Robust and annotation-free analysis of alternative splicing across diverse cell types in mice

Abstract

Although alternative splicing is a fundamental and pervasive aspect of gene expression in higher eukaryotes, it is often omitted from single-cell studies due to quantification challenges inherent to commonly used short-read sequencing technologies. Here, we undertake the analysis of alternative splicing across numerous diverse murine cell types from two large-scale single-cell datasets-the Tabula Muris and BRAIN Initiative Cell Census Network-while accounting for understudied technical artifacts and unannotated events. We find strong and general cell-type-specific alternative splicing, complementary to total gene expression but of similar discriminatory value, and identify a large volume of novel splicing events. We specifically highlight splicing variation across different cell types in primary motor cortex neurons, bone marrow B cells, and various epithelial cells, and we show that the implicated transcripts include many genes which do not display total expression differences. To elucidate the regulation of alternative splicing, we build a custom predictive model based on splicing factor activity, recovering several known interactions while generating new hypotheses, including potential regulatory roles for novel alternative splicing events in critical genes like Khdrbs3 and Rbfox1. We make our results available using public interactive browsers to spur further exploration by the community.

SPLASH: a statistical, reference-free genomic algorithm unifies biological discovery

“…NOMAD runs dramatically faster than existing algorithms that detect differential isoform expression, eg. SpliZ ( 10, 37 ). To achieve significance calls for donor 1, each cell required an average run time of 2.28 minutes and 758 MB of memory; for donor 2, each cell required an average run time of 3.5 minutes and 2.4 GB of memory on standard high performance compute architecture.…”

Splicing-aware resolution of scRNA-Seq data

“…However, the functional implementation of genetic programs occurs at multiple levels, not just at the level of the quantity of produced and stored RNA. Ideally, analyzing the transcriptomes of single cells could also reveal the proportion of spliced RNA molecules, which directly affect the functional activity of both mRNAs and non-coding RNAs, as well as offer the insights into alternative splicing 5,51 and trans-splicing 52 .…”

“…To date, splicing was studied in scRNA-Seq data in terms of transcriptional dynamics and cell-state transitions 5,6 , and only in a post hoc manner – after conventional clustering and dimensionality reduction. However, splicing information has not been used as an independent criterion to distinguish between stable functional cell subsets, implemented as an input at the level of cell clustering.…”

From words to complete phrases: insight into single-cell isoforms using short and long reads

“…They identified 78 differentially spliced exons that correlated with changes in expression and binding of neuronal splicing factors such as Nova1 , Rbfox1 and Mbnl2 , which can have downstream regulatory consequences. A more recent computational framework that uses generalized linear models (GLMs) to identify differential splicing across conditions while controlling for length biases in single-cell short read data was developed by Benegas et al [ 38 ]. They used this framework, scQuint, to identify thousands of alternative splicing events in the motor cortex and B-cell development in the bone marrow.…”