OmicVision develops the Deep Visual Proteomics platform for unbiased, single-cell spatial proteomics. This technology integrates high-content imaging, deep learning segmentation, and ultra-sensitive mass spectrometry to quantify thousands of proteins with spatial context. The platform supports precision medicine by enabling faster target discovery, biomarker identification, and patient stratification in drug development.
Funding
Funding not disclosed
Founders
Product
Problem
Current methods for understanding protein expression at the single-cell level are limited, hindering effective patient stratification and biomarker discovery in precision medicine. Existing technologies lack the resolution and throughput necessary to comprehensively analyze spatial proteomics in individual cells. This gap impedes the identification of druggable proteomes with precision, slowing down drug development and personalized treatment strategies.
Solution
OmicVision offers a Deep Visual Proteomics platform that combines high-resolution microscopy, deep learning, laser microdissection, and ultra-sensitive mass spectrometry to perform unbiased single-cell spatial proteomics. The platform enables the identification of disease-specific molecular signatures by classifying cells based on their phenotypes and measuring the proteome to highlight differential protein expression. By exploring protein expression in a spatial context, OmicVision facilitates a deeper understanding of cellular states and disease mechanisms. This technology supports improved patient stratification, biomarker identification, and indication expansion, accelerating drug development and improving patient care.
Target Audience
OmicVision's primary customers are pharmaceutical companies, biotech firms, and research institutions involved in drug discovery, biomarker development, and precision medicine initiatives.
Features
- High-resolution, high-content microscopy imaging for detailed cellular visualization
- Deep-learning-based single-cell phenotypic classification and segmentation for accurate cell identification
- Laser microdissection of single cells or phenotype-matched cells for targeted protein analysis
- Ultra-sensitive mass spectrometry for quantifying thousands of proteins with single-cell precision
- Proprietary AI platform for panoptic tissue segmentation and quantitative feature extraction
- Multimodal spatial tissue profiling for creating detailed spatial proteomic maps
- Computational cell segmentation and phenotyping to classify disease states