A primer to traction force microscopy

Zancla, Andrea; Mozetic, Pamela; Orsini, Monica; Forte, Giancarlo; Rainer, Alberto

doi:10.1016/j.jbc.2022.101867

Cited by 35 publications

(31 citation statements)

References 126 publications

(136 reference statements)

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“…Several types of image processing methods, including particle tracking/image velocimetry (PTV/PIV), correlation-based PTV [ 32 ], and feature-based registration [ 33 ], are typically used to determine the displacement field. After obtaining the displacement field the traction force can be calculated using a variety of computational methods [ 23 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Since the cells exert distributions of forces rather than discrete traction forces, the TFM image is interpreted as traction stress, i.e., force per unit area.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we calculate the traction stress field from the deformation field by using an FTTC algorithm as presented in references [ 34 , 35 , 39 ]. This method relies on the general relationship between the applied force on a solid object and the resulting deformations, which is valid in the linear elastic regime [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…For small deformations, the general equation that relates the displacements

with the traction field

can be written as a convolution (Boussinesq approximation):

where G is the Green’s function of the problem, i.e., the Fredholm integral for TFM [ 32 , 33 , 34 , 39 ]. To find

, the displacement field

is interpolated onto a rectangular grid where the spatial variable is discretized, and the traction force is described by a set of plane waves.…”

Section: Methodsmentioning

confidence: 99%

“…By taking the inverse Fourier transform of Equation (2), we obtain the solution for the traction field:

where

denotes the (two-dimensional) inverse Fourier transform operation, and the symbol

denotes the Fourier transform for the function with wave vector

. The expression for

can be written as [ 32 , 33 , 34 , 39 ]:

where A =

and E being the Poisson’s ratio and the Young’s modulus of the substrate, respectively. Equations (1)–(4) relate the traction forces with the corresponding substrate deformations.…”

Section: Methodsmentioning

confidence: 99%

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Combined Traction Force–Atomic Force Microscopy Measurements of Neuronal Cells

2022

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In the course of the development of the nervous system, neuronal cells extend (grow) axons, which navigate over distances of the order of many cell diameters to reach target dendrites from other neurons and establish neuronal circuits. Some of the central challenges in biophysics today are to develop a quantitative model of axonal growth, which includes the interactions between the neurons and their growth environment, and to describe the complex architecture of neuronal networks in terms of a small number of physical variables. To address these challenges, researchers need new experimental techniques for measuring biomechanical interactions with very high force and spatiotemporal resolutions. Here we report a unique experimental approach that integrates three different high-resolution techniques on the same platform—traction force microscopy (TFM), atomic force microscopy (AFM) and fluorescence microscopy (FM)—to measure biomechanical properties of cortical neurons. To our knowledge, this is the first literature report of combined TFM/AFM/FM measurements performed for any type of cell. Using this combination of powerful experimental techniques, we perform high-resolution measurements of the elastic modulus for cortical neurons and relate these values with traction forces exerted by the cells on the growth substrate (poly acrylamide hydrogels, or PAA, coated with poly D-lysine). We obtain values for the traction stresses exerted by the cortical neurons in the range 30–70 Pa, and traction forces in the range 5–11 nN. Our results demonstrate that neuronal cells stiffen when axons exert forces on the PAA substrate, and that neuronal growth is governed by a contact guidance mechanism, in which axons are guided by external mechanical cues. This work provides new insights for bioengineering novel biomimetic platforms that closely model neuronal growth in vivo, and it has significant impact for creating neuroprosthetic interfaces and devices for neuronal growth and regeneration.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…For small deformations, the general equation that relates the displacements

with the traction field

can be written as a convolution (Boussinesq approximation):

where G is the Green’s function of the problem, i.e., the Fredholm integral for TFM [ 32 , 33 , 34 , 39 ]. To find

, the displacement field

is interpolated onto a rectangular grid where the spatial variable is discretized, and the traction force is described by a set of plane waves.…”

