Single-cell transcriptomics unveils molecular signatures of neuronal vulnerability in a mouse model of prion disease that overlap with Alzheimer’s disease
Bibliographic record
Abstract
Understanding why certain neurons are more sensitive to dysfunction and death caused by misfolded proteins could provide therapeutically relevant insights into neurodegenerative disorders. Here, we harnessed single-cell transcriptomics to examine live neurons isolated from prion-infected female mice, aiming to identify and characterize prion-vulnerable neuronal subsets. Our analysis revealed distinct transcriptional responses across neuronal subsets, with a consistent pathway-level depletion of synaptic gene expression in damage-vulnerable neurons. By scoring neuronal damage based on the magnitude of depleted synaptic gene expression, we identified a diverse spectrum of prion-vulnerable glutamatergic, GABAergic, and medium spiny neurons. Comparison between prion-vulnerable and resistant neurons highlighted baseline gene expression differences that could influence neuronal vulnerability. For instance, the neuroprotective cold-shock protein Rbm3 exhibited higher baseline gene expression in prion-resistant neurons and was robustly upregulated across diverse neuronal classes upon prion infection. We also identified vulnerability-correlated transcripts that overlapped between prion and Alzheimer's disease. Our findings not only demonstrate the potential of single-cell transcriptomics to identify damage-vulnerable neurons, but also provide molecular insights into neuronal vulnerability and highlight commonalties across neurodegenerative disorders.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".