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Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
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High-throughput and high-accuracy single-cell RNA isoform analysis using PacBio circular consensus sequencing
Zhuo-Xing Shi1, Zhi-Chao Chen2, Jia-Yong Zhong1
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
Nature Communications
|May 6, 2023
Summary
HIT-scISOseq enhances single-cell RNA isoform sequencing by removing artifacts and concatenating cDNAs. This high-throughput method provides millions of accurate long-reads, accelerating single-cell transcriptomics research.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA isoform sequencing (scISO-Seq) offers insights into cellular RNA splicing.
- Existing scISO-Seq methods face limitations in read throughput, hindering comprehensive analysis.
Purpose of the Study:
- To develop a high-throughput and high-accuracy method for single-cell RNA isoform sequencing.
- To introduce a computational tool for accurate demultiplexing of concatenated cDNA reads.
Main Methods:
- HIT-scISOseq: A method involving artifact cDNA removal and cDNA concatenation for PacBio circular consensus sequencing (CCS).
- scISA-Tools: A computational tool for demultiplexing concatenated reads with high accuracy (>99.99%).
- Application to 3375 corneal limbus cells to analyze transcriptomes.
Main Results:
- HIT-scISOseq generates over 10 million high-accuracy long-reads per PacBio Sequel II SMRT Cell 8M.
- scISA-Tools achieves >99.99% accuracy and specificity in demultiplexing.
- Characterization of transcriptomes in 3375 corneal limbus cells, revealing cell-type-specific isoform expression.
Conclusions:
- HIT-scISOseq is a high-throughput, high-accuracy, and accessible method for long-read single-cell transcriptomics.
- The method and associated tools significantly advance the field of single-cell RNA isoform analysis.
- Enables deeper understanding of cellular heterogeneity and gene expression regulation.
