Non-invasive single-cell biomechanical analysis using live-imaging datasets

Yanthe E. Pearson, Amanda W. Lund, Alex W.H. Lin, Chee P. Ng, Aysha Alsuwaidi, Sara Azzeh, Deborah L. Gater, Jeremy Teo

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3351-3364
Number of pages14
JournalJournal of Cell Science
Volume129
Issue number17
DOIs
StatePublished - Jan 1 2016

Fingerprint

Single-Cell Analysis
Mesenchymal Stromal Cells
Endpoint Determination
T-Lymphocytes
Atomic Force Microscopy
Datasets
Biomechanical Phenomena
Viscosity
Phenotype

Keywords

  • Cell biomechanics
  • Cell migration
  • Live imaging
  • Mechanobiology
  • Mesenchymal stem cells
  • T-lymphocyte cells

ASJC Scopus subject areas

  • Cell Biology

Cite this

Pearson, Y. E., Lund, A. W., Lin, A. W. H., Ng, C. P., Alsuwaidi, A., Azzeh, S., ... Teo, J. (2016). Non-invasive single-cell biomechanical analysis using live-imaging datasets. Journal of Cell Science, 129(17), 3351-3364. https://doi.org/10.1242/jcs.191205

Non-invasive single-cell biomechanical analysis using live-imaging datasets. / Pearson, Yanthe E.; Lund, Amanda W.; Lin, Alex W.H.; Ng, Chee P.; Alsuwaidi, Aysha; Azzeh, Sara; Gater, Deborah L.; Teo, Jeremy.

In: Journal of Cell Science, Vol. 129, No. 17, 01.01.2016, p. 3351-3364.

Research output: Contribution to journalArticle

Pearson, YE, Lund, AW, Lin, AWH, Ng, CP, Alsuwaidi, A, Azzeh, S, Gater, DL & Teo, J 2016, 'Non-invasive single-cell biomechanical analysis using live-imaging datasets', Journal of Cell Science, vol. 129, no. 17, pp. 3351-3364. https://doi.org/10.1242/jcs.191205
Pearson YE, Lund AW, Lin AWH, Ng CP, Alsuwaidi A, Azzeh S et al. Non-invasive single-cell biomechanical analysis using live-imaging datasets. Journal of Cell Science. 2016 Jan 1;129(17):3351-3364. https://doi.org/10.1242/jcs.191205
Pearson, Yanthe E. ; Lund, Amanda W. ; Lin, Alex W.H. ; Ng, Chee P. ; Alsuwaidi, Aysha ; Azzeh, Sara ; Gater, Deborah L. ; Teo, Jeremy. / Non-invasive single-cell biomechanical analysis using live-imaging datasets. In: Journal of Cell Science. 2016 ; Vol. 129, No. 17. pp. 3351-3364.
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