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Peer-reviewedCC BY 4.0Nature Methods

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

  • Jose Nimo1,2,3
  • Sonja Fritzsche1,4
  • Daniela S. Valdes1
  • Minh Trinh1
  • Tancredi Pentimalli2,5
  • Melissa Klingeberg1,4
  • Simon Schallenberg3,6
  • Frederick Klauschen7,8,9
  • Florian Herse2,10,11
  • Stefan Florian3,6
  • Nikolaus Rajewsky2,3,5,12,13,14,15,16,17
  • Fabian Coscia1,3
17 affiliations
  1. 1Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Spatial Proteomics Group, Berlin, Germany
  2. 2Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
  3. 3German Cancer Consortium (DKTK), Partner Site Berlin, Heidelberg, Germany
  4. 4Humboldt-Universität zu Berlin, Institute of Biology, Berlin, Germany
  5. 5Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
  6. 6Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
  7. 7Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany
  8. 8German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
  9. 9Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
  10. 10Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany
  11. 11Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
  12. 12Cluster of Excellence NeuroCure, Berlin, Germany
  13. 13German Center for Cardiovascular Research (DZHK) Partner Site Berlin, Berlin, Germany
  14. 14National Center for Tumor Diseases Partner Site Berlin, Berlin, Germany
  15. 15German Center for Child and Adolescent Health (DZKJ) Partner Site Berlin, Berlin, Germany
  16. 16ImmunoPreCept Cluster of Excellence, Berlin, Germany
  17. 17Einstein Center for Early Disease Interception, Berlin, Germany
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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 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.

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Cite

BibTeXRIS
BibTeX
@article{nimo2026opendvp,
  title = {{OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics}},
  author = {Jose Nimo and Sonja Fritzsche and Daniela S. Valdes and Minh Trinh and Tancredi Pentimalli and Melissa Klingeberg and Simon Schallenberg and Frederick Klauschen and Florian Herse and Stefan Florian and Nikolaus Rajewsky and Fabian Coscia},
  journal = {Nature Methods},
  year = {2026},
  month = oct,
  doi = {10.1038/s41592-026-03262-z},
  url = {https://doi.org/10.1038/s41592-026-03262-z},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics
AU  - Jose Nimo
AU  - Sonja Fritzsche
AU  - Daniela S. Valdes
AU  - Minh Trinh
AU  - Tancredi Pentimalli
AU  - Melissa Klingeberg
AU  - Simon Schallenberg
AU  - Frederick Klauschen
AU  - Florian Herse
AU  - Stefan Florian
AU  - Nikolaus Rajewsky
AU  - Fabian Coscia
PY  - 2026
DA  - 2026/10/22
JO  - Nature Methods
DO  - 10.1038/s41592-026-03262-z
UR  - https://doi.org/10.1038/s41592-026-03262-z
AB  - 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.
SN  - 1548-7091
SN  - 1548-7105
ER  -