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

Nat Biotechnol. 2020 Jun;38(6):708-714. doi: 10.1038/s41587-020-0497-0. Epub 2020 May 4.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Alleles
  • Animals
  • Gene Expression Profiling / methods*
  • Humans
  • Mice
  • RNA / analysis*
  • RNA / genetics
  • RNA Isoforms / analysis
  • RNA Isoforms / genetics
  • Sensitivity and Specificity
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*
  • Transcriptome / genetics


  • RNA Isoforms
  • RNA