Phenotypic variability within isogenic population of lymphocytes

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We found that phenotypic variability can be driven by the heterogenous expression of key components within signal transduction cascade. Such variability has practical importance when modeling how cells vary in their drug response. In particular, we found that the topology of signal transduction cascades explain why small-drug inhibitors of proximal signaling components (e.g. Src) acted digitally (i.e. in an all-or-none manner) while inhibitors against distal signaling components (e.g. Mek) acted analogously (i.e. in a continuous manner) We expanded our findings on the phenotypic variability of cell signaling with a new methodology (termed "cell-to-cell variability analysis): such method relies on single-cell phospho-profiling of primary cells in preclinical and clinical settings to identify which biological components (receptor, kinase, phosphatase, transcription factor) is quantitatively limiting in terms of functional consequences. We illustrated the strength of this approach by showing how response to IL-2 and IL-7 are mutually exclusive within individual primary T cells. A computational model, based on biochemical modeling and Bayesian optimization, was introduced to test how sequestration of a shared but limited receptor chain could generate such flip-flop in cytokine signaling. This study provided a mechanistic explanation for the transition between effector and memory cells within an isogenic population of T cells (Cotari et al., Science Signaling, 2013). Concomitantly, we introduced and distributed a computer program (named ScatterSlice) that enables experimenters to analyze the cell-to-cell variability in their Flow Cytometry data (Cotari et al. Science Signaling, 2013). Such methodology has found applications in many clinical settings (Palomba et al., PLoS One, 2014; Kitano et al., Cancer Immunol Res, 2014). In parallel, we have been implementing mass cytometry (so-called CyTOF) at the NIH. CyTOF enables the multiplexing of large sets of antibodies (typically 40 at once) while bypassing issues of spectral overlap of classical fluorescence-based cytometry. We validated and optimized multiple antibody panels to profile multiple immunological systems: general profile of bone marrow in mouse and human, deepvprofile of T cell populations in mouse and human, human neutrophils and human B cells. We collaborated with clinical investigators at the NIH to profile patients' samples in the context of XMEN, ALPS, Lupus (PBMC) and melanoma (TIL). Moreover, we developed a method to pulse-chase IdU (a reagent that gets picked up and inserted in proliferating cells) and monitor the kinetics of differentiation of leukocytes in mice. We have expanded our robotic platform with higher multiplexing of sample preparation, of measurements (bulk cytokine and single-cell measurements) and used our well calibrated in mouse transgenic T cell model in order to map out the phenotypic diversity of human TILs and CAR-T cells (collaboration with Naomie Taylor and Nirali Shah in the CCR-POB). Finally, we introduced a machine-learning-based method to automatically identify clusters of differentiation amongst leukocytes under consideration: this method was applied to define a new T cell population whose phenotype correlates with positive clinical outcomes. We are pursuing our goal to better characterize the cellular complexity of immune responses, towards better classification of disease and therapeutic states, and better modeling. Finally, we are developing a platform to address leukocyte diversity (its origins and its functional significance). We built a custom-made robotic tissue culture system to systematically assemble immune responses ex vivo (using primary samples from mouse or human) and to monitor its time dynamics. Our system generates typically 500 conditions (as a convolution of cell contents, activation conditions, drug perturbations and time points) that get characterized for their soluble content (cytokine secretion) and cell composition (single-cell profiling by CyTOF and FACS). We then apply tools from the field of artificial intelligence to deconvolve the combinatorial complexity of these immune responses. Our goal is to identify new immune signatures & features that best classify immune responses, then to validate these signatures in models of immune responses (against vaccines and/or tumors).