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Cytoskeletal stiffening in synthetic hydrogels

Author

Listed:
  • Paula de Almeida

    (Institute for Molecules and Materials)

  • Maarten Jaspers

    (Institute for Molecules and Materials)

  • Sarah Vaessen

    (Institute for Molecules and Materials)

  • Oya Tagit

    (Radboud University Medical Centre, Radboud Institute for Molecular Life Sciences, Department of Tumor Immunology)

  • Giuseppe Portale

    (University of Groningen, Faculty of Science and Engineering)

  • Alan E. Rowan

    (Institute for Molecules and Materials
    Australian Institute for Bioengineering and Nanotechnology)

  • Paul H. J. Kouwer

    (Institute for Molecules and Materials)

Abstract

Although common in biology, controlled stiffening of hydrogels in vitro is difficult to achieve; the required stimuli are commonly large and/or the stiffening amplitudes small. Here, we describe the hierarchical mechanics of ultra-responsive hybrid hydrogels composed of two synthetic networks, one semi-flexible and stress-responsive, the other flexible and thermoresponsive. Heating collapses the flexible network, which generates internal stress that causes the hybrid gel to stiffen up to 50 times its original modulus; an effect that is instantaneous and fully reversible. The average generated forces amount to ~1 pN per network fibre, which are similar to values found for stiffening resulting from myosin molecular motors in actin. The excellent control, reversible nature and large response gives access to many biological and bio-like applications, including tissue engineering with truly dynamic mechanics and life-like matter.

Suggested Citation

  • Paula de Almeida & Maarten Jaspers & Sarah Vaessen & Oya Tagit & Giuseppe Portale & Alan E. Rowan & Paul H. J. Kouwer, 2019. "Cytoskeletal stiffening in synthetic hydrogels," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08569-4
    DOI: 10.1038/s41467-019-08569-4
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    Cited by:

    1. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    2. Bertasini, Davide & Battista, Federico & Rizzioli, Fabio & Frison, Nicola & Bolzonella, David, 2023. "Decarbonization of the European natural gas grid using hydrogen and methane biologically produced from organic waste: A critical overview," Renewable Energy, Elsevier, vol. 206(C), pages 386-396.

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