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ENH proofread most of aim 1

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@ -288,7 +288,10 @@
\DeclareSIUnit\IU{IU} \DeclareSIUnit\IU{IU}
\DeclareSIUnit\rpm{RPM} \DeclareSIUnit\rpm{RPM}
\DeclareSIUnit\carrier{carrier} \DeclareSIUnit\carrier{carrier}
\DeclareSIUnit\gauge{gauge}
\DeclareSIUnit\dms{DMS} \DeclareSIUnit\dms{DMS}
\DeclareSIUnit\stp{STP}
\DeclareSIUnit\snb{SNB}
\DeclareSIUnit\cell{cells} \DeclareSIUnit\cell{cells}
\DeclareSIUnit\ab{mAb} \DeclareSIUnit\ab{mAb}
\DeclareSIUnit\normal{N} \DeclareSIUnit\normal{N}
@ -370,6 +373,7 @@
\newcommand{\miltenyi}{Miltenyi Biotech} \newcommand{\miltenyi}{Miltenyi Biotech}
\newcommand{\bl}{Biolegend} \newcommand{\bl}{Biolegend}
\newcommand{\bd}{Becton Dickenson} \newcommand{\bd}{Becton Dickenson}
\newcommand{\pltread}{BioTek plate reader}
% the obligatory misc category % the obligatory misc category
\newcommand{\inlinecode}{\texttt} \newcommand{\inlinecode}{\texttt}
@ -1404,13 +1408,13 @@ novel considering the state-of-the-art technology for T cell manufacturing:
\section{Introduction} \section{Introduction}
The first aim was to develop a microcarrier system that mimics several key This aim was to develop a functionalized microcarrier system that mimics several
aspects of the \invivo{} lymph node microenvironment. We compared compare this key aspects of the \invivo{} lymph node microenvironment. We compared compare
system to state-of-the-art T cell activation technologies for both expansion this system to state-of-the-art T cell activation technologies for both
potential and memory cell formation. The governing hypothesis was that expansion potential and memory cell formation. The governing hypothesis was that
microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will provide
provide superior expansion and memory phenotype compared to state-of-the-art superior expansion and memory phenotype compared to state-of-the-art bead-based
bead-based T cell expansion technology\footnote{adapted from \dmspaper{}}. T cell expansion technology\footnote{adapted from \dmspaper{}}.
\section{Methods} \section{Methods}
@ -1428,41 +1432,62 @@ bead-based T cell expansion technology\footnote{adapted from \dmspaper{}}.
\end{figure*} \end{figure*}
\product{\gls{cus}}{\gehc}{DG-2001-OO} or \product{\gls{cug}}{\gehc}{DG-0001-OO} \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 were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} in a 15, 50, or 250
subsequent steps were done aseptically, and all reactions were carried out at \si{\ml} conical tube. The mass of the tube with the \gls{pbs} and microcarriers
\SI{20}{\mg\per\ml} carriers at room temperature and agitated using an orbital were recorded to the nearest millimeter (subsequently referred to here as
shaker with a \SI{3}{\mm} orbit diameter. After autoclaving, the microcarriers ``reaction mass''). The tube was centrifuged for \SI{30}{\second} at
were washed using sterile \gls{pbs} three times in a 10:1 volume ratio. \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} \product{\Gls{snb}}{\thermo}{21217} was dissolved at approximately \SI{10}{\uM}
in sterile ultrapure water, and the true concentration was then determined using 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 the \gls{haba} assay (see below). \SI{2.5}{\nmol\of{\snb}\per\mg\of{\carrier}}
to carrier suspension and allowed to react for \SI{60}{\minute} at (unless otherwise noted) was added to carrier suspension and allowed to react
\SI{700}{\rpm} of agitation. After the reaction, the amount of biotin remaining for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the reaction, the
in solution was quantified using the \gls{haba} assay (see below). The carriers amount of biotin attached to the microcarriers was determined indirectly by
were then washed three times, which entailed adding sterile \gls{pbs} in a 10:1 measuring the biotin in solution via the \gls{haba} assay (see below). The
volumetric ratio, agitating at \SI{900}{\rpm} for \SI{10}{\minute}, adding up to carriers were then washed three times, which entailed adding sterile \gls{pbs}
a 15:1 volumetric ratio (relative to reaction volume) of sterile \gls{pbs}, in a 10:1 volumetric ratio, agitating at \SI{900}{\rpm} for \SI{10}{\minute},
centrifuging at \SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid adding up to a 15:1 volumetric ratio (relative to reaction volume) of sterile
back down to the reaction volume. \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 To coat the microcarriers with \product{\gls{stp}}{Jackson
Immunoresearch}{016-000-114} was added and allowed to react for Immunoresearch}{016-000-114}, \SI{2}{\ug\of{\stp}\per\mg\of{\carrier}} was
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant added and allowed to react for \SI{60}{\minute} at \SI{700}{RPM} of agitation.
