From intra to intercellular regulatory networks that define cell type identity

项目摘要 细胞命运工程，例如定向分化多能干细胞或 体细胞类型之间的直接转换，对改善疾病大有可为 建模，药物筛选，并导致再生医学疗法。然而，我们的 保真度控制细胞命运的能力因不完整的 对支配分化的细胞间和细胞内网络的理解，以及 缺乏足够的计算工具来提取准确和可验证的假设 来自单细胞组学技术的海量数据。在……的初期 在米拉的资助下，我们开始通过开发新的 从单细胞RNA确定细胞类型同一性的理论和计算方法 SEQ(scRNA-Seq)数据，重点是出现的发育细胞类型 在中胚层发育和随后对滑膜谱系的承诺过程中 联合。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，我们采用了多能干细胞向软骨细胞分化的方案，我们 发明了一种用于在单个小区评估小区类型标识的通用平台 分辨率级别。我们还开发了一种计算方法来推断动态 准确的调控网络，并将其与信号通路相结合。现在，我们 建议解决以下未得到回答的问题和未得到满足的挑战。第一, 我们将大幅改进和扩展我们的计算方法来评估 通过将其扩展到更多数据类型来实现细胞命运工程的结果，从而 增加其结果的全面性，不仅通过预测细胞身份 而是功能。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 以染色质可获得性和表达数据的同时意图 发现孤儿转录因子的结合位点基序。第三，我们将制定和 实验测试计算方法以产生可靠的细胞命运工程 食谱不仅解释了转录网络，而且还解释了信号如何 路径告诉他们，这解释了时间动力学。最后，我们将跟随 将我们的动态网络工具应用于体外原肠形成的观察结果表明 表明一些信号通路通过重新连接对分化的影响更大 网络拓扑比通过直接影响效应器靶基因的表达更重要。 共同实现这些目标将有助于使细胞命运工程更加可靠 而且是可控的，它将揭示信号通路和细胞内 监管网络在发展过程中相互作用。

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.