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

• 批准号: 10619622
• 负责人: JAYAKUMAR RAJADAS
• 金额: $ 62.08万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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/10619622
• 关键词: 3-Dimensional; African American population; Artificial Intelligence; Asian population; Biochemical; Biocompatible Materials; Bioinformatics; Biological; Candidate Disease Gene; Cardiac; Cardiac Myocytes; Caucasians; Cell Communication; Cell Differentiation process; Cell Separation; Cells; Clover; Data Set; Development; Developmental Process; Elasticity; Embryonic Heart; Extracellular Matrix; Genomics; Heart; Heart Diseases; Hispanic Populations; Human; Image; 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; 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

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.

项目总结