Connecting images with single-cell resolution protein abundance measurements has been a challenge despite the advances of imaging-based and mass-spectrometry-based methods for spatial proteomics. Now, a novel method called, Deep Visual Proteomics (DVP), has been developed by an international team of researchers led by Copenhagen University and has been applied to cancer cells. The researchers believe using this technology, can effectively connect the physiological characteristics of cells seen under microscopes with the functions of proteins.
The findings are detailed in a study titled, “Deep Visual Proteomics defines single-cell identity and heterogeneity,” and published in the journal Nature Biotechnology.
“Here, we introduce DVP, which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry,” wrote the researchers. “DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context.”
“Our new concept, Deep Visual Proteomics, could become a game-changer for molecular pathology in the hospitals. With this method, we can identify thousands of proteins and determine how many of them are there,” explained Andreas Mund, associate professor at the Novo Nordisk Foundation Center for Protein Research (CPR), part of Matthias Mann’s team that spearheaded this development at CPR and the Max Planck Institute for Biochemistry, and is first author of the new study.
“We do this by taking a tissue sample and analyzing just the tumor cells in it. This ‘list’ of proteins is called proteome. These proteomes reveal the mechanisms that drive tumor development and directly expose new therapeutic targets from a single tissue slice of a cancer patient biopsy. It exposes a cosmos of molecules inside these cancer cells,” said Mund.
The researchers applied their method to cells from patients with acinic cell carcinoma and with melanoma. This was done in collaboration with researchers at the Zealand University Hospital.
“When something goes wrong inside our cells and we become sick, you can be sure that proteins are involved in a wide range of different ways. Because of this, mapping the protein landscape can help us determine why a tumor could develop in a particular patient, what vulnerabilities that tumor has and also what treatment strategy might prove the most beneficial,” added Mann.
“This unique method combines tissue architecture with the expression of thousands of proteins specific for selected cells. It enables researchers to investigate interactions between cancer cells and their surrounding cells with major implications for future clinical cancer treatment. Recently, we diagnosed a highly complex clinical case with two different components and the results from DVP analysis,” said Lise Mette Rahbek Gjerdrum, consultant and clinical research associate professor at the department of pathology, Zealand University Hospital and the department of clinical medicine, University of Copenhagen.
The new method combines advances from four different technologies into a single workflow. First, advanced microscopy generates high-resolution tissue maps. Second, machine learning algorithms are used to classify cells accurately before laser microdissections and single-cell collection. Then the normal or diseased cells of a particular type are analyzed by mass spectroscopy, mapping the protein landscape, and understanding the mechanisms of health and disease.
“Using this technology, we can effectively connect the physiological characteristics of cells seen under microscopes with the functions of proteins. This was not previously possible and we are very convinced that this method can be applied to other diseases, not just cancer,” said Mund.
“As a single slide can encompass hundreds of thousands of cells, DVP can discover and characterize rare cell states and interactions. In contrast to single-cell transcriptomics, DVP can readily analyze the ECMʼs subcellular structures and spatial dynamics. With further improvements in proteomics technology, DVP should also be suited to study proteoforms and post-translational modifications at a single-cell-type level,” concluded the researchers.
These findings may pave a path for new treatments and strategies for elusive diseases such as cancer and others.