Integrated Spatial Omics Mapping
Aligns spatial proteomic and transcriptomic data to reveal molecular phenotypes at high spatial resolution.
About Our Platform
Integrative Spatial Omics for True Biological Context
Despite rapid advancements in omics technologies, traditional approaches often sacrifice spatial context, limiting our ability to understand the cellular microenvironment that governs disease. SpatiOmics.Ai® addresses this gap by aligning spatial proteomics and transcriptomics on a unified tissue framework, enabling a new depth of biological interpretation.
Why Use SpatiOmics.Ai®
Translating Spatial Profiles into Cellular and Molecular Insight
Understanding how cells interact within their native tissue architecture is essential for uncovering mechanisms of disease progression, resistance, and therapeutic response. SpatiOmics.Ai® enables this by combining multiplexed protein marker analysis with spatially resolved gene expression profiling. By integrating these data layers at cellular resolution, the platform reconstructs molecular phenotypes, identifies disease-driving niches, and informs the development of targeted, spatially aware interventions.
SpatiOmics.Ai® aligns spatial proteomic and transcriptomic data within shared tissue segments. Using multiplexed protein markers, the platform performs cell segmentation and annotation, capturing gene expression in parallel. Integration algorithms map protein and RNA signatures to reconstruct cell states, interactions, and spatial domains. Downstream analyses include pathway enrichment, intercellular signaling inference, and identification of spatial drivers of therapy response and resistance.
Key Features & Capabilities
Single-Cell Segmentation and Phenotyping
Accurately segments cells and characterizes their states using multiplexed protein markers.
Identification of Disease-Driving Niches
Pinpoints cellular neighbourhoods and microenvironments contributing to progression, resistance, or therapeutic response.
Support for Spatially Informed Therapeutics
Guides development of treatments by linking spatial patterns to actionable biological insights.
Variant Pathogenicity Prediction
Uses EVO2 to assess likelihood of genetic variants being disease-causing.
Contextual Gene Expression Profiling
Captures transcriptomic signatures within the same spatial domain to enhance interpretation of cell functions.
High-Resolution Disease Stratification
Enables precise classification of tissue samples based on spatially resolved cellular and molecular features.
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