TY  - JOUR
A1  - Pokhilko, Alexandra
A1  - Handel, Adam E.
A1  - Curion, Fabiola
A1  - Volpato, Viola
A1  - Whiteley, Emma S.
A1  - Bøstrand, Sunniva
A1  - Newey, Sarah E.
A1  - Akerman, Colin J.
A1  - Webber, Caleb
A1  - Clark, Michael B.
A1  - Bowden, Rory
A1  - Cader, M. Zameel
T1  - Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories
Y1  - 2021/06/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1069 
EP  - 1081 
DO  - 10.1101/gr.273961.120 
VL  - 31 
IS  - 6 
UR  - http://genome.cshlp.org/content/31/6/1069.abstract 
N2  - Single-cell RNA sequencing (scRNA-seq) is a widely used method for identifying cell types and trajectories in biologically heterogeneous samples, but it 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 among neuronal populations, which were generated in two different laboratories 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 signaling in the developmental divergence between these different neuronal populations. Our results show 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 improve scRNA-seq resolution. 
ER  -