Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development

Abstract

RNA isoforms influence cell identity and function. Until recently, technological limitations prevented a genome-wide appraisal of isoform influence on cell identity in various parts of the brain. Using enhanced long-read single-cell isoform sequencing, we comprehensively analyze RNA isoforms in multiple mouse brain regions, cell subtypes, and developmental timepoints from postnatal day 14 (P14) to adult (P56). For 75% of genes, full-length isoform expression varies along one or more axes of phenotypic origin, underscoring the pervasiveness of isoform regulation across multiple scales. As expected, splicing varies strongly between cell types. However, certain gene classes including neurotransmitter release and reuptake as well as synapse turnover, harbor significant variability in the same cell type across anatomical regions, suggesting differences in network activity may influence cell-type identity. Glial brain-region specificity in isoform expression includes strong poly(A)-site regulation, whereas neurons have stronger TSS regulation. Furthermore, developmental patterns of cell-type specific splicing are especially pronounced in the murine adolescent transition from P21 to P28. The same cell type traced across development shows more isoform variability than across adult anatomical regions, indicating a coordinated modulation of functional programs dictating neural development. As most cell-type specific exons in P56 mouse hippocampus behave similarly in newly generated data from human hippocampi, these principles may be extrapolated to human brain. However, human brains have evolved additional cell-type specificity in splicing, suggesting gain-of-function isoforms. Taken together, we present a detailed single-cell atlas of full-length brain isoform regulation across development and anatomical regions, providing a previously unappreciated degree of isoform variability across multiple scales of the brain.

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

“…Furthermore, using LR var0. 25 to predict 𝛹 values of all exons resulted in lower performance for neurons compared to glia in both HPC and FC (Figure 1D, S3). Indicating that the learned splicing patterns for variable exons in neurons do not generalize to non-variable exons -likely because the underlying molecular grammar is different in the two exon sets.…”

“…As cell-type-specific alternative splicing is partially conserved between humans and mice 25 , we hypothesized that adding mouse data to our model would increase performance. We combined human HPC data with mouse HPC 25 . Since mouse FC data is not available, we combined human FC with data from the mouse visual cortex (VIS).…”

“…Long-read single-cell and single-nuclei sequencing in fresh 21,22 and frozen 23 tissue allows the study of alternative splicing at the cell-type level in the brain and other complex tissues. Such analyses revealed that most mouse genes show differential isoform expression across at least one pair of cell types, regions, and/or developmental time points in the brain 24,25 . In accordance with prior studies [26][27][28] , single-nuclei isoform RNA sequencing (SnISOr-Seq) of the human adult frontal cortex revealed that exons associated with autism spectrum disorder (ASD) are variably included across cell types 23 .…”

ScISOr-ATAC reveals convergent and divergent splicing and chromatin specificities between matched cell types across cortical regions, evolution, and in Alzheimer’s Disease

“…Both splicing 9,[11][12][13] and chromatin 14 organization are known to differentiate cell types within a brain region as well as matched cell types across brain regions 15 . Moreover, multiple modalities have undergone evolutionary changes and are affected in complex diseases such as Alzheimer's disease (AD) [16][17][18] .…”

Biosurfer for systematic tracking of regulatory mechanisms leading to protein isoform diversity

“…Transcript and, by extension, protein isoform diversity may now be globally characterized at great depth for individual samples (Glinos et al 2022;Reese et al 2023). Transcript diversity can be readily characterized by long read RNA-Seq, which employs single molecule sequencing of individual cDNA or RNA molecules to determine the sequence across the entire length of spliced transcripts (Sharon et al 2013;Workman et al 2019;Pardo Palacios et al 2021;Tian et al 2021;Joglekar et al 2023), with platforms from Oxford Nanopore and PacBio being most commonly used (Clarke et al 2009;Eid et al 2009). Long read RNA sequencing captures long range connectivity between multiple exons of a transcript and can reveal complex splice patterns unattainable by short read sequencing (De Paoli Iseppi et al 2021), including dependencies across distal splicing events (Anvar et al 2018) and alternative 5' and 3' transcript usage.…”