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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