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J Biomol Screen. 2016 Oct;21(9):887-96. doi: 10.1177/1087057116652064. Epub 2016 Jun 2.

A Novel Automated High-Content Analysis Workflow Capturing Cell Population Dynamics from Induced Pluripotent Stem Cell Live Imaging Data.

Author information

1
Centre for Stem Cells and Regenerative Medicine, King's College London, Tower Wing, Guy's Hospital, London, UK Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK National Institute for Health Research, Biomedical Research Centre for Mental Health, and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK maximilian.kerz@kcl.ac.uk.
2
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK National Institute for Health Research, Biomedical Research Centre for Mental Health, and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, UK.
3
Centre for Stem Cells and Regenerative Medicine, King's College London, Tower Wing, Guy's Hospital, London, UK.

Abstract

Most image analysis pipelines rely on multiple channels per image with subcellular reference points for cell segmentation. Single-channel phase-contrast images are often problematic, especially for cells with unfavorable morphology, such as induced pluripotent stem cells (iPSCs). Live imaging poses a further challenge, because of the introduction of the dimension of time. Evaluations cannot be easily integrated with other biological data sets including analysis of endpoint images. Here, we present a workflow that incorporates a novel CellProfiler-based image analysis pipeline enabling segmentation of single-channel images with a robust R-based software solution to reduce the dimension of time to a single data point. These two packages combined allow robust segmentation of iPSCs solely on phase-contrast single-channel images and enable live imaging data to be easily integrated to endpoint data sets while retaining the dynamics of cellular responses. The described workflow facilitates characterization of the response of live-imaged iPSCs to external stimuli and definition of cell line-specific, phenotypic signatures. We present an efficient tool set for automated high-content analysis suitable for cells with challenging morphology. This approach has potentially widespread applications for human pluripotent stem cells and other cell types.

KEYWORDS:

CellProfiler; HipDynamics; high-content screening; iPSC; live imaging

PMID:
27256155
PMCID:
PMC5030730
DOI:
10.1177/1087057116652064
[Indexed for MEDLINE]
Free PMC Article

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