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Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.
Simon Gordonov1, Mun Kyung Hwang, Alan Wells
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Integrative Biology : Quantitative Biosciences From Nano to Macro
|December 15, 2015
Summary
We developed SAPHIRE, a computational framework for analyzing live-cell imaging data. This method characterizes cell responses over time, revealing dynamic cellular behaviors for improved drug discovery and understanding drug mechanisms.
Area of Science:
- Cellular biology
- Computational biology
- Pharmacology
Background:
- Live-cell imaging captures dynamic cellular processes beyond fixed-cell methods.
- Analyzing temporal dynamics of individual cells requires advanced computational tools.
- Existing methods may not fully capture heterogeneous cellular responses.
Purpose of the Study:
- To present SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses), an experimental-computational framework.
- To characterize and classify temporal dynamics of individual cells from live-cell imaging datasets.
- To enable analysis of asynchronous cell populations.
Main Methods:
- Utilized hidden Markov modeling to infer morphological states and state-switching properties.
- Analyzed image-derived cell shape measurements from time-series data.
- Applied time-series modeling to individual cells, including those with actin and nuclear reporters.
Main Results:
- SAPHIRE effectively characterizes phenotypic cellular responses from time-series imaging.
- Time-series modeling captured heterogeneous dynamic cellular responses.
- Results demonstrated improved drug classification and insights into drug mechanisms compared to fixed-cell approaches.
Conclusions:
- SAPHIRE provides a robust framework for analyzing live-cell imaging data.
- The approach enhances understanding of dynamic cellular behaviors and drug action.
- This computational tool offers valuable insights for drug discovery and development.
