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Improving single-cell RNA counting

Hagemann-Jensen, M. et al. Nat. Biotechnol. 38, 708–714 (2020).

In single-cell RNA sequencing, full-length transcriptome coverage is important for reconstructing the RNA molecule and assigning its allelic origin and isoform. Smart-seq2 offers a sensitive approach to obtaining full-length coverage of transcripts and enables the quantification of isoform-level expression from single cells. Yet Smart-seq2 lacks a unique molecular identifier (UMI), valuable for profiling isoform-level RNA counting. Hagemann-Jensen et al. describe Smart-seq3, which further improves the sensitivity of Smart-seq2. They examined hundreds reaction conditions for preparing transcriptome libraries and identified the optimized reverse transcriptase and buffer conditions. In addition, they introduce a UMI, a partial Tn5 motif and a tag sequence in the template-switching oligonucleotide, which allows them to sequence different parts of the full-length cDNA and thus to reconstruct the RNA molecules in silico. They show that Smart-seq3 can detect thousands more transcripts than Smart-seq2 per cell.

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Correspondence to Lei Tang.

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Tang, L. Improving single-cell RNA counting. Nat Methods 17, 656 (2020).

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