Kerz Maximilian, Folarin Amos, Meleckyte Ruta, Watt Fiona M, Dobson Richard J, Danovi Davide
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
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.
J Biomol Screen. 2016 Oct;21(9):887-96. doi: 10.1177/1087057116652064. Epub 2016 Jun 2.
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.
大多数图像分析流程依赖于每个图像的多个通道以及用于细胞分割的亚细胞参考点。单通道相差图像往往存在问题，特别是对于形态不佳的细胞，如诱导多能干细胞（iPSC）。实时成像带来了进一步的挑战，因为引入了时间维度。评估不易与包括终点图像分析在内的其他生物学数据集整合。在此，我们提出了一种工作流程，该流程结合了一种基于CellProfiler的新型图像分析流程，通过强大的基于R的软件解决方案实现单通道图像的分割，将时间维度缩减为单个数据点。这两个软件包相结合，仅利用相差单通道图像就能对iPSC进行可靠的分割，并使实时成像数据能够轻松地整合到终点数据集中，同时保留细胞反应的动态变化。所描述的工作流程有助于表征实时成像的iPSC对外部刺激的反应，并定义细胞系特异性的表型特征。我们提出了一套适用于形态具有挑战性的细胞的自动化高内涵分析的高效工具集。这种方法对人类多能干细胞和其他细胞类型可能具有广泛的应用。
