使用Cell DIVE成像对同基因小鼠模型中肿瘤微环境进行多重空间分析

Comprehensive spatial and phenotypic characterization of the tumor immune microenvironment (TIME) in preclinical models is essential for understanding mechanisms of immune modulation and resistance. In this study, we employed the Cell DIVE™ multiplex immunofluorescence platform to spatially profile formalin-fixed paraffin-embedded (FFPE) sections from syngeneic mouse tumor models-MC38 (colorectal) and LL/2 (lung)-using a panel of antibodies from Cell Signaling Technology (CST), targeting epithelial, stromal, immune, and checkpoint markers. Multiplexed imaging data were analyzed using Aivia software to enable cell-level segmentation, phenotype classification, and spatial context analysis. We conducted a comprehensive comparison of the TME between the two tumor types, revealing distinct immune and stromal cell compositions, including differential infiltration of cytotoxic cells, regulatory T cells, and macrophage subsets. AI-powered spatial analyses uncovered unique patterns of immune cell localization and checkpoint molecule expression, highlighting potential mechanisms of immune exclusion and exhaustion. Co-localization and proximity analyses further elucidated interactions between immune and tumor cells, offering insights into the immunogenicity of each model. This study demonstrates the utility of multiplexed, spatially resolved immunofluorescence to dissect the complex tumor immune microenvironment in syngeneic mouse models. This approach enables a deeper understanding of tumor immunobiology in preclinical models and provides a foundation for identifying spatial biomarker patterns associated with therapeutic response. The combination of highly validated monoclonal antibody conjugates, Cell DIVE™ multiplexing, and advanced AI-powered image analysis, creates a more powerful and comprehensive tool for preclinical evaluation of immune-oncology (IO) therapeutics.