ENH make the modeling section better
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@ -2425,6 +2425,32 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
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publisher = {Wiley},
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}
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@Article{Qiu2011,
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author = {Peng Qiu and Erin F Simonds and Sean C Bendall and Kenneth D Gibbs and Robert V Bruggner and Michael D Linderman and Karen Sachs and Garry P Nolan and Sylvia K Plevritis},
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journal = {Nature Biotechnology},
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title = {Extracting a cellular hierarchy from high-dimensional cytometry data with {SPADE}},
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year = {2011},
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month = {oct},
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number = {10},
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pages = {886--891},
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volume = {29},
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doi = {10.1038/nbt.1991},
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publisher = {Springer Science and Business Media {LLC}},
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}
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@Article{Qiu2017,
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author = {Peng Qiu},
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journal = {Cytometry Part A},
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title = {Toward deterministic and semiautomated {SPADE} analysis},
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year = {2017},
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month = {feb},
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number = {3},
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pages = {281--289},
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volume = {91},
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doi = {10.1002/cyto.a.23068},
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publisher = {Wiley},
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}
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@Comment{jabref-meta: databaseType:bibtex;}
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@Comment{jabref-meta: grouping:
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@ -123,6 +123,7 @@
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\newacronym{colb}{COL-B}{collagenase B}
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\newacronym{cold}{COL-D}{collagenase D}
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\newacronym{tsne}{tSNE}{t-stochastic neighbor embedding}
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\newacronym{umap}{UMAP}{uniform manifold approximation and projection}
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\newacronym{anv}{AXV}{Annexin-V}
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\newacronym{pi}{PI}{propidium iodide}
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\newacronym{rt}{RT}{room temperature}
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@ -1103,48 +1104,68 @@ directions for future work. To this end, the types of \glspl{doe} we generally
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used in this work were fractional factorial designs with three levels, which
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enable the estimation of both main effects and second order quadratic effects.
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\subsection*{identification and standardization of CPPs and CQAs}\label{sec:background_cqa}
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\subsection*{identification and standardization of CPPs and
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CQAs}\label{sec:background_cqa}
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Ultimately the identification of relevant \glspl{cpp} and \glspl{cqa} is an
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interative process
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% BACKGROUND at least attempt to show that there is alot of work in the space
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% identifying signaling networks
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A number of multiomics strategies exist which can generate rich datasets for T
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cells. We will consider several multiomics strategies within this proposal:
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In the context of T cell manufacturing, ideally we would have a set of
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non-destructive biomarkers that could both identify functional T cells and
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predict when a process is on track to deliver such functional T cells. T cells
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secrete numerous cytokines and metabolites in the media, which may reflect the
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internal state accurately and thus serve as a potential set of \glspl{cqa}.
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The complexity of these pathways dictates that we take a big-data approach to
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this problem. To this end, there are several pertinent multi-omic (or simply
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`omic') techniques that can be used to collect such datasets, which can then be
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mined, modeled, and correlated to relevent responses (such as an endpoint
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quantification of memory T cells) to identify pertinent \glspl{cqa}.
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An overview of the techniques used in this work are:
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\begin{description}
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\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
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\item[Luminex --] This is a multiplexed bead-based assay similar to \gls{elisa} that can measure
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many bulk (not single cell) cytokine concentrations simultaneously
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in a media sample. Since this only requires media (as opposed to
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destructively measuring cells) we will use this as a longitudinal
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measurement.
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\item[Metabolomics:] It is well known that T cells of different
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in a media sample. This is a destructive assay but does not require cells to
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obtain a measurement.
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\item[\gls{nmr} --] It is well known that T cells of different
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lineages have different metabolic profiles; for instance memory T
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cells have larger aerobic capacity and fatty acid
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oxidation\cite{Buck2016, van_der_Windt_2012}. We will interrogate
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key metabolic species using \gls{nmr} in collaboration with the
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Edison Lab at the University of Georgia. This will be both a
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longitudinal assay using media samples (since some metabolites may
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be expelled from cells that are indicative of their phenotype) and
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at endpoint where we will lyse the cells and interogate their entire
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metabolome.
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\item[Flow and Mass Cytometry:] Flow cytometry using fluorophores has been used
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extensively for immune cell analysis, but has a practical limit of
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oxidation\cite{Buck2016, van_der_Windt_2012}. \gls{nmr} is a technique that
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can non-destructively quantify small molecules in a media sample, and thus is
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an attractive method that could be used for inline, real-time monitoring.
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\item[Flow and Mass Cytometry --] Flow cytometry using fluorophores has been
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used extensively for immune cell analysis, but has a practical limit of
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approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
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traditional flow cytometry except that it uses heavy-metal \gls{mab}
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conjugates, which has a practical limit of over 50 markers. This will be
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useful in determining precise subpopulations and phenotypes that may be
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influencing responses, especially when one considers that many cell types can
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be defined by more than one marker combination. We will perform this at
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endpoint. While mass cytometry is less practical than simple flow cytometers
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such as the BD Accuri, we may find that only a few markers are required to
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accurately predict performance, and thus this could easily translate to
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industry using relatively cost-effective equipment.
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conjugates, which has a practical limit of over 50 markers. While mass
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cytometry is less practical than simple flow cytometers such as the BD Accuri,
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we may find that only a few markers are required to accurately predict
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performance, and thus this could easily translate to industry using relatively
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cost-effective equipment. Both of these destructively analyze the cells
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themselves, but they have the advantage in that they are measuring a direct
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property of the cells and not a secreted product.
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\end{description}
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% TODO add a computational section
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% BACKGROUND what about ssRNAseq?
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% TODO add a section explaining causal inference since this is a big part of
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% the end of aim 1
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Upon collecting these omic datasets, determining the \glspl{cqa} becomes a
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computational problem. Predictions of the final product using data collected
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earlier in time can be made using any number of supervised learning techniques
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(linear and non-linear regression in all its forms) which in turn can be used to
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develop process control models. Unsupervised learning and dimensionality
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reduction techniques such as \gls{tsne}, \gls{umap}, and
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\gls{spade}\cite{Qiu2011, Qiu2017} can be performed to delineate clusters of
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interesting cell types and the markers that define them.
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Ultimately, identifying \glspl{cqa} will likely be an iterative process, wherein
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putative \glspl{cqa} will be identified, the corresponding \glspl{cpp} will be
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set and optimized to maximize products with these \glspl{cpp} and then
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additional data will be collected in the clinic as the product is tested on
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various patients with different indications. Additional \glspl{cqa} may be
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identified which better predict specific clinical outcomes, which can be fed
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back into the process model and optimized again.
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\section{Innovation}
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