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

The transduction of external forces to the internal components of the cell has critical implications for cell behavior in health and disease. Despite advances in techniques such as atomic force microscopy (AFM), accurately and reproducibly estimating the mechanical properties of multiple individual subcellular compartments remains challenging. This is often due to variability in measurements, the lack of a standard "inverse" approach to estimate unknown properties, and an often ill-posed inverse problem. This study presents an integrated experimental-computational framework for the optimal design of an inverse approach to estimate multi-compartment cell properties, focusing on the behavior of the nuclear membrane, cytoplasm, and nucleoplasm. The optimal design approach identifies an ideal set of AFM measurements that minimizes the dependence of the estimated multi-compartment properties on AFM probing locations, thereby improving the reproducibility of mechanical property estimations.

Super-resolution imaging was used to construct a 3-D computational model of a human umbilical vein endothelial cell (HUVEC). An inverse modeling approach was then used to fit experimental data to a hyperelastic constitutive model, accounting for large-deformation nonlinearities. Simulations incorporating visco-hyperelasticity were conducted to explore viscous effects.

Our approach leads to quantification of the mechanical properties of the nucleoplasm, nuclear membrane, and cytoplasm with minimal dependence on loading conditions.

We expect our approach to assist with standardizing the biomechanical characterizations of subcellular structures, improve the consistency and reproducibility of the estimations across mechanobiological studies, and ultimately improve our understanding of the role of mechanotransduction in disease progression.