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: 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
• 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

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.

项目摘要 细胞外基质（Extracellular matrix，ECM）是人体内最丰富的生物材料。在心脏发育过程中，ECM 在心脏的形状和模式的形成中起着关键作用，例如腔室和小梁， 与分化中的细胞进行复杂的相互作用。虽然ECM细胞相互作用的问题可能导致心脏病， 然而，在疾病中，由特定ECM组分激活的信号通路仍然知之甚少。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），可以 重演心脏发生。在这个多PI R 01的提案中，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， 新的iPSC双报告细胞系（Tbx 5-Clover 2-Nkx2.5-TagRFP），以阐明细胞组成对 iPSC-CO的形态发生（目的1）。之后，我们将筛选ECM组合物的36种不同组合， 其可以使用8个额外的iPSC系可靠地诱导iPSC-CO形成用于验证（目的2）。透过审慎 使用弹性性质测量和单细胞多组学分析用优化的ECM制备的iPSC-CO， 我们将阐明与ECM信号传导和机械传导相关的生物学和物理效应， 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 可能有助于发现新的生物材料或治疗心脏病的治疗方式。