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tex/thesis.tex
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tex/thesis.tex
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@ -2331,6 +2331,141 @@ IL13 and IL15 were found predictive in combination with these using SR
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\section{discussion}
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% optimization of process features
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% TODO this sounds like total fluff
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CPPs modeling and understanding are critical to new product development and in
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cell therapy development, it can have life-saving implications. The challenges
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for effective modeling grow with the increasing complexity of processes due to
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high dimensionality, and the potential for process interactions and nonlinear
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relationships. Another critical challenge is the limited amount of available
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data, mostly small DOE datasets. SR has the necessary capabilities to resolve
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the issues of process effects modeling and has been applied across multiple
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industries12. SR discovers mathematical expressions that fit a given sample and
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differs from conventional regression techniques in that a model structure is not
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defined a priori13. Hence, a key advantage of this methodology is that
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transparent, human-interpretable models can be generated from small and large
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datasets with no prior assumptions14,15.
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Since the model search process lets the data determine the model, diverse and
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competitive (e.g., accuracy, complexity) model structures are typically
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discovered. An ensemble of diverse models can be formed where its constituent
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models will tend to agree when constrained by observed data yet diverge in new
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regions. Collecting data in these regions helps to ensure that the target system
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is accurately modeled, and its optimum is accurately located14,15. Exploiting
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these features allows adaptive data collection and interactive modeling.
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Consequently, this adaptive-DOE approach is useful in a variety of scenarios,
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including maximizing model validity for model-based decision making, optimizing
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processing parameters to maximize target yields, and developing emulators for
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online optimization and human understanding14,15.
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% predictive features
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An in-depth characterization of potential DMS-based T-cell CQAs includes a list
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of cytokine and NMR features from media samples that are crucial in many aspects
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of T cell fate decisions and effector functions of immune cells. Cytokine
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features were observed to slightly improve prediction and dominated the ranking
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of important features and variable combinations when modeling together with NMR
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media analysis and process parameters (Fig.3b,d).
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Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and IL15 were
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biologically assessed in terms of their known functions and activities
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associated with T cells. T helper cells secrete more cytokines than T cytotoxic
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cells, as per their main functions, and activated T cells secrete more cytokines
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than resting T cells. It is possible that some cytokines simply reflect the
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CD4+/CD8+ ratio and the activation degree by proxy proliferation. However, the
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exact ratio of expected cytokine abundance is less clear and depends on the
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subtypes present, and thus examination of each relevant cytokine is needed.
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IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
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dampen its effect on T cells16. Since IL2R was much greater than IL2 in
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solution, this might reduce the overall effect of IL2, which could be further
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investigated by blocking IL2R with an antibody. In T cells, TNF can increase
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IL2R, proliferation, and cytokine production18. It may also induce apoptosis
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depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF
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has both a soluble and membrane-bound form, this may either increase or decrease
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CD4+ ratio and/or memory T cells depending on the ratio of the membrane to
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soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to
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understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13
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is known to be critical for Th2 response and therefore could be secreted if
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there are significant Th2 T cells already present in the starting population19.
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This cytokine has limited signaling in T cells and is thought to be more of an
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effector than a differentiation cytokine20. It might be emerging as relevant due
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to an initially large number of Th2 cells or because Th2 cells were
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preferentially expanded; indeed, IL4, also found important, is the conical
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cytokine that induces Th2 cell differentiation (Fig.3). The role of these
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cytokines could be investigated by quantifying the Th1/2/17 subsets both in the
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starting population and longitudinally. Similar to IL13, IL17 is an effector
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cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of
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T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells,
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but it is not clear if this cytokine is inducing differential expansion of CD8+
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T cells or if it is simply a covariate with another cytokine inducing this
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expansion22. Finally, IL15 has been shown to be essential for memory signaling
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and effective in skewing CAR-T cells toward the Tscm phenotype when using
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membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its
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ability to induce large numbers of memory T cells by functioning in an
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autocrine/paracrine manner and could be explored by blocking either the cytokine
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or its receptor.
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% FIGURE correlation plots from supplement (as alluded to here)
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Moreover, many predictive metabolites found here are consistent with metabolic
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activity associated with T cell activation and differentiation, yet it is not
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clear how the various combinations of metabolites relate with each other in a
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heterogeneous cell population. Formate and lactate were found to be highly
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predictive and observed to positively correlate with higher values of total live
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CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the
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one-carbon cycle implicated in promoting T cell activation24. Importantly, this
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cycle occurs between the cytosol and mitochondria of cells and formate
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excreted25. Mitochondrial biogenesis and function are shown necessary for memory
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cell persistence26,27. Therefore, increased formate in media could be an
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indicator of one-carbon metabolism and mitochondrial activity in the culture.
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In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate
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is the end-product of aerobic glycolysis, characteristic of highly proliferating
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cells and activated T cells28,29. Glucose import and glycolytic genes are
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immediately upregulated in response to T cell stimulation, and thus generation
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of lactate. At earlier time-points, this abundance suggests a more robust
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induction of glycolysis and higher overall T cell proliferation. Interestingly,
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our models indicate that higher lactate predicts higher CD4+, both in total and
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in proportion to CD8+, seemingly contrary to previous studies showing that CD8+
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T cells rely more on glycolysis for proliferation following activation30. It may
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be that glycolytic cells dominate in the culture at the early time points used
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for prediction, and higher lactate reflects more cells.
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% TODO not sure how much I should include here since I didn't do this analysis
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% AT ALL
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% Ethanol patterns are difficult to interpret since its production in mammalian
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% cells is still poorly understood31. Fresh media analysis indicates ethanol
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% presence in the media used, possibly utilized as a carrier solvent for certain
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% formula components. However, this does not explain the high variability and
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% trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
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% chemical, variation could be introduced by sample handling throughout the
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% analysis process. Nonetheless, it is also possible that ethanol excreted into
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% media over time, impacting processes regulating redox and reactive oxygen
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% species which have previously been shown to be crucial in T cell signaling and
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% differentiation32.
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% this looks fine since it is just parroting sources, just need to paraphrase a
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% little
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Metabolites that consistently decreased over time are consistent with the
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primary carbon source (glucose) and essential amino acids (BCAA, histidine) that
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must be continually consumed by proliferating cells. Moreover, the inclusion of
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glutamine in our predictive models also suggests the importance of other carbon
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sources for certain T cell subpopulations. Glutamine can be used for oxidative
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energy metabolism in T cells without the need for glycolysis30. Overall, these
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results are consistent with existing literature that show different T cell
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subtypes require different relative levels of glycolytic and oxidative energy
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metabolism to sustain the biosynthetic and signaling needs of their respective
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phenotypes33,34. It is worth noting that the trends of metabolite abundance here
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are potentially confounded by the partial replacement of media that occurred
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periodically during expansion (Methods), thus likely diluting some metabolic
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byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e.
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glucose, amino acids). More definitive conclusions of metabolic activity across
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the expanding cell population can be addressed by a closed system, ideally with
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on-line process sensors and controls for formate, lactate, along with ethanol
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and glucose.
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\chapter{aim 2b}\label{aim2b}
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\section{introduction}
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