Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-12-S13-S18
Title: Cell cycle phase classification in 3D in vivo microscopy of Drosophila embryogenesis
Authors: Du, T.H.
Puah, W.C.
Wasser, M. 
Issue Date: 30-Nov-2011
Citation: Du, T.H., Puah, W.C., Wasser, M. (2011-11-30). Cell cycle phase classification in 3D in vivo microscopy of Drosophila embryogenesis. BMC Bioinformatics 12 (SUPPL. 13) : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-12-S13-S18
Abstract: Background: Cell 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.Results: We 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.Conclusions: Our 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. © 2011 Du et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/114302
ISSN: 14712105
DOI: 10.1186/1471-2105-12-S13-S18
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
2011-cell_cycle_phase_classification_3d-published.pdf1.18 MBAdobe PDF

OPEN

PublishedView/Download

SCOPUSTM   
Citations

10
checked on Aug 15, 2018

WEB OF SCIENCETM
Citations

9
checked on Jul 23, 2018

Page view(s)

40
checked on May 11, 2018

Download(s)

7
checked on May 11, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.