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  • Featured in Physics
  • Open Access

Deep-Learning Generation of High-Resolution Images of Live Cells in Culture Using Tri-Frequency Acoustic Images

Natsumi Fujiwara*, Midori Uno*, Hiroki Fukuda, Akira Nagakubo, Shao Ying Tan, Masahiro Kino-oka, and Hirotsugu Ogi†

  • Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871, Japan

  • *These authors contributed equally to this work.
  • †Contact author: ogi@prec.eng.osaka-u.ac.jp
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Phys. Rev. X 15, 021015 – Published 15 April, 2025

DOI: https://doi.org/10.1103/PhysRevX.15.021015

Abstract

Ultrasound microscopy is the only technique that has the ability to monitor live-cell morphology over a long period of time without causing any damage to the cells, but its longer wavelength prevents one from obtaining high-resolution cell images. Here, we propose a deep-learning (DL) method for generating high-resolution acoustic images. By preparing datasets consisting of many pairs of acoustic and optical-microscope images for the same cells and training them, a high-resolution image comparable to optical microscopy is generated from an acoustic image. Importantly, the most accurate images are generated when three-layer (RGB) images containing not only high-frequency (approximately 180 MHz) images but also lower-frequency (approximately 100 MHz) images are used as the input images, which is attributed to enhanced acoustic absorption in the nucleus because the nucleus resonates in this low-frequency band. The DL scheme with the tri-frequency image input is applied to human mesenchymal stem cells and human induced pluripotent stem cells, and the high image-generation capability is demonstrated. As a result, high-resolution acoustic microscopy images are obtained for the same cells for over 24 h, without the typical cell damage encountered using optical imaging.

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synopsis

AI Gives Ultrasound Imaging a Boost

Published 15 April, 2025

A new method for obtaining high-resolution images of cells from low-resolution ultrasound data enables longer, noninvasive monitoring of organisms.

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Popular Summary

Ultrasound is widely recognized as a safe imaging technology, as evidenced by its use in prenatal scans. However, its long wavelength limits image resolution, making it difficult to capture fine details. In this study, we present a breakthrough in ultrasound imaging resolution using a novel AI-based approach. Unexpectedly, we find that incorporating both low- and high-frequency acoustic images significantly enhances resolution, enabling long-term observation of living cells at high frame rates.

Long-term imaging of live cells is crucial in biophysical science but has been constrained by the limitations of optical microscopy, where light exposure can damage cells over time. Our deep-learning methodology challenges the conventional belief that only high-frequency signals improve resolution. Instead, we discover that low-frequency components play a crucial role due to mechanical resonance effects in the cell nucleus. This novel insight enables high-resolution, damage-free imaging of live cells for days.

This AI-driven approach overturns the traditional reliance on short wavelengths for high resolution. The implications extend beyond cellular imaging, suggesting that by leveraging specific resonant frequencies of microscopic structures, AI can generate high-resolution images across various fields. Further integration with high-resolution fluorescence imaging will push the boundaries of our understanding of complex biological systems.

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References (44)

