Cell cycle phase classification in 3D in vivo microscopy of Drosophila embryogenesis

BMC Bioinformatics

Proceedings

Wee Choo Puah¹  Tie Hua Du¹  Martin Wasser² 

[1] Imaging Informatics Division, Live-Cell Imaging and Automation of Image Analysis Group Bioinformatics Institute (BII), Agency for Science, Technology and Research(A*STAR), Singapore;Imaging Informatics Division, Live-Cell Imaging and Automation of Image Analysis Group Bioinformatics Institute (BII), Agency for Science, Technology and Research(A*STAR), Singapore;Department of Biological Sciences, National University of Singapore, Singapore;

关键词: Support Vector Machine;    Classification Accuracy;    Linear Discriminant Analysis;    Cell Cycle Phase;    Back Propagation Neural Network;   

DOI  :  10.1186/1471-2105-12-S13-S18

来源: Springer

【 摘 要 】

BackgroundCell divisions play critical roles in disease and development. The analysis of cell division phenotypes in high content image-based screening and time-lapse microscopy relies on automated nuclear segmentation and classification of cell cycle phases. Automated identification of the cell cycle phase helps biologists quantify the effect of genetic perturbations and drug treatments. Most existing studies have dealt with 2D images of cultured cells. Few, if any, studies have addressed the problem of cell cycle classification in 3D image stacks of intact tissues.ResultsWe developed a workflow for the automated cell cycle phase classification in 3D time-series image datasets of live Drosophila embryos expressing the chromatin marker histone-GFP. Upon image acquisition by laser scanning confocal microscopy and 3D nuclear segmentation, we extracted 3D intensity, shape and texture features from interphase nuclei and mitotic chromosomes. We trained different classifiers, including support vector machines (SVM) and neural networks, to distinguish between 5 cell cycles phases (Interphase and 4 mitotic phases) and achieved over 90% accuracy. As the different phases occur at different frequencies (58% of samples correspond to interphase), we devised a strategy to improve the identification of classes with low representation. To investigate which features are required for accurate classification, we performed feature reduction and selection. We were able to reduce the feature set from 42 to 9 without affecting classifier performance. We observed a dramatic decrease of classification performance when the training and testing samples were derived from two different developmental stages, the nuclear divisions of the syncytial blastoderm and the cell divisions during gastrulation. Combining samples from both developmental stages produced a more robust and accurate classifier.ConclusionsOur study demonstrates that automated cell cycle phase classification, besides 2D images of cultured cells, can also be applied to 3D images of live tissues. We could reduce the initial 3D feature set from 42 to 9 without compromising performance. Robust classifiers of intact animals need to be trained with samples from different developmental stages and cell types. Cell cycle classification in live animals can be used for automated phenotyping and to improve the performance of automated cell tracking.

【 授权许可】

Unknown   
© Du et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.