:stp_diffusion_3}:
of the microcarriers was reached. This model was given by
\cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
\begin{equation}
\label{eqn:stp_diffusion_1}
@ -1738,24 +1783,22 @@ given by \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2,eqn:stp_diffusion_3}:
{\gls{sym:vol} (1 / \gls{sym:rad} - 1 / \gls{sym:mcrad})}
\end{equation}
\begin{equation}
\label{eqn:stp_diffusion_3}
\gls{sym:appdiff}=\gls{sym:diff} \gls{sym:geodiff}
\end{equation}
The diffusion rate of \gls{stp} was assumed to be
\SI{6.2e-7}{\cm\squared\per\second}\cite{Kamholz2001}. Since all but $\beta$ was
known, the experimental data was fit using these equations using
\inlinecode{ode45} in MATLAB and least squares as the fitting error. These
equations were then used analogously to describe the reaction profile of
\glspl{mab} assuming a diffusion rate of
fitted equations were then used to simulate the reaction profile of \glspl{mab}
assuming a diffusion rate of
\SI{4.8e-7}{\cm\squared\per\second}\cite{Sherwood1992}.
To model the washing of the microcarriers, they once again were assumed to be
porous spheres filled with whatever amount of reagent was left unbound from the
previous step (which was assumed to be equal to concentration in the
supernatent). The diffusion out of the microcarriers is given by the following
partial differential equation and boundary conditions:
supernatent). The fitted geometric diffusivity from above was used in these
washing calculations, and \SI{5.0e-6}{\cm\squared\per\second}\cite{Niether2020}
was used as the diffusion coefficient for free biotin. The diffusion out of the
microcarriers is given by the following partial differential equation and
boundary conditions:
\begin{equation}
\label{eqn:stp_washing}
@ -1786,24 +1829,18 @@ partial differential equation and boundary conditions:
\evalat{\gls{sym:bulkligconc}}{\gls{sym:time} = \infty}) / 2
\end{equation}
Note that in order to avoid solving a moving boundary value problem, the
concentration at the boundary of the microcarriers was fixed at the average of
the final and initial concentration expected to be observed in bulk. This should
be a reasonable assumption given that the volume inside the microcarriers is
tiny compared to the amount of volume added in the wash, thus the boundary
In order to avoid solving a moving boundary value problem, the concentration at
the boundary of the microcarriers was fixed at the average of the final and
initial concentration expected to be observed in bulk. This should be a
reasonable assumption given that the volume inside the microcarriers is tiny
compared to the amount of volume added in the wash, thus the boundary
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
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
\gls{pbs}.
See \cref{sec:appendix_binding} and \cref{sec:appendix_washing} for the MATLAB
code and derivations, as well as output in the case of the washing steps.
\gls{pbs}. See \cref{sec:appendix_binding} and \cref{sec:appendix_washing} for
the MATLAB code and derivations, as well as output in the case of the washing
steps.
\subsection{Luminex Analysis}\label{sec:luminex_analysis}
@ -1850,23 +1887,22 @@ notebooks (eg OneNote files) was not easily parsable, and thus this data was
summarized in YAML files. The data included in these YAML files included reagent
characteristics (vendor, catalog number, lot number, manufacturing date), cell
donor characteristics (age, \gls{bmi}, phenotype, demographic, gender), and all
experimental parameters such as the number of bead or \gls{dms} added.
experimental parameters such as the number of beads or \glspl{dms} added.
To aggregate the data in a database, we wrote a program using Python, R, and
Docker which retrieved the data from its source location and inserted the data
in a Postgres database (specifically Aurora implementation hosted on \gls{aws}).
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).
in a PostgreSQL database (specifically the Aurora implementation hosted on
\gls{aws}). 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,
significance was assessed using the \inlinecode{stat\_compare\_means} function
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.
For 2-way \gls{anova} analysis, significance of main and interaction effects
was determined using the \inlinecode{car} library in R.
