Spatial proteomics gets an open-source path from whole-slide image to mass spec, with neighborhood sampling about 100 times faster
Deep visual proteomics (DVP) lacked an open-source end-to-end workflow, which limited adoption and systematic benchmarking. openDVP links image processing, cell phenotyping, laser microdissection and mass-spectrometry proteomics into a modular pipeline, with a fast-track route that needs no image-analysis expertise. Across archival placenta, early lung cancer and relapsed breast cancer tissue, neighborhood-guided sampling collected tissue about 100 times faster than single-cell cutting and raised protein identifications by an average of 26%.