Section: Methodsmentioning

confidence: 99%

“…By taking the inverse Fourier transform of Equation (2), we obtain the solution for the traction field:

where

denotes the (two-dimensional) inverse Fourier transform operation, and the symbol

denotes the Fourier transform for the function with wave vector

. The expression for

can be written as [ 32 , 33 , 34 , 39 ]:

where A =

and E being the Poisson’s ratio and the Young’s modulus of the substrate, respectively. Equations (1)–(4) relate the traction forces with the corresponding substrate deformations.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Combined Traction Force–Atomic Force Microscopy Measurements of Neuronal Cells

2022

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“…6). Recently, Fourier transform traction cytometry, the FEM, TFM in image J, TFM in MATLAB, and other software tools have been reported to be used [151]. The transfer mapping of microscopy image data points to natural coordinate data points for computing requires a set of conversion algorithms to convert MATLAB image data into computational modeling.…”

Section: Tfm Modeling and Analysismentioning

confidence: 99%

The role of cellular traction forces in deciphering nuclear mechanics

et al. 2022

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Cellular forces exerted on the extracellular matrix (ECM) during adhesion and migration under physiological and pathological conditions regulate not only the overall cell morphology but also nuclear deformation. Nuclear deformation can alter gene expression, integrity of the nuclear envelope, nucleus-cytoskeletal connection, chromatin architecture, and, in some cases, DNA damage responses. Although nuclear deformation is caused by the transfer of forces from the ECM to the nucleus, the role of intracellular organelles in force transfer remains unclear and a challenging area of study. To elucidate nuclear mechanics, various factors such as appropriate biomaterial properties, processing route, cellular force measurement technique, and micromanipulation of nuclear forces must be understood. In the initial phase of this review, we focused on various engineered biomaterials (natural and synthetic extracellular matrices) and their manufacturing routes along with the properties required to mimic the tumor microenvironment. Furthermore, we discussed the principle of tools used to measure the cellular traction force generated during cell adhesion and migration, followed by recently developed techniques to gauge nuclear mechanics. In the last phase of this review, we outlined the principle of traction force microscopy (TFM), challenges in the remodeling of traction forces, microbead displacement tracking algorithm, data transformation from bead movement, and extension of 2-dimensional TFM to multiscale TFM.

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Emerging biotechnologies for screening electromechanical signals of cardiomyocytes

Tang,

Sun,

Shi

et al. 2024

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Cardiac diseases threaten human health and burden the global healthcare system. Cardiomyocytes (CMs) are considered the ideal model for studying the signal transduction and regulation of cardiac systems. Based on the principle of the rhythmical beating process (excitation‒contraction coupling mechanism of CMs), investigating the mechanical and electrophysiological signals offered new hope for cardiac disease detection, prevention, and treatment. Considerable technological success has been achieved in electromechanical signal recording. However, most drug assessment platforms attach importance to high‐throughput and dynamic monitoring of mechanical or electrical signals while overlooking the measuring principles and physiological significance of the signal. In this review, the development of biosensing platforms for CMs, sensing principles, key measured parameters, measurement accuracy, and limitations are discussed. Additionally, various approaches for the stimulation and measurement of CMs in vitro are discussed to further elucidate the response of these cells to external stimuli. Furthermore, disease modeling and drug screening are used as examples to intuitively demonstrate the contribution of in vitro CM measurement platforms to the biomedical field, thereby further illustrating the challenges and prospects of these sensing platforms.

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A primer to traction force microscopy

Cited by 35 publications

References 126 publications

Combined Traction Force–Atomic Force Microscopy Measurements of Neuronal Cells

Combined Traction Force–Atomic Force Microscopy Measurements of Neuronal Cells

The role of cellular traction forces in deciphering nuclear mechanics

Emerging biotechnologies for screening electromechanical signals of cardiomyocytes

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