scCensus: Off-target scRNA-seq reads reveal meaningful biology

Abstract

Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3′ end of polyadenylated RNAs, recent studies have shown that “off-target” reads can constitute a substantial portion of the read population. in this work, we introducedscCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We appliedscCensusto seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses.Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines.Data AvailabilityThe scripts for reproducing the results are available athttps://github.com/COMBINE-lab/sc-census. Supplementary files can be found athttps://doi.org/10.5281/zenodo.10520670.

Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads

“…distinct number of unique molecular identifiers (UMIs)] in scRNA-seq data ( 10x Genomics 2021 , He et al 2023 ), suggesting that, in addition to polyA tails, internal adenine-single nucleotide repeats (A-SNR or polyA)—predominantly on unspliced transcripts—are also frequently primed by oligo(dT) primers in scRNA-seq to generate sequenced scRNA-seq reads ( Nam et al 2002 , 10x Genomics 2021 , Svoboda et al 2022 ). Prior work has also shown that the information captured from unspliced transcripts can offer unprecedented insights into single-cell biology from a brand new perspective ( 10x Genomics 2022b , Gorin et al 2023 , Pool et al 2023 , Chamberlin et al 2024 , He et al 2024 ). For example, single-cell RNA velocity ( La Manno et al 2018 ) infers the cellular differentiation dynamics by proposing and performing inference in a model that uses the spliced and unspliced scRNA-seq reads separately to infer the transcriptional dynamics of the underlying genes and cells.…”