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Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
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2018 (English)In: SLAS Discovery: Advancing Life Sciences R&D, Vol. 23, no 10, p. 1030-1039Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 23, no 10, p. 1030-1039
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-368698DOI: 10.1177/2472555218791414OAI: oai:DiVA.org:uu-368698DiVA, id: diva2:1268693
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06

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Publisher's full texthttps://journals.sagepub.com/doi/10.1177/2472555218791414

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Matuszewski, Damian J.Wählby, CarolinaKrona, CeciliaNelander, SvenSintorn, Ida-Maria

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Matuszewski, Damian J.Wählby, CarolinaKrona, CeciliaNelander, SvenSintorn, Ida-Maria
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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLabDepartment of Immunology, Genetics and PathologyNeuro-Oncology
Computer Vision and Robotics (Autonomous Systems)

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