Single-nucleus transcriptome atlas of orbitofrontal cortex in ALS with a deep learning-based decoding of alternative polyadenylation mechanisms
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are fatal neurodegenerative diseases sharing clinical and pathological features. Both involve complex neuron-glia interactions, but cell-type-specific alterations remain poorly defined. We performed single-nucleus RNA sequencing of the frontal cortex from C9orf72-related ALS (with and without FTLD) and sporadic ALS (sALS). Neurons showed prominent changes in mitochondrial function, protein homeostasis, and chromatin remodeling. Comparison with independent datasets from other cortical regions revealed consistent pathway alterations, including upregulation of STMN2 and NEFL across brain regions and subtypes. We further examined dysregulation of alternative polyadenylation (APA), an understudied post-transcriptional mechanism, uncovering cell-type-specific APA patterns. To investigate its regulation, we developed the alternative polyadenylation network (APA-Net), a multi-modal deep learning model integrating transcript sequences and RNA-binding protein (RBP) expression profiles to predict APA. This atlas advances our understanding of ALS/FTLD molecular pathology and provides a valuable resource for future mechanistic studies.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".