ArticleBiology2026
Refined Mapping of Endothelial Cells, Brain Pericytes and Astrocytes Proteomes via Subcellular Fractionation and DDA Mass Spectrometry.
Article in Biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Proteomic profiling of brain microvascular cells, i.e., endothelial cells, brain pericytes, and glial cells, is essential for understanding their distinct roles in the neurovascular unit. In this study, we present a comparative data-dependent acquisition (DDA) analysis of endothelial cells (ECs), brain pericytes (BPs), and astrocytes (ACs), using both whole-cell lysates (WCL) and subcellular fractionation. Analysis of the WCL identified 2000-2300 proteins per cell type, revealing distinct biological signatures consistent with the known function of each cell type. Compared to the standard WCL protocol, subcellular fractionation significantly increased the depth of the proteome (by up to 93%), particularly for membrane and insoluble proteins. Functional enrichment analysis revealed sharper biological associations: angiogenesis in ECs, cell cycle regulation in BPs, and lipid metabolism in ACs. However, the results also revealed that the sampling method impacts the interpretation of cell-type specificity. For example, a disintegrin and metalloprotease 10 (ADAM10) appeared EC-specific in WCL data but was broadly detected after fractionation. Likewise, Glial Fibrillary Acidic Protein (GFAP) was unexpectedly identified in ECs, reflecting its transient expression in immature cells. To overcome such biases, we generated an integrative reference proteome for each cell type by combining data from different sampling strategies. These include 3605 proteins for ECs, 3378 for BPs, and 3976 for ACs, with consistent representation of membrane-associated sub-proteomes. This study highlights the importance of integrating sampling methods and considering cell differentiation status when interpreting proteomic data. It also provides valuable reference datasets for future neurovascular research.
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