Systematic evaluation of single-cell RNA-seq analyses performance based on long-read sequencing platforms

• Published in: Journal of advanced research 2024-05
• Main Authors: Deng, Enze, Shen, Qingmei, Zhang, Jingna, Fang, Yaowei, Chang, Lei, Luo, Guanzheng, Fan, Xiaoying
• Format: Article
• Language: English
• Subjects: Allele-specific gene/isoform; Cell type identification; Novel isoform; Single cell RNA-sequencing; Third-generation sequencing
• ISSN: 2090-1232; 2090-1224; 2090-1224
• DOI: 10.1016/j.jare.2024.05.020

[Display omitted] •The TGS-based scRNA-seq data could be independently used to generate singlecell gene/isoform expression matrices.•Although the gene detection sensitivity is relatively low due to limited sequencing throughput, the TGS-based scRNA-seq accurately captures all cell types.•PacBio demonstrates superior performance in discovering novel transcripts.•Both TGS techniques were able to determine the allelic origins of the transcript reads, and PacBio could specify more allele-specific transcripts. The rapid development of next-generation sequencing (NGS)-based single-cell RNA sequencing (scRNA-seq) allows for detecting and quantifying gene expression in a high-throughput manner, providing a powerful tool for comprehensively understanding cellular function in various biological processes. However, the NGS-based scRNA-seq only quantifies gene expression and cannot reveal the exact transcript structures (isoforms) of each gene due to the limited read length. On the other hand, the long read length of third-generation sequencing (TGS) technologies, including Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), enable direct reading of intact cDNA molecules. Both ONT and PacBio have been used in conjunction with scRNA-seq, but their performance in single-cell analyses has not been systematically evaluated. To address this, we generated ONT and PacBio data from the same single-cell cDNA libraries containing different amount of cells. Using NGS as a control, we assessed the performance of each platform in cell type identification. Additionally, the reliability in identifying novel isoforms and allele-specific gene/isoform expression by both platforms was verified, providing a systematic evaluation to design the sequencing strategies in single-cell transcriptome studies. Beyond gene expression analysis, which the NGS-based scRNA-seq only affords, TGS-based scRNA-seq achieved gene splicing analyses, identifying novel isoforms. Attribute to higher sequencing quality of PacBio, it outperforms ONT in accuracy of novel transcripts identification and allele-specific gene/isoform expression.