Phenotypic variability within isogenic population of lymphocytes

我们发现，表型变异可以由信号转导级联中关键成分的异质性表达驱动。在对细胞在药物反应中的变化进行建模时，这种可变性具有实际意义。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成分(如Src)的小药物抑制剂发挥数字作用(即以全有或全无的方式)，而针对远端信号成分的抑制剂(例如MEK)的作用类似(即以连续方式)。我们用一种新的方法扩展了我们对细胞信号表型可变性的发现(称为细胞-细胞变异性分析)：这种方法依赖于临床前和临床环境中原代细胞的单细胞磷酸化图谱，以确定哪些生物成分(受体、激酶、磷酸酶、转录因子)在功能结果方面具有数量上的局限性。我们通过展示单个原始T细胞对IL-2和IL-7的反应是如何相互排斥的，来说明这种方法的力量。基于生化建模和贝叶斯优化的计算模型被引入，以测试共享但有限的受体链的隔离如何在细胞因子信号中产生这种触发。这项研究为同基因T细胞群体中效应细胞和记忆细胞之间的转换提供了一个机制解释(Cotari等人，科学信号，2013)。与此同时，我们引入并分发了一个计算机程序(名为ScatterSlice)，它使实验人员能够分析他们的流式细胞术数据中的细胞间变异性(Cotari等人。科学信号，2013)。这种方法已在许多临床环境中得到应用(Palomba等人，PLoS One，2014年；Kitano等人，癌症免疫资源，2014年)。与此同时，我们已经在美国国立卫生研究院实施了质量细胞术(所谓的细胞学)。CyTOF使大量抗体(通常一次40个)的多路传输成为可能，同时绕过了经典的基于荧光的细胞术的光谱重叠问题。我们验证和优化了多个抗体面板，以分析多种免疫系统：小鼠和人的骨髓的一般分析，小鼠和人的T细胞群的深度分析，人的中性粒细胞和人的B细胞。我们与美国国立卫生研究院的临床研究人员合作，在XMEN、阿尔卑斯、狼疮(PBMC)和黑色素瘤(TIL)的背景下分析了患者的样本。此外，我们开发了一种方法来脉冲追逐IDU(一种被拾取并插入到增殖细胞中的试剂)，并监测小鼠白细胞分化的动力学。最后，我们介绍了一种基于机器学习的方法来自动识别正在考虑的白细胞之间的分化簇：该方法被应用于定义一个新的T细胞群体，其表型与积极的临床结果相关。我们正在追求我们的目标，以更好地表征免疫反应的细胞复杂性，朝着更好的疾病和治疗状态分类以及更好的建模。最后，我们正在开发一个解决白细胞多样性(其起源和功能意义)的平台。我们建立了一个定制的机器人组织培养系统，以系统地在体外组装免疫反应(使用来自小鼠或人类的原始样本)，并监测其时间动力学。我们的系统通常生成500种条件(细胞内容物、激活条件、药物扰动和时间点的卷积)，这些条件以其可溶性含量(细胞因子分泌)和细胞组成(细胞周期图和流式细胞仪的单细胞图谱)来表征。然后，我们应用人工智能领域的工具来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&对免疫反应进行最佳分类的特征，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).