ArticleActa biomaterialia2024
Multiscale elasticity mapping of biological samples in 3D at optical resolution.
Article in Acta biomaterialia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Overcoming Mechanical Blindness: Adaptive Surgical Instrument Design for the Heterogeneous Landscape of Uterine Fibroids.Research (Washington, D.C.) · 2026Review
- Biophysical, cellular, and mouse model approaches to investigate the mechanical regulation of folliculogenesis.Seminars in cell & developmental biology · 2025Review
- Probing the physical hallmarks of cancer.Nature methods · 2025Review
- Measuring and manipulating mechanical forces during development.Nature cell biology · 2025Review
- Micromechanics of lung capillaries across mouse lifespan and in positive- vs negative-pressure ventilation.npj biological physics and mechanics · 2025Article
- Mathematical Models for Ultrasound Elastography: Recent Advances to Improve Accuracy and Clinical Utility.Bioengineering (Basel, Switzerland) · 2024Review
- Mapping the strain-stiffening behavior of the lung and lung cancer at microscale resolution using the crystal ribcage.Frontiers in network physiology · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The mechanical properties of biological tissues have emerged as an integral determinant of tissue function in health and disease. Nonetheless, characterizing the elasticity of biological samples in 3D and at high resolution remains challenging. Here, we present a µElastography platform: a scalable elastography system that maps the elastic properties of tissues from cellular to organ scales. The platform leverages the use of a biocompatible, thermo-responsive hydrogel to deliver compressive stress to a biological sample and track its resulting deformation. By surrounding the specimen with a reference hydrogel of known Young's modulus, we are able to map the absolute values of elastic properties in biological samples. We validate the experimental and computational components of the platform using a hydrogel phantom and verify the system's ability to detect internal mechanical heterogeneities. We then apply the platform to map the elasticity of multicellular spheroids and the murine lymph node. With these applications, we demonstrate the platform's ability to map tissue elasticity at internal planes of interest, as well as capture mechanical heterogeneities neglected by most macroscale characterization techniques. The µElastography platform, designed to be implementable in any biology lab with access to 3D microscopy (e.g., confocal, multiphoton, or optical coherence microscopy), will provide the capability to characterize the mechanical properties of biological samples to labs across the large community of biological sciences by eliminating the need of specialized instruments such as atomic force microscopy. STATEMENT OF SIGNIFICANCE: Understanding the elasticity of biological tissues is of great importance, but characterizing these properties typically requires highly specialized equipment. Utilizing stimulus-responsive hydrogels, we present a scalable, hydrogel-based elastography method that uses readily available reagents and imaging modalities to generate resolved maps of internal elasticity within biomaterials and biological samples at optical resolution. This new approach is capable of detecting internal stiffness heterogeneities within the 3D bulk of samples and is highly scalable across both imaging modalities and biological length scales. Thus, it will have significant impact on the measurement capabilities of labs studying engineered biomaterials, mechanobiology, disease progression, and tissue engineering and development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.