Why don't we align to pre-RNA for typical single-cell RNA-seq data? (CellRanger or STARsolo GeneFull setting)

Question

[scideas]

I've noticed that CellRanger defaults to running STAR on sc-RNA-seq samples considering only reads that align to the exome.

However, they suggest modifying the default when running nuclei samples to align to exons + introns (pre-RNA).

Given that single-cell data includes cytosolic + nuclear RNA, why is it not standard practice to use the pre-RNA alignment strategy as the default for sc-RNA-seq experiments?

Since it generally improves alignment, it seems like a good idea, but am guessing there may be unintended consequences I'm not aware of.

Thanks in advance for any insights you might have!

[Jeremy Leipzig]

how do you distinguish this pre-RNA from genomic contamination?

Answer 1

[scideas]

I received this response after inquiring to 10x directly (summarized):

Other mechanisms could also contribute to intronic reads, such as internal poly-A priming (see the detailed technical note). By including intronic read counting, it will potentially have a bias for poly-A-rich genes (not referring to exons here). Aligning with introns increases the sensitivity of gene detection, since it helps the detection of genes that have intronic UMIs but 0 exonic UMIs in any cell.

Different samples have different intronic read mapping profiles (table 1 in above link). (Also, if there is a polyA stretch within an exon, then multiple 'unique' reads could potentially be generated from a single transcript). Therefore, if you expect a high fraction of intronic reads in your single-cell sample and would like to increase the gene detection sensitivity, you could include --include-introns.