Spatialproteomics: an interoperable toolbox for analyzing highly multiplexed fluorescence image data
Abstract Highly multiplexed immunofluorescence imaging visualizes and quantifies protein levels at single-cell resolution in intact tissues at low cost and high scalability. Analysis of these data involves multiple steps with many method and parameter choices that must be adapted to the data and analytical objectives. There is an unmet need for a toolbox that offers flexible end-to-end coverage of the workflow. Here we present ‘spatialproteomics’, a Python package that addresses these challenges. Spatialproteomics enables the processing and analysis of large imaging data, including steps such as segmentation, image processing and cell-type classification, while synchronizing shared coordinates across data modalities. We demonstrate spatialproteomics on images of reactive lymph nodes and B cell non-Hodgkin lymphomas from 132 patients. We showcase an end-to-end analysis from raw images to statistical characterization of how cell type composition and spatial distribution vary across indolent and aggressive lymphomas. Furthermore, we show how spatialproteomics can process Gigapixel whole-slide images.
Authors
- Jose Nimo (ORCID: https://orcid.org/0000-0002-1565-7799)
- Peter‐Martin Bruch (ORCID: https://orcid.org/0000-0002-9992-3109)
- Harald Vöhringer (ORCID: https://orcid.org/0000-0003-2513-4518)
- Luca Marconato (ORCID: https://orcid.org/0000-0003-3198-1326)
- Wolfgang K. Huber (ORCID: https://orcid.org/0000-0002-0474-2218)
- Alexander Brobeil (ORCID: https://orcid.org/0000-0002-1003-8577)
- Mikaela Koutrouli (ORCID: https://orcid.org/0000-0002-8953-3561)
- Sascha Dietrich (ORCID: https://orcid.org/0000-0002-0648-1832)
- Matthias Meyer-Bender (ORCID: https://orcid.org/0000-0002-0448-4546)
- E Chung (ORCID: https://orcid.org/0009-0003-5506-8248)
- Sarah Koziel (ORCID: https://orcid.org/0009-0003-8002-5127)
- Christina Schniederjohann (ORCID: https://orcid.org/0009-0001-2693-8086)
- Ekaterina Popova (ORCID: https://orcid.org/0000-0003-2559-3685)
- Theodoros Visvikis (ORCID: https://orcid.org/0009-0003-5579-5488)
- Aamir Munir (ORCID: https://orcid.org/0009-0009-0263-5943)
- Lucas Diedrich
- Nora Kolks
- Panos Roussos
- Wouter-Michiel Vierdag
- Lisa-Maria Held
- Nicklas Griese (ORCID: https://orcid.org/0009-0009-9461-4364)
- Vincenth Brennsteiner
- Erwin Schoof
- Scverse Community
Institutions
- European Molecular Biology Organization (DE)
- Max Delbrück Center (DE)
- Heidelberg University (DE)
- Maastricht University (NL)
- Düsseldorf University Hospital (DE)
- Integrated Oncology (United States) (US)
- James J. Peters VA Medical Center (US)
- European Molecular Biology Laboratory (DE)
- Max Planck Institute of Biochemistry (DE)
- European Molecular Biology Laboratory (DE)
- Heinrich Heine University Düsseldorf (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Technical University of Denmark (DK)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Nature Methods
- Published
- 2026-07-24
- DOI
- https://doi.org/10.1038/s41592-026-03155-1
- Citations
- 2
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- European Molecular Biology Laboratory
- European Commission
- Lundbeckfonden
- H. Lundbeck A/S
- Horizon 2020 Framework Programme