A Computational Workflow for Cell Line Profiling by Imaging Mass Cytometry

In single-cell spatial phenotyping biology, imaging mass cytometry (IMC) stands out as a cutting-edge, highly multiplexed technology driving discoveries across various disease areas. In vitro profiling relies on tumor-derived cancer cell lines, known for their diverse morphologies and phenotypes. A comprehensive analysis of those cell lines presents a direct challenge in predicting cancer progression and drug treatment responses. We demonstrate in our study an adaptable computational workflow we have named IMC Cell Line Profiler as a suite of versatile open-source tools for visual rendering and quantitative segmentation-based analysis of tumor-derived cell lines profiled by IMC. By leveraging our workflow with IMC resolution and multiplexing capabilities, we scrutinized the morphology, phenotypic traits, and spatial arrangement of 10 cultured cell lines from diverse tissues using custom panels of 20-30 metal-labeled antibodies. This allowed us to generate intricate high-dimensional datasets elucidating diverse cellular states with multiple markers. This computational approach was applied to a cisplatin exposure treatment in a resistant cell line to track morpho-phenotypic changes. Our IMC computational study presents new perspectives and applications to profile cell lines as new types of samples and expand the potential of IMC to new fields of discovery.