Decoding neuronal diversity by single-cell Convert-seq

Abstract

The conversion of cell fates is controlled by hierarchical gene regulatory networks (GRNs) that induce remarkable alterations in cellular and transcriptome states. The identification of key regulators within these networks from myriad of candidate genes, however, poses a major research challenge. Here we present Convert-seq, combining single-cell RNA sequencing (scRNA-seq) and pooled ectopic gene expression with a new strategy to discriminate sequencing reads derived from exogenous and endogenous transcripts. We demonstrate Convert-seq by associating hundreds of single cells during reprogramming of human fibroblasts to induced neurons (iN) with exogenous and endogenous transcriptional signatures. Convert-seq identified GRNs modulating the emergence of developmental trajectories and predicted combinatorial activation of exogenous transcription factors controlling iN subtype specification. Functional validation of iN subtypes generated by novel combinations of exogenous transcription factors establish Convert-seq as a broadly applicable workflow to rapidly identify key transcription factors and GRNs orchestrating the reprogramming of virtually any cell type.