SRA

Single cell RNA sequencing (scRNA-seq) is a widely used method for identifying cell types and trajectories in biologically heterogeneous samples, but is limited in its detection and quantification of lowly expressed genes. This results in missing important biological signals, such as the expression of key transcription factors (TFs) driving cellular differentiation. We show that targeted sequencing of ~1000 TFs (scCapture-seq) in iPSC-derived neuronal cultures greatly improves the biological information garnered from scRNA-seq. Increased TF resolution enhanced cell-type identification, developmental trajectories and gene regulatory networks. This allowed us to resolve differences amongst neuronal populations, which were generated in two different labs using the same differentiation protocol. ScCapture-seq improved TF gene-regulatory network inference and thus identified divergent patterns of neurogenesis into either excitatory cortical neurons or inhibitory interneurons. Furthermore, scCapture-seq revealed a role for of retinoic acid signalling in the developmental divergence between these different neuronal populations. Our results demonstrate that TF targeting improves the characterization of human cellular models and allows identification of the essential differences between cellular populations, which would otherwise be missed in traditional scRNA-seq. scCapture-seq TF targeting represents a cost-effective enhancement of scRNA-seq, which could be broadly applied to any cellular models to improve scRNA-seq resolution Overall design: RNAseq profiles of 188 intestinal stromal cells from UC patients made using the 10x droplet-based protocol (Kinchen et.al., Cell, 2018; PMID: 30270042). Post-capture libraries were sequenced on 1 lane of a HiSeq 4000 at 75 bp paired end. RNA from each cell was sequenced twice: the PRE-capture libraries have all human genome sequenced, while only chosen set of transcription factors was sequenced in POST-capture libraries