Divergent TDP43-regulated and TDP43-independent cryptic splicing in the cortex and spinal cord
Bibliographic record
Abstract
Abstract Mislocalization of the nuclear TAR DNA-binding protein 43 (TDP43) is a hallmark of ALS and FTD which leads to de-repression and inclusion of cryptic exons (CEs), promising biomarkers of TDP43 pathology in a spectrum of neurodegenerative diseases. However, most CEs to date have been identified from in vitro models or a single cortical FTD dataset, and little is known about cryptic splicing in the spinal cord, or within different neuronal subtypes. We meta-analyzed published bulk RNAseq datasets representing 1,778 RNAseq profiles of ALS and FTD post-mortem tissue, and in vitro models with experimentally depleted TDP43. We identified 142 cryptic splices, including 68 novel events. We found divergent cryptic splicing primarily between the spinal cord and cortex, validated in an independent ALS cohort by qPCR and supported by in situ hybridization (ISH). We also identified a set of cryptic splices observed in tissue but not in vitro , and, being present in either SOD1-ALS or MAPT-FTD subjects, likely TDP43 independent. Finally, leveraging multiple public single-nucleus RNAseq datasets of ALS and FTD motor and frontal cortex, we confirmed the elevation of cortical-enriched splices in disease and localized them to layer-specific neuronal populations. We provide a web interface to browse the meta-analysis results at https://go.roche.com/CrypticSplicingLandscape . This catalog of cryptic splices will inform efforts to develop biomarkers for tissue-specific and cell type-specific TDP43 pathology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".