For least-squares linear regression, statistical significance was evaluated the
\inlinecode{lm} function in R. All results with categorical variables are
@ -1942,7 +1978,7 @@ 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
\gls{snb}, we were able to 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
@ -1995,7 +2031,7 @@ of reaction parameters on biotin binding. The parameters included in this
\gls{doe} were temperature, microcarrier mass, and \gls{snb} input mass. These
were parameters that we specifically controlled but hypothesized might have some
sensitivity on the final biotin mass attachment rate depending on their noise
and uncertainty. In particular, temperature was `controlled' only by allowing
and uncertainty. In particular, temperature was ``controlled'' only by allowing
the microcarrier suspension to come to \gls{rt} after autoclaving. After
performing a full factorial \gls{doe} with three center points as the target
reaction conditions, we found that the final biotin binding mass is only highly
@ -2003,10 +2039,10 @@ dependent on biotin input concentration (\cref{fig:dms_qc_doe}). Overall,
temperature had no effect and carrier mass had no effect at higher temperatures,
but carrier mass had a slightly positive effect when temperature was low. This
could be because lower temperature might make spontaneous decay of \gls{snb}
occur slower, which would give \gls{snb} molecule more opportunity to diffuse
into the microcarriers and react with amine groups to form attachments. It seems
that concentration only has a linear effect and doesn't interact with any of the
other variables, which is not surprisingly given the behavior observed in
occur slower, which would give \gls{snb} molecules more opportunity to diffuse
into the microcarriers and react with amine groups to form attachments. It
seemed that concentration only has a linear effect and doesn't interact with any
of the other variables, which is not surprising given the behavior observed in
(\cref{fig:biotin_coating})
We also observed that the reaction pH does not influence the amount of biotin
@ -2016,7 +2052,7 @@ it also increases the number of \ce{OH-} groups which can spontaneously
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
increased the biotin binding rate (\cref{fig:dms_qc_washes}). While we did not
explore this further, one possible explanation for this behavior is that the
microcarriers have some loose protein in the form of powder or soluble peptides
that competes for \gls{snb} binding against the surface of the microcarriers,
@ -2031,14 +2067,14 @@ 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.
curve actually decreased slightly, possibly due to \gls{snb} absorbing to the
plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}
groups, 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
@ -2078,7 +2114,7 @@ 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 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}.
except for costing more time}.
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
@ -2088,46 +2124,46 @@ using the \gls{bca} assay to indirectly quantify the amount of attached
(\cref{fig:dms_stp_per_time}). Assuming a quasi-steady-state paradigm, we used
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
computed the number of ``receptors'' 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}).
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}.
Using this geometric diffusivity and the known diffusion coefficient of
\glspl{mab} 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 receptors 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
diffusivity of the microcarriers as calculated above. This is important, as
failing to wash out residual free \gls{snb} (for example) could occupy binding
sites on the \gls{stp} molecules, lowering the effective binding capacity of the
\gls{mab} downstream. Each wash was a 1:15 dilution (\SI{1}{\ml} reaction volume
in a \SI{15}{\ml} conical tube), and in the case of \gls{snb} we wished to wash
out enough biotin such that less than \SI{1}{\percent} of the binding sites in
\gls{stp} would be occupied. Given this dilution factor, a maximum of
\glspl{mab} downstream. Each wash was a 1:15 dilution (\SI{1}{\ml} reaction
volume in a \SI{15}{\ml} conical tube), and in the case of \gls{snb} we wished
to wash out enough biotin such that less than \SI{1}{\percent} of the binding
sites in \gls{stp} would be occupied. Given this dilution factor, a maximum of
\SI{20}{\nmol} of biotin remaining \cref{fig:biotin_coating} \SI{2.9}{\nmol}
biotin binding sites on \SI{40}{\ug} \gls{stp} (assuming 4 binding sites per
\gls{stp} protein), this turned out to be 3 washes. By similar logic, using 2
washes after the \gls{stp} binding step will ensure that the number of free
\gls{stp} binding sites is less than 20X the number of \gls{mab} molecules
added\footnote{This step may benefit from an additional wash, as the number of
washes used here was develop when \SI{40}{\ug} rather than \SI{4}{\ug}
washes used here was determined when \SI{40}{\ug} rather than \SI{4}{\ug}
\gls{mab} was used to coat the \gls{dms}, yielding a much wider margin.
