Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma.

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 Compute and Storage [Service]

PubMed 30074852

DOI 10.1177/2472555218791414

Crossref 10.1177/2472555218791414

pii: S2472-5552(22)06926-X

Publications 9.5.0