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!
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][1]). 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.

  [1]: https://assets.ctfassets.net/an68im79xiti/awNZTarmwqmwxKcvDv9wv/b05500661e36290b8ce59689ff889ea8/CG000376_TechNote_Antisense_Intronic_Reads_SingleCellGeneExpression_RevA.pdf

