The cell type resolved mouse transcriptome in neuron-enriched brain tissues from the hippocampus and cerebellum during prion disease
Bibliographic record
Abstract
Multiple cell types and complex connection networks are an intrinsic feature of brain tissue. In this study we used expression profiling of specific microscopic regions of heterogeneous tissue sections isolated by laser capture microdissection (LCM) to determine insights into the molecular basis of brain pathology in prion disease. Temporal profiles in two mouse models of prion disease, bovine spongiform encephalopathy (BSE) and a mouse-adapted strain of scrapie (RML) were performed in microdissected regions of the CA1 hippocampus and granular layer of the cerebellum which are both enriched in neuronal cell bodies. We noted that during clinical disease the number of activated microglia and astrocytes that occur in these areas are increased, thereby likely diluting the neuronal gene expression signature. We performed a comparative analysis with gene expression profiles determined from isolated populations of neurons, microglia and astrocytes to identify transcripts that are enriched in each of these cell types. Although the incubation periods of these two models are quite different, over 300 days for BSE and ~160 days for RML scrapie, these regional microdissections revealed broadly similar profiles. Microglial and astrocyte-enriched genes contributed a profound inflammatory profile consisting of inflammatory cytokines, genes related to phagocytosis, proteolysis and genes coding for extracellular matrix proteins. CA1 pyramidal neurons displayed a net upregulation of transcription factors and stress induced genes at pre-clinical stages of disease while all tissues showed profound decrease of overlapping genes related to neuronal function, in particular transcripts related to neuronal communication including glutamate receptors, phosphatase subunits and numerous synapse-related markers. Of note, we found a small number of genes expressed in neurons that were upregulated during clinical disease including, COX6A2, FZD9, RXRG and SOX11, that may be biomarkers of 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".