Predicting the Heterogeneity of Cell-Fate Decisions

项目摘要 在肿瘤中，遗传异质性是许多研究的焦点，因为它在肿瘤中起重要作用 通过驱动表型多样性来控制疾病进展和耐药性。在这里，我们考虑另一种类型的 异质性，其中在遗传上相同的哺乳动物细胞中蛋白质水平的天然细胞间变异性 导致相同的刺激产生不同的细胞命运，如生或死。我们称这种现象为“自然 表型趋异”（NPD）。NPD可以表现为，例如，持续的抗癌药物耐药性， 细胞亚群，了解它对于预测癌症治疗效果很重要。然而，在这方面， 从基于细胞的实验中预测NPD的方法尚未开发，并且是本发明的主题。 提议我们假设NPD可以通过表征多变量内源性蛋白质 表达噪声通过信号网络非线性传播以调节细胞命运。是 内源性表达和降解噪音在信号网络内的多种蛋白质的水平， 统称为NPD。我们将结合实验和计算来检验这一假设 检测非转化MCF 10A细胞的基于NPD的增殖的方法。这种扩散是 由表皮生长因子、胰岛素和皮质醇的组合诱导，并由 ERK、Akt、JNK和SGK通路。首先，在实验上，我们将使用活细胞成像方法与FRET 同时测量实时信号网络动态和增殖的探针。虽然我们可以 一次只测量一个通路，我们随后使用计算的，动态的模块化反应， 分析理论使我们能够重建这些途径如何以刺激特异性方式动态相互作用 来控制随机增殖的命运其次，我们将建立一个基于化学动力学的，随机的 一个计算模型，模拟NPD背后的蛋白质表达变异性如何传播到 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， 多变量波动对基于NPD的增殖具有很大的影响。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 这些细胞是否应该增殖或不响应定义的扰动，并测试预测， 观察这些细胞中的实际增殖决定。我们将测试这些预测不仅在 标准2D细胞培养模型，而且在3D培养腺泡形成的背景下。如果成功，这将 这是第一次证明可以根据以下因素预测单个活细胞的随机命运： 生物标志物在扰动之前存在。这将是识别生物标志物集的重要一步 为个体患者和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.