Particle Retracking Algorithm Capable of Quantifying Large, Local Matrix Deformation for Traction Force Microscopy

Abstract Deformation measurement is a key process in traction force microscopy (TFM). Conventionally, particle image velocimetry (PIV) or correlation-based particle tracking velocimetry (cPTV) have been used for such a purpose. Using simulated bead images, we show that those methods fail to capture large displacement vectors and that it is due to a poor cross-correlation. Here, to redeem the potential large vectors, we propose a two-step deformation tracking algorithm that combines cPTV, which performs better for small displacements than PIV methods and a newly-designed retracking algorithm that exploits statistically confident vectors from the initial cPTV to guide the selection of correlation peak which are not necessarily the global maximum. As a result, the new method, named ‘cPTV-Retracking’, or cPTVR, was able to track more than 92% of large vectors whereas conventional methods could track 43-77% of those. Correspondingly, traction force reconstructed from cPTVR showed better recovery of large traction than the old methods. cPTVR applied on the experimental bead images has shown a better resolving power of the traction with different-sized cell-matrix adhesions than conventional methods. Altogether, cPTVR method enhances the accuracy of TFM in the case of large deformations present in soft substrates. We share this advance via our TFMPackage software.

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Three-Dimensional Traction Force Microscopy: A New Tool for Quantifying Cell-Matrix Interactions

PLoS ONE ◽

10.1371/journal.pone.0017833 ◽

2011 ◽

Vol 6 (3) ◽

pp. e17833 ◽

Cited By ~ 146

Author(s):

Christian Franck ◽

Stacey A. Maskarinec ◽

David A. Tirrell ◽

Guruswami Ravichandran

Keyword(s):

Three Dimensional ◽

Traction Force ◽

Traction Force Microscopy ◽

Force Microscopy ◽

Cell Matrix ◽

Matrix Interactions ◽

Cell Matrix Interactions

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877 Integration of magnetic tweezers and traction force microscopy for exploring the mechanobiology of keratinocyte cell-cell and cell-matrix anchoring junctions

Journal of Investigative Dermatology ◽

10.1016/j.jid.2018.03.888 ◽

2018 ◽

Vol 138 (5) ◽

pp. S149

Author(s):

W.I. Moghram ◽

A. Kruger ◽

E.A. Sander ◽

J.C. Selby

Keyword(s):

Traction Force ◽

Magnetic Tweezers ◽

Traction Force Microscopy ◽

Force Microscopy ◽

Cell Matrix ◽

Keratinocyte Cell ◽

Cell Cell

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Traction Force Microscopy by Deep Learning

10.1101/2020.05.20.107128 ◽

2020 ◽

Author(s):

Y.L. Wang ◽

Y.-C. Lin

Keyword(s):

Neural Network ◽

Deep Learning ◽

Vector Fields ◽

Traction Force ◽

Traction Force Microscopy ◽

Force Microscopy ◽

Large Sets ◽

Novel Approach ◽

Conventional Methods ◽

Speed Accuracy

AbstractCells interact mechanically with their surrounding by exerting forces and sensing forces or force-induced displacements. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posted mathematical problem, its implementation has involved regularization and the associated compromises in accuracy and resolution. Here we applied neural network-based deep learning as a novel approach for TFM. We modified a network for processing images to process vector fields of stress and strain. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and strains for training the network. We found that deep learning-based TFM yielded results qualitatively similar to those from conventional methods but at a higher accuracy and resolution. The speed and performance of deep learning TFM make it an appealing alternative to conventional methods for characterizing mechanical interactions between cells and the environment.Statement of SignificanceTraction Force Microscopy has served as a fundamental driving force for mechanobiology. However, its nature as an ill-posed inverse problem has posed serious challenges for conventional mathematical approaches. The present study, facilitated by large sets of simulated stresses and strains, describes a novel approach using deep learning for the calculation of traction stress distribution. By adapting the UNet neural network for handling vector fields, we show that deep learning is able to minimize much of the limitations of conventional approaches to generate results with speed, accuracy, and resolution.

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

Nature Communications ◽

10.1038/s41467-021-22377-9 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

Liliana Barbieri ◽

Huw Colin-York ◽

Kseniya Korobchevskaya ◽

Di Li ◽

Deanna L. Wolfson ◽

...

Keyword(s):

Resolution Enhancement ◽

Traction Force ◽

Super Resolution ◽

Traction Force Microscopy ◽

Two Dimensional ◽

Structured Illumination ◽

Structured Illumination Microscopy ◽

Force Microscopy ◽

Health And Disease ◽

Spatio Temporal

AbstractQuantifying 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.

