Wnt dose escalation during the exit from pluripotency identifies tranilast as a regulator of cardiac mesoderm.

Wnt signaling is a critical determinant of cell lineage development. This study used Wnt dose-dependent induction programs to gain insights into molecular regulation of stem cell differentiation. We performed single-cell RNA sequencing of hiPSCs responding to a dose escalation protocol with Wnt agonist CHIR-99021 during the exit from pluripotency to identify cell types and genetic activity driven by Wnt stimulation. Results of activated gene sets and cell types were used to build a multiple regression model that predicts the efficiency of cardiomyocyte differentiation. Cross-referencing Wnt-associated gene expression profiles to the Connectivity Map database, we identified the small-molecule drug, tranilast. We found that tranilast synergistically activates Wnt signaling to promote cardiac lineage differentiation, which we validate by in vitro analysis of hiPSC differentiation and in vivo analysis of developing quail embryos. Our study provides an integrated workflow that links experimental datasets, prediction models, and small-molecule databases to identify drug-like compounds that control cell differentiation.

HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function.

Genomic regulation of cardiomyocyte differentiation is central to heart development and function. This study uses genetic loss-of-function human-induced pluripotent stem cell-derived cardiomyocytes to evaluate the genomic regulatory basis of the non-DNA-binding homeodomain protein HOPX. We show that HOPX interacts with and controls cardiac genes and enhancer networks associated with diverse aspects of heart development. Using perturbation studies in vitro, we define how upstream cell growth and proliferation control HOPX transcription to regulate cardiac gene programs. We then use cell, organoid, and zebrafish regeneration models to demonstrate that HOPX-regulated gene programs control cardiomyocyte function in development and disease. Collectively, this study mechanistically links cell signaling pathways as upstream regulators of HOPX transcription to control gene programs underpinning cardiomyocyte identity and function.

New Drug Targets and Preclinical Modelling Recommendations for Treating Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is the leading cause of morbidity and mortality worldwide and the primary underlying risk factor for heart failure. Despite decades of research and clinical trials, there are no drugs currently available to prevent organ damage from acute ischaemic injuries of the heart. In order to address the increasing global burden of heart failure, drug, gene, and cell-based regeneration technologies are advancing into clinical testing. In this review we highlight the burden of disease associated with AMI and the therapeutic landscape based on market analyses. New studies revealing the role of acid-sensitive cardiac ion channels and other proton-gated ion channels in cardiac ischaemia are providing renewed interest in pre- and post-conditioning agents with novel mechanisms of action that may also have implications for gene- and cell-based therapeutics. Furthermore, we present guidelines that couple new cell technologies and data resources with traditional animal modelling pipelines to help de-risk drug candidates aimed at treating AMI. We propose that improved preclinical pipelines and increased investment in drug target identification for AMI is critical to stem the increasing global health burden of heart failure.

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

Vascular cells improve functionality of human cardiac organoids.

Crosstalk between cardiac cells is critical for heart performance. Here we show that vascular cells within human cardiac organoids (hCOs) enhance their maturation, force of contraction, and utility in disease modeling. Herein we optimize our protocol to generate vascular populations in addition to epicardial, fibroblast, and cardiomyocyte cells that self-organize into in-vivo-like structures in hCOs. We identify mechanisms of communication between endothelial cells, pericytes, fibroblasts, and cardiomyocytes that ultimately contribute to cardiac organoid maturation. In particular, (1) endothelial-derived LAMA5 regulates expression of mature sarcomeric proteins and contractility, and (2) paracrine platelet-derived growth factor receptor ß (PDGFRß) signaling from vascular cells upregulates matrix deposition to augment hCO contractile force. Finally, we demonstrate that vascular cells determine the magnitude of diastolic dysfunction caused by inflammatory factors and identify a paracrine role of endothelin driving dysfunction. Together this study highlights the importance and role of vascular cells in organoid models.

Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.

Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein-protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.