Using mechanics for improved tissue engineering

In this webinar, Andrew Miller explains how stiffness testing and nanoindentation can strengthen tissue engineering and regenerative medicine workflows. Using published examples, he shows how mechanical measurements help characterize healthy versus diseased tissues, evaluate wound healing outcomes, and validate biomaterials and scaffolds before moving to downstream studies. The session also introduces what nanoindentation can measure beyond Young’s modulus, including viscoelasticity, creep, adhesion, and stress relaxation, with a Q&A focused on practical lab implementation.

Key takeaways and what you will learn

  • Why mechanical properties matter when moving from 2D to 3D biological models
  • What nanoindentation measures, including Young’s modulus, viscoelasticity, creep, stress relaxation, and adhesion
  • How stiffness mapping and matrix scans reveal local heterogeneity that bulk tests can miss
  • How stiffness can support tissue characterization, including differentiating healthy, fibrotic, and cancerous regions
  • How stiffness readouts can help compare wound healing strategies and hydrogel performance, including consistency and variability
  • How mechanics can be used to validate scaffold formulations for physiological relevance before in vivo or downstream studies
  • Practical considerations, including sample immobilization, measurement duration choices, and pairing with microscopy

Who should watch?

This webinar is a fit for:

  • Anyone evaluating nanoindentation as a complement to imaging, staining, and molecular assays
  • Researchers using hydrogels, scaffolds, or tissue slices and looking for quantitative mechanical readouts
  • Teams working on wound healing, fibrosis, tissue regeneration, and 3D tissue models
  • Scientists building artificial tissues, bioprinted constructs, or ECM-mimicking materials who need confidence in physiological stiffness ranges
  • Labs interested in stiffness mapping, heterogeneity analysis, and mechanical biomarkers for disease modeling

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