Lineage Analysis of Cellular States Predicting Reprogramming into iPSCs

项目摘要 异位表达α基因诱导分化的体细胞分化为多能干细胞 重新编程因子的鸡尾酒是一种有前途的、特定于患者的疾病建模和 再生医学。然而，实际上只有极少数细胞(1%)暴露在重新编程因素下 成为ipscs。此外，我们不知道这些稀有细胞有什么不同，如果有什么不同的话 重新编程。虽然重新编程结果的可变性通常归因于技术问题，但Low 即使在将重编程因素克隆并稳定地集成到 基因组。这表明这种可变性是由于染色质状态、基因的单细胞差异所致。 表达和蛋白质信号传递(即细胞状态)。在这里，我们展示了明显和稳定的细胞的证据 稀有细胞子集中的状态已准备好重新编程。我们假设细胞可以上下波动 这些准备好的状态，它们的收购可以成功地重新编程到IPSC中。的根本目标是 我们的建议是识别、表征并最终操纵这些启动状态以增加IPSC 重新编程的效率。然而，识别标记这一罕见的启动细胞子集的事后相关因素 这是一个重大的概念和技术挑战。因此，我们建议使用蜂窝“时光机” 回溯终极表型的时间，以鉴定准备成为原始细胞中的ipscs的细胞 通过条形码、RNA FISH、成像和流分类的新组合进行种群。我们的初步数据 演示此方法可以基于特定细胞未来的倾向来标记、分离和分析特定细胞 当暴露在重新编程因素下时，重新编程到iPSC。在目标1中，我们将使用此方法分离 这些细胞后来会从几种不同的起始细胞类型中产生IPSCs。通过执行RNA-seq和 在分离的细胞上，我们将鉴定这些启动细胞的标志物和表观遗传调节因子，并 使用化学和基于CRISPR的扰动从功能上验证它们。除基线外 重新编程，我们想了解增加IPSC重新编程效率的扰动是如何(即 助推器)特别增加了细胞成为ipscs的比例。在《目标2》中，我们将使用时光机 分离并分析仅在使用Booster重新编程时才会产生IPSCs的多余细胞。我们将决定 通过比较分子水平，它们与不含助推器的可重编程细胞的初始子集有何不同 签名。然后，我们将识别和验证在这些额外细胞中调解重新编程的因素 助推器或跨助推器从分子上了解助推器如何招募更多的细胞亚群成为 IPSCs。这项工作准备回答长期存在的关于稀有细胞存在和性质的问题 用于重新编程。更广泛地说，它将帮助我们确定操纵IPSC重新编程和 揭示看似分化的细胞可塑性的分子基础。

Project Summary Induced pluripotent stem cells (iPSCs) derived from differentiated somatic cells via ectopic expression of a cocktail of reprogramming factors are a promising, patient-specific resource for disease modeling and regenerative medicine. However, only a rare subset of cells (<1%) exposed to the reprogramming factors actually become iPSCs. Furthermore, we do not know what, if anything, is different about these rare cells capable of reprogramming. While variability in reprogramming outcomes is often ascribed to technical issues, low reprogramming efficiency remains even when the reprogramming factors are integrated clonally and stably into the genome. This suggests that this variability is instead due to single-cell differences in chromatin state, gene expression, and protein signaling (i.e. cell states). Here, we demonstrate evidence of distinct and stable cell states in the rare subset of cells “primed” to reprogram. We hypothesize that cells can fluctuate in and out of these primed states whose acquisition enables successful reprogramming into iPSCs. The underlying goal of our proposal is to identify, characterize, and eventually manipulate these primed states to increase iPSC reprogramming efficiency. Yet, identifying post-facto relevant factors marking this rare subset of primed cells represents a major conceptual and technical challenge. Therefore, we propose to use a cellular “Time Machine” to rewind back time from the ultimate phenotype to identify cells primed to become iPSCs in the original population via a novel combination of barcoding, RNA FISH, imaging, and flow sorting. Our preliminary data demonstrate that this method can label, isolate, and profile specific cells based on their future propensity to reprogram into iPSCs when exposed to the reprogramming factors. In Aim 1, we will use this method to isolate cells that would later give rise to iPSCs from several different starting cell types. By performing RNA-seq and ATAC-seq on the isolated cells, we will identify markers and epigenetic regulators of these primed cells and validate them functionally using chemical and CRISPR-based perturbations. In addition to baseline reprogramming, we want to understand how perturbations that increase iPSC reprogramming efficiency (i.e. boosters) specifically increase the fraction of cells becoming iPSCs. In Aim 2, we will use Time Machine to isolate and profile the extra cells that give rise to iPSCs only when reprogrammed with booster. We will determine how they are different from the initial subset of reprogrammable cells without booster by comparing molecular signatures. Then, we will identify and validate factors mediating reprogramming in these extra cells with a specific booster or across boosters to molecularly understand how boosters recruit additional subsets of cells to become iPSCs. This work is poised to answer longstanding questions about the existence and nature of rare cells primed for reprogramming. More broadly, it will help us identify new pathways to manipulate iPSC reprogramming and reveal the molecular basis of plasticity in seemingly differentiated cells.