Nucleai announced its contribution to a collaborative international study published in Nature Communications that explores how spatial organization and metabolic characteristics of tumor cells are associated with response and resistance to immunotherapy in non-small cell lung cancer (NSCLC).
The study, led by academic researchers at The University of Queensland and Yale School of Medicine, applied multiplex immunofluorescence (mIF) and computational approaches to analyze tumor tissue at single-cell resolution. By examining how different cell populations are organized within the tumor microenvironment and how they metabolize glucose, the researchers identified distinct spatial and metabolic patterns associated with immunotherapy outcomes.
As part of the collaboration, Nucleai’s AI-powered multiplex immunofluorescence (mIF) analysis pipeline enabled accurate identification and classification of tumor and immune cell populations at scale, providing a consistent and reproducible foundation for downstream spatial and metabolic analyses conducted by the academic research teams.
Findings from this study suggest that spatially defined metabolic features within tumors may help explain variability in treatment response, reinforcing the need for more nuanced approaches to characterizing tumor biology beyond traditional single-marker assessments.
This work builds on Nucleai’s broader multimodal spatial AI platform, which is designed to support scalable and rapid spatial profiling across large research cohorts. By transforming complex multiplex imaging data into structured, quantitative spatial insights, the platform supports collaborative efforts to advance precision oncology research.
Visit Nucleai for more news
