{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Spatial Proteomics Group, Berlin, Germany",
    "Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany",
    "German Cancer Consortium (DKTK), Partner Site Berlin, Heidelberg, Germany",
    "Humboldt-Universität zu Berlin, Institute of Biology, Berlin, Germany",
    "Laboratory for Systems Biology of Regulatory Elements, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany",
    "Institute of Pathology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany",
    "Institute of Pathology, Ludwig Maximilians University Hospital Munich, Munich, Germany",
    "German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany",
    "Berlin Institute for the Foundations of Learning and Data, Berlin, Germany",
    "Max Delbrück Center for Molecular Medicine in the Helmholtz Association and the Charité – Universitätsmedizin Berlin, Experimental and Clinical Research Center, Berlin, Germany",
    "Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany",
    "Cluster of Excellence NeuroCure, Berlin, Germany",
    "German Center for Cardiovascular Research (DZHK) Partner Site Berlin, Berlin, Germany",
    "National Center for Tumor Diseases Partner Site Berlin, Berlin, Germany",
    "German Center for Child and Adolescent Health (DZKJ) Partner Site Berlin, Berlin, Germany",
    "ImmunoPreCept Cluster of Excellence, Berlin, Germany",
    "Einstein Center for Early Disease Interception, Berlin, Germany"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jose Nimo",
      "orcid": "0000-0002-1565-7799"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Sonja Fritzsche",
      "orcid": "0000-0003-3335-3534"
    },
    {
      "affs": [
        0
      ],
      "name": "Daniela S. Valdes"
    },
    {
      "affs": [
        0
      ],
      "name": "Minh Trinh",
      "orcid": "0009-0000-2727-8449"
    },
    {
      "affs": [
        1,
        4
      ],
      "name": "Tancredi Pentimalli"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Melissa Klingeberg",
      "orcid": "0009-0002-4778-2874"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Simon Schallenberg",
      "orcid": "0000-0002-7897-7116"
    },
    {
      "affs": [
        6,
        7,
        8
      ],
      "name": "Frederick Klauschen"
    },
    {
      "affs": [
        1,
        9,
        10
      ],
      "name": "Florian Herse",
      "orcid": "0000-0002-9305-8134"
    },
    {
      "affs": [
        2,
        5
      ],
      "name": "Stefan Florian"
    },
    {
      "affs": [
        1,
        2,
        4,
        11,
        12,
        13,
        14,
        15,
        16
      ],
      "name": "Nikolaus Rajewsky",
      "orcid": "0000-0002-4785-4332"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Fabian Coscia",
      "orcid": "0000-0002-2244-5081"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/axsxt75yxcbq1.bib",
  "cited_by": 6,
  "doi": "10.1038/s41592-026-03262-z",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736673/",
  "id": "axsxt75yxcbq1",
  "issns": [
    "1548-7091",
    "1548-7105"
  ],
  "landing_url": "https://doi.org/10.1038/s41592-026-03262-z",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
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  "openalex": "W7223175165",
  "pdf_url": "https://www.nature.com/articles/s41592-026-03262-z.pdf",
  "pmcid": "PMC13736673",
  "pmid": "42918851",
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/axsxt75yxcbq1.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:04Z",
      "native_id": "PMC13736673",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41592-026-03262-z"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics",
  "title_text": "OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:04Z",
  "url": "https://research.draconitic.com/paper/axsxt75yxcbq1",
  "venue": "Nature Methods",
  "versions": []
}