Decoding neuronal diversity by single cell Convert-seq

The conversion of cell fates is controlled by hierarchical gene regulatory networks (GRNs) that induce remarkable alterations of 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 (mutiplexed) 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 at multiple time-points during direct conversion of human fibroblasts to induced neurons (iN) with exogenous and endogenous transcriptional signatures. Convert-seq simultaneously identified GRNs that modulate the emergence of parallel developmental trajectories during iN conversion and predicted combinatorial interactions of exogenous transcription factors controlling iN subtype specification. 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 direct conversion of virtually any cell type. Single-cell transcriptomes of human fibroblasts reprogrammed into induced neurons via chemicals or ectopic expression of transcription factors at multiple time-points