Comparative analysis of single-cell RNA-seq protocols for transcript quantification

Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for studying cellular heterogeneity and identifying biomarkers across various cell types. However, current scRNA-seq protocols have limitations in isoform-level analysis due to biases such as 3'-coverage biases and low RNA capture efficiency. To address these limitations, Smart-Seq technologies have been developed with limited throughput, enabling the capture of full-length transcripts in single cells, particularly useful for single-cell alternative splicing analysis. Meanwhile, single-cell long-read sequencing (MAS-seq) covers the entire length of transcripts, overcoming coverage bias, but suffers from high sequencing error and low throughput.

Since it is currently unclear how these new technologies perform in isoform-level analysis, we systematically compared scRNA-seq full-length transcript methods, including Chromium 10x, Smart-Seq2/3 and MAS-seq sequenced with Sequel and Revio. We analyzed 5 data sets from human PBMC and performed downsample analysis to account for cell number variations. Our study provides insights into the effectiveness of different technologies in isoform-level analysis. We found that Smart-Seq3 shows superior performance in mapping quality, coverage bias, junction detection and isoform detection. In conclusion, Smart-Seq3 is currently a superior technology for isoform-level analysis in single-cell RNA sequencing, while long-read methods (MAS-seq) still require improvement in RNA capture efficiency.