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Record W4414281027 · doi:10.1016/j.xgen.2025.101007

Single-nucleus transcriptome atlas of orbitofrontal cortex in ALS with a deep learning-based decoding of alternative polyadenylation mechanisms

2025· article· en· W4414281027 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Genomics · 2025
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsThe Wilson CentreSinai Health SystemSunnybrook Health Science CentreUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer CentreHealth Sciences CentreOccupational Cancer Research Centre
FundersCanadian Institutes of Health ResearchALS Society of CanadaMcGill UniversityConsortium canadien en neurodégénérescence associée au vieillissementALS AssociationFoundation for the National Institutes of Health
KeywordsPolyadenylationFrontotemporal lobar degenerationAmyotrophic lateral sclerosisTranscriptomeFrontotemporal dementiaOrbitofrontal cortexC9orf72microRNAPrefrontal cortex

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it