Optimal design for multi-compartment cell elasticity estimation using AFM and super-resolution imaging.

Area of Science:

• Cellular biomechanics
• Mechanobiology
• Biophysics

Background:

• Estimating mechanical properties of subcellular compartments is crucial for understanding cell behavior in health and disease.
• Existing methods like atomic force microscopy (AFM) face challenges in accuracy and reproducibility due to measurement variability and ill-posed inverse problems.
• Quantifying the mechanical properties of the nuclear membrane, cytoplasm, and nucleoplasm is particularly complex.

Purpose of the Study:

• To develop an integrated experimental-computational framework for optimal inverse approach design.
• To estimate multi-compartment cell mechanical properties with improved accuracy and reproducibility.
• To minimize the dependence of property estimations on AFM probing locations.

Main Methods:

• Constructed a 3-D computational model of a human umbilical vein endothelial cell (HUVEC) using super-resolution imaging.
• Employed an inverse modeling approach to fit experimental data to a hyperelastic constitutive model, incorporating large-deformation nonlinearities.
• Utilized visco-hyperelasticity simulations to investigate viscous effects.

Main Results:

• Successfully quantified the mechanical properties of the nucleoplasm, nuclear membrane, and cytoplasm.
• Achieved minimal dependence of these property estimations on loading conditions.
• Demonstrated an approach that improves the reproducibility of mechanical property estimations.

Conclusions:

• The developed framework aids in standardizing biomechanical characterizations of subcellular structures.
• Enhances consistency and reproducibility in mechanobiological studies.
• Contributes to a better understanding of mechanotransduction's role in disease progression.