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

Eroles, Mar; López-Alonso, Javier; Ortega, Alexandre; Boudier, Thomas; Gharzeddine, Khaldoun; Lafont, Frank; Franz, Clemens M.; Millet, Arnaud; Valotteau, Claire; Rico, Félix

doi:10.1039/d3nr00757j

Cited by 4 publications

(2 citation statements)

References 124 publications

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“…This work successfully reproduces ECM remodeling dynamics observed both with and without scarring. Recent experimental work highlights the importance of viscoelastic behaviors in cell and tissue function (Alegre-Cebollada et al, 2023;Eroles et al, 2023), which is subsequently incorporated into corresponding hybrid models (Pensalfini and Tepole, 2023). To investigate dermal wound healing, Guo et al (2022b) couple a microscale stochastic cell adhesion model with a macroscale continuum mechanics ECM modeled as a fiber-reinforced material with hyperviscoelastic constitutive behavior to represent fibroblast contraction.…”

Section: Wound Healingmentioning

confidence: 99%

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Crossley,

Johnson,

Tsingos

et al. 2024

Front. Cell Dev. Biol.

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The extracellular matrix (ECM) is a highly complex structure through which biochemical and mechanical signals are transmitted. In processes of cell migration, the ECM also acts as a scaffold, providing structural support to cells as well as points of potential attachment. Although the ECM is a well-studied structure, its role in many biological processes remains difficult to investigate comprehensively due to its complexity and structural variation within an organism. In tandem with experiments, mathematical models are helpful in refining and testing hypotheses, generating predictions, and exploring conditions outside the scope of experiments. Such models can be combined and calibrated with in vivo and in vitro data to identify critical cell-ECM interactions that drive developmental and homeostatic processes, or the progression of diseases. In this review, we focus on mathematical and computational models of the ECM in processes such as cell migration including cancer metastasis, and in tissue structure and morphogenesis. By highlighting the predictive power of these models, we aim to help bridge the gap between experimental and computational approaches to studying the ECM and to provide guidance on selecting an appropriate model framework to complement corresponding experimental studies.

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Section: Wound Healingmentioning

confidence: 99%

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Crossley,

Johnson,

Tsingos

et al. 2024

Front. Cell Dev. Biol.

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“…As a consequence of this combination of singular features, the number of AFM studies on these systems has increased steadily. Thus, AFM has allowed, for instance, the measurement of the elastic and viscoelastic properties of various biological systems [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Identification of Individual Target Molecules Using Antibody-Decorated DeepTipTM Atomic-Force Microscopy Probes

Corregidor-Ortiz,

Daza,

Colchero

et al. 2024

Biomimetics

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A versatile and robust procedure is developed that allows the identification of individual target molecules using antibodies bound to a DeepTipTM functionalized atomic-force microscopy probe. The model system used for the validation of this process consists of a biotinylated anti-lactate dehydrogenase antibody immobilized on a streptavidin-decorated AFM probe. Lactate dehydrogenase (LDH) is employed as target molecule and covalently immobilized on functionalized MicroDeckTM substrates. The interaction between sensor and target molecules is explored by recording force–displacement (F–z) curves with an atomic-force microscope. F–z curves that correspond to the genuine sensor–target molecule interaction are identified based on the following three criteria: (i) number of peaks, (ii) value of the adhesion force, and (iii) presence or absence of the elastomeric trait. The application of these criteria leads to establishing seven groups, ranging from no interaction to multiple sensor–target molecule interactions, for which force–displacement curves are classified. The possibility of recording consistently single-molecule interaction events between an antibody and its specific antigen, in combination with the high proportion of successful interaction events obtained, increases remarkably the possibilities offered by affinity atomic-force microscopy for the characterization of biological and biomimetic systems from the molecular to the tissue scales.

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

López-Alonso,

Eroles,

Janel

et al. 2023

Open Res Europe

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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.

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

Abstract: Coupled atomic force microscopy viscoelastic mapping and interference contrast microscopy revealed larger adhesion, stiffening and solidification during monocyte differentiation into macrophages.

Cited by 4 publications

References 124 publications

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Modeling the extracellular matrix in cell migration and morphogenesis: a guide for the curious biologist

Identification of Individual Target Molecules Using Antibody-Decorated DeepTipTM Atomic-Force Microscopy Probes

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

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