Deep Visual Proteomics (DVP) is a groundbreaking method that combines high-end microscopy, AI, and ultra-sensitive mass spectrometry for comprehensive proteomic analysis within the native spatial context of cells. By utilizing AI to identify different cell types, this technology allows an in-depth study of individual cells, increasing the precision and effectiveness of cellular phenotyping.
The DVP workflow involves high-resolution imaging for single-cell phenotyping, where distinct cell types are identified utilizing AI. The process is facilitated through Biology Image Analysis Software (BIAS), enabling seamless integration between advanced imaging and proteomic technologies. The identified cell segments are isolated using an automated laser microdissection system, after which the isolated cells are subject to ultra-high sensitivity mass spectrometry for detailed proteomic profiling.
The Extracted samples are then subjected to high-sensitivity mass spectrometry. This process provides an extensive proteomic outline while preserving spatial information. This innovative approach offers an advanced method for research in cell and disease biology, expanding the scope to characterizing proteomic differences in disease tissues such as melanoma and salivary gland carcinoma.
Moreover, DVP enables the study of functional differences among phenotypically distinct cells at the subcellular level. Research using this methodology on an unperturbed cancer cell line allowed the processing of minute samples and direct analysis using advanced mass spectrometry. It identified six nuclei classes with significant morphological and proteomic differences, demonstrating the alignment of visible cellular phenotypes to unique proteome profiles, a crucial insight into cell cycle regulation and cancer prognostic markers.
When applied to cancer tissue, DVP offers a comprehensive, impartial proteomic profile of distinct cell classes within their spatial constraints. Examination of archived salivary gland acinic cell carcinoma tissue showed significant proteomic differences between normal cells and cancerous ones. Furthermore, DVP also identified unique proteomic signatures related to melanoma progression and prognosis, indicating its potential for specific molecular disease classification and guiding clinical decision-making.
In comparison to conventional methods that focus on a limited subset of proteins, DVP changes the way cellular phenotypes are analyzed. The combination of advanced microscopy, AI, and ultra-sensitive mass spectrometry allows rapid scanning and isolation of rare cell states for proteomic analysis, providing a much more detailed insight into differing cell types. DVP is a robust method with broad potential applications in biology and biomedicine, particularly oncology. By providing a comprehensive proteomic context, DVP could enhance digital pathology, becoming the new standard for cellular and disease biological research.
