UNASSIGNED: Atomic force microscopy (AFM) is one of the main techniques used to characterize the mechanical properties of soft biological samples and biomaterials at the nanoscale. Despite efforts made by the AFM community to promote open-source data analysis tools, standardization continues to be a significant concern in a field that requires common analysis procedures. AFM-based mechanical measurements involve applying a controlled force to the sample and measure the resulting deformation in the so-called force-distance curves. These may include simple approach and retract or oscillatory cycles at various frequencies (microrheology). To extract quantitative parameters, such as the elastic modulus, from these measurements, AFM measurements are processed using data analysis software. Although open tools exist and allow obtaining the mechanical properties of the sample, most of them only include standard elastic models and do not allow the processing of microrheology data. In this work, we have developed an open-source software package (called PyFMLab, as of python force microscopy laboratory) capable of determining the viscoelastic properties of samples from both conventional force-distance curves and microrheology measurements.
UNASSIGNED: PyFMLab has been written in Python, which provides an accessible syntax and sufficient computational efficiency. The software features were divided into separate, self-contained libraries to enhance code organization and modularity and to improve readability, maintainability, testability, and reusability. To validate PyFMLab, two AFM datasets, one composed of simple force curves and another including oscillatory measurements, were collected on HeLa cells.
UNASSIGNED: The viscoelastic parameters obtained on the two datasets analysed using PyFMLab were validated against data processing proprietary software and against validated MATLAB routines developed before obtaining equivalent results.
UNASSIGNED: Its open-source nature and versatility makes PyFMLab an open-source solution that paves the way for standardized viscoelastic characterization of biological samples from both force-distance curves and microrheology measurements.
Just like we can test the ripeness of fruit by touching it, we can use our hands to gently touch an object and determine if it is soft or stiff. Doctors use this technique, called palpation, to explore our organs and check for signs of disease. We can think about doing something similar, but on a much smaller scale — the nanoscale — so small that you cannot even see it with your naked eye. Atomic force microscopy (AFM) allows to apply palpation at the nanoscale. AFM is a powerful tool that allows scientists to examine incredibly small objects, like individual cells or molecules. AFM uses super sensitive “fingers” to touch and explore things that are too small to be seen under a regular microscope. During the European project Phys2BioMed, we explored how to apply AFM to diagnose diseases using nanopalpation. For example, touching samples from biopsies of patients and determining how soft or stiff they are. Here\'s the catch: there is not a single, standardized method or software that can efficiently process all the data obtained from AFM. It\'s a bit like having a lot of different languages but no universal translator. Just like a scale or a measuring cup are standardized, scientists need to analyze AFM data accurately and consistently. This is crucial to ensure reliable comparisons between results obtained by different researchers on different instruments, something particularly important when the results are to be used for diagnostic or predictive purposes. To help tackle this problem, we have developed PyFMLab. This software is a reliable and easy-to-use tool that translates AFM data into insights about the tiny structures being studied. By providing a standardized, open-source, modular and accessible way to analyze AFM data, PyFMLab democratizes access to this field of Biophysics, paving the way for clinical applications of AFM.

Studies of cell and tissue mechanics have shown that significant changes in cell and tissue mechanics during lesions and cancers are observed, which provides new mechanical markers for disease diagnosis based on machine learning. However, due to the lack of effective mechanic markers, only elastic modulus and iconographic features are currently used as markers, which greatly limits the application of cell and tissue mechanics in disease diagnosis. Here, we develop a liver pathological state classifier through a support vector machine method, based on high dimensional viscoelastic mechanical data. Accurate diagnosis and grading of hepatic fibrosis facilitates early detection and treatment and may provide an assessment tool for drug development. To this end, we used the viscoelastic parameters obtained from the analysis of creep responses of liver tissues by a self-similar hierarchical model and built a liver state classifier based on machine learning. Using this classifier, we implemented a fast classification of healthy, diseased, and mesenchymal stem cells (MSCs)-treated fibrotic live tissues, and our results showed that the classification accuracy of healthy and diseased livers can reach 0.99, and the classification accuracy of the three liver tissues mixed also reached 0.82. Finally, we provide screening methods for markers in the context of massive data as well as high-dimensional viscoelastic variables based on feature ablation for drug development and accurate grading of liver fibrosis. We propose a novel classifier that uses the dynamical mechanical variables as input markers, which can identify healthy, diseased, and post-treatment liver tissues.

