Deep visual proteomics: integrating artificial intelligence and mass spectrometry for cellular phenotyping:
Deep visual proteomics (DVP) revolutionizes cellular phenotype analysis by combining advanced microscopy, artificial intelligence, and ultrasensitive mass spectrometry (MS). Traditional methods typically focus on a limited subset of proteins, but DVP expands this capability by enabling comprehensive proteomic analysis within the native spatial context of cells. This approach involves high-resolution imaging for single-cell phenotyping, ai-driven cell segmentation, and automated laser microdissection to precisely isolate cellular or subcellular regions of interest. These isolated samples are then subjected to ultra-high-sensitivity mass spectrometry to obtain detailed proteomic profiles.
Powered by Biology Image Analysis Software (BIAS), DVP facilitates the seamless integration of imaging and proteomics technologies. It enables the identification of distinct cell types and states based on ai-defined features, improving the accuracy and efficiency of cellular phenotyping. DVP’s applications range from studying single cell heterogeneity to characterizing proteomic differences in pathological tissues such as melanoma and salivary gland carcinoma. By preserving spatial information alongside molecular insights, DVP offers a powerful tool to advance research and clinical diagnostics in cellular and pathological biology.
Image processing and single cell isolation workflow in deep visual proteomics:
The single cell isolation and image processing workflow in DVP integrates cutting-edge microscopy technologies with advanced ai-driven image analysis and automated laser microdissection. Beginning with high-resolution scanning microscopy, the process involves capturing images of whole slides that are processed using BIAS. BIAS supports various microscopy formats and uses deep learning algorithms to accurately segment cellular components such as nuclei and cytoplasm. This includes innovative techniques such as image style transfer to optimize deep learning model training for specific biological contexts. BIAS facilitates seamless interaction with laser microdissection systems such as the ZEISS PALM MicroBeam and Leica LMD6 and 7, ensuring precise transfer and automated targeted cell extraction. This integrated workflow enables fast and accurate single cell isolation, which is crucial for in-depth proteomic analysis of cell and tissue samples in DVP applications.
Characterizing single cell heterogeneity by deep visual proteomics:
DVP enables the characterization of functional differences between phenotypically distinct cells at the subcellular level. By applying this workflow to an unperturbed cancer cell line, the researchers used deep learning-based segmentation to isolate and analyze individual cells and nuclei. This approach addressed the challenges of processing tiny samples, allowing direct analysis of 384 wells using advanced mass spectrometry. The proteomic profiles of whole cells and isolated nuclei were distinct, with high reproducibility. Machine learning identified six classes of nuclei with significant morphological and proteomic differences. This demonstrated that visible cellular phenotypes correspond to distinct proteomic profiles, offering insights into cell cycle regulation and potential cancer prognostic markers.
DVP reveals the heterogeneity of cancer tissue:
DVP provides unbiased, high-resolution proteomic profiling of distinct cell classes within their spatial environments. Applied to archived salivary gland acinar cell carcinoma tissue, DVP revealed significant proteomic differences between normal and cancer cells. Normal acinar cells showed high expression of secretory proteins, whereas cancer cells exhibited elevated interferon response proteins and the SRC proto-oncogene. Extending this to melanoma, DVP differentiated central tumor cells from those at the tumor-stroma boundary, identifying distinct proteomic signatures linked to disease progression and prognosis. These findings underscore the potential of DVP for accurate molecular subtyping of disease, guiding clinical decision-making.
Outlook for DVP:
The DVP system integrates high-resolution microscopy with advanced image recognition, automated laser microdissection, and ultrasensitive mass spectrometry-based proteomics. This robust system is applicable to diverse biological systems that can be imaged microscopically, from cell cultures to pathological specimens. DVP enables rapid scanning of slides to isolate rare cellular states and study the proteomic composition of the extracellular matrix. With the potential of super-resolution microscopy, DVP can achieve accurate cell state classification. By combining powerful imaging technologies with unbiased proteomics, DVP offers significant applications in basic biology and biomedicine, particularly in oncology, where it enhances digital pathology by providing comprehensive proteomic context.
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Sana Hassan, a Consulting Intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and ai to address real-world challenges. With a keen interest in solving practical problems, she brings a fresh perspective to the intersection of ai and real-life solutions.
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