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

Hagemann-Jensen M, Ziegenhain C, Chen P, Ramsk├Âld D, Hendriks GJ, Larsson AJM, Faridani OR, Sandberg R

Nat. Biotechnol. 38 (6) 708-714 [2020-06-00; online 2020-05-04]

Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. 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 cells2,3. 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.

NGI Stockholm (Genomics Applications) [Service]

NGI Stockholm (Genomics Production) [Service]

NGI Uppsala (Uppsala Genome Center) [Service]

National Genomics Infrastructure [Service]

PubMed 32518404

DOI 10.1038/s41587-020-0497-0

Crossref 10.1038/s41587-020-0497-0

pii: 10.1038/s41587-020-0497-0

Publications 9.5.0