Article

Biophysical and biomolecular determination of cellular age in humans

  • Nature Biomedical Engineering 1, Article number: 0093 (2017)
  • doi:10.1038/s41551-017-0093
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Abstract

Ageing research has focused either on assessing organ- and tissue-based changes, such as lung capacity and cardiac function, or on changes at the molecular scale such as gene expression, epigenetic modifications and metabolism. Here, by using a cohort of 32 samples of primary dermal fibroblasts collected from individuals between 2 and 96 years of age, we show that the degradation of functional cellular biophysical features—including cell mechanics, traction strength, morphology and migratory potential—and associated descriptors of cellular heterogeneity predict cellular age with higher accuracy than conventional biomolecular markers. We also demonstrate the use of high-throughput single-cell technologies, together with a deterministic model based on cellular features, to compute the cellular age of apparently healthy males and females, and to explore these relationships in cells from individuals with Werner syndrome and Hutchinson–Gilford progeria syndrome, two rare genetic conditions that result in phenotypes that show aspects of premature ageing. Our findings suggest that the quantification of cellular age may be used to stratify individuals on the basis of cellular phenotypes and serve as a biological proxy of healthspan.

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Acknowledgements

We acknowledge the financial support for this work by the National Institutes of Health; grant numbers U54CA143868 (D.W.), R01CA174388 (D.W.) and P30AG021334 Johns Hopkins Older Americans Independence Center (J.W.). Special thanks to M. Maggioni, and Q-L. Xue for feedback and discussion with regards to the statistical methodologies employed in this study.

Author information

Affiliations

  1. Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.

    • Jude M. Phillip
    • , Pei-Hsun Wu
    • , Wadsworth Williams
    • , Shaun McGovern
    • , Jena Daya
    • , Ivie Aifuwa
    • , Jerry S. H. Lee
    •  & Denis Wirtz
  2. Johns Hopkins Physical Sciences—Oncology Center, Johns Hopkins University, Baltimore, Maryland 21218, USA.

    • Jude M. Phillip
    • , Pei-Hsun Wu
    • , Daniele M. Gilkes
    • , Ivie Aifuwa
    •  & Denis Wirtz
  3. Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, USA.

    • Jude M. Phillip
    • , Pei-Hsun Wu
    • , Daniele M. Gilkes
    • , Wadsworth Williams
    • , Shaun McGovern
    • , Jena Daya
    • , Ivie Aifuwa
    •  & Denis Wirtz
  4. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

    • Daniele M. Gilkes
    •  & Denis Wirtz
  5. Department of Biomedical Engineering, Yale University, New Haven, Connecticut 06520, USA.

    • Jonathan Chen
    •  & Rong Fan
  6. Center for Strategic Scientific Initiatives, Office of the Director, National Cancer Institute, National Institute of Health, Bethesda, Maryland 20892, USA.

    • Jerry S. H. Lee
  7. Department of Medicine, Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA.

    • Jeremy Walston
  8. Department of Pathology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.

    • Denis Wirtz

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Contributions

J.M.P., D.W., J.W. and J.S.H.L. conceived and designed the study. J.M.P., W.W., S.M., J.D. and I.A. performed the experiments. J.M.P., P.-H.W. and D.W. analysed the results. P.-H.W. and J.M.P. developed the analysis software and algorithms. J.C. and R.F. generated and analysed the secretion microchip data. D.W., P.-H.W., D.M.G. and J.W. supervised the study. J.M.P., D.W. and J.W. wrote and edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Denis Wirtz.

Supplementary information

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    Supplementary Information

    Supplementary figures, tables and references.

Excel files

  1. 1.

    Supplementary dataset

    Replicative history of the cells, and parameter list and correlations.