Dysregulation of neuroprotective astrocytes, a spectrum of microglial activation states, and altered hippocampal neurogenesis are revealed by single-cell RNA sequencing in prion disease
Bibliographic record
Abstract
Prion diseases are neurodegenerative disorders with long asymptomatic incubation periods, followed by a rapid progression of cognitive and functional decline culminating in death. The complexity of intercellular interactions in the brain is challenging to unravel and the basis of disease pathobiology remains poorly understood. In this study, we employed single cell RNA sequencing (scRNAseq) to produce an atlas of 147,536 single cell transcriptomes from cortex and hippocampus of mice infected with prions and showing clinical signs. We identified transcriptionally distinct populations and sub-populations of all the major brain cell-types. Disease-related transcription was highly specific to not only overarching cell-types, but also to sub-populations of glia and neurons. Most striking was an apparent decrease in relative frequency of astrocytes expressing genes that are required for brain homeostasis such as lipid synthesis, glutamate clearance, synaptic modulation and regulation of blood flow. Additionally, we described a spectrum of microglial activation states that suggest delineation of phagocytic and neuroinflammatory functions in different cell subsets. Differential responses of immature and mature neuron populations were also observed, alongside abnormal hippocampal neurogenesis. Our scRNAseq library provides a new layer of knowledge on single cell gene expression in prion disease, and is a basis for a more detailed understanding of cellular interplay that leads to neurodegeneration.
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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 | 0.000 |
| 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".