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

Authors

  • Jose Nimo
  • Sonja Fritzsche
  • Daniela S. Valdes
  • Minh Tien Trinh
  • Tancredi Pentimalli
  • Simon Schallenberg
  • Frederick Klauschen
  • Florian Herse
  • Stefan Florian
  • Nikolaus Rajewsky
  • Fabian Coscia

Journal

  • bioRxiv

Citation

  • bioRxiv

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 openDVP's 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.


DOI

doi:10.1101/2025.07.13.662099