was taken for the \product{\gls{bca} assay}{\thermo}{23225}, and the carriers After the reaction, \SI{400}{\ul} supernatant (regardless of tube size) was
were washed analogously to the previous wash step to remove the biotin, except taken for the \product{\gls{bca} assay}{\thermo}{23225} in order to indirectly
two washes were done and the agitation time was \SI{30}{\minute}. Biotinylated 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} \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 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 \SI{0.2}{\ug\of{\ab}\per\mg\of{\carrier}}. \glspl{mab} were allowed to bind to
\product{\gls{bsa}}{\sigald}{A9576} was added to a final concentration of the carriers for \SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding,
\SI{2}{\percent} in order to prevent non-specific binding of the antibodies to \SI{400}{\ul} supernatant was sampled to indirectly quantify \gls{mab}
the reaction tubes. \glspl{mab} were allowed to bind to the carriers for attachment using an \product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}.
\SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding, supernatants were Fully functionalized \glspl{dms} were washed in sterile \gls{pbs} analogous to
sampled to quantify remaining \gls{mab} concentration using an the previous washing step to remove excess \gls{stp}.
\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.
\begin{table}[!h] \centering \begin{table}[!h] \centering
\caption{Microcarrier properties} \caption{Microcarrier properties}
@ -1470,7 +1495,9 @@ media to be used for the T cell expansion.
\input{../tables/carrier_properties.tex} \input{../tables/carrier_properties.tex}
\end{table} \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 \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
@ -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 premix. All quantifications of \gls{haba} were performed on an Eppendorf D30
Spectrophotometer using \product{\SI{70}{\ul} cuvettes}{BrandTech}{759200}. The 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}\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 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 \pltread{}.
The \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 \glspl{mab} 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. This assay was quantified using a \pltread{}.
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
quantified indirectly using \product{\gls{fitcbt}}{\thermo}{B10570}. 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 \pltread{}.
\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
@ -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 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 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 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 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
@ -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 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 \cdp{19} K562 cells transformed with
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}. \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}. Cells
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml} 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{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar}
\product{monensin}{eBioscience}{ 00-4505-51}, and \SI{1}{\ug\per\ml} \product{monensin}{eBioscience}{ 00-4505-51}, and \SI{1}{\ug\per\ml}
\product{\acd{28}}{eBioscience}{302914} (all functional grade \glspl{mab}) with \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} \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 promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core. \product{FUGW transfer plasmid}{Addgene}{14883} kindly provided by the Emory
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the Viral Vector Core. Lentiviral vectors were synthesized by the Emory Viral Vector
Cincinnati Children's Hospital Medical Center Viral Vector Core. RNA titer was Core or the Cincinnati Children's Hospital Medical Center Viral Vector Core. RNA
calculated using a \product{Lenti-X \gls{qpcr} titer kit}{Takara}{631235}. To titer was calculated using a \product{Lenti-X \gls{qpcr} titer
transduce primary human T cells, \product{retronectin}{Takara}{T100A} was coated kit}{Takara}{631235}. To transduce primary human T cells,
onto non-TC treated 96 well plates and used to immobilize lentiviral vector \product{retronectin}{Takara}{T100A} was coated onto non-TC treated 96 well
particles according to the manufacturer's instructions. Briefly, retronectin plates and used to immobilize lentiviral vector particles according to the
solution was adsorbed overnight at \SI{4}{\degreeCelsius} and blocked the next manufacturer's instructions. Briefly, retronectin solution was adsorbed
day using \gls{bsa}. Prior to transduction, lentiviral supernatant was overnight at \SI{4}{\degreeCelsius} and blocked the next day using \gls{bsa}.
