Single-cell transcriptomic analysis reveals age-related remodeling of brain endothelial cells
Bibliographic record
Abstract
Blood–brain barrier (BBB) integrity naturally declines with age. Brain endothelial cells (ECs) and pericytes (PCs) form the BBB, and aging impairs tight junctions, likely via altered PC-to-EC signaling. However, the molecular mechanisms underlying this impairment remain unclear. Using single-cell RNA sequencing, we profiled 68,316 brain ECs expressing 15,564 genes from young and old mice. Unsupervised clustering and annotation revealed five distinct EC subtypes—Capillary EC1, Capillary EC2, Arterial EC, Venous EC1, and Venous EC2—defined by marker genes Mfsd2a, Plvap, Bmx, Nr2f2, and Vcam1, respectively. Aging shifted EC subtype distribution, with reduced Capillary EC1 (45% vs. 57%) and increased Arterial (33% vs. 16%) and Venous ECs (12% vs. 2%) compared with young mice. Mio analysis further showed that Capillary EC1 and Venous EC2 neighborhoods were less abundant in aged brains. Biotin metabolism was decreased in old vs. young mice, particularly within Capillary EC1, Capillary EC2, and Arterial EC. Although widespread gene downregulation was observed across EC subsets, overall expression trends were largely consistent among clusters. Key genes—Ramp2, Hbb-bs, Ly6c1, Calm1—were less abundant, whereas Rasgrf2 was uniquely enriched in aged mice. Immunohistochemistry confirmed reduced LY6C and RAMP2 and elevated RASGRF2 in aged mouse and human brains. Cell–cell interaction analyses revealed fewer ligand–receptor interactions in aged mice, suggesting weakened intercellular communication. Enrichment analyses implicated pathways involved in neurovascular integrity, inflammation, amyloid processing, and vascular remodeling. Collectively, these findings show that aging reprograms EC subtype composition, gene expression, and metabolism, thereby contributing to BBB disruption and neurovascular dysfunction.
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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 | 0.001 |
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".