A better understanding of the underlying pathomechanisms of diastolic dysfunction is crucial for the development of targeted therapeutic options with the aim to increase the patients\' quality of life. In order to shed light on the processes involved, suitable models are required. Here, effects of endothelin-1 (ET-1) treatment on cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) were investigated. While it is well established, that ET-1 treatment induces hypertrophy in cardiomyocytes, resulting changes in cell mechanics and contractile behavior with focus on relaxation have not been examined before. Cardiomyocytes were treated with 10 nM of ET-1 for 24 h and 48 h, respectively. Hypertrophy was confirmed by real-time deformability cytometry (RT-DC) which was also used to assess the mechanical properties of cardiomyocytes. For investigation of the contractile behavior, 24 h phase contrast video microscopy was applied. To get a deeper insight into changes on the molecular biological level, gene expression analysis was performed using the NanoString nCounter® cardiovascular disease panel. Besides an increased cell size, ET-1 treated cardiomyocytes are stiffer and show an impaired relaxation. Gene expression patterns in ET-1 treated hiPSC derived cardiomyocytes showed that pathways associated with cardiovascular diseases, cardiac hypertrophy and extracellular matrix were upregulated while those associated with fatty acid metabolism were downregulated. We conclude that alterations in cardiomyocytes after ET-1 treatment go far beyond hypertrophy and represent a useful model for diastolic dysfunction.

Cell mechanics are a biophysical indicator of cell state, such as cancer metastasis, leukocyte activation, and cell cycle progression. Atomic force microscopy (AFM) is a widely used technique to measure cell mechanics, where the Young modulus of a cell is usually derived from the Hertz contact model. However, the Hertz model assumes that the cell is an elastic, isotropic, and homogeneous material and that the indentation is small compared to the cell size. These assumptions neglect the effects of the cytoskeleton, cell size and shape, and cell environment on cell deformation. In this study, we investigated the influence of cell size on the estimated Young\'s modulus using liposomes as cell models. Liposomes were prepared with different sizes and filled with phosphate buffered saline (PBS) or hyaluronic acid (HA) to mimic the cytoplasm. AFM was used to obtain the force indentation curves and fit them to the Hertz model. We found that the larger the liposome, the lower the estimated Young\'s modulus for both PBS-filled and HA-filled liposomes. This suggests that the Young modulus obtained from the Hertz model is not only a property of the cell material but also depends on the cell dimensions. Therefore, when comparing or interpreting cell mechanics using the Hertz model, it is essential to account for cell size.

Brain injuries resulting from mechanical trauma represent an ongoing global public health issue. Several in vitro and in vivo models for traumatic brain injury (TBI) continue to be developed for delineating the various complex pathophysiological processes involved in its onset and progression. Developing an in vitro TBI model that is based on cortical spheroids is especially of great interest currently because they can replicate key aspects of in vivo brain tissue, including its electrophysiology, physicochemical microenvironment, and extracellular matrix composition. Being able to mechanically deform the spheroids are a key requirement in any effective in vitro TBI model. The spheroids\' shape and size, however, make mechanically loading them, especially in a high-throughput, sterile, and reproducible manner, quite challenging. To address this challenge, we present an idea for a spheroid-based, in vitro TBI model in which the spheroids are mechanically loaded by being spun by a centrifuge. (An experimental demonstration of this new idea will be published shortly elsewhere.) An issue that can limit its utility and scope is that imaging techniques used in 2D and 3D in vitro TBI models cannot be readily applied in it to determine spheroid strains. In order to address this issue, we developed a continuum mechanics-based theory to estimate the spheroids\' strains when they are being spun at a constant angular velocity. The mechanics theory, while applicable here to a special case of the centrifuge-based TBI model, is also of general value since it can help with the further exploration and development of TBI models.

