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Isolation of vascular nuclei from frozen post-mortem brain tissue

Post-mortem fresh-frozen hippocampus and superior frontal cortex tissue were obtained from the Stanford/VA/NIA Aging Clinical Research Center (ACRC) with approval from local ethics committees and patient consent. Group characteristics are presented in Supplementary Table 1. Individuals were grouped by clinical diagnosis, with two of the individuals with NCI exhibiting β-amyloid plaque staining in the hippocampus, although not to a sufficient degree for an expert pathologist to diagnose AD by histopathological criteria. Clinical instead of pathological diagnosis was chosen because of potentially vascular contributions to AD independent of the well-known hallmarks of AD, β-amyloid and tau pathophysiology6. All procedures were carried out on ice in a 4 °C cold room as rapidly as possible. Brain tissue (0.3 grams or more) was thawed on ice for 5 min with 5 ml of nuclei buffer (NB): 1% BSA containing 0.2 U μl−1 RNase inhibitor (Takara, 2313A) and EDTA-free protease inhibitor cocktail (Roche, 11873580001). Tissue was quickly minced and homogenized with 7-ml glass douncers (357424, Wheaton) until no visible chunks of debris remained. Similar to before52, homogenates were transferred into 50-ml tubes containing 35 ml of chilled 32% dextran (D8821, Sigma) in HBSS. Samples were vigorously mixed before centrifugation at 4,400g for 20 min with no brake. After centrifugation, samples separate into a top myelin layer, middle parenchymal layer and vascular-enriched pellet. The myelin layer was aspirated, tips were changed and the parenchymal layer was carefully removed without disturbing the pellet. Pellets were resuspended in 8 ml of 32% dextran, transferred to 15-ml falcon tubes, and centrifuged again. Vascular-enriched pellets were gently resuspended in 1 ml of NB and added to pre-wetted 40-μm strainers sitting on top of 50-ml falcon tubes. From here diverging from prior protocol, strainers were washed with 10 ml of cold 0.32 M sucrose in PBS and 90 ml PBS until flow through the strainers was unimpeded to deplete contaminating parenchymal cells from trapped microvessels. At this step, retained microvessels turn white in colour, indicating the removal of circulating blood cells. Strainers were switched to new collection 50-ml falcon tubes. Various techniques were tested and optimized to extract vascular cells from the isolated microvessels (for example, enzymatic digestion, TisssueRuptor, sonication and so on), but nearly all resulted in loss of nuclei integrity or low nuclei complexity (fewer than 50 median genes per nucleus). Eventually, adapting a method for the isolation of mouse splenocytes proved successful: vascular fragments were mashed four times through the cell strainer using the plunger end of a 3-ml syringe, with intermittent elution via 10 ml of 0.32 M sucrose and 40 ml of PBS. Liberated vascular cells were pelleted at 500g for 10 min and resuspended in 1.5 ml of EZ Prep Lysis Buffer (Sigma, NUC101) spiked with 0.2 U μl−1 RNase inhibitor (Takara, 2313A) and EDTA-free protease inhibitor cocktail (Roche, 11873580001). Nuclei were homogenized with 2-ml glass douncers (D8938, Sigma) 20 times with pestle B (pestle A optional). Spiked EZ lysis buffer was added to samples up to 4 ml and incubated on ice for 5 min before pelleting at 500g for 6 min. This incubation step was repeated. Debris was depleted via a sucrose gradient before flow cytometry isolation of nuclei. In brief, pelleted nuclei were resuspended in 0.5 ml of NB before the addition of 0.9 ml of 2.2 M sucrose in PBS. This mixture was layered on top of 0.5 ml of 2.2 M sucrose and samples were centrifuged at 14,000g for 45 min at 4 °C, with no brake. Pellets were aspirated in 1 ml of NB, filtered through a 40-μm strainer (Flowmi), transferred to FACS tubes, stained with Hoechst 3342 (1:2,000, Thermo Fisher Scientific) and rabbit monoclonal anti-NeuN Alexa Fluor 647 (1:500, Abcam, ab190565), and nuclei collected on a SH800S Cell Sorter into chilled tubes containing 1 ml of NB without protease inhibitor. In pilot runs, we noticed that the cytometer overestimated nuclei counts by around 3.4 times, and thus we sorted around 34,000 nuclei to target around 10,000 nuclei per sample. Sorted samples were inspected for lack of debris on a bright-field microscope. We note that an iodixanol gradient53 can substitute for the 2.2 M sucrose, but that unfortunately with either gradient, flow sorting is required—unlike parenchymal myelin debris, vascular debris is not sufficiently removed by gradient centrifugation alone. Vascular debris will confound downstream cDNA traces with higher background and low molecular weight peaks.

