High throughput single-cell whole transcriptome analysis of induced cardiomyocytes during direct cardiac reprogramming [RNA-seq]

Direct lineage conversion offers a new strategy for tissue regeneration and disease modeling. Despite recent success in directly reprogramming fibroblasts into a wide spectrum of cell types, the precise changes that fibroblasts undergo as they progress to target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process make it difficult to study using bulk genomic techniques. Therefore, a fundamental and detailed understanding of global transcriptome changes at the single cell level is necessary to better optimize reprogramming for therapeutic purposes and to advance the understanding of cell plasticity and cell identity acquisition. Here, we applied single-cell RNA-seq to analyze global transcriptome changes at early stages of induced cardiomyocyte (iCM) reprogramming. Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells along the reprogramming process from fibroblasts to iCMs, including a novel intermediate state that exhibited molecular signatures of both fibroblast and cardiomyocyte. In summary, our single cell transcriptomics approaches enabled us to construct a reprogramming trajectory and uncover the intermediate cell populations, regulatory pathways and genes putatively involved in iCM induction. Neonatal mouse cardiac fibroblasts were isolated and reprogrammed with Mef2c, Gata4, and Tbx5. Single cells were captured on day 3 with the microfluidic system C1 in seven separate experiments including two plates of control cells (C1 and C2, uninfected and retroviral transduction control), three plates of reprogramming cells (M1, M2 and M3) and two plates of half-to-half mixed reprogramming cells and retroviral-infected control cells (M4 and M5). External RNA-spike in were added before lysis and used to calculate technical size factor during normalization. Lysate from 574 wells containing single cells (four zero-cell control were included in four of the seven plates) were selected for mRNA sequencing (574 raw data files) and a total of 513 high-quality single-cell transcriptomes were analyzed (513 processed data).