Damage and repair are recurring processes in tissues, with fibroblasts playing key roles by remodeling extracellular matrices (ECM) through protein synthesis, proteolysis, and cell contractility. Dysregulation of fibroblasts can lead to fibrosis and tissue damage, as seen in idiopathic pulmonary fibrosis (IPF). In advanced IPF, tissue damage manifests as honeycombing, or voids in the lungs. This study explores how transforming growth factor-beta (TGF-β), a crucial factor in IPF, induces lung fibroblast spheroids to create voids in reconstituted collagen through proteolysis and cell contractility, a process is termed as hole formation. These voids reduce when proteases are blocked. Spheroids mimic fibroblast foci observed in IPF. Results indicate that cell contractility mediates tissue opening by stretching fractures in the collagen meshwork. Matrix metalloproteinases (MMPs), including MMP1 and MT1-MMP, are essential for hole formation, with invadopodia playing a significant role. Blocking MMPs reduces hole size and promotes wound healing. This study shows how TGF-β induces excessive tissue destruction and how blocking proteolysis can reverse damage, offering insights into IPF pathology and potential therapeutic interventions.

Endothelial cells are constantly exposed to mechanical stimuli, of which mechanical stretch has shown various beneficial or deleterious effects depending on whether loads are within physiological or pathological levels, respectively. Vascular properties change with age, and on a cell-scale, senescence elicits changes in endothelial cell mechanical properties that together can impair its response to stretch. Here, high-rate uniaxial stretch experiments were performed to quantify and compare the stretch-induced damage of monolayers consisting of young, senescent, and aged endothelial populations. The aged and senescent phenotypes were more fragile to stretch-induced damage. Prominent damage was detected by immunofluorescence and scanning electron microscopy as intercellular and intracellular void formation. Damage increased proportionally to the applied level of deformation and, for the aged and senescent phenotype, induced significant detachment of cells at lower levels of stretch compared to the young counterpart. Based on the phenotypic difference in cell-substrate adhesion of senescent cells indicating more mature focal adhesions, a discrete network model of endothelial cells being stretched was developed. The model showed that the more affine deformation of senescent cells increased their intracellular energy, thus enhancing the tendency for cellular damage and impending detachment. Next to quantifying for the first-time critical levels of endothelial stretch, the present results indicate that young cells are more resilient to deformation and that the fragility of senescent cells may be associated with their stronger adhesion to the substrate.

Desmosomes are relatives of ancient cadherin-based junctions, which emerged late in evolution to ensure the structural integrity of vertebrate tissues by coupling the intermediate filament cytoskeleton to cell-cell junctions. Their ability to dynamically counter the contractile forces generated by actin-associated adherens junctions is particularly important in tissues under high mechanical stress, such as the skin and heart. Much more than the simple cellular \'spot welds\' depicted in textbooks, desmosomes are in fact dynamic structures that can sense and respond to changes in their mechanical environment and external stressors like ultraviolet light and pathogens. These environmental signals are transmitted intracellularly via desmosome-dependent mechanochemical pathways that drive the physiological processes of morphogenesis and differentiation. This Cell Science at a Glance article and the accompanying poster review desmosome structure and assembly, highlight recent insights into how desmosomes integrate chemical and mechanical signaling in the epidermis, and discuss desmosomes as targets in human disease.

A simple machine is a basic of device that takes mechanical advantage to apply force. Animals and plants self-assemble through the operation of a wide variety of simple machines. Embryos of different species actuate these simple machines to drive the geometric transformations that convert a disordered mass of cells into organized structures with discrete identities and function. These transformations are intrinsically coupled to sequential and overlapping steps of self-organization and self-assembly. The processes of self-organization have been explored through the molecular composition of cells and tissues and their information networks. By contrast, efforts to understand the simple machines underlying self-assembly must integrate molecular composition with the physical principles of mechanics. This primer is concerned with effort to elucidate the operation of these machines, focusing on the \"problem\" of morphogenesis. Advances in understanding self-assembly will ultimately connect molecular-, subcellular-, cellular- and meso-scale functions of plants and animals and their ability to interact with larger ecologies and environmental influences.

Cell mechanics is gaining attraction in drug screening, but the applicable methods have not yet become part of the standardized norm. This review presents the current state of the art for atomic force microscopy, which is the most widely available method. The field is first motivated as a new way of tracking pharmaceutical effects, followed by a basic introduction targeted at pharmacists on how to measure cellular stiffness. The review then moves on to the current state of the knowledge in terms of experimental results and supplementary methods such as fluorescence microscopy that can give relevant additional information. Finally, rheological approaches as well as the theoretical interpretations are presented before ending on additional methods and outlooks.