CamoTSS: analysis of alternative transcription start sites for cellular phenotypes and regulatory patterns from 5' scRNA-seq data

An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.

A single-cell genomic strategy for alternative transcript start sites identification

Alternative transcription start sites (TSSs) usage plays a critical role in gene transcription regulation in mammals. However, precisely identifying alternative TSSs remains challenging at the genome-wide level. We report a single-cell genomic technology for alternative TSSs annotation and cell heterogeneity detection. In the method, we utilize Fluidigm C1 system to capture individual cells of interest, SMARTer cDNA synthesis kit to recover full-length cDNAs, then dual priming oligonucleotide system to specifically enrich TSSs for genomic analysis. We apply this method to a genome-wide study of alternative TSSs identification in two different IFN-β stimulated mouse embryonic fibroblasts (MEFs). The data clearly discriminate two IFN-β stimulated MEFs. Moreover, our results indicate 81% expressed genes in these two cell types containing multiple TSSs, which is much higher than previous predictions based on Cap-Analysis Gene Expression (CAGE) (58%) or empirical determination (54%) in various cell types. This indicates that alternative TSSs are more pervasive than expected and implies our strategy could position them at an unprecedented sensitivity. It would be helpful for elucidating their biological insights in future.