StrataQuest from TissueGnostics is a contextual image analysis platform offering single-cell resolution and spatial phenotyping directly within tissue sections. It combines advanced machine learning–based workflows, which includes deep learning for nuclear segmentation and tissue classification. It also contains a library of prebuilt “apps” for tasks such as phenotyping, spatial interaction mapping, and dimensionality reduction. The software supports both brightfield and fluorescence images and delivers scalable, reproducible analysis across study cohorts, making population-level imaging data both interpretable and quantifiable.
Integration with TissueFAXS
Combine StrataQuest with TissueFAXS for a streamlined end-to-end workflow that preserves spatial and analytical context at every step. Scanned images and metadata move into analysis without format changes, enabling immediate processing of completed scans and ongoing scans.
Research Benefits
- Deep learning–based segmentation and classification excel in complex or weakly stained samples
- Prebuilt apps simplify setup and reproducibility across projects, with the ability to assemble custom pipelines
- Built-in spatial tools (e.g. proximity mapping, cell neighborhood analysis, and t-SNE/UMAP visualization)
- Exploration of tissue microenvironments
- Optimized for throughput: Users can scale experiments without disrupting consistency from single slides to full cohorts