This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Fabian Coscia and his team have published in Nature Methods an open-source workflow that makes Deep Visual Proteomics easier to use, adapt and scale. OpenDVP enables scientists to spatially map thousands of proteins inside cells and their cellular environments, while preserving their tissue context.
Today's commercial satellites can image landscapes with a level of detail unthinkable 20 years ago: they capture individual trees, cracks in infrastructure and even details of road signs. In proteomic research, Deep Visual Proteomics (DVP) enables scientists to do something similar at the cellular and molecular scale—they can zoom in on selected cells and cellular communities and measure thousands of proteins while preserving information about where those cells are located in a tissue. Now researchers in the Spatial Proteomics lab headed by Coscia at the Max Delbrück Center have built the first open-source end-to-end workflow called openDVP to make the technology easier to use, adapt and scale.
The Coscia lab co-developed the DVP method, which was first published in 2022. Based on the conceptual novelty of the method and its proven translational impact, Nature Methods highlighted it as a "Method of the Year 2024" in spatial proteomics, explains Coscia, senior author of the openDVP study. "We are very excited that the method has advanced in the last few years, which has been a joint effort of several international groups," he adds.
"But we also realized that its further development and community adoption required open-source tools. We built openDVP to bridge that critical gap." DVP combines high-resolution imaging, artificial intelligence and mass spectrometry to connect the location of cells in tissues with their proteomes—the thousands of proteins that determine much of what a cell actually does. The ability to catalog, visualize and quantify proteins within their native tissues in such fine detail can deepen our understanding of how diseases develop and ultimately help researchers design better disease interventions and treatments.
DVP applications are not limited to any disease and can, in principle, be applied to any type of biological sample. A major challenge of DVP, however, is connecting several technically distinct steps: imaging tissue, analyzing images, identifying cells or tissue regions, physically isolating them, studying samples with mass spectrometry and subsequently analyzing the data. openDVP now brings these steps together in a modular, open-source framework. The team has streamlined the image analysis process and developed a fast-track workflow that allows researchers to quickly annotate, collect and measure large numbers of individual cells or areas of interest without requiring extensive expertise in computational image analysis.
They have also introduced a new tissue-sampling strategy called cellular neighborhood-guided DVP, which informs tissue collection based on recurring, functional cellular groups. In cancer, for example, the identity of the cells that surround a tumor can often predict therapeutic outcomes and whether the disease will progress. "But we know very little about what these cells are actually doing," says Jose Nimo, first author of the study.
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