Reliable, standardized measurements for cell mechanical properties.

Atomic force microscopy (AFM) has become indispensable for studying biological and medical samples. More than two decades of experiments have revealed that cancer cells are softer than healthy cells (for measured cells cultured on stiff substrates). The softness or, more precisely, the larger deformability of cancer cells, primarily independent of cancer types, could be used as a sensitive marker of pathological changes. The wide application of biomechanics in clinics would require designing instruments with specific calibration, data collection, and analysis procedures. For these reasons, such development is, at present, still very limited, hampering the clinical exploitation of mechanical measurements. Here, we propose a standardized operational protocol (SOP), developed within the EU ITN network Phys2BioMed, which allows the detection of the biomechanical properties of living cancer cells regardless of the nanoindentation instruments used (AFMs and other indenters) and the laboratory involved in the research. We standardized the cell cultures, AFM calibration, measurements, and data analysis. This effort resulted in a step-by-step SOP for cell cultures, instrument calibration, measurements, and data analysis, leading to the concordance of the results (Young's modulus) measured among the six EU laboratories involved. Our results highlight the importance of the SOP in obtaining a reproducible mechanical characterization of cancer cells and paving the way toward exploiting biomechanics for diagnostic purposes in clinics.

Coupled mechanical mapping and interference contrast microscopy reveal viscoelastic and adhesion hallmarks of monocyte differentiation into macrophages.

Monocytes activated by pro-inflammatory signals adhere to the vascular endothelium and migrate from the bloodstream to the tissue ultimately differentiating into macrophages. Cell mechanics and adhesion play a crucial role in macrophage functions during this inflammatory process. However, how monocytes change their adhesion and mechanical properties upon differentiation into macrophages is still not well understood. In this work, we used various tools to quantify the morphology, adhesion, and viscoelasticity of monocytes and differentiatted macrophages. Combination of atomic force microscopy (AFM) high resolution viscoelastic mapping with interference contrast microscopy (ICM) at the single-cell level revealed viscoelasticity and adhesion hallmarks during monocyte differentiation into macrophages. Quantitative holographic tomography imaging revealed a dramatic increase in cell volume and surface area during monocyte differentiation and the emergence of round and spread macrophage subpopulations. AFM viscoelastic mapping showed important stiffening (increase of the apparent Young's modulus, E0) and solidification (decrease of cell fluidity, ß) on differentiated cells that correlated with increased adhesion area. These changes were enhanced in macrophages with a spread phenotype. Remarkably, when adhesion was perturbed, differentiated macrophages remained stiffer and more solid-like than monocytes, suggesting a permanent reorganization of the cytoskeleton. We speculate that the stiffer and more solid-like microvilli and lamellipodia might help macrophages to minimize energy dissipation during mechanosensitive activities. Thus, our results revealed viscoelastic and adhesion hallmarks of monocyte differentiation that may be important for biological function.

Comparison of Rheological Properties of Healthy versus Dupuytren Fibroblasts When Treated with a Cell Contraction Inhibitor by Atomic Force Microscope.

Mechanical properties of healthy and Dupuytren fibroblasts were investigated by atomic force microscopy (AFM). In addition to standard force curves, rheological properties were assessed using an oscillatory testing methodology, in which the frequency was swept from 1 Hz to 1 kHz, and data were analyzed using the structural damping model. Dupuytren fibroblasts showed larger apparent Young's modulus values than healthy ones, which is in agreement with previous results. Moreover, cell mechanics were compared before and after ML-7 treatment, which is a myosin light chain kinase inhibitor (MLCK) that reduces myosin activity and hence cell contraction. We employed two different concentrations of ML-7 inhibitor and could observe distinct cell reactions. At 1 µM, healthy and scar fibroblasts did not show measurable changes in stiffness, but Dupuytren fibroblasts displayed a softening and recovery after some time. When increasing ML-7 concentration (3 µM), the majority of cells reacted, Dupuytren fibroblasts were the most susceptible, not being able to recover from the drug and dying. These results suggested that ML-7 is a potent inhibitor for MLCK and that myosin II is essential for cytoskeleton stabilization and cell survival.

PyFMLab: Open-source software for atomic force microscopy microrheology data analysis.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.