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Matuszewski DJ, Wählby C, Krona C, Nelander S, Sintorn IM
SLAS DISCOVERY: Advancing Life Sciences R&D 23 (10) 1030-1039 [2018-12-00; online 2018-08-03]
Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.
BioImage Informatics [Collaborative]
Bioinformatics Support for Computational Resources [Service]
PubMed 30074852
DOI 10.1177/2472555218791414
Crossref 10.1177/2472555218791414
pii: S2472-5552(22)06926-X