spinoculated at \SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}. Prior to transduction, lentiviral supernatant was spinoculated at
T cells were activated in 96 well plates using beads or \glspl{dms} for \SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}. T cells were
\SI{24}{\hour}, and then cells and beads/\glspl{dms} were transferred onto activated in 96 well plates using beads or \glspl{dms} for \SI{24}{\hour}, and
lentiviral vector coated plates and incubated for another \SI{24}{\hour}. Cells then cells and beads/\glspl{dms} were transferred onto lentiviral vector coated
and beads/\glspl{dms} were removed from the retronectin plates using vigorous plates and incubated for another \SI{24}{\hour}. Cells and beads/\glspl{dms}
pipetting and transferred to another 96 well plate wherein expansion continued. 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 % METHOD fill in missing product numbers
\gls{bcma} \gls{car} lentiviral vector was synthesized in house as \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} \ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
\end{equation} \end{equation}
Measuring the hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb} Measuring the hydrolysis of \gls{snb} was performed spectroscopically as the
was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well extinction coefficient of \ce{NHS-} is well-known. \gls{snb} was added to either
plate. Kinetic analysis using a BioTek plate reader began immediately after \gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic analysis
prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}. 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} \subsection{Reaction Kinetics Quantification}
The diffusion of \gls{stp} into biotin-coated microcarriers was determined The reaction kinetics of \gls{stp} attaching to biotin-coated microcarriers was
experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of determined experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple
biotin-coated microcarriers, and supernatents were taken at fixed intervals and batches of biotin-coated microcarriers, and supernatents were taken at fixed
quantified for \gls{stp} protein using the \gls{bca} assay. 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 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 with a fixed number of uniformly distributed ``receptors'' equal to the number
of \gls{stp} molecules (here called ``ligands'') experimentally determined to 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), \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 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 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 of the microcarriers was reached. This model was given by
microcarriers were large enough that the interactions between the \gls{stp} and \cref{eqn:stp_diffusion_1,eqn:stp_diffusion_2}:
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}:
\begin{equation} \begin{equation}
\label{eqn:stp_diffusion_1} \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})} {\gls{sym:vol} (1 / \gls{sym:rad} - 1 / \gls{sym:mcrad})}
\end{equation} \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 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 \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 known, the experimental data was fit using these equations using
\inlinecode{ode45} in MATLAB and least squares as the fitting error. These \inlinecode{ode45} in MATLAB and least squares as the fitting error. These
equations were then used analogously to describe the reaction profile of fitted equations were then used to simulate the reaction profile of \glspl{mab}
\glspl{mab} assuming a diffusion rate of assuming a diffusion rate of
\SI{4.8e-7}{\cm\squared\per\second}\cite{Sherwood1992}. \SI{4.8e-7}{\cm\squared\per\second}\cite{Sherwood1992}.
