Lineage Analysis of Cellular States Predicting Reprogramming into iPSCs

预测重编程为 iPSC 的细胞状态谱系分析

• 批准号: 10470914
• 负责人: Naveen Jain
• 金额: $ 3.41万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10470914
• 关键词: ATAC-seq; Back; Bar Codes; Carbon; Cell Differentiation process; Cell Fraction; Cell division; Cells; Chemicals; Chromatin; Clone Cells; Clustered Regularly Interspaced Short Palindromic Repeats; DNA; Data; Disease model; Ectopic Expression; Enabling Factors; Engineering; Epigenetic Process; Exposure to; Fibroblasts; Fluorescent Probes; Frequencies; Future; Gene Expression; Genes; Genome; Germ Cells; Goals; Hepatocyte; Image; Label; Logic; Maintenance; Malignant Neoplasms; Mediating; Methods; Modeling; Molecular; Molecular Profiling; Nature; Outcome; Pathogenesis; Pathway interactions; Patients; Phenotype; Population; Publishing; RNA; Regenerative Medicine; Reporting; Research; Resources; Signaling Protein; Sister; Somatic Cell; Sorting - Cell Movement; Source; System; Testing; Time; Validation; Work; base; cell type; clinical application; design; drug discovery; fluorescence imaging; induced pluripotent stem cell; interest; keratinocyte; live cell imaging; molecular marker; non-genetic; novel; novel strategies; predictive test; protein biomarkers; recruit; stem cell biology; stem cell differentiation; time use; tissue repair; transcriptome sequencing

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.

项目摘要 诱导多能干细胞（iPSC）通过异位表达a 重编程因子的鸡尾酒是一种有前途的、患者特异性的疾病建模资源， 再生医学然而，实际上，只有少数细胞（<1%）暴露于重编程因子， 成为iPSC。此外，我们不知道这些罕见的细胞有什么不同，如果有的话， 重新编程虽然重编程结果的可变性通常归因于技术问题，但低 即使当重编程因子被克隆地和稳定地整合到细胞中， 基因组这表明，这种变异性是由于单细胞染色质状态的差异， 表达和蛋白质信号传导（即细胞状态）。在这里，我们展示了独特而稳定的细胞 状态在罕见的细胞亚群“准备”重新编程。我们假设细胞可以波动进出 这些启动状态的获得使得能够成功地重编程为iPSC。的基本目标 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 重编程效率然而，识别标记这一罕见的引发细胞亚群的事后相关因素， 这是一个重大的概念和技术挑战。因此，我们建议使用细胞“时间机器” 从最终的表型倒回时间，以识别原始细胞中准备成为iPSC的细胞。 通过条形码、RNA FISH、成像和流式分选的新组合来检测群体。我们的初步数据 证明这种方法可以标记，分离，并根据其未来的倾向， 当暴露于重编程因子时，细胞重编程为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iPSC。通过进行RNA测序和 ATAC-seq，我们将鉴定这些引发细胞的标志物和表观遗传调节因子， 使用化学和基于CRISPR的扰动来验证它们的功能。除了基线 为了研究iPSC重编程，我们想了解增加iPSC重编程效率的扰动（即， 增强剂）特异性地增加成为iPSC的细胞的分数。在目标2中，我们将使用时间机器 分离并分析仅在用加强剂重编程时产生iPSC的额外细胞。我们将确定 通过比较分子水平， 签名.然后，我们将确定和验证在这些额外的细胞中介导重编程的因素， 助推器或跨助推器，从分子上了解助推器如何招募额外的细胞亚群， iPSCs。这项工作准备回答长期存在的问题，即稀有细胞的存在和性质。 重新编程更广泛地说，它将帮助我们识别操纵iPSC重编程的新途径， 揭示了看似分化的细胞可塑性的分子基础。