A computerized cellular imaging system for high content analysis in Monastrol suppressor screens

Authors:
Xiaobo Zhou;Xinhua Cao;Zach Perlman;Stephen T. C. Wong
Affiliations:
Harvard Center for Neurodegeneration and Repair--Center for Bioinformatics, Harvard Medical School, Boylston, Boston, MA and Department of Radiology, Brigham and Women's Hospital, Boston, MA;Harvard Center for Neurodegeneration and Repair--Center for Bioinformatics, Harvard Medical School, Boylston, Boston, MA;Department of Systems Biology, Harvard Medical School, Boston, MA;Harvard Center for Neurodegeneration and Repair--Center for Bioinformatics, Harvard Medical School, Boylston, Boston, MA and Department of Radiology, Brigham and Women's Hospital, Boston, MA
Venue:
Journal of Biomedical Informatics
Year:
2006

Citing 1
Cited 5

Fundamentals of digital image processing

Fundamentals of digital image processing

Automated recognition of cellular phenotypes by support vector machines with feature reduction

International Journal of Knowledge-based and Intelligent Engineering Systems - Extended papers selected from KES-2006
Hierarchical SOMs: Segmentation of Cell-Migration Images

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Annotation and retrieval of cell images

IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Automated analysis of remyelination therapy for spinal cord injury

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Automated recognition of cellular phenotypes by support vector machines with feature reduction

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we describe a new bioimage informatics system developed for high content screening (HCS) applications with the goal to extract and analyze phenotypic features of hundreds of thousands of mitotic cells simultaneously. The system introduces the algorithm of multi-phenotypic mitotic analysis (MMA) and integrates that with algorithms of correlation analysis and compound clustering used in gene microarray studies. The HCS-MMA system combines different phenotypic information of cellular images obtained from three-channel acquisitions to distinguish and label individual cells at various phases of mitosis. The proposed system can also be used to extract and count the number of cells in each phase in cell-based assay experiments and archive the extracted data into a structured database for more sophisticated statistical and data analysis. To recognize different mitotic phases, binary patterns are set up based on a known biological mitotic spindle model to characterize cellular morphology of actin, microtubules, and DNA. To illustrate its utility, the HCS-MMA system has been applied to screen the quantitative response of 320 different drug compounds in suppressing Monastrol. The results are validated and evaluated by comparing the performance of HCS-MMA with visual analysis, as well as clustering of the drug compounds under evaluation.