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Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images

Honghan Li, View ORCID ProfileDaiki Matsunaga, Tsubasa S. Matsui, Hiroki Aosaki, Koki Inoue, View ORCID ProfileAmin Doostmohammadi, Shinji Deguchi
doi: https://doi.org/10.1101/2021.02.01.429065
Honghan Li
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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Daiki Matsunaga
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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  • For correspondence: daiki.matsunaga@me.es.osaka-u.ac.jp deguchi@me.es.osaka-u.ac.jp
Tsubasa S. Matsui
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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Hiroki Aosaki
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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Koki Inoue
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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Amin Doostmohammadi
bNiels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen, Denmark
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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Shinji Deguchi
aDivision of Bioengineering, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama Toyonaka, Osaka, 5608531, Japan
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  • For correspondence: daiki.matsunaga@me.es.osaka-u.ac.jp deguchi@me.es.osaka-u.ac.jp
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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.

Significance Statement Cell-generated forces are indispensable determinants of fundamental cell functions such as motility and cell division. As such, quantifying how the forces change upon perturbations to the cells such as gene mutations and drug administration is of profound importance. Here we present a novel machine learning based system that allows for efficient estimations of the forces that are determined only by “observing” microscope images. Given that the cellular traction forces are regulated downstream of diverse signaling pathways, our system – that helps significantly improve the throughput of the measurements – presents a new, high throughput platform for real time analysis of the effects of a massive number of genetic and molecular perturbations on the forces and resulting cell mechanics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • H.L. and D.M developed the machine learning system, and K.I. supported the implementation. H.A. and T.S.M. designed and worked on the cell experiments. H.L., H.A. and D.M. analyzed the experimental data. D.M., A.D. and S.D. conceived the idea of simultaneous TFM with wrinkle extraction. D.M. and A.D. analyzed the physics. H.L., D.M., A.D. and S.D. wrote the article and designed the research.

  • The authors declare no competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted February 23, 2021.
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Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images
Honghan Li, Daiki Matsunaga, Tsubasa S. Matsui, Hiroki Aosaki, Koki Inoue, Amin Doostmohammadi, Shinji Deguchi
bioRxiv 2021.02.01.429065; doi: https://doi.org/10.1101/2021.02.01.429065
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Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images
Honghan Li, Daiki Matsunaga, Tsubasa S. Matsui, Hiroki Aosaki, Koki Inoue, Amin Doostmohammadi, Shinji Deguchi
bioRxiv 2021.02.01.429065; doi: https://doi.org/10.1101/2021.02.01.429065

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