  1. D. J. Stephens and V. J. Allan, Light microscopy techniques for live cell imaging, Science 300, 82 (2003).
  2. Y. F. Dufrêne, T. Ando, R. Garcia, D. Alsteens, D. Martinez-Martin, A. Engel, C. Gerber, and D. J. Müller, Imaging modes of atomic force microscopy for application in molecular and cell biology, Nat. Nanotechnol. 12, 295 (2017).
  3. J. R. McIntosh, Electron microscopy of cells: A new beginning for a new century, J. Cell Biol. 153, F25 (2001).
  4. J. R. Swedlow and M. Platani, Live cell imaging using wide-field microscopy and deconvolution, Cell Struct. Funct. 27, 335 (2002).
  5. N. R. Subedi, S. Stolyar, S. J. Tuson, C. J. Marx, and A. E. Vasdekis, Scattered-light-sheet microscopy with sub-cellular resolving power, J. Biophoton. 16, e202300068 (2023).
  6. A. Godin, B. Lounis, and L. Cognet, Super-resolution microscopy approaches for live cell imaging, Biophys. J. 107, 1777 (2014).
  7. E. C. Jensen, Overview of live-cell imaging: Requirements and methods used, Anat. Rec. 296, 1 (2013).
  8. R. Cole, Live-cell imaging, Cell Adhes. Migr. 8, 452 (2014).
  9. M. M. Frigault, J. Lacoste, J. L. Swift, and C. M. Brown, Live-cell microscopy—Tips and tools, J. Cell Sci. 122, 753 (2009).
  10. J. Rösner, A. Liotta, E. Angamo, C. Spies, U. Heinemann, and R. Kovács, Minimizing photodecomposition of flavin adenine dinucleotide fluorescence by the use of pulsed LEDs, J. Microsc. 264, 215 (2016).
  11. R. J. Wang, Effect of room fluorescent light on the deterioration of tissue culture medium, In vitro 12, 19 (1976).
  12. J. Icha, M. Weber, J. C. Waters, and C. Norden, Phototoxicity in live fluorescence microscopy, and how to avoid it, BioEssays 39, 1700003 (2017).
  13. J.-Y. Tinevez, J. Dragavon, L. Baba-Aissa, P. Roux, E. Perret, A. Canivet, V. Galy, and S. Shorte, in Imaging and Spectroscopic Analysis of Living Cells, Methods in Enzymology Vol. 506, edited by P. M. Conn (Academic, New York, 2012), Chap. 15, pp. 291–309.
  14. V. Magidson and A. Khodjakov, Circumventing photodamage in live-cell microscopy, Methods Cell Biol. 114, 545 (2013).
  15. S. Wäldchen, J. Lehmann, T. Klein, S. van de Linde, and M. Sauer, Light-induced cell damage in live-cell super-resolution microscopy, Sci. Rep. 5, 15348 (2015).
  16. V. Magidson and A. Khodjakov, in Digital Microscopy, Methods in Cell Biology Vol. 114, edited by G. Sluder and D. E. Wolf (Academic, New York, 2013), Chap. 23, pp. 545–560.
  17. W. C. Lemon and K. McDole, Live-cell imaging in the era of too many microscopes, Curr. Opin. Cell Biol. 66, 34 (2020).
  18. H. T. Beier and B. L. Ibey, Experimental comparison of the high-speed imaging performance of an EM-CCD and SCMOS camera in a dynamic live-cell imaging test case, PLoS One 9, e84614 (2014).
  19. J. H. Stockley, K. Evans, M. Matthey, K. Volbracht, S. Agathou, J. Mukanowa, J. Burrone, and R. T. Káradóttir, Surpassing light-induced cell damage in vitro with novel cell culture media, Sci. Rep. 7, 849 (2017).
  20. C. Belthangady and L. A. Royer, Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction, Nat. Methods 16, 1215 (2019).
  21. J. W. Pylvänäinen, E. G. de Mariscal, R. Henriques, and G. Jacquemet, Live-cell imaging in the deep learning era, Curr. Opin. Cell Biol. 85, 102271 (2023).
  22. H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Günayd𝚤n, L. A. Bentolila, C. Kural, and A. Ozcan, Deep learning enables cross-modality super-resolution in fluorescence microscopy, Nat. Methods 16, 103 (2019).
  23. R. F. Laine, G. Jacquemet, and A. Krull, Imaging in focus: An introduction to denoising bioimages in the era of deep learning, Int. J. Biochem. Cell Biol. 140, 106077 (2021).
  24. M. Weigert et al., Content-aware image restoration: Pushing the limits of fluorescence microscopy, Nat. Methods 15, 1090 (2018).
  25. B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, Image reconstruction by domain-transform manifold learning, Nature (London) 555, 487 (2018).
  26. W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, Deep learning massively accelerates super-resolution localization microscopy, Nat. Biotechnol. 36, 460 (2018).
