Simultaneous Quantification of the Interplay Between Molecular Turnover and Cell Mechanics by AFM-FRAP

Quantifying biomolecular organisation in membranes with brightness-transit statistics

Cells crucially rely on the interactions of biomolecules at their plasma membrane to maintain homeostasis. Yet, a methodology to systematically quantify biomolecular organisation, measuring diffusion dynamics and oligomerisation, represents an unmet need. Here, we introduce the brightness-transit statistics (BTS) method based on fluorescence fluctuation spectroscopy and combine information from brightness and transit times to elucidate biomolecular diffusion and oligomerisation in both cell-free in vitro and in vitro systems incorporating living cells. We validate our approach in silico with computer simulations and experimentally using oligomerisation of EGFP tethered to supported lipid bilayers. We apply our pipeline to study the oligomerisation of CD40 ectodomain in vitro and endogenous CD40 on primary B cells. While we find a potential for CD40 to oligomerize in a concentration or ligand depended manner, we do not observe mobile oligomers on B cells. The BTS method combines sensitive analysis, quantification, and intuitive visualisation of dynamic biomolecular organisation.

In vitro investigation of the mechanics of fixed red blood cells based on optical trap micromanipulation and image analysis

Erythrocyte deformability correlates with various diseases. Single-cell measurements via optical tweezers (OTs) enable quantitative exploration but may encounter inaccuracies due to erythrocyte life cycle mixing. We present a three-step methodology to address these challenges. Firstly, density gradient centrifugation minimizes erythrocyte variations. Secondly, OTs measure membrane shear force across layers. Thirdly, MATLAB analyzes dynamic cell areas. Results combined with membrane shear force data reveal erythrocyte deformational capacity. To further characterize the deformability of diseased erythrocytes, the experiments used glutaraldehyde-fixed erythrocytes to simulate diseased cells. OTs detect increased shear modulus, while image recognition indicates decreased deformation. The integration of OTs and image recognition presents a comprehensive approach to deformation analysis, introducing novel ideas and methodologies for investigating erythrocytic lesions.

A MEC-2/stomatin condensate liquid-to-solid phase transition controls neuronal mechanotransduction during touch sensing

A growing body of work suggests that the material properties of biomolecular condensates ensuing from liquid-liquid phase separation change with time. How this aging process is controlled and whether the condensates with distinct material properties can have different biological functions is currently unknown. Using Caenorhabditis elegans as a model, we show that MEC-2/stomatin undergoes a rigidity phase transition from fluid-like to solid-like condensates that facilitate transport and mechanotransduction, respectively. This switch is triggered by the interaction between the SH3 domain of UNC-89 (titin/obscurin) and MEC-2. We suggest that this rigidity phase transition has a physiological role in frequency-dependent force transmission in mechanosensitive neurons during body wall touch. Our data demonstrate a function for the liquid and solid phases of MEC-2/stomatin condensates in facilitating transport or mechanotransduction, and a previously unidentified role for titin homologues in neurons.

In Situ Measurements of Cell Mechanical Properties Using Force Spectroscopy

Mechanobiology focuses on how physical forces and the mechanical properties of cells and whole tissues affect their function. The mechanical properties of cells are of particular interest to developmental biology and stem cell differentiation, lymphocyte activation and phagocytic action in phagocytes, and development of malignant tumors and metastases. These properties can be measured on whole tissue and cell culture. Advances in instrument sensitivity and design, as well as improved techniques and scientific know-how achieved over the past few decades, allow researchers to study the mechanical properties of single cells and even at the subcellular level. Particularly, nanoindentation measurements using atomic force microscopy (AFM) mechanically probes single cells and even allows mapping of these traits. This chapter discusses these measurements from the experimental design to the analysis.

Targeting cell-matrix interface mechanobiology by integrating AFM with fluorescence microscopy

Mechanosensing at the interface of a cell and its surrounding microenvironment is an essential driving force of physiological processes. Understanding molecular activities at the cell-matrix interface has the potential to provide novel targets for improving tissue regeneration and early disease intervention. In the past few decades, the advancement of atomic force microscopy (AFM) has offered a unique platform for probing mechanobiology at this crucial microdomain. In this review, we describe key advances under this topic through the use of an integrated system of AFM (as a biomechanical testing tool) with complementary immunofluorescence (IF) imaging (as an in situ navigation system). We first describe the body of work investigating the micromechanics of the pericellular matrix (PCM), the immediate cell micro-niche, in healthy, diseased, and genetically modified tissues, with a focus on articular cartilage. We then summarize the key findings in understanding cellular biomechanics and mechanotransduction, in which, molecular mechanisms governing transmembrane ion channel-mediated mechanosensing, cytoskeleton remodeling, and nucleus remodeling have been studied in various cell and tissue types. Lastly, we provide an overview of major technical advances that have enabled more in-depth studies of mechanobiology, including the integration of AFM with a side-view microscope, multiple optomicroscopy, a fluorescence recovery after photobleaching (FRAP) module, and a tensile stretching device. The innovations described here have contributed greatly to advancing the fundamental knowledge of extracellular matrix biomechanics and cell mechanobiology for improved understanding, detection, and intervention of various diseases.

