Single-nucleus transcriptome atlas of orbitofrontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".