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Different Cell Migration Modes: Insights from Traction Force Microscopy and Modeling

Biophysical Journal ◽

10.1016/j.bpj.2020.11.905 ◽

2021 ◽

Vol 120 (3) ◽

pp. 113a

Author(s):

Wouter-Jan Rappel ◽

Elisabeth Ghabache ◽

Yuansheng Cao ◽

Yuchuan Miao ◽

Alexander Groisman ◽

...

Keyword(s):

Cell Migration ◽

Traction Force ◽

Traction Force Microscopy ◽

Force Microscopy

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Epifluorescence-based three-dimensional traction force microscopy

Scientific Reports ◽

10.1038/s41598-020-72931-6 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Lauren Hazlett ◽

Alexander K. Landauer ◽

Mohak Patel ◽

Hadley A. Witt ◽

Jin Yang ◽

...

Keyword(s):

Spatial Frequency ◽

Open Source ◽

Single Particle ◽

Three Dimensional ◽

Traction Force ◽

High Spatial Frequency ◽

Epifluorescence Microscopy ◽

Traction Force Microscopy ◽

Force Microscopy ◽

Out Of Plane

Abstract We introduce a novel method to compute three-dimensional (3D) displacements and both in-plane and out-of-plane tractions on nominally planar transparent materials using standard epifluorescence microscopy. Despite the importance of out-of-plane components to fully understanding cell behavior, epifluorescence images are generally not used for 3D traction force microscopy (TFM) experiments due to limitations in spatial resolution and measuring out-of-plane motion. To extend an epifluorescence-based technique to 3D, we employ a topology-based single particle tracking algorithm to reconstruct high spatial-frequency 3D motion fields from densely seeded single-particle layer images. Using an open-source finite element (FE) based solver, we then compute the 3D full-field stress and strain and surface traction fields. We demonstrate this technique by measuring tractions generated by both single human neutrophils and multicellular monolayers of Madin–Darby canine kidney cells, highlighting its acuity in reconstructing both individual and collective cellular tractions. In summary, this represents a new, easily accessible method for calculating fully three-dimensional displacement and 3D surface tractions at high spatial frequency from epifluorescence images. We released and support the complete technique as a free and open-source code package.

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Traction Force Microscopy Based on an Active Cable Network Model

Biophysical Journal ◽

10.1016/j.bpj.2013.11.2393 ◽

2014 ◽

Vol 106 (2) ◽

pp. 425a

Author(s):

Jerome Soine ◽

Christoph Brand ◽

Jonathan Stricker ◽

Patrick W. Oakes ◽

Margaret L. Gardel ◽

...

Keyword(s):

Network Model ◽

Traction Force ◽

Traction Force Microscopy ◽

Force Microscopy ◽

Cable Network

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pyTFM: A tool for traction force and monolayer stress microscopy

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008364 ◽

2021 ◽

Vol 17 (6) ◽

pp. e1008364

Author(s):

Andreas Bauer ◽

Magdalena Prechová ◽

Lena Fischer ◽

Ingo Thievessen ◽

Martin Gregor ◽

...

Keyword(s):

Image Annotation ◽

Traction Force ◽

Theoretical Background ◽

Ease Of Use ◽

Traction Force Microscopy ◽

Force Transmission ◽

Force Microscopy ◽

Wide Range ◽

Cell Layers ◽

Cellular Stresses

Cellular force generation and force transmission are of fundamental importance for numerous biological processes and can be studied with the methods of Traction Force Microscopy (TFM) and Monolayer Stress Microscopy. Traction Force Microscopy and Monolayer Stress Microscopy solve the inverse problem of reconstructing cell-matrix tractions and inter- and intra-cellular stresses from the measured cell force-induced deformations of an adhesive substrate with known elasticity. Although several laboratories have developed software for Traction Force Microscopy and Monolayer Stress Microscopy computations, there is currently no software package available that allows non-expert users to perform a full evaluation of such experiments. Here we present pyTFM, a tool to perform Traction Force Microscopy and Monolayer Stress Microscopy on cell patches and cell layers grown in a 2-dimensional environment. pyTFM was optimized for ease-of-use; it is open-source and well documented (hosted at https://pytfm.readthedocs.io/) including usage examples and explanations of the theoretical background. pyTFM can be used as a standalone Python package or as an add-on to the image annotation tool ClickPoints. In combination with the ClickPoints environment, pyTFM allows the user to set all necessary analysis parameters, select regions of interest, examine the input data and intermediary results, and calculate a wide range of parameters describing forces, stresses, and their distribution. In this work, we also thoroughly analyze the accuracy and performance of the Traction Force Microscopy and Monolayer Stress Microscopy algorithms of pyTFM using synthetic and experimental data from epithelial cell patches.

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