Understanding how immune cells interact with tumor cells remains a central challenge in cancer research. Traditional biomarker analysis often faces trade-offs between spatial resolution and multiplexing capacity, limiting insights into immune exclusion, infiltration, and phenotypic diversity. A streamlined workflow combining multiplexed staining, AI-based cell segmentation, and spatial data analysis enables researchers to more effectively quantify and interpret immune cell phenotypes within tumor nests.
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- Rapid design and optimization of multiplex antibody panels for automated sequential immunofluorescence
- Techniques for accurate cell segmentation and phenotyping using deep learning and spatial analysis tools
- Insights into tumor-immune interactions in oral cancer through quantitative spatial metrics
