Oral Presentation 43rd Lorne Genome Conference 2022

Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories (#6)

Michael B Clark 1 , Alexandra Pokhilko 2 , Adam E Handel 2 , Fabiola Curion 3 , Viola Volpato 4 , Emma S Whiteley 5 , Sunniva Bostrand 5 , Sarah E Newey 5 , Colin J Akerman 5 , Caleb Webber 4 , Rory Bowden 3 6 , Zameel Cader 2
  1. University of Melbourne, Parkville, VIC, Australia
  2. Weatherall Institute of Molecular Medicine, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  3. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
  4. UK Dementia Research Institute, Cardiff University, Cardiff, UK
  5. Department of Pharmacology, University of Oxford, Oxford, UK
  6. The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia

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 the absence of 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 signalling 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 with standard scRNA-seq. scCapture-seq TF targeting represents a cost-effective enhancement of scRNA-seq, which could be broadly applied to improve scRNA-seq resolution.