Isolation of endothelial cells, pericytes and astrocytes from mouse brain
Bibliographic record
Abstract
Primary cell isolation from the central nervous system (CNS) has allowed fundamental understanding of blood-brain barrier (BBB) properties. However, poorly described isolation techniques or suboptimal cellular purity has been a weak point of some published scientific articles. Here, we describe in detail how to isolate and enrich, using a common approach, endothelial cells (ECs) from adult mouse brains, as well as pericytes (PCs) and astrocytes (ACs) from newborn mouse brains. Our approach allowed the isolation of these three brain cell types with purities of around 90%. Furthermore, using our protocols, around 3 times more PCs and 2 times more ACs could be grown in culture, as compared to previously published protocols. The cells were identified and characterized using flow cytometry and confocal microscopy. The ability of ECs to form a tight monolayer was assessed for passages 0 to 3. The expression of claudin-5, occludin, zonula occludens-1, P-glycoprotein-1 and breast cancer resistance protein by ECs, as well as the ability of the cells to respond to cytokine stimuli (TNF-α, IFN-γ) was also investigated by q-PCR. The transcellular permeability of ECs was evaluated in the presence of pericytes or astrocytes in a Transwell® model by measuring the transendothelial electrical resistance (TEER), dextran-FITC and sodium fluorescein permeability. Overall, ECs at passages 0 and 1 featured the best properties valued in a BBB model. Furthermore, pericytes did not increase tightness of EC monolayers, whereas astrocytes did regardless of their seeding location. Finally, ECs resuspended in fetal bovine serum (FBS) and dimethyl sulfoxide (DMSO) could be cryopreserved in liquid nitrogen without affecting their phenotype nor their capacity to form a tight monolayer, thus allowing these primary cells to be used for various longitudinal in vitro studies of the blood-brain barrier.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".