Reverse engineering neuron-type-specific and type-orthogonal splicing-regulatory networks using diverse cellular transcriptomes.

Abstract

Cell-type-specific alternative splicing (AS) enables differential gene isoform expression between diverse neuron types with distinct identities and functions. Current studies linking individual RNA-binding proteins (RBPs) to AS in a limited number of neuron types underscore the need for holistic modeling. Here, we use network reverse engineering to derive a map of the neuron-type-specific AS-regulatory landscape of 133 mouse neocortical cell types using pseudobulk transcriptomes derived from single-cell data. We infer the regulons of 350 RBPs and their cell-type-specific activities, among which we validate Elavl2 as a key RBP for medial ganglionic eminence (MGE)-specific splicing in GABAergic interneurons using an in vitro embryonic stem cell (ESC) differentiation system. We also identify a module of exons and candidate regulators specific to long- and short-projection neurons across multiple neuronal classes. This study provides a resource for elucidating splicing-regulatory programs that drive neuronal molecular diversity, including those that do not align with gene-expression-based classifications.