From intra to intercellular regulatory networks that define cell type identity

从细胞内到细胞间的调节网络定义细胞类型身份

• 批准号: 10404834
• 负责人: Patrick Cahan
• 金额: $ 45.85万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10404834
• 关键词: Address; Adopted; Binding Sites; Biological; Cells; Chondrocytes; Chromatin; Computing Methodologies; Data; Development; Disease model; Drug Screening; Engineering; Funding; Generations; Genes; Genetic Transcription; Goals; In Vitro; Intention; Lead; Light; Mesoderm; Orphan; Outcome; Pluripotent Stem Cells; Recipe; Regenerative Medicine; Resolution; Signal Pathway; Somatic Cell; Synovial joint; Technology; Testing; Work; algorithm development; cell type; computerized tools; differentiation protocol; directed differentiation; follow-up; gastrulation; improved; meetings; novel; single-cell RNA sequencing; tool; transcription factor

Project Abstract Cell fate engineering, for example the directed differentiation of pluripotent stem cells or the direct conversion among somatic cell types, holds great promise to improve disease modeling, drug screening, and to lead to regenerative medicine therapies. However, our ability to engineering cell fate with fidelity has been impeded by an incomplete understanding of inter- and intracellular networks that govern differentiation, and by the lack of adequate computational tools to distill accurate and testable hypothesis from the mountains of data coming from single cell omics technologies. In the initial period of funding under the MIRA, we started to address these challenges by developing novel theoretical and computational methods to define cell type identity from single cell RNA- Seq (scRNA-Seq) data with an emphasis on developmental cell types that emerge during mesoderm development and subsequent commitment to lineages of the synovial joint. As part of this work, we generated scRNA-Seq data of the developing synovial joint, we adopted a pluripotent stem cell-to-chondrocyte differentiation protocol, and we invented a generally applicable platform for assessing cell type identity at the single cell level of resolution. We also developed a computational method to infer dynamic regulatory networks accurately and to integrate them with signaling pathways. Now, we propose to address the following unanswered questions and unmet challenges. First, we will substantially improve and extend our computational methods that assess the outcomes of cell fate engineering by extending them to more data types, and thus increasing the comprehensiveness of its results, and by predicting not only cell identity but function. Second, we will substantially improve and extend our regulatory network tools so that their predictions are statistically calibrated and so that they can be applied to chromatin accessibility and expression data simultaneously with the intention of discovering binding site motifs of orphan transcription factors. Third, we will devise and experimentally test computational methods to generate reliable cell fate engineering recipes that account for not only transcriptional networks but also how signaling pathways inform them, and that account for temporal dynamics. Finally, we will follow up on observations from applying our dynamic network tool to in vitro gastrulation that indicates that some signaling pathways influence differentiation more by re-wiring network topology than by directly impacting expression of effector target genes. Collectively meeting these goals will help to make cell fate engineering more reliable and controllable, and it will shed light on how signaling pathways and intracellular regulatory networks interact during development.

项目摘要 细胞命运工程，例如多能干细胞的定向分化或 体细胞类型之间的直接转换有望改善疾病 建模、药物筛选以及再生医学疗法。然而，我们的 不完整的细胞命运阻碍了忠实地改造细胞命运的能力 了解控制分化的细胞间和细胞内网络，并通过 缺乏足够的计算工具来提取准确且可检验的假设 来自单细胞组学技术的大量数据。在最初的时期 在 MIRA 的资助下，我们开始通过开发新颖的产品来应对这些挑战 从单细胞 RNA 定义细胞类型同一性的理论和计算方法 Seq (scRNA-Seq) 数据，重点关注出现的发育细胞类型 在中胚层发育和随后形成滑膜谱系的过程中 联合的。作为这项工作的一部分，我们生成了发育中滑膜的 scRNA-Seq 数据 联合起来，我们采用了多能干细胞到软骨细胞的分化方案，并且我们 发明了一种普遍适用的平台，用于评估单细胞的细胞类型身份 分辨率级别。我们还开发了一种计算方法来推断动态 准确地调控网络并将其与信号通路整合。现在，我们 建议解决以下未解答的问题和未应对的挑战。第一的， 我们将大幅改进和扩展我们评估的计算方法 通过将细胞命运工程扩展到更多数据类型来获得结果，从而 不仅通过预测细胞身份来提高结果的全面性 但功能。二是大幅完善和拓展监管网络 工具，以便对他们的预测进行统计校准，以便可以应用它们 同时检测染色质可及性和表达数据，目的是 发现孤儿转录因子的结合位点基序。第三，我们将设计并 实验测试计算方法以生成可靠的细胞命运工程 食谱不仅解释了转录网络，还解释了信号传导的方式 路径告诉他们，这解释了时间动态。最后我们将跟随 根据将我们的动态网络工具应用于体外原肠胚形成的观察结果 表明一些信号通路通过重新布线更多地影响分化 网络拓扑而不是直接影响效应靶基因的表达。 共同实现这些目标将有助于使细胞命运工程更加可靠 并且是可控的，它将揭示信号通路和细胞内如何 监管网络在发展过程中相互作用。