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

Xiaobo Zhou, Xinhua Cao, Zach Perlman, Stephen T.C. Wong

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)115-125
Number of pages11
JournalJournal of Biomedical Informatics
Issue number2
StatePublished - Apr 2006


  • Cellular phenotyping
  • Compound clustering
  • High content analysis
  • High content screening
  • Image analysis
  • Pattern recognition

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Computer Science (miscellaneous)
  • Catalysis


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