Single-cell analysis of mesenchymal cells in permeable neural vasculature reveals novel diverse subpopulations of fibroblasts
Bibliographic record
Abstract
Abstract Background In the choroid plexus and pituitary gland, vasculature is known to have a permeable, fenestrated phenotype which allows for the free passage of molecules in contrast to the blood brain barrier observed in the rest of the CNS. The endothelium of these compartments, along with secretory, neural-lineage cells (choroid epithelium and pituitary endocrine cells) have been studied in detail, but less attention has been given to the perivascular mesenchymal cells of these compartments. Methods The Hic1CreERT2 Rosa26LSL−TdTomato mouse model was used in conjunction with a PdgfraH2B−EGFP mouse model to examine mesenchymal cells, which can be subdivided into Pdgfra+ fibroblasts and Pdgfra− pericytes within the choroid plexus (CP) and pituitary gland (PG), by histological, immunofluorescence staining and single-cell RNA-sequencing analyses. Results We found that both CP and PG possess substantial populations of distinct Hic1+ mesenchymal cells, including an abundance of Pdgfra+ fibroblasts. Within the pituitary, we identified distinct subpopulations of Hic1+ fibroblasts in the glandular anterior pituitary and the neurosecretory posterior pituitary. We also identified multiple distinct markers of CP, PG, and the meningeal mesenchymal compartment, including alkaline phosphatase, indole-n-methyltransferase and CD34. Conclusions Novel, distinct subpopulations of mesenchymal cells can be found in permeable vascular interfaces, including the CP, PG, and meninges, and make distinct contributions to both organs through the production of structural proteins, enzymes, transporters, and trophic molecules.
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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) | 0.001 | 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".