Journal
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Volume 59, Issue 9, Pages 1851-1864Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-021-02418-7
Keywords
Phenotype; Clustering; Cell tracking; Cell morphology; Cell motility
Categories
Funding
- National Science Foundation [CMMI-1334611]
- IGERT fellowship under NSF-DGE [1068780]
- Ronald E. McNair Program
- Renee Crown University Honors Program
- Donofrio Scholars Program
- LSAMP program
- Division Of Graduate Education
- Direct For Education and Human Resources [1068780] Funding Source: National Science Foundation
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In this study, a computational, image analysis-based approach was introduced to accurately identify and characterize distinct functional cell phenotypic subpopulations. Using a heterogeneous model system of endothelial and smooth muscle cells, it was found that cells exhibited different motility rates in co-culture compared to monoculture. This non-destructive and non-invasive imaging method can be broadly applied to heterogeneous cell culture model systems to advance understanding of how heterogeneity affects cell phenotype.
In vitro cell culture model systems often employ monocultures, despite the fact that cells generally exist in a diverse, heterogeneous microenvironment in vivo. In response, heterogeneous cultures are increasingly being used to study how cell phenotypes interact. However, the ability to accurately identify and characterize distinct phenotypic subpopulations within heterogeneous systems remains a major challenge. Here, we present the use of a computational, image analysis-based approach-comprising automated contour-based cell tracking for feature identification, principal component analysis for feature reduction, and partitioning around medoids for subpopulation characterization-to non-destructively and non-invasively identify functionally distinct cell phenotypic subpopulations from live-cell microscopy image data. Using a heterogeneous model system of endothelial and smooth muscle cells, we demonstrate that this approach can be applied to both mono and co-culture nuclear morphometric and motility data to discern cell phenotypic subpopulations. Morphometric clustering identified minimal difference in mono- versus co-culture, while motility clustering revealed that a portion of endothelial cells and smooth muscle cells adopt increased motility rates in co-culture that are not observed in monoculture. We anticipate that this approach using non-destructive and non-invasive imaging can be applied broadly to heterogeneous cell culture model systems to advance understanding of how heterogeneity alters cell phenotype.
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