Reelin expression in brain endothelial cells: an electron microscopy study
Bibliographic record
Abstract
BACKGROUND: Reelin expression and function have been extensively studied in the brain, although its expression has been also reported in other tissues including blood. This raises the possibility that reelin might be able to cross the blood-brain barrier, which could be functionally relevant. Up-to-date no studies have been conducted to assess if reelin is present in the blood-brain barrier, which is mainly constituted by tightly packed endothelial cells. In this report we assessed the expression of reelin in brain capillaries using immunocytochemistry and electron microscopy. RESULTS: At the light microscope, reelin immunolabeling appeared in specific endothelial cells in brain areas that presented abundant diffuse labeling for this protein (e.g., layer I of the cortex, or the stratum lacunosum moleculare of the hippocampus), while it was mostly absent from capillaries in other brain areas (e.g., deeper cortical layers, or the CA1 layer of the hippocampus). As expected, at the electron microscope reelin labeling was observed in neurons of the cortex, where most of the labeling was associated with the rough endoplasmic reticulum. Importantly, reelin was also observed in some endothelial cells located in small capillaries, which confirmed the findings obtained at the light microscope. In these cells, reelin labeling was located primarily in caveolae (i.e., vesicles of transcytosis), and associated with the plasma membrane of the luminal side of endothelial cells. In addition, some scarce labeling was observed in the nuclear membrane. CONCLUSIONS: The presence of reelin immunolabeling in brain endothelial cells, and particularly in caveolar vesicles within these cells, suggests that reelin and/or reelin peptides may be able to cross the blood-brain barrier, which could have important physiological, pathological, and therapeutic implications.
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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 | 0.000 |
| 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".