“OpenDVP: Open-Source Workflow Makes Deep Visual Proteomics Easier to Use” — what this page in the network covers. Fabian Coscia and his team have published an open-source workflow called OpenDVP in Nature Methods, designed to make Deep Visual Proteomics more accessible and scalable for researchers. OpenDVP enables scientists to spatially map thousands of proteins within cells and their surrounding tissue environments, preserving critical spatial context that is often lost in traditional proteomics methods. Deep Visual Proteomics is a powerful technique that combines high-content imaging, laser microdissection, and ultra-sensitive mass spectrometry to analyze the proteome of individual cells in situ.
However, its complexity and lack of standardized tools have limited broader adoption. OpenDVP addresses these barriers by providing a modular, open-source framework that streamlines data analysis, visualization, and integration, allowing laboratories to adapt the workflow to their specific needs. This listing links straight to the original page, where the content is available in full.