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Single-cell RNA counting at allele and isoform resolution using Smart-seq3


Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states(1). However, current short-read single-cell RNA-sequencing methods have limited ability to count RNAs at allele and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells(2,3). Here we introduce Smart-seq3, which combines full-length transcriptome coverage with a 5' unique molecular identifier RNA counting strategy that enables in silico reconstruction of thousands of RNA molecules per cell. Of the counted and reconstructed molecules, 60% could be directly assigned to allelic origin and 30-50% to specific isoforms, and we identified substantial differences in isoform usage in different mouse strains and human cell types. Smart-seq3 greatly increased sensitivity compared to Smart-seq2, typically detecting thousands more transcripts per cell. We expect that Smart-seq3 will enable large-scale characterization of cell types and states across tissues and organisms.

Type Journal
ISBN 1546-1696 (Electronic) 1087-0156 (Linking)
Authors Hagemann-Jensen, M.; Ziegenhain, C.; Chen, P.; Ramskold, D.; Hendriks, G. J.; Larsson, A. J. M.; Faridani, O. R.; Sandberg, R.
Responsible Garvan Author Omid Faridani
Published Date 2020-06-30
Published Volume 38
Published Issue 6
Published Pages 708-714
Status Published in-print
DOI 10.1038/s41587-020-0497-0
URL link to publisher's version