Automated analysis of time-lapse fluorescence microscopy images: from live cell images to intracellular foci

Motivation: Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems f...

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Bibliographic Details
Published in:Bioinformatics 2010-10, Vol.26 (19), p.2424-2430
Main Authors: Dzyubachyk, Oleh, Essers, Jeroen, Cappellen, Wiggert A. van, Baldeyron, Céline, Inagaki, Akiko, Niessen, Wiro J., Meijering, Erik
Format: Article
Language:English
Subjects:
Algorithms
Automation
Biological and medical sciences
Deoxyribonucleic acid
DNA Damage
Fluorescence
Fundamental and applied biological sciences. Psychology
General aspects
Image Interpretation, Computer-Assisted
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Matlab
Microscopy
Microscopy, Fluorescence - methods
Pattern analysis
Pattern Recognition, Automated
Segmentation
Software
Tasks
Time-Lapse Imaging - methods
Tracking
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Summary:Motivation: Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. Results: Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. Availability and implementation: The described system was implemented in MATLAB (The MathWorks, Inc., USA) and compiled to run within the MATLAB environment. The routines together with four sample datasets are available at http://celmia.bigr.nl/. The software is planned for public release, free of charge for non-commercial use, after publication of this article. Contact: meijering@imagescience.org
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btq434