To model the washing of the microcarriers, they once again were assumed to be 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 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 previous step (which was assumed to be equal to concentration in the
supernatent). The diffusion out of the microcarriers is given by the following supernatent). The fitted geometric diffusivity from above was used in these
partial differential equation and boundary conditions: 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} \begin{equation}
\label{eqn:stp_washing} \label{eqn:stp_washing}
@ -1786,24 +1829,18 @@ partial differential equation and boundary conditions:
\evalat{\gls{sym:bulkligconc}}{\gls{sym:time} = \infty}) / 2 \evalat{\gls{sym:bulkligconc}}{\gls{sym:time} = \infty}) / 2
\end{equation} \end{equation}
Note that in order to avoid solving a moving boundary value problem, the In order to avoid solving a moving boundary value problem, the concentration at
concentration at the boundary of the microcarriers was fixed at the average of the boundary of the microcarriers was fixed at the average of the final and
the final and initial concentration expected to be observed in bulk. This should initial concentration expected to be observed in bulk. This should be a
be a reasonable assumption given that the volume inside the microcarriers is reasonable assumption given that the volume inside the microcarriers is tiny
tiny compared to the amount of volume added in the wash, thus the boundary compared to the amount of volume added in the wash, thus the boundary
concentration should change little. 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} 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
\gls{pbs}. \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
See \cref{sec:appendix_binding} and \cref{sec:appendix_washing} for the MATLAB steps.
code and derivations, as well as output in the case of the washing steps.
\subsection{Luminex Analysis}\label{sec:luminex_analysis} \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 summarized in YAML files. The data included in these YAML files included reagent
characteristics (vendor, catalog number, lot number, manufacturing date), cell characteristics (vendor, catalog number, lot number, manufacturing date), cell
donor characteristics (age, \gls{bmi}, phenotype, demographic, gender), and all 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 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 Docker which retrieved the data from its source location and inserted the data
in a Postgres database (specifically Aurora implementation hosted on \gls{aws}). in a PostgreSQL database (specifically the Aurora implementation hosted on
This program included checks to ensure the integrity of source data (for \gls{aws}). This program included checks to ensure the integrity of source data
example, flagging entries which had a reagent whose manufacturing date was after (for example, flagging entries which had a reagent whose manufacturing date was
the date the experiment started, which signifies a human input error). after 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,
significance was assessed using the \inlinecode{stat\_compare\_means} function significance was assessed using the \inlinecode{stat\_compare\_means} function
with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R. 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, significance of main and interaction effects
was determined using the car library in R. was determined using the \inlinecode{car} library in R.
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. All results with categorical variables are \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 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} overall \gls{stp} binding compared to \gls{cug}-based \glspl{dms}
(\cref{fig:cug_vs_cus}). We showed that by varying the concentration of (\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:biotin_coating}), mass of attached \gls{stp}
(\cref{fig:stp_coating}), and mass of attached \glspl{mab} (\cref{fig:stp_coating}), and mass of attached \glspl{mab}
(\cref{fig:mab_coating}). Furthermore, we showed that the microcarriers were (\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 \gls{doe} were temperature, microcarrier mass, and \gls{snb} input mass. These
were parameters that we specifically controlled but hypothesized might have some were parameters that we specifically controlled but hypothesized might have some
sensitivity on the final biotin mass attachment rate depending on their noise 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 the microcarrier suspension to come to \gls{rt} after autoclaving. After
performing a full factorial \gls{doe} with three center points as the target 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 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, 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 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} could be because lower temperature might make spontaneous decay of \gls{snb}
occur slower, which would give \gls{snb} molecule more opportunity to diffuse occur slower, which would give \gls{snb} molecules more opportunity to diffuse
into the microcarriers and react with amine groups to form attachments. It seems into the microcarriers and react with amine groups to form attachments. It
that concentration only has a linear effect and doesn't interact with any of the seemed that concentration only has a linear effect and doesn't interact with any
other variables, which is not surprisingly given the behavior observed in of the other variables, which is not surprising given the behavior observed in
(\cref{fig:biotin_coating}) (\cref{fig:biotin_coating})
We also observed that the reaction pH does not influence the amount of biotin 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}). 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 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 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 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, 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} 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 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 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 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-}, but plate surface. \gls{snb} is known to hydrolyze in the presence of \ce{OH-}
the lack of hydrolysis in \gls{di} water can be explained by the fact that groups, but the lack of hydrolysis in \gls{di} water can be explained by the
biotin itself is acidic, and thus the reaction is self-inhibitory in an fact that biotin itself is acidic, and thus the reaction is self-inhibitory in
unbuffered and neutral pH system. Because we dissolve our \gls{snb} in \gls{di} an unbuffered and neutral pH system. Because we dissolve our \gls{snb} in
water prior to adding it to the microcarrier suspension (which itself is in \gls{di} water prior to adding it to the microcarrier suspension (which itself
\gls{pbs}) this result indicated that hydrolysis is not of concern when adding is in \gls{pbs}) this result indicated that hydrolysis is not of concern when
\gls{snb} within minutes. adding \gls{snb} within minutes.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \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 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 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 \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 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
@ -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 (\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 this experimental binding data to compute the geometric diffusivity of the
microcarriers and fit a continuous model for the \gls{stp} binding reaction. We 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 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} (\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}).
