Integrative Analysis of Single-Nucleus RNA-Seq and ATAC-Seq of C9orf72 Knockout Mouse Brain: Unraveling the Molecular Mechanisms Underlying Neurodegeneration in Amyotrophic Lateral Sclerosis and Frontotemporal Dementia
Bibliographic record
Abstract
Hexanucleotide repeat expansions in the first intron of C9orf72 are the most frequent genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). These expansions result in neurodegeneration related to haploinsufficiency of C9orf72, though the mechanisms remain unclear. To investigate the effects of C9orf72 haploinsufficiency, I conducted single nucleus RNA-seq (snRNA-seq) and single nucleus assay for transposase-accessible chromatin sequencing (snATAC-seq) on hippocampus and frontal cortex tissues from C9orf72 heterozygous-knockout (C9-HET) and homozygous-knockout (C9-KO) mice. In the hippocampus, C9-HET mice showed downregulation of excitotoxicity-related pathways in neurons, whereas C9-KO mice showed upregulation. In the frontal cortex, C9-KO showed dysregulation of protein folding, ATP synthesis, and epigenomic dysregulation in glial cells and layer 2-3 excitatory neurons, highlighting a potential vulnerability of specific cell types. This multiome analysis provides insights into the molecular mechanisms underlying C9orf72-related ALS and FTD.
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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 | 0.000 | 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 | 0.001 |
| 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".