RT Journal Article
SR Electronic
T1 Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 817924
DO 10.1101/817924
A1 Michael Hagemann-Jensen
A1 Christoph Ziegenhain
A1 Ping Chen
A1 Daniel Ramsköld
A1 Gert-Jan Hendriks
A1 Anton J.M. Larsson
A1 Omid R. Faridani
A1 Rickard Sandberg
YR 2019
UL http://biorxiv.org/content/early/2019/10/25/817924.abstract
AB Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) 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 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.