Using this geometric diffusivity and the known diffusion coefficient of a Using this geometric diffusivity and the known diffusion coefficient of
\gls{mab} protein in water, we calculated the binding of \glspl{mab} per time \glspl{mab} in water, we calculated the binding of \glspl{mab} per time onto the
onto the microcarriers (this obviously assumes that the effectively diffusivity microcarriers (this obviously assumes that the effectively diffusivity is
is independent of the protein used, which should be reasonable given that the independent of the protein used, which should be reasonable given that the pores
pores of the microcarriers are huge compared to the proteins, and we don't of the microcarriers are huge compared to the proteins, and we don't expect any
expect any significant reaction between the protein and the microcarrier surface significant reaction between the protein and the microcarrier surface save for
save for the \gls{stp}-biotin binding reaction). Once again, we used the maximum the \gls{stp}-biotin binding reaction). Once again, we used the maximum number
number of \glspl{mab} observed to determine the number of `binding sites' for of \glspl{mab} observed to determine the number of receptors for \glspl{mab} on
\glspl{mab} on the microcarriers, which should correspond to the upper-bound for the microcarriers, which should correspond to the upper-bound for the reaction
the reaction time (\cref{fig:mab_coating}). According to this model, the time (\cref{fig:mab_coating}). According to this model, the \gls{mab} binding
\gls{mab} binding reaction should be complete within \SI{75}{\minute} under the reaction should be complete within \SI{75}{\minute} under the conditions used
conditions used for our protocol (\cref{fig:dms_mab_per_time})\footnote{We for our protocol (\cref{fig:dms_mab_per_time})\footnote{we actually used
actually used \SI{60}{\minute} as describe in the method section as this model \SI{60}{\minute} as describe in the method section as this model was not
was not updated with new parameters until recently; however, we should point updated with new parameters until recently; however, we should point out that
out that even at \SI{60}{\minute} the reaction appears to be even at \SI{60}{\minute} the reaction appears to be >\SI{95}{\percent}
>\SI{95}{\percent} complete}. 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
diffusivity of the microcarriers as calculated above. This is important, as diffusivity of the microcarriers as calculated above. This is important, as
failing to wash out residual free \gls{snb} (for example) could occupy binding 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 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 \glspl{mab} downstream. Each wash was a 1:15 dilution (\SI{1}{\ml} reaction
in a \SI{15}{\ml} conical tube), and in the case of \gls{snb} we wished to wash volume in a \SI{15}{\ml} conical tube), and in the case of \gls{snb} we wished
out enough biotin such that less than \SI{1}{\percent} of the binding sites in to wash out enough biotin such that less than \SI{1}{\percent} of the binding
\gls{stp} would be occupied. Given this dilution factor, a maximum of 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} \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 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 \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 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 \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 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. \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} 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} 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 bulk concentration of the previous binding step, and calculated the amount of
time it would take for the concentration profile inside the microcarriers to 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 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 \SI{15}{\minute}, and \SI{17}{\minute} respectively. We verified that the
\gls{snb} was totally undetectable after washing (\cref{fig:dms_biotin_washed}). \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 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 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
one that could be optimized). 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}. \cref{sec:appendix_washing}.