Droplet-based snRNA-seq

For droplet-based snRNA-seq, libraries were prepared using the Chromium Single Cell 3ʹ v3 according to the manufacturer’s protocol (10x Genomics), targeting 10,000 nuclei per sample after flow sorting (Sony SH800S Cell Sorter). Fifteen PCR cycles were applied to generate cDNA before 16 cycles for final library generation. Generated snRNA-seq libraries were sequenced on S4 lanes of a NovaSeq 6000 (150 cycles, Novogene).

snRNA-seq quality control

Gene counts were obtained by aligning reads to the hg38 genome (refdata-gex-GRCh38-2020-A) using CellRanger software (v.4.0.0) (10x Genomics). To account for unspliced nuclear transcripts, reads mapping to pre-mRNA were counted. As previously published, a cut-off value of 200 unique molecular identifiers (UMIs) was used to select single nuclei for further analysis16,54. As initial reference, the entire dataset was projected onto two-dimensional space using UMAP on the top 30 principal components55. Three approaches were combined for strict quality control: (1) outliers with a high ratio of mitochondrial (more than 5%, fewer than 200 features) relative to endogenous RNAs and homotypic doublets (more than 5,000 features) were removed in Seurat56; (2) after scTransform normalization and integration, doublets and multiplets were filtered out using DoubletFinder57; and (3) after DoubletFinder, nuclei were manually inspected using known cell-type-specific marker genes, with nuclei expressing more than one cell-type-specific marker further filtered16,18,57,58,59,60,61. For example, BEC nuclei containing any reads for the following cell type markers were subsequently filtered: MOBP, MBP, MOG, SLC38A11, LAMA2, PDGFRB, GFAP, SLC1A2 and AQP4. We note that the vascular nuclei in previous human single-cell datasets exhibit contamination with other cell-type-specific gene markers, which potentially confounds downstream analysis. After applying these filtering steps, the dataset contained 143,793 high-quality, single nuclei.

Cell annotations and differential gene expression analysis

Seurat’s integration function was used to align data with default settings. Genes were projected into principal component (PC) space using the principal component analysis (RunPCA). The first 30 dimensions were used as inputs into Seurat’s FindNeighbors, FindClusters (at 0.2 resolution) and RunUMAP functions. In brief, a shared-nearest-neighbour graph was constructed based on the Euclidean distance metric in PC space, and cells were clustered using the Louvain method. RunUMAP functions with default settings were used to calculate two-dimensional UMAP coordinates and search for distinct cell populations. Positive differential expression of each cluster against all other clusters (model-based analysis of single-cell transcriptomics; MAST) was used to identify marker genes for each cluster62. We annotated cell-types using previously published marker genes16,19,61,63. For BECs, zonation specificity scores for each gene were calculated separately for arterial, capillary, and venous segments as in the following example for a given gene in capillaries:

$${rm{Capillary}},{rm{specificity}},{rm{score}}={rm{Average}}left[log left(frac{{rm{Capillary}},{rm{logCPM}}}{{rm{Arterial}},{rm{logCPM}}}right),,mathrm{log}left(frac{{rm{Capillary}},{rm{logCPM}}}{{rm{Veinous}},{rm{logCPM}}}right)right].$$

Differential gene expression of genes comparing AD, ApoE4, and NCI samples—or comparing cell type subcluster markers—was done using the MAST62 algorithm, which implements a two-part hurdle model. Seurat natural log(fold change) > 0.5 (absolute value), adjusted P value (Bonferroni correction) < 0.01, and expression in greater than 10% of cells in both comparison groups were required to consider a gene differentially expressed for subcluster analysis and natural log(fold change) > 0.3 (absolute value), adjusted P value (Bonferroni correction) < 0.01, and expression in greater than 10% of cells in both comparison groups for AD and ApoE4 comparisons, both more stringent than the default Seurat settings. We incorporated age, gender and batch as covariates in our model. A more lenient threshold of the above but with natural log(fold change) > 0.2 (absolute value) was used for brain region (that is, hippocampus versus cortex). Biological pathway and gene ontology enrichment analyses were performed using Enrichr64 or Metascape65 with the input species set to Homo sapiens65. UpSet plots were generated using identified DEGs as inputs using the R package UpSetR66. Diagrams were created with BioRender.

