Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

• Published in: bioRxiv : the preprint server for biology
• Main Authors: McKeever, Paul M, Sababi, Aiden M, Sharma, Raghav, Khuu, Nicholas, Xu, Zhiyu, Shen, Shu Yi, Xiao, Shangxi, McGoldrick, Philip, Orouji, Elias, Ketela, Troy, Sato, Christine, Moreno, Danielle, Visanji, Naomi, Kovacs, Gabor G, Keith, Julia, Zinman, Lorne, Rogaeva, Ekaterina, Goodarzi, Hani, Bader, Gary D, Robertson, Janice
• Format: Journal Article
• Language: English
• Published: United States 23.12.2023

The understanding of how different cell types contribute to amyotrophic lateral sclerosis (ALS) pathogenesis is limited. Here we generated a single-nucleus transcriptomic and epigenomic atlas of the frontal cortex of ALS cases with C9orf72 (C9) hexanucleotide repeat expansions and sporadic ALS (sALS). Our findings reveal shared pathways in C9-ALS and sALS, characterized by synaptic dysfunction in excitatory neurons and a disease-associated state in microglia. The disease subtypes diverge with loss of astrocyte homeostasis in C9-ALS, and a more substantial disturbance of inhibitory neurons in sALS. Leveraging high depth 3'-end sequencing, we found a widespread switch towards distal polyadenylation (PA) site usage across ALS subtypes relative to controls. To explore this differential alternative PA (APA), we developed APA-Net, a deep neural network model that uses transcript sequence and expression levels of RNA-binding proteins (RBPs) to predict cell-type specific APA usage and RBP interactions likely to regulate APA across disease subtypes.