Predicting the Heterogeneity of Cell-Fate Decisions

描述(申请人提供)：在癌症中，遗传异质性是许多研究的焦点，因为它通过驱动表型多样性在肿瘤进展和耐药性中发挥重要作用。在这里，我们考虑另一种类型的异质性，在这种异质性中，遗传相同的哺乳动物细胞中蛋白质水平的自然细胞间差异导致相同的刺激。 以产生不同的细胞命运，例如生或死。我们称这种现象为“自然表型差异”(NPD)。例如，NPD可以表现为持续的抗癌药物耐药细胞亚群，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，这是该提案的主题。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如何通过信号网络非线性传播来调节细胞命运来预测。它是信号网络中多种蛋白质水平上的内源性表达和降解噪声，共同表现为NPD。我们将通过实验和计算相结合的方法来检验这一假设，以检验未转化的MCF10A细胞基于NPD的增殖。这种增殖是由表皮生长因子、胰岛素和皮质醇联合诱导的，并由ERK、Akt、JNK和SGK通路的激活介导。首先，在实验上，我们将使用带有FRET探针的活细胞成像方法来同时测量实时信令网络动态和增殖。尽管我们一次只能测量一条通路，但我们随后使用计算、动态模块反应分析理论，允许我们重建这些通路如何以刺激特定的方式动态相互作用，以控制随机增殖命运。其次，我们将建立一个基于化学动力学的随机计算模型，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。对这一模型的分析将提出一组关键蛋白质，它们的集体、多变量波动对基于NPD的增殖有很大影响。最后，我们将测量单个ve细胞中这些关键蛋白水平的波动，使用我们的计算模型来预测这些细胞是否应该在特定的扰动下增殖，并通过观察这些相同细胞中的实际增殖决定来测试预测。我们将不仅在标准的2D细胞培养模型中，而且在3D培养的腺泡形成的背景下测试这样的预测。如果成功，这将是第一次证明，单个活细胞的随机命运可以基于扰动之前存在的生物标记物来预测。这将是朝着为个别患者确定生物标记物集和形成个性化治疗策略迈出的重要一步。

DESCRIPTION (provided by applicant): 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 ive 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 firt 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.