Abstract
In pharmaceutical research, high-content screening is an integral part of lead candidate development. Drug response in vitro over 40 parameters including biomarkers, signaling molecules, cell morphological changes, proliferation indexes and toxicity in a single sample could significantly enhance discovery of new therapeutics. As a proof of concept, we present 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 anti-correlation with cell surface markers. Our study demonstrates that IMC data expands 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.
Footnotes
Abbreviations: IMC, Imaging Mass Cytometry; CellProfiler, CP; histoCAT, histology topography cytometry analysis toolbox; NCI-60, National Cancer Institute 60; MCF-7, Michigan Cancer Foundation-7; SNE, stochastic neighbor embedding; EGF, epidermal growth factor; 4E-BP1, eukaryotic initiation factor 4E-binding protein.