\subsection{DMSs Can Efficiently Expand T Cells Compared to Beads} \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 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 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 per the manufacturer’s protocol, with the exception that the starting cell
densities were matched between the beads and carriers to densities were matched between the beads and \glspl{dms} to
~\SI{2.5e6}{\cell\per\ml}. Throughout the culture we observed that T cells in ~\SI{2.5e6}{\cell\per\ml}. We observed that T cells in \gls{dms} culture grew in
\gls{dms} culture grew in tight clumps on the surface of the \glspl{dms} as well tight clumps on the surface of the \glspl{dms} as well as inside the pores of
as inside the pores of the \glspl{dms} the \glspl{dms} (\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore,
(\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore, we observed that we observed that the \glspl{dms} conferred greater expansion compared to
the \glspl{dms} conferred greater expansion compared to traditional beads, and traditional beads, and significantly greater expansion after \SI{12}{\day} of
significantly greater expansion after \SI{12}{\day} of culture culture (\cref{fig:dms_expansion_bead}). We also observed no T cell expansion
(\cref{fig:dms_expansion_bead}). We also observed no T cell expansion using using \glspl{dms} coated with an isotype control mAb compared to \glspl{dms}
\glspl{dms} coated with an isotype control mAb compared to \glspl{dms} coated coated with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}),
with \acd{3}/\acd{28} \glspl{mab} (\cref{fig:dms_expansion_isotype}), confirming confirming specificity of the expansion method. Given that \il{2} does not lead
specificity of the expansion method. Given that \il{2} does not lead to to expansion on its own, we know that the expansion of the T cells shown here is
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}. due to the \acd{3} and \acd{28} \glspl{mab}\cite{Waysbort2013}.
\begin{figure*}[ht!] \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 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 the cell, but only penetrates necrotic cells which have a perforated cell
membrane. When staining for these two markers and assessing via flow cytometry, 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 apoptotic and necrotic cells (\cref{fig:apoptosis_annV}). Furthermore, we
stained our cultures with CellEvent dye, which is an indicator of \gls{cas37}, stained our cultures with CellEvent dye, an indicator of \gls{cas37} which is
which is activated in apoptotic cells. In line with the \gls{pi}/\gls{anv} activated in apoptotic cells. In line with the \gls{pi}/\gls{anv} results, we
results, we observed that the \gls{dms} T cells had lower frequency of observed that the \gls{dms} T cells had lower frequency of \gls{cas37}
\gls{cas37} expression, indicating less apoptosis for our method expression, indicating less apoptosis for our method (\cref{fig:apoptosis_cas}).
(\cref{fig:apoptosis_cas}). Finally, we lysed our cells and stained for Finally, we lysed our cells and stained for \gls{bcl2}, which is also
\gls{bcl2}, which is also upregulated in apoptosis. In this case, some (but not upregulated in apoptosis. In this case, some (but not all) of the bead-expanded
all) of the bead-expanded cultures showed higher \gls{bcl2} expression, which cultures showed higher \gls{bcl2} expression, which could indicate more
could indicate more apoptosis in those groups (\cref{fig:apoptosis_bcl2}). None apoptosis in those groups (\cref{fig:apoptosis_bcl2}). None of the \gls{dms}
of the \gls{dms} cultures showed similar heightened expression. Taken together, cultures showed similar heightened expression. Taken together, these data
these data suggest that the \gls{dms} platform at least in part achieves higher suggest that the \gls{dms} platform at least in part achieves higher expansion
expansion by lowering apoptosis of the cells in culture. by lowering apoptosis.
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \begingroup
@ -2281,19 +2316,19 @@ expansion by lowering apoptosis of the cells in culture.