However, it is also not clear to what extent this matters, as the \gls{mab}
have multiple biotin molecules per \gls{mab} protein, and thus one \gls{mab}
@ -2140,15 +2176,15 @@ microcarriers to be porous spheres, this time with an initial concentration of
bulk concentration of the previous binding step, and calculated the amount of
time it would take for the concentration profile inside the microcarriers to
equilibrate to the bulk in the wash step. Using this model, we found that the
wash times for \gls{snb}, \gls{stp}, and \glspl{mab} was \SI{3}{\minute},
wash time for \gls{snb}, \gls{stp}, and \glspl{mab} was \SI{3}{\minute},
\SI{15}{\minute}, and \SI{17}{\minute} respectively. We verified that the
\gls{snb} was totally undetectable after washing (\cref{fig:dms_biotin_washed}).
The other two species need to be verified in a similar manner; however, we
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).
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
MATLAB code and output for all wash step calculations are given in
\cref{sec:appendix_washing}.
\subsection{DMSs Can Efficiently Expand T Cells Compared to Beads}
@ -2195,18 +2231,17 @@ MATLAB code and output for all the wash step calculations are given in
We next sought to determine how our \glspl{dms} could expand T cells compared to
state-of-the-art methods used in industry. All bead expansions were performed as
per the manufacturer’s protocol, with the exception that the starting cell
densities were matched between the beads and carriers to
~\SI{2.5e6}{\cell\per\ml}. Throughout the culture we observed that T cells in
\gls{dms} culture grew in tight clumps on the surface of the \glspl{dms} as well
as inside the pores of the \glspl{dms}
(\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore, we observed that
the \glspl{dms} conferred greater expansion compared to traditional beads, and
significantly greater expansion after \SI{12}{\day} of culture
(\cref{fig:dms_expansion_bead}). We also observed no T cell expansion using
\glspl{dms} coated with an isotype control mAb compared to \glspl{dms} coated
with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}), confirming
specificity of the expansion method. Given that \il{2} does not lead to
expansion on its own, we know that the expansion of the T cells shown here is
densities were matched between the beads and \glspl{dms} to
~\SI{2.5e6}{\cell\per\ml}. We observed that T cells in \gls{dms} culture grew in
tight clumps on the surface of the \glspl{dms} as well as inside the pores of
the \glspl{dms} (\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore,
we observed that the \glspl{dms} conferred greater expansion compared to
traditional beads, and significantly greater expansion after \SI{12}{\day} of
culture (\cref{fig:dms_expansion_bead}). We also observed no T cell expansion
using \glspl{dms} coated with an isotype control mAb compared to \glspl{dms}
coated with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}),
confirming specificity of the expansion method. Given that \il{2} does not lead
to expansion on its own, we know that the expansion of the T cells shown here is
due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
\begin{figure*}[ht!]
@ -2241,19 +2276,19 @@ usually present only on the cytoplasmic surface of the cell membrane, but flips
to the outside when the cell becomes apoptotic. \gls{pi} stains the nucleus of
the cell, but only penetrates necrotic cells which have a perforated cell
membrane. When staining for these two markers and assessing via flow cytometry,
we observe that the \gls{dms}-expanded T cells have lower frequencies of
we observed that the \gls{dms}-expanded T cells have lower frequencies of
apoptotic and necrotic cells (\cref{fig:apoptosis_annV}). Furthermore, we
stained our cultures with CellEvent dye, which is an indicator of \gls{cas37},
which is activated in apoptotic cells. In line with the \gls{pi}/\gls{anv}
results, we observed that the \gls{dms} T cells had lower frequency of
\gls{cas37} expression, indicating less apoptosis for our method
(\cref{fig:apoptosis_cas}). Finally, we lysed our cells and stained for
\gls{bcl2}, which is also upregulated in apoptosis. In this case, some (but not
all) of the bead-expanded cultures showed higher \gls{bcl2} expression, which
could indicate more apoptosis in those groups (\cref{fig:apoptosis_bcl2}). None
of the \gls{dms} cultures showed similar heightened expression. Taken together,
these data suggest that the \gls{dms} platform at least in part achieves higher
expansion by lowering apoptosis of the cells in culture.
stained our cultures with CellEvent dye, an indicator of \gls{cas37} which is
activated in apoptotic cells. In line with the \gls{pi}/\gls{anv} results, we
observed that the \gls{dms} T cells had lower frequency of \gls{cas37}
expression, indicating less apoptosis for our method (\cref{fig:apoptosis_cas}).
