Single Cell: Brain Vasculature In Alzheimer's Disease
Single-cell multi-region dissection of brain vasculature in Alzheimer’s Disease
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, pervascular 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.
Figure 1 Brain vasculature characterization across six brain regions
Proportion / cell numbers across different cell information
In this tab, users can visualise the composition of single cells based on one discrete
cell information across another discrete cell information.
Usage examples include the library or cellcycle composition across clusters.
In this tab, users can visualise the gene expression patterns of
multiple genes grouped by categorical cell information (e.g. library / cluster).
The normalised expression are averaged, log-transformed and then plotted.