Predicting the Heterogeneity of Cell-Fate Decisions

项目总结 在癌症中，遗传异质性是许多研究的焦点，因为它在肿瘤中起着重要的作用。 通过驱动表型多样性的进展和耐药性。在这里，我们考虑另一种类型的 异质性，在遗传相同的哺乳动物细胞中，细胞间蛋白质水平的自然变异性 导致相同的刺激产生不同的细胞命运，例如生或死。我们把这种现象称为“自然现象”。 表型分化“(NPD)。例如，NPD可表现为持久的抗癌药物耐药 细胞亚群，并了解它对于预测癌症治疗效果很重要。然而， 从基于细胞的实验中预测NPD的方法还没有开发出来，这是 求婚。我们假设NPD可以通过表征多变量的内源性蛋白质 表达噪声通过信号网络非线性传播，以调节细胞命运。它是 信号网络中多种蛋白质水平的内源性表达和降解噪声 集体表现为NPD。我们将通过实验和计算相结合的方法来检验这一假设 检测未转化的MCF10A细胞基于NPD的增殖的方法。这种扩散是 由表皮生长因子、胰岛素和皮质醇联合诱导，并通过激活 ERK、AKT、JNK和SGK路径。首先，在实验上，我们将在FRET中使用活细胞成像方法 探索同时测量实时信令网络动态和扩散。尽管我们可以 一次只测量一条路径，我们随后使用的是计算的动态模块响应 分析理论允许我们重建这些通路是如何以特定于刺激的方式动态相互作用的 来控制随机扩散的命运。第二，我们将构建以化学动力学为基础的、随机的 模拟NPD背后的蛋白质表达可变性如何传播到 信号动力学异质性。对这个模型的分析将提出一组关键蛋白质，它们的集体， 多变量波动对NPD的增殖有很大影响。最后，我们将测量 在单个活细胞中这些关键蛋白质水平的波动，使用我们的计算模型来预测 这些细胞是否应该在定义的扰动下增殖，并通过以下方式测试预测 观察这些细胞的实际增殖决定。我们将测试这样的预测，不仅在 标准的2D细胞培养模型，也可在3D培养的背景下形成腺泡。如果成功，这将是 是第一个可以根据以下条件预测单个活细胞的随机命运的证据 生物标志物存在于扰动之前。这将是朝着识别生物标记物集合迈出的重要一步 针对个别患者，形成个性化的治疗策略。

PROJECT SUMMARY In cancer, genetic heterogeneity is the focus of many investigations as it plays important roles in tumor progression and drug resistance by driving phenotypic diversity. Here, we consider another type of heterogeneity, one where natural cell-to-cell variability in protein levels in genetically-identical mammalian cells causes the same stimuli to yield different cell fates, such as life or death. We term this phenomenon "natural phenotypic divergence" (NPD). NPD can manifest as, for example, a persistent anticancer drug resistant subpopulation of cells, and understanding it is important for predicting cancer treatment efficacy. However, means to predict NPD from cell-based experiments have not been developed and are the subject of the proposal. We hypothesize that NPD can be predicted by characterizing how multivariate, endogenous protein expression noise is propagated non-linearly through signaling networks to regulate cell fate. It is the endogenous expression and degradation noise in the levels of multiple proteins within a signaling network that collectively manifest as NPD. We will test this hypothesis by combining experimental and computational approaches to examine NPD-based proliferation of non-transformed MCF10A cells. This proliferation is induced by combinations of epidermal growth factor, insulin, and cortisol and mediated by activation of the ERK, Akt, JNK, and SGK pathways. First, experimentally, we will use live-cell imaging approaches with FRET probes to measure real-time signaling network dynamics and proliferation simultaneously. Although we can only measure one pathway at a time, our subsequent use of computational, dynamic modular response analysis theory allows us to reconstruct how these pathways dynamically interact in a stimulus-specific fashion to control stochastic proliferation fates. Second, we will build a chemical kinetics-based, stochastic computational model that simulates how the protein expression variability underlying NPD propagates into signaling dynamics heterogeneity. Analysis of this model will suggest sets of key proteins whose collective, multivariate fluctuations have a large influence on NPD-based proliferation. Finally, we will measure fluctuations in the levels of these key proteins in single live cells, use our computational models to predict whether these cells should proliferate or not in response to defined perturbations, and test the predictions by observing the actual proliferation decision in those same cells. We will test such predictions not only in standard 2D cell culture models, but also in the context of 3D culture acini formation. If successful, this would be the first demonstration that the stochastic fates of individual live cells could be predicted based on biomarkers present prior to perturbation. This would be an important step towards identifying biomarker sets for individual patients and fashioning personalized therapeutic strategies.