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| Item Type: | Preprint |
|---|---|
| Title: | OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics |
| Creators Name: | Nimo, Jose, Fritzsche, Sonja, Valdes, Daniela S., Trinh, Minh Tien, Pentimalli, Tancredi, Schallenberg, Simon, Klauschen, Frederick, Herse, Florian, Florian, Stefan, Rajewsky, Nikolaus and Coscia, Fabian |
| 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. |
| Source: | bioRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2025.07.13.662099 |
| Date: | 16 July 2025 |
| Official Publication: | https://doi.org/10.1101/2025.07.13.662099 |
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