Finally, we lysed our cells and stained for \gls{bcl2}, which is also
upregulated in apoptosis. In this case, some (but not all) of the bead-expanded
cultures showed higher \gls{bcl2} expression, which could indicate more
apoptosis in those groups (\cref{fig:apoptosis_bcl2}). None of the \gls{dms}
cultures showed similar heightened expression. Taken together, these data
suggest that the \gls{dms} platform at least in part achieves higher expansion
by lowering apoptosis.
\begin{figure*}[ht!]
\begingroup
@ -2281,19 +2316,19 @@ expansion by lowering apoptosis of the cells in culture.
\input{../tables/inside_fraction_regression.tex}
\end{table}
We also asked how many cells were inside the \glspl{dms} vs. free-floating in
suspension and/or loosely attached to the surface. We qualitatively verified the
presence of cells inside the \glspl{dms} using a \gls{mtt} stain to opaquely
mark cells and enable visualization on a brightfield microscope
(\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different densities and
expanding for \SI{14}{\day}, we filtered the \glspl{dms} out of the cell
suspension and digested them using dispase to free any cells attached on the
inner surface. We observed that approximately \SI{15}{\percent} of the total
cells after \SI{14}{\day} were on the interior surface of the \glspl{dms}
We also asked how many cells were inside the \glspl{dms} instead of
free-floating in suspension and/or loosely attached to the surface. We
qualitatively verified the presence of cells inside the \glspl{dms} using a
\gls{mtt} stain to opaquely mark cells and enable visualization on a brightfield
microscope (\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different
densities and expanding for \SI{15}{\day}, we filtered the \glspl{dms} out of
the cell suspension and digested them using dispase to free any cells attached
on the inner surface. We observed that approximately \SI{15}{\percent} of the
total cells after \SI{15}{\day} were on the interior surface of the \glspl{dms}
(\cref{fig:dms_inside_regression,tab:inside_regression}). Performing linear
regression on this data revealed that the percentage of T cells inside the
\glspl{dms} does not depend on the initial starting cell density (at least when
harvested after \SI{14}{\day}) (\cref{tab:inside_regression}).
harvested after \SI{15}{\day}) (\cref{tab:inside_regression}).
\subsection{DMSs Lead to Greater Expansion and High-Quality Phenotypes}
@ -2332,7 +2367,7 @@ 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
as \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
@ -2364,25 +2399,23 @@ true when observing the CD4+ and CD8+ fractions of the naïve/memory subset
\label{fig:dms_phenotype}
\end{figure*}
We also observed that, at least with the donors and conditions tested in these
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})\footnote{these where not the same donors as used for
\cref{fig:dms_exp}}. 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 \gls{dms} platform as opposed to the beads (or
alternatively, the \gls{dms} has less preferential expansion for CD8 T cells).
We cannot say the same about the \ptmemp{} since we did not have the initial
data for this phenotype; however (although it should be the vast majority of
cells given that cryopreserved T cells from a healthy donor should generally be
composed of circulated memory and naive T cells). Taken together, these data
indicate the \gls{dms} platform has the capacity to expand higher numbers and
percentages of highly potent \ptmem{} and \pth{} T cells compared to
state-of-the-art bead technology.
We also observed that, at least among some donors and conditions\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})\footnote{these where not the same donors
as used for \cref{fig:dms_exp}}. 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 \gls{dms} platform as
opposed to the beads (or alternatively, the \gls{dms} has less preferential
expansion for \cdp{8} T cells). We cannot say the same about the \ptmemp{} since
we did not have the initial data for this phenotype; (although memory and naive
cells should be the vast majority of cells given that \glspl{pbmc} is taken from
blood which has mostly these cell types). Taken together, these data indicate
the \gls{dms} platform has the capacity to expand higher numbers and percentages
of highly potent \ptmem{} and \pth{} T cells compared to state-of-the-art bead
technology.
\subsection{DMSs Produce Functional CAR T Cells}
@ -2390,12 +2423,12 @@ 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 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_cd19_flow,fig:car_cd19_endpoint}). 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}.
insert an anti-CD19 \gls{car}\cite{Milone2009} with a \gls{moi} of 10 and
subsequently measured the surface expression of the \gls{car} on day 14
(\cref{fig:car_cd19_flow,fig:car_cd19_endpoint}). We noted that there was robust
\gls{car} expression in over \SI{25}{\percent} of expanded T cells, and there
was no observable difference in \gls{car} expression between beads and
\glspl{dms}.
