Spatial Proteomics Using S4P.

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. Key features • Achieves whole-tissue slice coverage for spatial proteomics mapping. • Enables reconstruction of spatial protein distribution using sparse sampling with multi-angle strip projections. • Combines mass spectrometry-based proteomics with deep learning-based image reconstruction. • Reduces required mass spectrometry time by 50%-90% compared to gridding-based approaches.