Time series modeling of live-cell shape dynamics for image-based phenotypic profiling. Issue 1 (11th December 2015)
- Record Type:
- Journal Article
- Title:
- Time series modeling of live-cell shape dynamics for image-based phenotypic profiling. Issue 1 (11th December 2015)
- Main Title:
- Time series modeling of live-cell shape dynamics for image-based phenotypic profiling
- Authors:
- Gordonov, Simon
Hwang, Mun Kyung
Wells, Alan
Gertler, Frank B.
Lauffenburger, Douglas A.
Bathe, Mark - Abstract:
- Abstract : Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. 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 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.Abstract : Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. 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 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. The software is available at Web:http://saphire-hcs.org . … (more)
- Is Part Of:
- Integrative biology. Volume 8:Issue 1(2016:Jan.)
- Journal:
- Integrative biology
- Issue:
- Volume 8:Issue 1(2016:Jan.)
- Issue Display:
- Volume 8, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 8
- Issue:
- 1
- Issue Sort Value:
- 2016-0008-0001-0000
- Page Start:
- 73
- Page End:
- 90
- Publication Date:
- 2015-12-11
- Subjects:
- Biology -- Periodicals
Technology -- Periodicals
Biological systems -- Periodicals
570.5 - Journal URLs:
- http://www.rsc.org/Publishing/Journals/ib/Index.asp ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/c5ib00283d ↗
- Languages:
- English
- ISSNs:
- 1757-9694
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9830.238000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 1687.xml