Time series modeling of live-cell shape dynamics for image-based phenotypic profiling
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Bathe_Time series modeling.pdf
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Author(s)
Wells, Alan
Gordonov, Simon
Hwang, Mun Kyung
Gertler, Frank
Lauffenburger, Douglas A
Bathe, Mark
Date Issued
November 2015
Journal
Integrative Biology
Publisher
Royal Society of Chemistry
Citation
Gordonov, Simon et al. “Time Series Modeling of Live-Cell Shape Dynamics for Image-Based Phenotypic Profiling.” Integr. Biol. 8.1 (2016): 73–90.
Version
Author's final manuscript
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 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. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations.
Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton–regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Koch Institute for Integrative Cancer Research at MIT
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1039/c5ib00283d
