Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Abstract

To design and engineer materials inspired by the properties of the brain, whether for mechanical simulants or for tissue regeneration studies, the brain tissue itself must be well characterized at various length and time scales. Like many biological tissues, brain tissue exhibits a complex, hierarchical structure. However, in contrast to most other tissues, brain is of very low mechanical stiffness, with Young's elastic moduli E on the order of 100s of Pa. This low stiffness can present challenges to experimental characterization of key mechanical properties. Here, we demonstrate several mechanical characterization techniques that have been adapted to measure the elastic and viscoelastic properties of hydrated, compliant biological materials such as brain tissue, at different length scales and loading rates. At the microscale, we conduct creep-compliance and force relaxation experiments using atomic force microscope-enabled indentation. At the mesoscale, we perform impact indentation experiments using a pendulum-based instrumented indenter. At the macroscale, we conduct parallel plate rheometry to quantify the frequency dependent shear elastic moduli. We also discuss the challenges and limitations associated with each method. Together these techniques enable an in-depth mechanical characterization of brain tissue that can be used to better understand the structure of brain and to engineer bio-inspired materials.

Peptide‐modified, hyaluronic acid‐based hydrogels as a 3D culture platform for neural stem/progenitor cell engineering

“…The average complex shear modulus of HA hydrogels was found to be 188 AE 42 Pa with a range of 134-282 Pa, which is consistent with native CNS tissue. 55,70,71 Hydrogel storage modulus was not significantly affected by the addition of up to 500 μM peptide ( Supplementary Fig. 1).…”

Physical and Mechanical Characterization of Fibrin-Based Bioprinted Constructs Containing Drug-Releasing Microspheres for Neural Tissue Engineering Applications

“…Their results emphasize the importance of considering substrate stiffness in the development of neural tissue-engineered scaffolds [20]. Given that neural tissues have low mechanical stiffness-with Young s elastic moduli (E) in the order of 0.1 kilopascal (kPa)-compared to other tissues present in the body [21,22], it is challenging to measure the mechanical properties using standard techniques, such as atomic force microscopy (AFM), impact indentation, and rheometry because these methods are indirect and require several assumptions that lead to variances in data across groups [19,21,[23][24][25][26][27][28]. Such methods use mathematical modeling to correlate experimental measurements, such as displacement and load to stress and strain, to evaluate elastic modulus [19].…”

Viscoelastic Cell Microenvironment: Hydrogel‐Based Strategy for Recapitulating Dynamic ECM Mechanics

“…Reproduced with permission. [ 144 ] Copyright 2016, Journal of Visualized Experiments. G) Passive microrheology technique.…”

“…With a rapid development of material characterization technology, some techniques, for instance, indentation‐based techniques and particle‐based microrheology, are available to study viscoelastic properties at the microscale that is more closely to the scale of cell–matrix interactions. [ 59,60,139 ] Indentation‐based techniques, mainly including depth‐sensing nanoindentation [ 140–143 ] and atomic force microscopy (AFM)‐based indentation, [ 144–148 ] have been widely used to explore the microscale viscoelasticity of the hydrogels, tissues, and even cells. For depth‐sensing nanoindentation, a calibrated force is applied through a probe tip, and the displacement of probe motion in a plane perpendicular to the sample is directly measured, thereby obtaining the indentation depth (Figure 3E).…”

“…This technique has been used to determine the viscoelasticity of anatomically distinct regions of the brain, for instance, the corpus callosum and cortex. [ 144 ] With raw data from the constant applied depth, measured force, and knowledge of the probe geometry, relaxation modulus G R (t) could be computed and obtained.…”