Background Advances in therapies targeting immuno-oncological processes that dictate tumor growth, metastasis, and immune response require comprehensive preclinical research. Mouse models have proven to be a preferred tool for determining important factors that influence tumor development. Conducting multiparametric analysis on mouse tumor tissue has the potential to significantly expand capabilities of tumor-targeting therapies. Imaging Mass Cytometry™ (IMC™) is a proven high-plex imaging technology that enables deep characterization of the complexity of tumor tissue to capture spatial context while eliminating artifacts caused by spectral overlap and background autofluorescence. The Hyperion™ Imaging System utilizes IMC technology to simultaneously assess 40-plus individual structural and functional markers in tissues. Here, we showcase the Maxpar® OnDemand Mouse Immuno-Oncology IMC Panel Kit (PN 9100005) for application on mouse tumor tissues.

Methods We compiled a 33-plex Mouse Immuno-Oncology IMC Panel Kit to evaluate immuno-oncological-related processes and applied it to a tissue microarray containing a large variety of mouse tumors including non-small-cell lung cancer, B cell lymphoma, colon adenocarcinoma, and renal carcinoma. We digitized high-plex data from mouse tissues using the Hyperion Imaging System and generated images demonstrating the detailed layout of the tumor immune microenvironment (TIME). We conducted single-cell analysis to identify specific and relevant populations of tumor and immune cells. We further applied neighborhood analysis to determine spatial relationships between selected cellular clusters distributed across the TIME.

Results The Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kit successfully detected immune cell infiltration and activation, signaling pathway activation, biomarkers of epithelial-to-mesenchymal transition (EMT), metabolic activity, growth, and the tumor tissue architecture. Single-cell analysis of non-small-cell lung carcinoma, B cell lymphoma, colon adenocarcinoma, and renal carcinoma separated distinct cellular clusters representing tumor, immune, stromal, and vascular cells. Neighborhood analysis pinpointed spatial relationships between specific cellular clusters within the TIME.

Conclusions Our quantitative analysis of tumor composition revealed critical insights regarding prognostic parameters such as metastatic and growth potential of tumor cells and activity of immune cell infiltrates. Overall, this work demonstrates the tumor spatial profiling capabilities of IMC technology and provides evidence of its successful application in mouse tumor models.

Ethics Approval The samples obtained for this study were sourced from an accredited commercial provider.