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Cell organization in soft media due to active mechanosensing

Open access
Bischofs, I. B., Schwarz, U. S.·2003·Proceedings of the National Academy of Sciences

Abstract

Adhering cells actively probe the mechanical properties of their environment and use the resulting information to position and orient themselves. We show that a large body of experimental observations can be consistently explained from one unifying principle, namely that cells strengthen contacts and cytoskeleton in the direction of large effective stiffness. Using linear elasticity theory to model the extracellular environment, we calculate optimal cell organization for several situations of interest and find excellent agreement with experiments for fibroblasts, both on elastic substrates and in collagen gels: cells orient in the direction of external tensile strain; they orient parallel and normal to free and clamped surfaces, respectively; and they interact elastically to form strings. Our method can be applied for rational design of tissue equivalents. Moreover, our results indicate that the concept of contact guidance has to be reevaluated. We also suggest that cell–matrix contacts are up-regulated by large effective stiffness in the environment because, in this way, build-up of force is more efficient.

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Crossref · 10.1073/pnas.1233544100

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Metadata

Authors
Bischofs, I. B., & Schwarz, U. S.
Venue
Proceedings of the National Academy of Sciences
Published
2003-07-25
Volume
100
Issue
16
Pages
9274-9279
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2013541867
Cited by
304
References
2
FWCI
4.35
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Cell organization in soft media due to active mechanosensing. https://app.cochat.ai/papers/10.1073/pnas.1233544100
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Bischofs, I. B., & Schwarz, U. S. (2003). Cell organization in soft media due to active mechanosensing. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1233544100

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.