OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics
Open accessAbstract
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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Cites
Deep Visual Proteomics defines single-cell identity and heterogeneity
Mund, A., Coscia, F., Kriston, A., Hollandi, R., Kovács, F., Brunner, A.-D., Migh, E., Schweizer, L., Santos, A., Bzorek, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01302-5 · Cited by 562 · see citing works
Self-citationSpatial single-cell mass spectrometry defines zonation of the hepatocyte proteome
Rosenberger, F. A., Thielert, M., Strauss, M. T., Schweizer, L., Ammar, C., Mädler, S. C., Metousis, A., Skowronek, P., Wahle, M., Madden, K., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02007-6 · Cited by 176 · see citing works
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- Authors
- Nimo, J., Fritzsche, S., Valdes, D. S., Trinh, M., Pentimalli, T., Klingeberg, M., Schallenberg, S., Klauschen, F., Herse, F., Florian, S., Rajewsky, N., & Coscia, F.
- Affiliations
- Max Delbrück Center (DE)
- Humboldt-Universität zu Berlin (DE)
- German Cancer Research Center (DE)
- Corresponding author
- Coscia, Fabian
- ORCID
- Venue
- Nature Methods
- Published
- 2026-10-22
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W7223175165
- Cited by
- 0
- References
- 2
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (HE 6249/5-3)
- Bundesministerium für Bildung und Forschung (01ZX1917A, 161L0222, 03LW0239K)
- Medizinische Universität Graz
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- deep visual proteomics, open source software, spatial proteomics, image analysis workflow, multi-omics integration, image segmentation, cellular neighborhoods, human placenta, lung cancer, breast cancer, archival tissue
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