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

Abstract

Abstract 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 an open-source end-to-end workflow within a community-driven ecosystem. Here we introduce openDVP, an experimental and computational framework for accessible and scalable DVP. OpenDVP combines open-source image analysis tools (MCMICRO, QuPath and napari) with scverse formats (AnnData and SpatialData) for multiomics integration. It provides two workflows: a fast-track pipeline requiring no image analysis expertise, and an artificial intelligence-powered pipeline incorporating state-of-the-art methods for image preprocessing, segmentation and spatial analysis. We apply openDVP across three archival tissue studies (human placenta, early stage lung cancer and locally relapsed breast cancer), integrating spatial single-cell phenotypes with exploratory proteomic measurements. Finally, we introduce deep proteomic profiling of cellular neighborhoods as a scalable approach for spatial discovery proteomics.