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July 16th, 2025
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Max Delbrueck Center, Berlin, Germany
systems biology
biorxiv

OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics

Nimo, J.Open in Google Scholar•Fritzsche, S.Open in Google Scholar•Valdes, D. S.Open in Google Scholar•Trinh, M. T.Open in Google Scholar•Pentimalli, T. M.Open in Google Scholar•Schallenberg, S.Open in Google Scholar•Klauschen, F.Open in Google Scholar•Herse, F.Open in Google Scholar•Florian, S.Open in Google Scholar•Rajewsky, N.Open in Google Scholaret al.

Deep visual proteomics (DVP) is an emerging approach for cell type-specific and spatially resolved proteomics. However, its broad adoption has been constrained by the lack of open-source end-to-end workflows in a community-driven ecosystem. Here, we introduce openDVP, an experimental and computational framework for simplifying and democratizing DVP. OpenDVP integrates open-source software for image analysis, including MCMICRO, QuPath, and Napari, and uses scverse data formats AnnData and SpatialData for multi-omics integration. It offers two workflows: a fast-track pipeline requiring no image analysis expertise and an artificial intelligence (AI)-powered pipeline with recent algorithms for image pre-processing, segmentation, and spatial analysis. We demonstrate openDVP\'s versatility in three archival tissue studies, profiling human placenta, early-stage lung cancer, and locally relapsed breast cancer. Our framework provided insights into health and disease states by connecting spatial single-cell phenotypes with exploratory proteomic data. Finally, we introduce deep proteomic profiling of cellular neighborhoods as a scalable approach to accelerate spatial discovery proteomics across biological systems.

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