Characterizing the primary squamous cell carcinoma tumor microenvironment by Multiplex Ion Beam Imaging (MIBI) and Co-Detection by indEXing (CODEX) multiplex imaging

Solid tumors are composed of a diverse mixture of cancer, immune, and stromal cells. Understanding their spatial organization within the tumor microenvironment (TME) is critical for deciphering mechanisms of tumor progression and response to therapy. Targeted spatial proteomics for spatial organization studies of the TME is poised to revolutionize clinical practice as a natural evolution of immunohistochemistry (IHC), a technique routinely used for clinical diagnostics. However, direct comparisons that evaluate the relative capabilities and trade-offs of the major available targeted spatial proteomics platforms are currently scarce. In this study, we systematically evaluate two leading targeted spatial proteomics platforms: Co-Detection by Indexing (CODEX), a cyclic immunofluorescence technology, and Multiplexed Ion Beam Imaging (MIBI), an imaging mass spectrometry technique. Using a tissue microarray from a clinical cohort of 85 patient-derived head and neck squamous cell carcinoma samples, we processed a section from the same tissue blocks on each platform. To enable a side-by-side evaluation, we applied a comparable Python pipeline for image pre-processing, segmentation, and iterative clustering. Our results demonstrate that despite inherent differences in sample preparation and imaging approaches, both technologies can yield consistent and spatially relevant biological conclusions. For example, using antibody markers such as CD4, FOXP3, γH2AX, and cytokeratin, we successfully differentiated and localized CD4+ T cells, regulatory T cells, and cancer cells while simultaneously assessing cellular states like DNA damage. In conclusion, both technologies enable multiplexed, spatially resolved characterization of cellular architecture and heterogeneity in the TME. Future work will focus on a deeper spatial characterization of the dataset, alongside a direct comparison of the resolution and dynamic range of the two platforms. Once completed, this study will provide a robust framework for understanding the respective strengths and limitations of each technology, helping researchers select the optimal platform to accelerate the application of spatial biology in cancer research.