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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.


Michael Hagemann-Jensen, Christoph Ziegenhain, Ping Chen, Daniel Ramsköld, Gert-Jan Hendriks, Anton J M Larsson, Omid R Faridani, Rickard Sandberg. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nature biotechnology. 2020 Jun;38(6):708-714

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PMID: 32518404

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