Monocle trajectory analysis

Monocle was used to generate the pseudotime trajectory analysis in brain endothelial and mural cells24. Cells were clustered in Seurat and cluster markers used as input into Monocle to infer arteriovenous relationships within endothelial cells and pericytes. Specifically, UMAP embeddings and cell subclusters generated from Seurat were converted to a cell_data_set object using SeuratWrappers (v.0.2.0) and then used as input to perform trajectory graph learning and pseudotime measurement through independent component analysis (ICA) with Monocle. Cluster marker genes identified in Seurat were used to generate a pseudotime route and plotted using the ‘plot_pseudotime_heatmap’ function. For mural cells, variable genes were limited to those with log[average expression] > 1 (Seurat) for even more robust cell ordering.

Cell–cell communication

Cell–cell interactions based on the expression of known ligand–receptor pairs in different cell types were inferred using CellChatDB67 (v.0.02). In brief, we followed the official workflow and loaded the normalized counts into CellChat and applied the preprocessing functions ‘identifyOverExpressedGenes’, ‘identifyOverExpressedInteractions’ and ‘projectData’ with standard parameters set. As database we selected the ‘Secreted Signaling’ pathways and used the pre-compiled human ‘Protein-Protein-Interactions’ as a priori network information. For the main analyses the core functions ‘computeCommunProb’, ‘computeCommunProbPathway’ and ‘aggregateNet’ were applied using standard parameters and fixed randomization seeds. Finally, to determine the senders and receivers in the network the function ‘netAnalysis_signalingRole’ was applied on the ‘netP’ data slot.


Aged C57BL/6 male mice (19 months old) were obtained from the National Institute on Aging rodent colony. Young male C57BL/6 mice (3 months old) were obtained from The Jackson Laboratory or Charles River Laboratories. Thy1-hAPPLon,Swe male mice and littermate wild-type control37 mice were raised until 12–14 months of age. All mice were kept on a 12-h light–dark cycle and provided ad libitum access to food and water. All animal care and procedures complied with the Animal Welfare Act and were in accordance with institutional guidelines and approved by the V.A. Palo Alto Committee on Animal Research and the institutional administrative panel of laboratory animal care at Stanford University.

Mouse wild-type and APP T41B BEC single-cell and nuclei sequencing

Whole cell isolation from the central nervous system followed previously described methods68,69,70. In brief, cortices and hippocampi were microdissected, minced and digested using the Neural Dissociation Kit (Miltenyi). Suspensions were filtered through a 100-µm strainer and myelin removed by centrifugation in 0.9 M sucrose. The remaining myelin-depleted cell suspension was blocked for 10 min with Fc preblock (CD16/CD32, BD 553141) on ice and stained for 20 min with antibodies to distinguish BECs (CD31+/CD45). BECs from 12–14-month-old Thy1-hAPPLon,Swe mice and littermate wild-type control37 mice (pool of 4–6 mice per group) were sorted into PBS with 0.1% BSA. Nuclei isolation from 4–6 month-old mouse hippocampi followed protocols adapted from previous studies16,17,53,61,71. In brief, tissue was homogenized using a glass douncer in 2 ml of ice-cold EZ PREP buffer (Sigma, N3408) and incubated on ice for 5 min. Centrifuged nuclei were resuspended in 1% BSA in PBS with 0.2 U μl−1 RNase inhibitor and filtered through a 40-μm cell strainer. Cells or nuclei were immediately counted using a Neubauer haemocytometer and loaded on a Chromium Single-Cell Instrument (10x Genomics) to generate single-cell gel-bead in emulsions (GEMs). The 10x Genomics v3 libraries were prepared as per the manufacturer’s instructions. Libraries were sequenced on an Illumina NextSeq 550 (paired-end; read 1: 28 cycles; i7 index: 8 cycles, i5 index: 0 cycles; read 2: 91 cycles). De-multiplexing was performed using the Cellranger toolkit (v.3.0.0) ‘cellranger mkfastq’ command and the ‘cellranger count’ command for alignment to the mouse transcriptome, cell barcode partitioning, collapsing UMIs to transcripts, and gene-level quantification. Around 70% sequencing saturation (more than 20,000 reads per cell) was achieved, for a median of around 2,000 genes detected per cell and around 16,500 genes detected in total. Downstream analysis using the Seurat package (v.3)72 was performed as previously described12, applying standard algorithms for cell filtration, feature selection and dimensionality reduction. Samples with fewer than 1,000 and more than 4,000 unique feature counts, samples with more than 15% mitochondrial RNA, samples with more than 15% small subunit ribosomal genes (Rps), and counts of more than 10,000 were excluded from the analysis. Genes were projected into PC space using the principal component analysis (RunPCA). The first 30 dimensions were used as inputs into Seurat’s FindNeighbors and RunTsne functions. In brief, a shared-nearest-neighbour graph was constructed based on the Euclidean distance metric in PC space, and cells were clustered using the Louvain method. RunTsne functions with default settings was used to calculate two-dimensional t-distributed stochastic neighbour embedding (t-SNE) coordinates and search for distinct cell populations. Cells and clusters were then visualized using three-dimensional t-SNE embedding on the same distance metric. Differential gene expression analysis was done by applying MAST. Significant DEGs in Thy1-hAPPLon,Swe BECs were called by log(fold change) > 0.15 (absolute value), adjusted P value (Bonferroni correction) < 0.01. This lowered log(fold change) was to ensure our claims of limited overlap with human AD BECs were robust.

