Single-cell dissection of the human brain vasculature

Abstract

Despite the importance of the blood-brain barrier in maintaining normal brain physiology and in understanding neurodegeneration and CNS drug delivery, human cerebrovascular cells remain poorly characterized due to their sparsity and dispersion. Here, we perform the first single-cell characterization of the human cerebrovasculature using both ex vivo fresh-tissue experimental enrichment and post mortem in silico sorting of human cortical tissue samples. We capture 31,812 cerebrovascular cells across 17 subtypes, including three distinct subtypes of perivascular fibroblasts as well as vasculature-coupled neurons and glia. We uncover human-specific expression patterns along the arteriovenous axis and determine previously uncharacterized cell type-specific markers. We use our newly discovered human-specific signatures to study changes in 3,945 cerebrovascular cells of Huntington's disease patients, which reveal an activation of innate immune signaling in vascular and vasculature-coupled cell types and the concomitant reduction to proteins critical for maintenance of BBB integrity. Finally, our study provides a comprehensive resource molecular atlas of the human cerebrovasculature to guide future biological and therapeutic studies.

Single-cell RNA sequencing reveals dysregulation of spinal cord cell types in a severe spinal muscular atrophy mouse model

“…CD31 and Col3a1 were nearly 100% co-localized (Fig. 6G), indicating that vascular endothelial cells and fibroblasts coexisted in a spatial and functionally relevant manner, consistent with fibroblasts' potential role as vessel supporting cell [51,52]. Notably, along with the reduction of CD31 in the spinal cord of SMA at P4 [21], the expression of Col3a1 was dramatically decreased (Fig.…”

Huntington’s Disease Produces Multiplexed Transcriptional Vulnerabilities of Striatal D1-D2 and Striosome-Matrix Neurons

“…3), which seems a class of striatal interneuron according to our preliminary data (Pineda et al, in preparation). These cell types, identified by ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] , co-clustered perfectly atop each other between controls and the HD samples (magenta and cyan dots, Fig. 1A-C), affirming the consistency of cell-type annotations across phenotypes.…”

“…We analyzed snRNA-seq data by using ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (Extended Data Fig. 1), from striatal samples harvested from human striatum and from R6/2 and zQ175 HD model mice, and originally reported in an initial study without attention to the coordinated compartmental transcriptomics examined here (Fig.…”

“…We used the ACTIONet and scran R packages to normalize, batch correct, and cluster single-nucleus gene counts. Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph.…”

“…Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph. A modified version of the stochastic gradient descent-based layout method was used in the uniform manifold approximation and projection (UMAP) algorithm 59 , to visualize the ACTIONet graph.…”