The physiological state of a cell is governed by a multitude of processes and can be described by a combination of mechanical, spatial and temporal properties. Quantifying cell dynamics at multiple scales is essential for comprehensive studies of cellular function, and remains a challenge for traditional end-point assays. We introduce an efficient, non-invasive computational tool that takes time-lapse images as input to automatically detect, segment and analyze unlabeled live cells; the program then outputs kinematic cellular shape and migration parameters, while simultaneously measuring cellular stiffness and viscosity. We demonstrate the capabilities of the program by testing it on human mesenchymal stem cells (huMSCs) induced to differentiate towards the osteoblastic (huOB) lineage, and T-lymphocyte cells (T cells) of naïve and stimulated phenotypes. The program detected relative cellular stiffness differences in huMSCs and huOBs that were comparable to those obtained with studies that utilize atomic force microscopy; it further distinguished naïve from stimulated T cells, based on characteristics necessary to invoke an immune response. In summary, we introduce an integrated tool to decipher spatiotemporal and intracellular dynamics of cells, providing a new and alternative approach for cell characterization.
The authors declare no competing or financial interests.
Y.E.P. and J.C.M.T. conceived, developed, implemented and analyzed the data output of CSPA and image-morphing programs respectively, together both authors wrote the paper. C.P.N. and J.C.M.T. designed the huMSC study and collected the data. A.W.L. and J.C.M.T. designed the T cell study and collected the data. All authors contributed to the application of algorithms, manual data collection and review of the manuscript. D.L.G. edited the manuscript.
This manuscript was made possible through Khalifa University of Science, Technology and Research internal research funds [grant number KUIRF 210034]; and the Al Jalila Foundation [grant number AJF201407].
A comprehensive user guide for both CSPA and image morphing with a tutorial and detailed explanation of the model and algorithms, together with the MATLAB source code is available for download here: https://figshare.com/s/a207830096d7cdd32164
Supplementary information available online at http://jcs.biologists.org/lookup/doi/10.1242/jcs.191205.supplemental
- Received April 19, 2016.
- Accepted July 12, 2016.
- © 2016. Published by The Company of Biologists Ltd