Open AccessMultidimensional profiling of drug‐treated cells by Imaging Mass Cytometry
Author(s) -
Bouzekri Alexandre,
Esch Amanda,
Ornatsky Olga
Publication year - 2019
Publication title -
febs open bio
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 31
ISSN - 2211-5463
DOI - 10.1002/2211-5463.12692
Subject(s) - mass cytometry , nocodazole , cytometry , flow cytometry , high content screening , drug discovery , micronucleus test , cell , mitosis , biology , cell growth , computational biology , chemistry , microbiology and biotechnology , bioinformatics , cytoskeleton , biochemistry , toxicity , organic chemistry , gene , phenotype
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 pHistone3 S28 , Ki‐67, and p4E‐BP1 T37/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.