Quantification of substrate and cellular strains in stretchable 3D cell cultures: an experimental and computational framework.

The mechanical cell environment is a key regulator of biological processes . In living tissues, cells are embedded into the 3D extracellular matrix and permanently exposed to mechanical forces. Quantification of the cellular strain state in a 3D matrix is therefore the first step towards understanding how physical cues determine single cell and multicellular behaviour. The majority of cell assays are, however, based on 2D cell cultures that lack many essential features of the in vivo cellular environment. Furthermore, nondestructive measurement of substrate and cellular mechanics requires appropriate computational tools for microscopic image analysis and interpretation. Here, we present an experimental and computational framework for generation and quantification of the cellular strain state in 3D cell cultures using a combination of 3D substrate stretcher, multichannel microscopic imaging and computational image analysis. The 3D substrate stretcher enables deformation of living cells embedded in bead-labelled 3D collagen hydrogels. Local substrate and cell deformations are determined by tracking displacement of fluorescent beads with subsequent finite element interpolation of cell strains over a tetrahedral tessellation. In this feasibility study, we debate diverse aspects of deformable 3D culture construction, quantification and evaluation, and present an example of its application for quantitative analysis of a cellular model system based on primary mouse hepatocytes undergoing transforming growth factor (TGF-ß) induced epithelial-to-mesenchymal transition.

Contactless determination of nuclear compressibility using 3D image- and model-based analysis of drug-induced cellular deformation.

Mechanical properties of the chromatin-bearing nucleus in normal and pathological cells are of general interest for epigenetics and medicine. Conventional techniques for quantitative measurements of material properties of cellular matter are based on application of controlled forces onto the cellular or nuclear boundary and do not allow probing intracellular structures that are not directly accessible for physical contact inside the living cell. In this work, we present a novel approach for contactless determination of the nuclear compressibility (i.e. the Poisson's ratio ν) in living cells by means of image- and model-based analysis of drug-induced cell deformation. The Poisson's ratio of the HeLa cell nucleus is determined from time-series of 3D images as a parameter of constitutive model that minimizes the dissimilarity between the numerically predicted and experimentally observed images.

TGFß-induced cytoskeletal remodeling mediates elevation of cell stiffness and invasiveness in NSCLC.

Importance of growth factor (GF) signaling in cancer progression is widely acknowledged. Transforming growth factor beta (TGFß) is known to play a key role in epithelial-to-mesenchymal transition (EMT) and metastatic cell transformation that are characterized by alterations in cell mechanical architecture and behavior towards a more robust and motile single cell phenotype. However, mechanisms mediating cancer type specific enhancement of cell mechanical phenotype in response to TGFß remain poorly understood. Here, we combine high-throughput mechanical cell phenotyping, microarray analysis and gene-silencing to dissect cytoskeletal mediators of TGFß-induced changes in mechanical properties of on-small-cell lung carcinoma (NSCLC) cells. Our experimental results show that elevation of rigidity and invasiveness of TGFß-stimulated NSCLC cells correlates with upregulation of several cytoskeletal and motor proteins including vimentin, a canonical marker of EMT, and less-known unconventional myosins. Selective probing of gene-silenced cells lead to identification of unconventional myosin MYH15 as a novel mediator of elevated cell rigidity and invasiveness in TGFß-stimulated NSCLC cells. Our experimental results provide insights into TGFß-induced cytoskeletal remodeling of NSCLC cells and suggest that mediators of elevated cell stiffness and migratory activity such as unconventional cytoskeletal and motor proteins may represent promising pharmaceutical targets for restraining invasive spread of lung cancer.

Shape normalization of 3D cell nuclei using elastic spherical mapping.

Topological analysis of cells and subcellular structures on the basis of image data, is one of the major trends in modern quantitative biology. However, due to the dynamic nature of cell biology, the optical appearance of different cells or even time-series of the same cell is undergoing substantial variations in shape and texture, which makes a comparison of shapes and distances across different cells a nontrivial task. In the absence of canonical invariances, a natural approach to the normalization of cells consists of spherical mapping, enabling the analysis of targeted regions in terms of canonical spherical coordinates, that is, radial distances and angles. In this work, we present a physically-based approach to spherical mapping, which has been applied for topological analysis of multichannel confocal laser scanning microscopy images of human fibroblast nuclei. Our experimental results demonstrate that spherical mapping of entire nuclear domains can automatically be obtained by inverting affine and elastic transformations, performed on a spherical finite element template mesh.

3D finite element analysis of uniaxial cell stretching: from image to insight.

Mechanical forces play an important role in many microbiological phenomena such as embryogenesis, regeneration, cell proliferation and differentiation. Micromanipulation of cells in a controlled environment is a widely used approach for understanding cellular responses with respect to external mechanical forces. While modern micromanipulation and imaging techniques provide useful optical information about the change of overall cell contours under the impact of external loads, the intrinsic mechanisms of energy and signal propagation throughout the cell structure are usually not accessible by direct observation. This work deals with the computational modelling and simulation of intracellular strain state of uniaxially stretched cells captured in a series of images. A nonlinear elastic finite element method on tetrahedral grids was applied for numerical analysis of inhomogeneous stretching of a rat embryonic fibroblast 52 (REF 52) using a simplified two-component model of a eukaryotic cell consisting of a stiffer nucleus surrounded by a softer cytoplasm. The difference between simulated and experimentally observed cell contours is used as a feedback criterion for iterative estimation of canonical material parameters of the two-component model such as stiffness and compressibility. Analysis of comparative simulations with varying material parameters shows that (i) the ratio between the stiffness of cell nucleus and cytoplasm determines intracellular strain distribution and (ii) large deformations result in increased stiffness and decreased compressibility of the cell cytoplasm. The proposed model is able to reproduce the evolution of the cellular shape over a sequence of observed deformations and provides complementary information for a better understanding of mechanical cell response.

Anatomy- and physics-based facial animation for craniofacial surgery simulations.

A modelling approach for the realistic simulation of facial expressions of emotion in craniofacial surgery planning is presented. The method is different from conventional, non-physical techniques for character animation in computer graphics. A consistent physiological mechanism for facial expressions was assumed, which was the effect of contracting muscles on soft tissues. For the numerical solution of the linear elastic boundary values, the finite element method on tetrahedral grids was used. The approach was validated on a geometrical model of a human head derived from tomographic data. Using this model, individual facial expressions of emotion were estimated by the superpositioning of precomputed single muscle actions.