Abstract
In pharmaceutical research, high‐content screening is an integral part of lead candidate development. Measuring drug response in vitro by examining over 40 parameters, including biomarkers, signaling molecules, cell morphological changes, proliferation indices, and toxicity in a single sample, could significantly enhance discovery of new therapeutics. As a proof of concept, we present here a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single‐cell quantitative information visualized as t‐SNE plots and heatmaps. Human breast cancer‐derived cell lines SKBR3, HCC1143, and MCF‐7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF‐7 cells dosed with three target‐specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole, and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3S28, Ki‐67, and p4E‐BP1T37/T46 in classified mitotic cells and anticorrelation with cell surface markers. Our study demonstrates that IMC data expand the number of measured parameters in single cells and brings higher‐dimension analysis to the field of cell‐based screening in early lead compound discovery.
Highlights
Discovery of new treatments in oncology research relies extensively on the use of humanderived cell culture models [1]
Predicted pharmacodynamic effects were studied in MCF-7 cells dosed with three target-specific compounds: growth stimulatory epidermal growth factor (EGF), microtubule depolymerization agent nocodazole and genotoxic chemotherapeutic drug etoposide [22, 23]
Human epidermal growth factor receptor 2 (HER2) and EGFR are seen at the surface membrane, while tumor suppressor and transcription factor p53 is localized in the nuclei
Summary
Discovery of new treatments in oncology research relies extensively on the use of humanderived cell culture models [1]. High-content cell-based screens are widely applied in pharmaceutical drug development to prioritize lead molecules for animal testing [2] These assays rely on the use of primary and cancer cell lines and mostly monitor cytotoxicity and proliferation. There is no single software package or analysis workflow that could currently be applied to answer specific biological questions In this proof-of-principle study, we set out to develop a comprehensive workflow for IMC data analysis based on recent advances in imaging algorithmic methods to visualize and measure multiple biomarkers in model cell lines cultured in chamber slides (Fig. 1). Future improvements of the technology toward a higher acquisition speed of multiple samples will expand IMC applications as an imaging platform for in vitro cell-based drug screening
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