\input{../tables/inside_fraction_regression.tex} \input{../tables/inside_fraction_regression.tex}
\end{table} \end{table}
We also asked how many cells were inside the \glspl{dms} vs. free-floating in We also asked how many cells were inside the \glspl{dms} instead of
suspension and/or loosely attached to the surface. We qualitatively verified the free-floating in suspension and/or loosely attached to the surface. We
presence of cells inside the \glspl{dms} using a \gls{mtt} stain to opaquely qualitatively verified the presence of cells inside the \glspl{dms} using a
mark cells and enable visualization on a brightfield microscope \gls{mtt} stain to opaquely mark cells and enable visualization on a brightfield
(\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different densities and microscope (\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different
expanding for \SI{14}{\day}, we filtered the \glspl{dms} out of the cell densities and expanding for \SI{15}{\day}, we filtered the \glspl{dms} out of
suspension and digested them using dispase to free any cells attached on the the cell suspension and digested them using dispase to free any cells attached
inner surface. We observed that approximately \SI{15}{\percent} of the total on the inner surface. We observed that approximately \SI{15}{\percent} of the
cells after \SI{14}{\day} were on the interior surface of the \glspl{dms} 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 (\cref{fig:dms_inside_regression,tab:inside_regression}). Performing linear
regression on this data revealed that the percentage of T cells inside the 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 \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} \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 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} 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 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 \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 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 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} \label{fig:dms_phenotype}
\end{figure*} \end{figure*}
We also observed that, at least with the donors and conditions tested in these We also observed that, at least among some donors and conditions\footnote{these
experiments\footnote{these results were not always consistent, see the results were not always consistent, see the metaanalysis at the end of this
metaanalysis at the end of this aim for an in-depth quantification of this aim for an in-depth quantification of this observation} that the fraction of
observation} that the fraction of \ptmem{} and \pth{} T cells was higher in \ptmem{} and \pth{} T cells was higher in the \gls{dms} groups compared to the
the \gls{dms} groups compared to the bead groups bead groups (\cref{fig:dms_phenotype})\footnote{these where not the same donors
(\cref{fig:dms_phenotype})\footnote{these where not the same donors as used for as used for \cref{fig:dms_exp}}. Note that in the case of \pthp{}, the donors
\cref{fig:dms_exp}}. This result was seen for multiple donors. We should note we used had an initial \pthp{} that was much higher (healthy donors generally
that in the case of \pthp{}, the donors we used had an initial \pthp{} that was have a CD4:CD8 ratio of 2:1), so the proper interpretation of this is that the
much higher (healthy donors generally have a CD4:CD8 ratio of 2:1), so the \pthp{} decreases less over the culture period with the \gls{dms} platform as
proper interpretation of this is that the \pthp{} decreases less over the opposed to the beads (or alternatively, the \gls{dms} has less preferential
culture period with the \gls{dms} platform as opposed to the beads (or expansion for \cdp{8} T cells). We cannot say the same about the \ptmemp{} since
alternatively, the \gls{dms} has less preferential expansion for CD8 T cells). we did not have the initial data for this phenotype; (although memory and naive
We cannot say the same about the \ptmemp{} since we did not have the initial cells should be the vast majority of cells given that \glspl{pbmc} is taken from
data for this phenotype; however (although it should be the vast majority of blood which has mostly these cell types). Taken together, these data indicate
cells given that cryopreserved T cells from a healthy donor should generally be the \gls{dms} platform has the capacity to expand higher numbers and percentages
composed of circulated memory and naive T cells). Taken together, these data of highly potent \ptmem{} and \pth{} T cells compared to state-of-the-art bead
indicate the \gls{dms} platform has the capacity to expand higher numbers and technology.
percentages of highly potent \ptmem{} and \pth{} T cells compared to
state-of-the-art bead technology.
\subsection{DMSs Produce Functional CAR T Cells} \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 \glspl{dms} were compatible with lentiviral transduction protocols used to
generate \gls{car} T cells\cite{Tumaini2013, Lamers2014}. We added a 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 \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 insert an anti-CD19 \gls{car}\cite{Milone2009} with a \gls{moi} of 10 and
surface expression of the \gls{car} on day 14 subsequently measured the surface expression of the \gls{car} on day 14
(\cref{fig:car_cd19_flow,fig:car_cd19_endpoint}). We noted (\cref{fig:car_cd19_flow,fig:car_cd19_endpoint}). We noted that there was robust
that there was robust \gls{car} expression in over \SI{25}{\percent} of expanded \gls{car} expression in over \SI{25}{\percent} of expanded T cells, and there
T cells, and there was no observable difference in \gls{car} expression between was no observable difference in \gls{car} expression between beads and
beads and \glspl{dms}. \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
degranulation assay\cite{Zheng2012}. Briefly, T cells were cocultured with 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] \caption[\acrshort{car} T Cell Functionality]
{\glspl{dms} produce functional \gls{car} T cells. {\glspl{dms} produce functional \gls{car} T cells.
