2016
DOI: 10.1039/c5ib00283d
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Time series modeling of live-cell shape dynamics for image-based phenotypic profiling

Abstract: Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time ser… Show more

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Cited by 65 publications
(71 citation statements)
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“…However, individual gene products typically only reflect partial dynamical information of a CPT process, and simultaneous fluorescence labeling of multiple genes is challenging. Recently, tracking cell morphological features through live cell imaging, emerges as a means of extracting temporal information about cellular processes in conjunction with expression-based cell state characterization( 11, 3336 ). Cellular and subcellular morphology reflects collective gene expression pattern and cell phenotype ( 37, 38 ).…”
Section: Discussionmentioning
confidence: 99%
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“…However, individual gene products typically only reflect partial dynamical information of a CPT process, and simultaneous fluorescence labeling of multiple genes is challenging. Recently, tracking cell morphological features through live cell imaging, emerges as a means of extracting temporal information about cellular processes in conjunction with expression-based cell state characterization( 11, 3336 ). Cellular and subcellular morphology reflects collective gene expression pattern and cell phenotype ( 37, 38 ).…”
Section: Discussionmentioning
confidence: 99%
“…The necessity of live cell trajectories has been illustrated in a number of studies, such as the information capacity of a signal transduction network ( 9 ), incoherent-feedforward loops to detect only fold change but not the absolute change of an input signal ( 10 ), and step-wise cellular responses to drugs ( 11 ). Two recent studies conclude a linear path for EMT from analyzing single cell RNA-seq and proteomic data ( 12, 13 ).…”
Section: Introductionmentioning
confidence: 99%
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“…A number of studies focus on the dynamics of cell shape rather than static shapes, e.g. by using time series of shape descriptors (42) or modeling trajectories in shape space (43).…”
Section: Further Reading and Softwarementioning
confidence: 99%
“…While these tools allow us to acquire substantially more data, it is still incumbent on the scientist to transform data into useful and actionable information. Manual image processing is unfeasible for those datasets, and automated data analysis techniques and methods are continuously being developed, even more now with the advent of machine learning tools, that often perform on par with human observers. Open‐source artificial intelligence (AI) software libraries such as Keras and TensorFlow, enable the power of neural networks and AI for the analysis of the datasets.…”
Section: Introductionmentioning
confidence: 99%