title: A high content imaging-based approach for classifying cellular phenotypes.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA. P41 EB001046
authors.forename: Joseph J
authors.initials: JJ
authors.lastname: Kim
authors.forename: Sebastián L
authors.initials: SL
authors.lastname: Vega
authors.forename: Prabhas V
authors.initials: PV
authors.lastname: Moghe
mesh_terms: D001672:Biocompatible Materials / Q000737:chemistry; D015415:Biomarkers; D001854:Bone Marrow Cells; D018929:Cell Culture Techniques; D002454:Cell Differentiation; D002478:Cells, Cultured; D006801:Humans; D059630:Mesenchymal Stem Cells / Q000166:cytology*; D061848:Optical Imaging / Q000379:methods*; D039904:Pluripotent Stem Cells / Q000166:cytology*
chemical_list: D001672:Biocompatible Materials; D015415:Biomarkers