The study investigated the molecular mechanisms underlying frontotemporal lobar degeneration (FTLD) associated with amyotrophic lateral sclerosis (ALS), particularly the characterization of dysregulated alternative polyadenylation (APA) patterns and the development of a deep learning model to predict APA regulation. To achieve this, a single-cell atlas was generated from 103,076 nuclei (23 snRNA-seq samples) from the orbitofrontal cortex of ALS individuals. This was sampled from ALS cases caused by mutations in the C9orf72 gene (C9-ALS), both with and without FTLD, and sporadic ALS cases, where there was no clear genetic cause. Analysis revealed that chaperone-mediated protein folding pathways are upregulated in C9-ALS with FTLD, whereas chromatin remodeling pathways are dysregulated across upper-layer excitatory neurons, inhibitory neurons, oligodendrocytes, and astrocytes. Cell-type-specific metabolic changes occurred in neurons, microglia, and oligodendrocyte precursor cells, as well as mitochondrial dysfunction driven by the widespread dysregulation of ribosomal complexes across populations. Neurons exhibited downregulation of associated RNA binding proteins (RBP) (TARDBP, FUS, ATXN2, SETX, and ANXA11), dysregulation of APA patterns, and lengthening of alternatively spliced transcripts. The study also noted consistent upregulation of STMN2 and NEFL across brain regions. To investigate APA regulation, a deep learning network, APA-Net, was developed and revealed the involvement of RBPs in APA regulation. Overall, this work established the transcriptomic alterations and post-transcriptional regulation across ALS/FTLD subtypes, providing a molecular atlas and predictive model to guide to therapeutic advancements.