Tight Junction ZO Proteins Maintain Tissue Fluidity, Ensuring Efficient Collective Cell Migration

Tight junctions (TJs) are essential components of epithelial tissues connecting neighboring cells to provide protective barriers. While their general function to seal compartments is well understood, their role in collective cell migration is largely unexplored. Here, the importance of the TJ zonula occludens (ZO) proteins ZO1 and ZO2 for epithelial migration is investigated employing video microscopy in conjunction with velocimetry, segmentation, cell tracking, and atomic force microscopy/spectroscopy. The results indicate that ZO proteins are necessary for fast and coherent migration. In particular, ZO1 and 2 loss (dKD) induces actomyosin remodeling away from the central cortex towards the periphery of individual cells, resulting in altered viscoelastic properties. A tug-of-war emerges between two subpopulations of cells with distinct morphological and mechanical properties: 1) smaller and highly contractile cells with an outward bulging apical membrane, and 2) larger, flattened cells, which, due to tensile stress, display a higher proliferation rate. In response, the cell density increases, leading to crowding-induced jamming and more small cells over time. Co-cultures comprising wildtype and dKD cells migrate inefficiently due to phase separation based on differences in contractility rather than differential adhesion. This study shows that ZO proteins are necessary for efficient collective cell migration by maintaining tissue fluidity and controlling proliferation.

Extended mechanical force measurements using structured illumination microscopy

Quantifying cell generated mechanical forces is key to furthering our understanding of mechanobiology. Traction force microscopy (TFM) is one of the most broadly applied force probing technologies, but its sensitivity is strictly dependent on the spatio-temporal resolution of the underlying imaging system. In previous works, it was demonstrated that increased sampling densities of cell derived forces permitted by super-resolution fluorescence imaging enhanced the sensitivity of the TFM method. However, these recent advances to TFM based on super-resolution techniques were limited to slow acquisition speeds and high illumination powers. Here, we present three novel TFM approaches that, in combination with total internal reflection, structured illumination microscopy and astigmatism, improve the spatial and temporal performance in either two-dimensional or three-dimensional mechanical force quantification, while maintaining low illumination powers. These three techniques can be straightforwardly implemented on a single optical set-up offering a powerful platform to provide new insights into the physiological force generation in a wide range of biological studies. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Two-dimensional TIRF-SIM-traction force microscopy (2D TIRF-SIM-TFM)

Quantifying small, rapidly evolving forces generated by cells is a major challenge for the understanding of biomechanics and mechanobiology in health and disease. Traction force microscopy remains one of the most broadly applied force probing technologies but typically restricts itself to slow events over seconds and micron-scale displacements. Here, we improve >2-fold spatially and >10-fold temporally the resolution of planar cellular force probing compared to its related conventional modalities by combining fast two-dimensional total internal reflection fluorescence super-resolution structured illumination microscopy and traction force microscopy. This live-cell 2D TIRF-SIM-TFM methodology offers a combination of spatio-temporal resolution enhancement relevant to forces on the nano- and sub-second scales, opening up new aspects of mechanobiology to analysis.

Quantitative Methodologies to Dissect Immune Cell Mechanobiology

Mechanobiology seeks to understand how cells integrate their biomechanics into their function and behavior. Unravelling the mechanisms underlying these mechanobiological processes is particularly important for immune cells in the context of the dynamic and complex tissue microenvironment. However, it remains largely unknown how cellular mechanical force generation and mechanical properties are regulated and integrated by immune cells, primarily due to a profound lack of technologies with sufficient sensitivity to quantify immune cell mechanics. In this review, we discuss the biological significance of mechanics for immune cells across length and time scales, and highlight several experimental methodologies for quantifying the mechanics of immune cells. Finally, we discuss the importance of quantifying the appropriate mechanical readout to accelerate insights into the mechanobiology of the immune response.

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks

Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model, this can be time consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data are used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further development of machine learning-based estimation in FRAP. LAY DESCRIPTION: Fluorescence recovery after photobleaching (FRAP) is one of the most frequently used methods for microscopy-based diffusion measurements and broadly used in materials science, pharmaceutics, food science and cell biology. In a FRAP experiment, a laser is used to photobleach fluorescent particles in a region. By analysing the recovery of the fluorescence intensity due to the diffusion of still fluorescent particles, the diffusion coefficient and other parameters can be estimated. Typically, a confocal laser scanning microscope (CLSM) is used to image the time evolution of the recovery, and a model is fit using least squares to obtain parameter estimates. In this work, we introduce a new, fast and accurate method for analysis of data from FRAP. The new method is based on using artificial neural networks to predict parameter values, such as the diffusion coefficient, effectively circumventing classical least squares fitting. This leads to a dramatic speed-up, especially noticeable when analysing large numbers of FRAP data sets, while still producing results in excellent agreement with least squares. Further, the neural network estimates can be used as very good initial guesses for least squares estimation in order to make the least squares optimization convergence much faster than it otherwise would. This provides for obtaining, for example, diffusion coefficients as soon as possible, spending minimal time on data analysis. In this fashion, the proposed method facilitates efficient use of the experimentalist's time which is the main motivation to our approach. The concept is demonstrated on pure diffusion. However, the concept can easily be extended to the diffusion and binding case. The concept is likely to be useful in all application areas of FRAP, including diffusion in cells, gels and solutions.