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Tools
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| Item Type: | Dataset |
|---|---|
| Title: | OpenDVP: an experimental and computational framework for community-empowered deep visual proteomics |
| Creators: |
Coscia, Fabian  ORCID: https://orcid.org/0000-0002-2244-5081 and Nimo, Jose ORCID: https://orcid.org/0000-0002-1565-7799
|
| Abstract: | This dataset supports the publication on openDVP, an open-source framework for Deep Visual Proteomics (DVP). It contains imaging and proteomics data from three studies on archival human tissues, aimed at demonstrating a spatially resolved, cell type-specific proteomics workflow. The data is organized into two biological contexts: (1) Early-Stage Lung Cancer: To profile the tumor and its surroundings. (2) Locally Relapsed Breast Cancer: To investigate the characteristics of recurrent disease. For each study, the dataset includes: > Raw multiplexed tissue images. > Pre-processed images and single-cell segmentation masks generated using the openDVP pipeline, which integrates MCMICRO, QuPath, and Napari. These datasets enable the deep proteomic profiling of cellular neighborhoods, providing a resource to explore the connection between spatial cell organization and proteomic states in health and disease. |
| Keywords: | Multiplex Imaging, Cancer, Tumor Microenvironment, Spatial Proteomics |
| Source: | BioImage Archive |
| Publisher: | EMBL-EBI |
| Date: | 31 May 2026 |
| Official Publication: | https://doi.org/10.6019/S-BIAD2194 |
| Related to: |
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