A High Content Imaging-Based Approach for Classifying Cellular Phenotypes

  • Joseph J. Kim
  • Sebastián L. Vega
  • Prabhas V. Moghe
Part of the Methods in Molecular Biology book series (MIMB, volume 1052)


Current methods to characterize cell–biomaterial interactions are population-based and rely on imaging or biochemical analysis of end-point biological markers. The analysis of stem cells in cultures is further challenged by the heterogeneous nature and divergent fates of stem cells, especially in complex, engineered microenvironments. Here, we describe a high content imaging-based platform capable of identifying cell subpopulations based on cell phenotype-specific morphological descriptors. This method can be utilized to identify microenvironment-responsive morphological descriptors, which can be used to parse cells from a heterogeneous cell population based on emergent phenotypes at the single-cell level and has been successfully deployed to forecast long-term cell lineage fates and screen regenerative phenotype-prescriptive biomaterials.


Bioimage informatics High content image analysis Pluripotent stem cells Phenotypic classification Immunocytochemistry 



This study was partially supported by NIH P41 EB001046 (RESBIO, Integrated Resources for Polymeric Biomaterials), Rutgers University Academic Excellence Fund, and NSF Stem Cell IGERT 0801620.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Joseph J. Kim
    • 1
  • Sebastián L. Vega
    • 2
  • Prabhas V. Moghe
    • 1
  1. 1.Department of Biomedical Engineering, Chemical and Biochemical EngineeringRutgers UniversityPiscatawayUSA
  2. 2.Chemical and Biochemical EngineeringRutgers UniversityPiscatawayUSA

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