Systems Biology

Spatial proteomics enables the mapping of protein distribution within tissues, which is crucial for understanding cellular functions in their native context. While spatial transcriptomics has seen rapid advancement, spatial proteomics faces challenges due to protein non-amplifiability and mass spectrometry sensitivity limitations. This protocol describes a sparse sampling strategy for spatial proteomics (S4P) that combines multi-angle tissue strip microdissection with deep learning–based image reconstruction. The method achieves whole-tissue slice coverage with significantly reduced sampling requirements, enabling mapping of over 9,000 proteins in mouse brain tissue at 525 μm resolution within 200 h of mass spectrometry time. Key advantages include reduced sample processing time, deep proteome coverage, and applicability to centimeter-sized tissue samples.

The cellular compartments of eukaryotic cells are defined by their specific protein compositions. Different strategies are used for the identification of the subcellular proteomes, such as fractionation by differential centrifugation of cellular extracts. The localization of mitochondrial proteins is particularly challenging, as mitochondria consist of two membranes of different protein composition and two aqueous subcompartments, the intermembrane space (IMS) and the matrix. Previous studies identified subcompartment-specific proteomes by using combinations of hypotonic swelling and protease digestion followed by mass spectrometry. Here, we present an alternative, more unbiased method to identify the proteomes of mitochondrial subcompartments by use of an improved ascorbate peroxidase (APEX2) that is targeted to the IMS and the matrix. This method allows the subcompartment-specific labeling of proteins in mitochondria isolated from cells of the baker’s yeast Saccharomyces cerevisiae, followed by their purification on streptavidin beads. With this method, the proteins located in the different mitochondrial subcompartments of yeast cells can be efficiently and comprehensively identified.

Brain endothelial cells, which constitute the cerebrovasculature, form the first interface between the blood and brain and play essential roles in maintaining central nervous system (CNS) homeostasis. These cells exhibit strong apicobasal polarity, with distinct luminal and abluminal membrane compositions that crucially mediate compartmentalized functions of the vasculature. Existing transcriptomic and proteomic profiling techniques often lack the spatial resolution to discriminate between these membrane compartments, limiting insights into their distinct molecular compositions and functions. To overcome these limitations, we developed an in vivo proteomic strategy to selectively label and enrich luminal cerebrovascular proteins. In this approach, we perfuse a membrane-impermeable biotinylation reagent into the vasculature to covalently tag cell surface proteins exposed on the luminal side. This is followed by microvessel isolation and streptavidin-based enrichment of biotinylated proteins for downstream mass spectrometry analysis. Using this method, we robustly identified over 1,000 luminally localized proteins via standard liquid chromatography–tandem mass spectrometry (LC–MS/MS) techniques, achieving substantially improved enrichment of canonical luminal markers compared with conventional vascular proteomic approaches. Our method enables the generation of a high-confidence, compartment-resolved atlas of the luminal cerebrovascular proteome and offers a scalable platform for investigating endothelial surface biology in both healthy and disease contexts.