Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates.
Here researchers from the Murdoch Childrens Research Institute investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. They found that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), the researchers found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer.
Gene length bias is present in non-UMI protocols
Three different datasets were analysed: (a–c) mouse embryonic stem cells, n=530, (d–f) human primordial germ cells, n=226, (g–i) human brain whole organoids, n=494. For all plots (a–i), the x-axis shows 10 gene length bins all containing roughly equal numbers of genes. The left panel shows gene-wise average log counts, the middle panel shows proportion of zeroes in each gene (dropout rate per gene), and the right panel shows average log counts corrected for gene length (RPKM).
The researchers found that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, they illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.