Elucidating ECM Signaling in Cardiac Organoids with Machine Learning and Single-cell Multiomics

利用机器学习和单细胞多组学阐明心脏类器官中的 ECM 信号转导

基本信息

批准号：
10435045
负责人：
JAYAKUMAR RAJADAS
金额：
$ 62.86万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-09 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10435045
关键词：
3-Dimensional African American population Artificial Intelligence Asian population Biochemical Biocompatible Materials Bioinformatics Biological Candidate Disease Gene Cardiac Cardiac Myocytes Cardiac development Caucasians Cell Communication Cell Differentiation process Cell Separation Cells Data Set Development Developmental Process Elasticity Embryonic Heart Extracellular Matrix Genomics Heart Heart Diseases Hispanic Populations Human Image Lead Life Machine Learning Measurement Measures Mechanics Modality Modeling Modulus Morphogenesis Morphology Organoids Pattern Pattern Formation Play Property Reporter Reproducibility Research Resolution Role Shapes Signal Pathway Signal Transduction Standardization System Therapeutic Training Validation Variant Wing base cardiogenesis cell behavior congenital heart disorder deep neural network differentiation protocol improved induced pluripotent stem cell machine learning prediction malformation mechanical signal mechanotransduction multiple omics novel prevent screening

项目摘要

Project Summary Extracellular matrix (ECM) is the most abundant biomaterial in the body. During cardiac development, the ECM plays critical roles in the formation of shapes and patterns of the heart such as chambers and trabeculae through elaborate interactions with differentiating cells. Although problems in ECM-cell interactions can lead to heart diseases, signaling pathways activated by the specific ECM components are still poorly understood. We recently succeeded in developing human induced pluripotent stem cell-derived cardiac organoids (iPSC-COs) that can recapitulate cardiogenesis. In this multi-PI R01 proposal, our team will further elucidate the mechanisms of ECM- cell interactions that influence cardiac differentiation and morphogenesis. We will apply machine learning and novel iPSC double reporter lines (Tbx5-Clover2-Nkx2.5-TagRFP) to elucidate the effect of cell composition on morphogenesis of iPSC-COs (Aim 1). Afterwards, we will screen 36 different combinations of ECM compositions that can reliably induce iPSC-CO formation using 8 additional iPSC lines for validation (Aim 2). By rigorously analyzing iPSC-COs made with optimized ECM using elastic property measurement and single-cell multiomics, we will elucidate the biological and physical effects associated with ECM signaling and mechanotransduction at single-cell resolution (Aim 3). In summary, understanding the exact role and mechanism of ECM-cell interactions may contribute to finding new biomaterials or therapeutic modalities for treatment of heart diseases.

项目概要细胞外基质（ECM）是体内最丰富的生物材料。在心脏发育过程中，ECM 在心脏形状和模式（例如心室和小梁）的形成中起着关键作用与分化细胞的复杂相互作用。尽管 ECM 与细胞相互作用的问题可能会导致心脏对于疾病中特定 ECM 成分激活的信号通路仍知之甚少。我们最近成功开发出人类诱导多能干细胞衍生的心脏类器官（iPSC-CO）重述心脏发生。在这个多PI R01提案中，我们的团队将进一步阐明ECM的机制- 影响心脏分化和形态发生的细胞相互作用。我们将应用机器学习和新型 iPSC 双报告系 (Tbx5-Clover2-Nkx2.5-TagRFP) 阐明细胞组成对 iPSC-CO 的形态发生（目标 1）。之后，我们将筛选36种不同的ECM成分组合使用 8 个额外的 iPSC 系进行验证，可以可靠地诱导 iPSC-CO 形成（目标 2）。通过严格使用弹性特性测量和单细胞多组学分析由优化的 ECM 制成的 iPSC-CO，我们将阐明与 ECM 信号传导和机械转导相关的生物和物理效应单细胞分辨率（目标 3）。总之，了解 ECM-细胞相互作用的确切作用和机制可能有助于寻找新的生物材料或治疗心脏病的治疗方式。