Phenotypic variability within isogenic population of lymphocytes

我们发现，表型变异可以驱动的信号转导级联中的关键组件的异质性表达。当对细胞在其药物反应中如何变化进行建模时，这种可变性具有实际重要性。特别是，我们发现，信号转导级联的拓扑结构解释了为什么近端信号组分的小分子药物抑制剂（例如Src）以数字方式执行（即以全或无的方式），而针对远端信号传导组分的抑制剂（例如Mek）类似地行动（即以连续的方式）我们用一种新的方法扩展了我们对细胞信号传导的表型变异性的发现。（称为“细胞-细胞变异性分析"）：这种方法依赖于在临床前和临床环境中原代细胞的单细胞磷酸分析，以鉴定哪些生物组分（受体、激酶、磷酸酶、转录因子）在功能后果方面是定量限制性的。我们通过显示对IL-2和IL-7的应答如何在单个原代T细胞内相互排斥来说明这种方法的强度。基于生物化学建模和贝叶斯优化的计算模型被引入来测试共享但有限的受体链的隔离如何在细胞因子信号传导中产生这种触发器。该研究提供了T细胞的同基因群体内效应细胞和记忆细胞之间的转变的机制解释（Cotari等人，Science Signaling，2013）。同时，我们引入并分发了一个计算机程序（名为ScatterSlice），该程序使实验人员能够分析流式细胞术数据中的细胞间变异性（Cotari et al. Science Signaling，2013）。这种方法已经在许多临床环境中得到应用（Palomba等人，PLoS One，2014; Kitano等人，Cancer Immunol Res，2014）。与此同时，我们一直在NIH实施质量细胞计数（所谓的CyTOF）。CyTOF使大量抗体（通常一次40个）的多路复用成为可能，同时绕过经典的基于荧光的细胞术的光谱重叠问题。我们验证并优化了多个抗体组，以分析多个免疫系统：小鼠和人骨髓的一般特征，小鼠和人T细胞群的深度特征，人中性粒细胞和人B细胞。我们与NIH的临床研究人员合作，在XMEN、ALPS、狼疮（PBMC）和黑色素瘤（TIL）的背景下分析患者样本。此外，我们开发了一种脉冲追踪IdU（一种被拾取并插入增殖细胞中的试剂）并监测小鼠白细胞分化动力学的方法。最后，我们引入了一种基于机器学习的方法来自动识别所考虑的白细胞之间的分化簇：该方法用于定义一种新的T细胞群体，其表型与阳性临床结果相关。我们的目标是更好地表征免疫反应的细胞复杂性，更好地分类疾病和治疗状态，以及更好地建模。最后，我们正在开发一个平台来解决白细胞多样性（其起源和功能意义）。我们建立了一个定制的机器人组织培养系统，以系统地组装离体免疫反应（使用来自小鼠或人类的原始样品）并监测其时间动态。我们的系统通常生成500个条件（作为细胞内容物、活化条件、药物扰动和时间点的卷积），这些条件针对其可溶性内容物（细胞因子分泌）和细胞组成（通过CyTOF和FACS进行的单细胞分析）进行表征。然后，我们应用人工智能领域的工具去卷积这些免疫反应的组合复杂性。我们的目标是识别新的免疫特征&最好地分类免疫反应的特征，然后在免疫反应模型中验证这些特征（针对疫苗和/或肿瘤）。

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. 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).