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

Nimo, J.; Fritzsche, S.; Valdes, D. S.; Trinh, M. T.; Pentimalli, T. M.; Schallenberg, S.; Klauschen, F.; Herse, F.; Florian, S.; Rajewsky, N.; Coscia, F.

doi:10.1101/2025.07.13.662099

bioRxiv · 10.1101/2025.07.13.662099

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

Nimo, J.·Fritzsche, S.·Valdes, D. S.·Trinh, M. T.·Pentimalli, T. M.·Schallenberg, S.·Klauschen, F.·Herse, F.·Florian, S.·Rajewsky, N.·Coscia, F.

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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 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 the 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 openDVPs versatility in three archival tissue studies, profiling human placenta, early-stage lung cancer, and locally relapsed breast cancer. In each study, our framework provided insights into health and disease states by integrating 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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Nimo, J., Fritzsche, S., Valdes, D. S., Trinh, M. T., Pentimalli, T. M., Schallenberg, S., Klauschen, F., Herse, F., Florian, S., Rajewsky, N., Coscia, F.. 2025-07-16. OpenDVP: An experimental and computational framework for community-empowered deep visual proteomics. https://doi.org/10.1101/2025.07.13.662099

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