RNA editing enhances the diversity of gene products at the post-transcriptional level. Approaches for genome-wide identification of RNA editing face two main challenges: separating true editing sites from false discoveries and accurate estimation of editing levels. We developed an approach to analyze transcriptome sequencing data (RNA-seq) for global identification of RNA editing in cells for which whole-genome sequencing data are available. We applied the method to analyze RNA-seq data of a human glioblastoma cell line, U87MG. Around 10,000 DNA-RNA differences were identified, the majority being putative A-to-I editing sites. These predicted A-to-I events were associated with a low false-discovery rate (~5%). Moreover, the estimated editing levels from RNA-seq correlated well with those based on traditional clonal sequencing. Our results further facilitated unbiased characterization of the sequence and evolutionary features flanking predicted A-to-I editing sites and discovery of a conserved RNA structural motif that may be functionally relevant to editing. Genes with predicted A-to-I editing were significantly enriched with those known to be involved in cancer, supporting the potential importance of cancer-specific RNA editing. A similar profile of DNA-RNA differences as in U87MG was predicted for another RNA-seq data set obtained from primary breast cancer samples. Remarkably, significant overlap exists between the putative editing sites of the two transcriptomes despite their difference in cell type, cancer type, and genomic backgrounds. Our approach enabled de novo identification of the RNA editome, which sets the stage for further mechanistic studies of this important step of post-transcriptional regulation.

SUMMARY Cardiac differentiation of human pluripotent stem cells (hPSCs) requires orchestration of dynamic gene regulatory networks during stepwise fate transitions, but often generates immature cell types that do not fully recapitulate properties of their adult counterparts, suggesting incomplete activation of key transcriptional networks. We performed extensive single-cell transcriptomic analyses to map fate choices and gene expression programs during cardiac differentiation of hPSCs, and identified strategies to improve in vitro cardiomyocyte differentiation. Utilizing genetic gain- and loss-of-function approaches, we found that hypertrophic signaling is not effectively activated during monolayer-based cardiac differentiation, thereby preventing expression of HOPX and its activation of downstream genes that govern late stages of cardiomyocyte maturation. This study therefore provides a key transcriptional roadmap of in vitro cardiac differentiation at single-cell resolution, revealing fundamental mechanisms underlying heart development and differentiation of hPSC-derived cardiomyocytes.

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Gastrulation of the mouse embryo entails progressive restriction of lineage potency and the organization of the lineage progenitors into a body plan. Here we performed a high-resolution RNA sequencing analysis on single mid-gastrulation mouse embryos to collate a spatial transcriptome that correlated with the regionalization of cell fates in the embryo. 3D rendition of the quantitative data enabled the visualization of the spatial pattern of all expressing genes in the epiblast in a digital whole-mount in situ format. The dataset also identified genes that (1) are co-expressed in a specific cell population, (2) display similar global pattern of expression, (3) have lineage markers, (4) mark domains of transcriptional and signaling activity associated with cell fates, and (5) can be used as zip codes for mapping the position of single cells isolated from the mid-gastrula stage embryo and the embryo-derived stem cells to the equivalent epiblast cells for delineating their prospective cell fates.

Conventional gene expression studies analyze multiple cells simultaneously or single cells, for which the exact in vivo or in situ position is unknown. Although cellular heterogeneity can be discerned when analyzing single cells, any spatially defined attributes that underpin the heterogeneous nature of the cells cannot be identified. Here, we describe how to use Geo-seq, a method that combines laser capture microdissection (LCM) and single-cell RNA-seq technology. The combination of these two methods enables the elucidation of cellular heterogeneity and spatial variance simultaneously. The Geo-seq protocol allows the profiling of transcriptome information from only a small number cells and retains their native spatial information. This protocol has wide potential applications to address biological and pathological questions of cellular properties such as prospective cell fates, biological function and the gene regulatory network. Geo-seq has been applied to investigate the spatial transcriptome of mouse early embryo, mouse brain, and pathological liver and sperm tissues. The entire protocol from tissue collection and microdissection to sequencing requires ∼5 d, Data analysis takes another 1 or 2 weeks, depending on the amount of data and the speed of the processor.

Rationale Accurate and comprehensive de novo transcriptome profiling in heart is a central issue to better understand cardiac physiology and diseases. Although significant progress has been made in genome-wide profiling for quantitative changes in cardiac gene expression, current knowledge offers limited insights to the total complexity in cardiac transcriptome at individual exon level. Objective To develop more robust bioinformatic approaches to analyze high-throughput RNA sequencing (RNA-Seq) data, with the focus on the investigation of transcriptome complexity at individual exon and transcript levels. Methods and Results In addition to overall gene expression analysis, the methods developed in this study were used to analyze RNA-Seq data with respect to individual transcript isoforms, novel spliced exons, novel alternative terminal exons, novel transcript clusters (i.e., novel genes) and long non-coding RNA genes. We applied these approaches to RNA-Seq data obtained from mouse hearts following pressure-overload induced by trans-aortic constriction. Based on experimental validations, analyses of the features of the identified exons/transcripts, and expression analyses including previously published RNASeq data, we demonstrate that the methods are highly effective in detecting and quantifying individual exons and transcripts. Novel insights inferred from the examined aspects of the cardiac transcriptome open ways to further experimental investigations. Conclusions Our work provided a comprehensive set of methods to analyze mouse cardiac transcriptome complexity at individual exon and transcript levels. Applications of the methods may infer important new insights to gene regulation in normal and disease hearts in terms of exon utilization and potential involvement of novel components of cardiac transcriptome.

Graphical AbstractHighlights d A comprehensive scRNA-seq roadmap of early mouse development before gastrulation d Three cellular states of the epiblast cells transit the pluripotency continuum d X-reactivation in the epiblast initiates before completion of imprinted X-inactivation d Faster X-inactivation in visceral endoderm than in the extraembryonic ectoderm SUMMARY Following implantation, the epiblast (EPI) cells transit from the naive to primed pluripotency, accompanied by dynamic changes in X chromosome activity in females. To investigate the molecular attributes of this process, we performed single-cell RNA-seq analysis of 1,724 cells of E5.25, E5.5, E6.25, and E6.5 mouse embryos. We identified three cellular states in the EPI cells that capture the transition along the pluripotency continuum and the acquisition of primitive streak propensity. The transition of three EPI states was driven by inductive signaling activity emanating from the visceral endoderm (VE). In the EPI of female embryos, X chromosome reactivation (XCR) was initiated prior to the completion of imprinted X chromosome inactivation (XCI), and the ensuing random XCI was highly asynchronous. Moreover, imprinted paternal XCI proceeded faster in the VE than the extraembryonic ectoderm. Our study has provided a detailed molecular roadmap of the emergent lineage commitment before gastrulation and characterized X chromosome dynamics during early mouse development.