Advanced Functional MaterialsBioengineeringPeer-reviewed journal article
Programmable viscoelastic hydrogels uncover mechano-sensing timescales and direct cell polarity
Syuan‐Ku HsiaoiD, Markus MukenhirniD, Carsten WerneriD, Alf HonigmanniD, Elisha KriegiD
Abstract
Mechanical interactions between cells and their three-dimensional environment govern fundamental processes in tissue development and disease. Yet, how cells interrogate and respond to mechanical cues remains incompletely understood. Reproducing the mechanical properties of natural tissues in vitro is a key challenge, as these tissues exhibit complex viscoelastic behaviors and undergo continuous remodeling throughout an organisms development. Here, we leverage principles of dynamic DNA nanotechnology to introduce programmable mechanical cues into a synthetic hydrogel matrix that guides and interrogates the development of embedded cells. By systematically modulating matrix stress relaxation and stiffness, we uncover two distinct timescales of mechano-sensitive processes controlling apical-basal polarity in Madin-Darby Canine Kidney (MDCK) cells: the fast timescale (1-3 min) is linked to rapid integrin-mediated signaling, while the slower process (3-9 h) is associated with cytoskeletal reinforcements. Our analysis highlights that the commonly reported matrix "stiffness" value, often measured as the storage modulus at [~]1 Hz, has limited physiological relevance. These findings prompted us to develop a novel switchable DNA crosslinker that enables dynamic changes in viscoelasticity during ongoing cell culture. This controlled matrix reconfiguration induces reversible cell polarity inversions and guides the morphogenesis of complex multicellular structures. The material platform therefore offers unprecedented control over the mechanical microenvironment, opening new avenues for advanced applications in biophysics, tissue engineering, and disease modeling.
- research focus: cell signaling & trafficking
- research focus: development & morphogenesis
- approach: deep learning
- approach: experimental assay
- Citations
- 3
- FWCI
- 1.01
- Field percentile
- top 26%
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