Measuring isoform co-expression in single-cell RNAseq data to decode splicing coordination
Creators: name: Luque, Ángeles Arzalluz
name: Tarazona, Sonia
name: Conesa, Ana
Publisher: name: F1000 Research Limited
Year: 2019

description: Single-cell RNAseq studies have mainly focused on the discovery and characterization of new cell types, leaving areas such as isoform expression dynamics unexplored. Furthermore, splicing has almost solely been analyzed in an exon-wise manner. Instead, we hypothesize that splicing can be understood as a regulatory layer that acts coordinately to change the biological properties of entire transcripts and proteins, depending on the context of the cell. This mechanism can be observed as a series of transcript modules that present co-variation across cell types and states, in a manner that is independent of gene-level expression. In this work, we have developed the first computational pipeline that makes use of single-cell RNAseq data to infer such relationships, while also overcoming three main limitations of previous analyses: (1) assessment of differential expression across multiple groups, avoiding pairwise comparisons, (2) new correlation metric that uses the distribution of expression of an isoform in a cell type instead of the raw expression values, and hence reduces the negative effect of single-cell noise and captures relevant co-expression relationships, and (3) new clustering strategy to group transcripts with similar expression profiles, inferring differential isoform usage and co-usage beyond pairwise analyses. As a result, we are able to obtain genes with multimodal differential isoform usage (i.e. differential splicing) by selecting the sets of isoforms that belong to the same gene, but are assigned to different clusters and therefore are expressed in a different modality across cell types. This allows us to extract co-differentially spliced sets of genes, namely the groups of differentially spliced genes whose isoforms are assigned to the same clusters. Using a functionally-annotated transcriptome and our recently released tool tappAS, we applied our pipeline to a publicly available dataset (Tasic et al. 2016) and performed a functional analysis of the results. In particular, we analyzed 47 genes that coordinately switch their isoform usage between neural cell types (GABA-ergic and Glutamatergic neurons) and oligodendrocytes. We found that isoforms from these genes expressed in neurons had consistently longer 3’UTRs, and moreover, that these UTRs were highly populated with miRNA and RBP binding motifs, providing evidence of implications for transcript expression regulation and splicing. At the protein level, we found several genes whose oligodendrocyte-specific isoforms tend to present inclusion of functional elements that might increase nuclear functionality, such as zinc finger domains or NLS, and protein binding ability, such as EF-hand domains. Together, these results point towards a coordinated action of splicing that provides isoforms of these genes with cell type specific functional properties, both at the protein and transcript levels. Interestingly, functional enrichment results show that these differentially co-spliced genes share biological functions such as cytoskeleton remodelling and binding, mitochondrial activity and membrane-related processes such as ion transport and cell projection establishment, opening an interesting path for further analysis and biological interpretation.