The differences in transcription start sites (TSS) and transcription end sites (TES) among gene isoforms can affect the stability, localization, and translation efficiency of mRNA. Isoforms also allow a single gene different functions across various tissues and cells However, methods for efficient genome-wide identification and quantification of RNA isoforms in single cells are still lacking. Here, we introduce single cell Cap And Tail sequencing (scCAT-seq). In conjunction with a novel machine learning algorithm developed for TSS/TES characterization, scCAT-seq can demarcate transcript boundaries of RNA transcripts, providing an unprecedented way to identify and quantify single-cell full-length RNA isoforms based on short-read sequencing. Compared with existing long-read sequencing methods, scCAT-seq has higher efficiency with lower cost. Using scCAT-seq, we identified hundreds of previously uncharacterized full-length transcripts and thousands of alternative transcripts for known genes, quantitatively revealed cell-type specific isoforms with alternative TSSs/TESs in dorsal root ganglion (DRG) neurons, mature oocytes and ageing oocytes, and generated the first atlas of the non-human primate cornea. The approach described here can be widely adapted to other short-read or long-read methods to improve accuracy and efficiency in assessing RNA isoform dynamics among single cells.
scCAT-seq:single-cell identification and quantification of mRNA isoforms by cost-effective short-read sequencing of cap and tail
Youjin Hu,Jiawei Zhong,Yuhua Xiao,Zheng Xing,K. Sheu,Shuxin Fan,Qin An,Yuanhui Qiu,Yingfeng Zheng,Xialin Liu,G. Fan,Yizhi Liu
Published 2019 in bioRxiv
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2019
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bioRxiv
- Publication date
2019-12-12
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Biology, Materials Science, Computer Science
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