About This Resource

Live-cell imaging can be used to capture spatial-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, we developed the computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling (HMM) is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements, with Bayesian model selection performed to ensure that the simplest, most parsimonious representation is selected.

For more information on SAPHIRE, please see our recent publication:

Gordonov, S., Hwang, M.K., Wells, A., Gertler, F.B., Lauffenburger, D., Bathe, M. Time-series modeling of live-cell shape dynamics for image-based phenotypic profiling. Integrative Biology, DOI: 10.1039/C5IB00283D (2015).