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Cardiac regeneration occurs primarily through proliferation of existing cardiomyocytes, yet the regenerative response also involves complex interactions between distinct cardiac cell types including not only cardiomyocytes, but also non-cardiomyocytes (nonCMs). However, the subpopulations, distinguishing molecular features, cellular functions, and intercellular interactions of nonCMs in heart regeneration remain largely unexplored. Using the LIGER algorithm, we assembled an atlas of cell states from 61,977 individual nonCM scRNA-seq profiles isolated at multiple time points during heart regeneration in both wildtype and mutant fish. This analysis revealed extensive nonCM cell diversity, including multiple macrophage, fibroblast and endothelial subpopulations with unique spatiotemporal distributions and cooperative interactions during the process of cardiac regeneration. Genetic and pharmacological perturbation of macrophage functional dynamics compromised interactions among nonCM subpopulations, reduced cardiomyocyte proliferation, and caused defective cardiac regeneration. Furthermore, we developed a computational algorithm called Topologizer to map the topological relationships and dynamics of nonCMs during heart regeneration. We uncovered dynamic transitions between macrophage functional states and identified factors involved in mRNA processing and transcriptional regulation associated with the transition. Together, our single-cell transcriptomic analysis of nonCMs during cardiac regeneration provides a blueprint for interrogating the molecular and cellular basis of cardiac regeneration.

During pathological hypertrophy, the heart undergoes extensive metabolic changes prior to the onset of structural remodeling, yet, it remains unknown whether and how this metabolic remodeling could contribute to cardiac hypertrophic growth. Here, we identified the RNA-binding protein Lin28a as a critical regulator of pathological cardiac hypertrophy and metabolic re-patterning. Cardiac specific deletion of Lin28a attenuated pressure overload-induced hypertrophy and cardiac dysfunction. Transcriptomics and metabolomic analysis highlighted roles for Lin28a in promoting cardiac glycolysis and anabolic biosynthesis. Mechanistically, Lin28a directly bound to mitochondrial phosphoenolpyruvate carboxykinase 2 ( Pck2 ) mRNA and positively modulated its transcript level. By utilizing the neonatal rat cardiomyocytes (NRCMs) hypertrophy model, we found that manipulation of Pck2 expression phenocopied the metabolic and hypertrophic phenotypes of manipulating Lin28a expression. Furthermore, epistatic analyses demonstrated that overexpression of Pck2 reversed the attenuation of norepinephrine-induced enhancement of cardiac glycolysis and cardiac hypertrophy by loss of Lin28a function, and knockdown of Pck2 suppressed Lin28a-induced increase in cardiac glycolysis and cardiomyocyte hypertrophic growth. Thus, our study revealed a critical role of Lin28a in the regulation of pathological cardiac hypertrophic growth through Pck2-mediated regulation of cardiac metabolism.

Reprogramming from fibroblasts into induced cardiomyocytes (iCMs) offers alternative strategies for cardiac disease modeling and cardiac regeneration. Cellular reprogramming is closely associated with global re-patterning of the epigenetic landscape. The reprogramming cells must overcome the epigenetic barriers to acquire the target cell-like epigenetic pattern. We have recently demonstrated that stoichiometry of iCM reprogramming factors Gata4 (G), Mef2c (M) and Tbx5 (T) has profound impact on reprogramming efficiency and quality, yet the underlying mechanism remains unclear. Here, we demonstrate that the optimal stoichiometry combination MGT resulted in a reduced binding of the repressive marker histone 3 lysine 27 trimethlytion (H3K27me3) and an enhanced binding of the active marker histone 3 lysine 4 trimethlytion (H3K4me3) on the cardiac gene loci, compared to the least optimal combination GTM. We found that this epigenetic change occurred as early as day 3 when cardiac marker genes started to be induced in the reprogramming fibroblasts. Additionally, we determined the DNA methylation status in two cardiac gene loci during MGT and GTM induced reprogramming. Subsets of the CpG islands in these promoters were significantly more demethylated in MGT than GMT-transduced cells. We thus propose these CpGs islands as the “regulatory” CpGs critical for iCM reprogramming. In conclusion, we demonstrated that stoichiometry of G, M, T influences the epigenetic status of the iCM cells, suggesting a potential mechanism of how stoichiometry of G,M,T influences reprogramming.

Rationale: Generation of induced cardiac myocytes (iCMs) directly from fibroblasts offers great opportunities for cardiac disease modeling and cardiac regeneration. A major challenge of iCM generation is the low conversion rate of fibroblasts to fully reprogrammed iCMs, which could in part be attributed to unbalanced expression of reprogramming factors Gata4 (G), Mef2c (M), and Tbx5 (T) using the current gene delivery approach. Objective: We aimed to establish a system to express distinct ratios of G, M, T proteins in fibroblasts and determine the effect of G, M, T stoichiometry on iCM reprogramming. Methods and Results: We took advantage of the inherent feature of the polycistronic system and generated all possible combinations of G, M, T with identical 2A sequences in a single transgene. We demonstrated that each splicing order of G, M, T gave rise to distinct G, M, T protein expression levels. Combinations that resulted in higher protein level of Mef2c with lower levels of Gata4 and Tbx5 significantly enhanced reprogramming efficiency compared with separate G, M, T transduction. Importantly, after further optimization, the MGT vector resulted in more than 10-fold increase in the number of mature beating iCM loci. Molecular characterization revealed that more optimal G, M, T stoichiometry correlated with higher expression of mature cardiac myocyte markers. Conclusions: Our results demonstrate that stoichiometry of G, M, T protein expression influences the efficiency and quality of iCM reprogramming. The established optimal G, M, T expression condition will provide a valuable platform for future iCM studies.