We also verified the functionality of expanded \gls{car} T cells using a
degranulation assay\cite{Zheng2012}. Briefly, T cells were cocultured with
@ -2451,7 +2484,7 @@ showing that migration was likely independent of \gls{car} transduction.
\caption[\acrshort{car} T Cell Functionality]
{\glspl{dms} produce functional \gls{car} T cells.
\subcap{fig:car_degran_flow}{Representative flow plot for
degenerating T cells.}
degranulating T cells.}
\subcap{fig:car_degran_endpoint}{Endpoint plots for transduced or
untransduced T cells stained with \cd{107a} for the degranulation assay.}
\subcap{fig:car_degran_migration}{Endpoint plot for transmigration assay
@ -2461,9 +2494,9 @@ showing that migration was likely independent of \gls{car} transduction.
\end{figure*}
In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms}
platform can be used to expand \gls{car} T cells against \gls{bcma}. Analogously
to the case with CD19, \gls{dms} and bead produced similar fractions of \ptcar{}
cells (albeit in this case at day 7 and with an undefined \gls{moi})
platform can be used to expand \gls{car} T cells against \gls{bcma}. Analogous
to CD19, \gls{dms} and bead produced similar fractions of \ptcar{} cells (albeit
in this case at day 7 and with an undefined \gls{moi})
(\cref{fig:car_bcma_percent}). Also consistent with CD19 and non-\gls{car} data,
we also found that the number of \ptcar{} T cells was greater for \gls{dms} than
for bead (\cref{fig:car_bcma_total}).
@ -2509,25 +2542,23 @@ for bead (\cref{fig:car_bcma_total}).
\label{fig:grex_results}
\end{figure*}
We also asked if the \gls{dms} platform could expand T cells in a static
bioreactor such a Grex. We incubated T cells in a Grex analogously to that for
plates and found that T cells in Grex bioreactors expanded as efficiently as
bead over \SI{14}{\day} and had similar viability
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent
with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to
beads and higher \ptmemp{} compared to beads (\cref{fig:grex_mem,fig:grex_cd4}).
Overall the \ptmemp{} was much lower than that seen from cultures grown in
tissue-treated plates (\cref{fig:dms_phenotype_mem}).
We also asked if the \gls{dms} platform could expand T cells in a Grex
bioreactor. We incubated T cells in a Grex analogously to plates and found that
T cells in Grex bioreactors expanded as efficiently as beads over \SI{14}{\day}
and had similar viability
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Consistent with past
results, \glspl{dms}-expanded T cells had higher \pthp{} and \ptmemp{} compared
to beads (\cref{fig:grex_mem,fig:grex_cd4}). Overall the \ptmemp{} was lower
than that seen in standard plates (\cref{fig:dms_phenotype_mem}).
These discrepancies might be explained in light of our other data as follows.
The Grex bioreactor has higher media capacity relative to its surface area, and
we did not move the T cells to a larger bioreactor as they grew in contrast with
These discrepancies might be explained in light of other data as follows. The
Grex bioreactor has higher media capacity relative to its surface area, and 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
\glspl{teff}, which was why the \ptmemp{} was so low compared to other data
(\cref{fig:dms_phenotype_mem}).
constraints, which may have nullified any advantage to the expansion seen in
standard plates (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
area could mean increased signaling and \gls{teff} differentiation, which was
why the \ptmemp{} was low compared to past data (\cref{fig:dms_phenotype_mem}).
\begin{figure*}[ht!]
\begingroup
@ -2543,9 +2574,9 @@ area could mean higher signaling and higher differentiation rate to
We also quantified the cytokines released during the Grex expansion using
Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
released higher concentrations of cytokines compared to beads
(\cref{fig:grex_luminex}). This included higher concentrations of
pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This
demonstrates that \gls{dms} could lead to more robust activation and fitness.
(\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory
cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This demonstrates that
\glspl{dms} could lead to more robust activation.
Taken together, these data suggest that \gls{dms} also lead to robust expansion
in Grex bioreactors, although more optimization may be necessary to maximize the