Discrimination of cell cycle phases in PCNA-immunolabeled cells. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Schönenberger, Felix; Deutzmann, Anja; Ferrando-May, Elisa; Merhof, Dorit
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Classification -- Image analysis -- Feature selection -- Cell cycle phases; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0618-9
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images. Results We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images. Conclusion Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.