Comparing 10x Genomics single-cell 3’ and 5’ assay in short-and long-read sequencing

Abstract

Barcoding strategies are fundamental to droplet-based single-cell sequencing, and understanding the biases and caveats between approaches is essential. Here, we comprehensively evaluated both short and long reads of the cDNA obtained through the two marketed approaches from 10x Genomics, the "3' assay" and the "5' assay", which attach barcodes at different ends of the mRNA molecule. Although the barcode detection, cell-type identification, and gene expression profile are similar in both assays, the 5' assay captured more exonic molecules and fewer intronic molecules compared to the 3' assay. We found that 13.7% of genes sequenced have longer average read lengths and are more complete (spanning both polyA-site and TSS) in the long reads from the 5' assay compared to the 3' assay. These genes are characterized by long average transcript length, high intron number, and low expression overall. Despite these differences, cell-type-specific isoform profiles observed from the two assays remain highly correlated. This study provides a benchmark for choosing the single-cell assay for the intended research question, and insights regarding platform-specific biases to be mindful of when analyzing data, particularly across samples and technologies.

Splicing-aware resolution of scRNA-Seq data

“…However, this has proven methodologically challenging, as the use of either 5’ or 3’ end-labeling of RNA molecules with molecular barcodes—alongside inherent limitations of high-throughput sequencing methods—have restricted our ability to comprehensively derive such information for a given RNA molecule 3,51 .…”

“…Direct estimation of the proportion of spliced versus unspliced mRNA for each gene in scRNA-Seq data is confounded by the oligo-dT primers used to enrich for polyadenylated mRNA molecules, and the limited coverage and biases of currently-available information obtained via either 5'-or 3'-high-throughput transcriptomics 3 . We settled on the veloVI framework 10 , which is based on the proportions of spliced and unspliced unique molecular identifiers (UMIs), where each UMI-labeled molecule containing a read mapping to an intronic region is counted as an unspliced molecule.…”

“…Correspondingly, rational utilization of splicing information in scRNA-Seq data analysis could reveal multiple functional aspects of cell biology. However, quantitative analysis of splicing is rarely included in scRNA-Seq studies due to the difficulties inherent to the short-read sequencing technologies [3][4] . Coverage bias across genes and sequencing technologies, inability to detect all splicing junctions, insufficient sequencing depth, and high dropout rate prevent direct estimation of splicing by distinguishing spliced and unspliced molecules 3 .…”

De novo detection of somatic variants in long-read single-cell RNA sequencing data

“…LongSom excludes SNVs called in noncancer cells to filter germline SNVs, possibly leading to false negative somatic SNVs, as shown by the matched panel-seq. Second, read coverage is also uneven within a transcript, as transcripts produced by 10X Genomics Chromium remain incomplete on the 5' end (Hsu et al 2022). Third, RNA-seq is inherently limited to detecting only expressed SNVs and fusions.…”

Multi-omic analyses in immune cell development with lessons learned from T cell development

“…To also capture information about the TCRs, the barcoded 5′ cDNA libraries can be used to enrich for the full V(D) J sequence by targeted PCR using reverse primers designed on the constant regions of alpha and beta chains ( Matuła et al, 2020 ) or gamma and delta chains ( Cordes et al, 2022 ). While 3′end sequencing approaches are standard practice for gene expression quantification, a recent comparison study between 3′ and 5′ 10X generated data shows that the 5′ assays captures more exonic UMIs and gene expression of genes with longer transcript lengths ( Hsu et al, 2022 ). While V(D) J sequencing gives valuable information about the rearrangement processes of developing T cells, no actual protein data is investigated.…”