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Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images
Biological Physics · Submitted 24 Feb 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment.
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The abstract, sentence by sentence
Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. | contribution 1.00 | 0.86 | 0.28 | asserted 0.97 |
| s1 | The full process can be divided into three steps. | method 0.99 | 0.30 | 0.02 | asserted 0.98 |
| s2 | First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. | method 1.00 | 0.88 | 0.03 | asserted 1.00 |
| s3 | The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. | method 0.93 | 0.64 | 0.03 | asserted 1.00 |
| s4 | Second, the wrinkle positions are extracted from the microscope images. | method 1.00 | 0.88 | 0.02 | asserted 0.99 |
| s5 | Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. | method 1.00 | 0.93 | 0.05 | asserted 1.00 |
| s6 | The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. | method 0.97 | 0.71 | 0.07 | asserted 1.00 |
| s7 | After sufficient training, the network is utilized to predict the cellular forces just from the input images. | method 0.93 | 0.83 | 0.19 | asserted 1.00 |
| s8 | Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. | interpretation 0.92 | 0.73 | 0.61 | asserted 1.00 |
| s9 | Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells. | future work 0.58 | 0.23 | 0.17 | hedged possibility 0.84 |
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Original abstract
Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope images. The full process can be divided into three steps. First, we culture the cells on a special substrate allowing to measure both the cellular traction force on the substrate and the corresponding substrate wrinkles simultaneously. The cellular forces are obtained using the traction force microscopy (TFM), at the same time that cell-generated contractile forces wrinkle their underlying substrate. Second, the wrinkle positions are extracted from the microscope images. Third, we train the machine learning system with GAN (generative adversarial network) by using sets of corresponding two images, the traction field and the input images (raw microscope images or extracted wrinkle images), as the training data. The network understands the way to convert the input images of the substrate wrinkles to the traction distribution from the training. After sufficient training, the network is utilized to predict the cellular forces just from the input images. Our system provides a powerful tool to evaluate the cellular forces efficiently because the forces can be predicted just by observing the cells under the microscope, which is a way simpler method compared to the TFM experiment. Additionally, the machine learning based approach presented here has the profound potential for being applied to diverse cellular assays for studying mechanobiology of cells.
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Uses its method or tool 1 paper
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We compute a coarse-grained stress field [ref] , $σ_ij=(1/(2V_cg))∑_m∈V_cg(t⃗_i⊗e⃗^j_n+t⃗_j⊗e⃗^i_n)$ where $x⃗_0$ represents the center of the coarse-grained volume, $V_cg=ℓ_cg^3$, corresponding to coarse-graining length $ℓ_cg$, $t⃗_i=t⃗_i(x⃗_m)$ and unit vector $e⃗_n^i=e⃗_n^i(x⃗_m,x⃗_0)$.
Mechanical basis and topological routes to cell elimination · Introduction · 1.00
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