Single-cell multi-region dissection of brain vasculature in Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Cerebrovascular breakdown occurs early in Alzheimer’s Disease (AD), but its cell-type-specific molecular basis remains uncharacterized. Here, we characterize single-cell transcriptomic differences in human cerebrovasculature across 220 AD and 208 control individuals and across 6 brain regions. We annotate 22,514 cerebrovascular cells in 11 subtypes of endothelial, pericyte, smooth muscle, perivascular fibroblast, and ependymal cells, and how they differ in abundance and gene expression between brain regions. We identify 2,676 AD-differential genes, including lower expression of PDGFRB in pericytes, and ABCB1 and ATP10A in endothelial cells. These AD-differential genes reveal common upstream regulators, including MECOM, EP300, and KLF4, whose targeting may help restore vasculature function. We find coordinated vasculature-glial-neuronal co-expressed gene modules supported by ligand-receptor pairs, involved in axon growth/degeneration and neurogenesis, suggesting mechanistic mediators of neurovascular unit dysregulation in AD. Integration with AD genetics reveals 125 AD-differential genes directly linked to AD-associated genetic variants (through vasculature-specific eQTLs, Hi-C, and correlation-based evidence), 559 targeted by AD-associated regulators, and 661 targeted by AD-associated ligand-receptor signaling. Lastly, we show that APOE4-genotype associated differences are significantly enriched among AD-associated genes in capillary and venule endothelial cells, and subsets of pericytes and fibroblasts, which underlie the vascular dysregulation in APOE4-associated cognitive decline. Overall, our multi-region molecular atlas of differential human cerebrovasculature genes and pathways in AD can help guide early-stage AD therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".