\subcap{fig:car_degran_flow}{Representative flow plot for \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 \subcap{fig:car_degran_endpoint}{Endpoint plots for transduced or
untransduced T cells stained with \cd{107a} for the degranulation assay.} untransduced T cells stained with \cd{107a} for the degranulation assay.}
\subcap{fig:car_degran_migration}{Endpoint plot for transmigration 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*} \end{figure*}
In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms} 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 platform can be used to expand \gls{car} T cells against \gls{bcma}. Analogous
to the case with CD19, \gls{dms} and bead produced similar fractions of \ptcar{} to CD19, \gls{dms} and bead produced similar fractions of \ptcar{} cells (albeit
cells (albeit in this case at day 7 and with an undefined \gls{moi}) 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, (\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 we also found that the number of \ptcar{} T cells was greater for \gls{dms} than
for bead (\cref{fig:car_bcma_total}). for bead (\cref{fig:car_bcma_total}).
@ -2509,25 +2542,23 @@ for bead (\cref{fig:car_bcma_total}).
\label{fig:grex_results} \label{fig:grex_results}
\end{figure*} \end{figure*}
We also asked if the \gls{dms} platform could expand T cells in a static We also asked if the \gls{dms} platform could expand T cells in a Grex
bioreactor such a Grex. We incubated T cells in a Grex analogously to that for bioreactor. We incubated T cells in a Grex analogously to plates and found that
plates and found that T cells in Grex bioreactors expanded as efficiently as T cells in Grex bioreactors expanded as efficiently as beads over \SI{14}{\day}
bead over \SI{14}{\day} and had similar viability and had similar viability
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent (\cref{fig:grex_results_fc,fig:grex_results_viability}). Consistent with past
with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to results, \glspl{dms}-expanded T cells had higher \pthp{} and \ptmemp{} compared
beads and higher \ptmemp{} compared to beads (\cref{fig:grex_mem,fig:grex_cd4}). to beads (\cref{fig:grex_mem,fig:grex_cd4}). Overall the \ptmemp{} was lower
Overall the \ptmemp{} was much lower than that seen from cultures grown in than that seen in standard plates (\cref{fig:dms_phenotype_mem}).
tissue-treated plates (\cref{fig:dms_phenotype_mem}).
These discrepancies might be explained in light of our other data as follows. These discrepancies might be explained in light of other data as follows. The
The Grex bioreactor has higher media capacity relative to its surface area, and Grex bioreactor has higher media capacity relative to its surface area, and we
we did not move the T cells to a larger bioreactor as they grew in contrast with 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 seen in
seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth standard plates (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
area could mean higher signaling and higher differentiation rate to area could mean increased signaling and \gls{teff} differentiation, which was
\glspl{teff}, which was why the \ptmemp{} was so low compared to other data why the \ptmemp{} was low compared to past data (\cref{fig:dms_phenotype_mem}).
(\cref{fig:dms_phenotype_mem}).
\begin{figure*}[ht!] \begin{figure*}[ht!]
\begingroup \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 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 Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
released higher concentrations of cytokines compared to beads released higher concentrations of cytokines compared to beads
(\cref{fig:grex_luminex}). This included higher concentrations of (\cref{fig:grex_luminex}), including higher concentrations of pro-inflammatory
pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This demonstrates that
demonstrates that \gls{dms} could lead to more robust activation and fitness. \glspl{dms} could lead to more robust activation.
Taken together, these data suggest that \gls{dms} also lead to robust expansion 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 in Grex bioreactors, although more optimization may be necessary to maximize the