GWAS analysis

For calculation of proportional cell-type-specific gene expression, we followed the EWCE method described in a previous study42, and used previously on human snRNA-seq data17. For AD analysis, we compiled a list of top GWAS risk genes from ref. 39, ref. 40 and ref. 41, sorted descending by approximate P value. The expression of each gene sums to 1 across the cell types, with each heat map cell showing the fraction of total gene expression as determined from EWCE analysis. The set of 720 AD and AD-related trait GWAS genes were obtained from ref. 17, and using EWCE analysis, the strongest expressing cell type was determined for each gene. The original list was slightly parsed to 720, as several genes were not detected as expressed in our dataset.


Fresh-frozen human brain tissue from individuals with NCI and from individuals with AD (hippocampus and superior frontal cortex adjacent to tissue processed for snRNA-seq as well as meninges) was subjected to immunohistochemistry (IHC). Ten-micrometre sections mounted on SuperFrost Plus glass slides were fixed with 4% paraformaldehyde (Electron Microscopy Services, 15714S) diluted in PBS at 4 °C for 15 min before dehydration via an ethanol series or air drying. Sections were blocked in TBS++ (TBS +  3% donkey serum (130787, Jackson ImmunoResearch) + 0.25% Triton X-100 (T8787, Sigma-Aldrich)) for 1.5 h at room temperature. Sections were incubated with primary antibodies at 4 °C overnight: goat polyclonal anti-collagen type IV (1:200, AB769, Sigma), rabbit polyclonal anti-CYP1B1 (1:100, HPA026863, Atlas Antibodies), rabbit polyclonal anti-SLC4A4 (1:100, HPA035628, Atlas Antibodies), rabbit polyclonal anti-SLC47A1 (1:100, HPA021987, Atlas Antibodies), rabbit polyclonal anti-ABCA8 (1:100, HPA044914, Atlas Antibodies), mouse monoclonal anti-CD31 (1:100, JC70A, Dako), rabbit polyclonal anti-VWF (1:100, GA527, Dako), rabbit polyclonal anti-SLC39A10 (1:100, HPA066087, Atlas Antibodies), rabbit polyclonal anti-ALPL (1:100, HPA007105, Atlas Antibodies), rabbit polyclonal anti-A2M (1:100, HPA002265, Atlas Antibodies), rabbit monoclonal anti-β-amyloid (1:500, clone D54D2 XP, CST) and mouse monoclonal anti-actin, α-smooth muscle–Cy3 (1:100, clone 1A4, Sigma). Sections were washed, stained with Alexa Fluor-conjugated secondary antibodies (1:250) and Hoechst 33342 (1:2,000, H3570, Thermo Fisher Scientific), mounted and coverslipped with ProLong Gold (Life Technologies) or VECTASHIELD (Vector Laboratories) before imaging on a confocal laser scanning microscope (Zeiss LSM880). Age-related autofluorescence was quenched before mounting with Sudan Black B, as before8,70. National Institutes of Health ImageJ software was used to quantify the number of Hoechst+ nuclei per image, the percentage of vasculature (collagen IV), the number of Hoechst+ nuclei within collagen IV+ vasculature, or the predicted DEG SLC39A10 among CD31+ vasculature, following previously described protocols8,15,73. In short, at least five images were stained per patient, and imaging and analyses were performed by a blinded observer.

Statistics and reproducibility

Immunostaining validation experiments were repeated independently at least twice with similar results. As indicated in the figure legends, some immunostaining images come from the Human Protein Atlas25,74 and are available at

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

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