  27. C. Ounkomol, S. Seshamani, M. M. Maleckar, F. Collman, and G. R. Johnson, Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy, Nat. Methods 15, 917 (2018).
  28. E. M. Christiansen, S. J. Yang, D. M. Ando, A. Javaherian, G. Skibinski, S. Lipnick, E. Mount, A. O’Neil, K. Shah, A. K. Lee, P. Goyal, W. Fedus, R. Poplin, A. Esteva, M. Berndl, L. L. Rubin, P. Nelson, and S. Finkbeiner, In silico labeling: Predicting fluorescent labels in unlabeled images, Cell 173, 792 (2018).
  29. A. Kinoshita, S. Senda, K. Mizushige, H. Masugata, S. Sakamoto, H. Kiyomoto, and H. Matsuo, Evaluation of acoustic properties of the live human smooth-muscle cell using scanning acoustic microscopy, Ultrasound Med. Biol. 24, 1397 (1998).
  30. E. M. Strohm and M. C. Kolios, Measuring the mechanical properties of cells using acoustic microscopy, in Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE, New York, 2009), p. 6042, 10.1109/IEMBS.2009.5334535.
  31. P. Anastasiadis and P. V. Zinin, High-frequency time-resolved scanning acoustic microscopy for biomedical applications, Open Neuroimaging J. 12, 69 (2018).
  32. R. J. G. van Sloun, R. Cohen, and Y. C. Eldar, Deep learning in ultrasound imaging, Proc. IEEE 108, 11 (2020).
  33. Ákos Makra, W. Bost, I. Kalló, A. Horváth, M. Fournelle, and M. Gyöngy, Enhancement of acoustic microscopy lateral resolution: A comparison between deep learning and two deconvolution methods, IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67, 136 (2020).
  34. P. Banerjee, S. Akarte, P. Kumar, M. Shamsuzzaman, A. Butola, K. Agarwal, D. Prasad, F. Melandso, and A. Habib, High-resolution imaging in acoustic microscopy using deep learning, Machine Learning: Sci. Technol. 5, 015007 (2024).
  35. N. Fujiwara, S. Y. Tan, T. Matsumoto, A. Nagakubo, M. Kino-oka, and H. Ogi, Development of a focused-ultrasound spectroscopic-imaging system combined with optical imaging for applying mechanical stimulation on living cells and the possibility of selective stimulation for the nucleus, Jpn. J. Appl. Phys. 63, 03SP65 (2024).
  36. M. Nakagawa, Y. Taniguchi, S. Senda, N. Takizawa, T. Ichisaka, K. Asano, A. Morizane, D. Doi, J. Takahashi, M. Nishizawa, Y. Yoshida, T. Toyoda, K. Osafune, K. Sekiguchi, and S. Yamanaka, A novel efficient feeder-free culture system for the derivation of human induced pluripotent stem cells, Sci. Rep. 4, 3594 (2014).
  37. O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, edited by N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi (Springer International Publishing, Cham, 2015), pp. 234–241.
  38. See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevX.15.021015 for figures that show generated images for various MSCs (S1) and iPSC colonies (S3) and nuclei of MSCs and iPSCs (S2). Movies S1 and S2 show standard time-lapse images obtained by SAM and those by the DL method for MSCs and iPSCs, respectively.
  39. V. Galstyan, O. S. Pak, and H. A. Stone, A note on the breathing mode of an elastic sphere in Newtonian and complex fluids, Phys. Fluids 27, 032001 (2015).
  40. R. Kato, M. Matsumoto, H. Sasaki, R. Joto, M. Okada, Y. Ikeda, K. Kanie, M. Suga, M. Kinehara, K. Yanagihara, Y. Liu, K. Uchio-Yamada, T. Fukuda, H. Kii, T. Uozumi, H. Honda, Y. Kiyota, and M. K. Furue, Parametric analysis of colony morphology of non-labelled live human pluripotent stem cells for cell quality control, Sci. Rep. 6, 34009 (2016).
  41. M. Suga, H. Kii, K. Niikura, Y. Kiyota, and M. K. Furue, Development of a monitoring method for nonlabeled human pluripotent stem cell growth by time-lapse image analysis, Stem Cells Transl. Med. 4, 720 (2015).
  42. S. K. Schnyder, J. J. Molina, and R. Yamamoto, Control of cell colony growth by contact inhibition, Sci. Rep. 10, 6713 (2020).
  43. T. N. T. Nguyen, K. Sasaki, and M. Kino-oka, Elucidation of human induced pluripotent stem cell behaviors in colonies based on a kinetic model, J. Biosci. Bioeng. 127, 625 (2019).
  44. S. Wakao, M. Kitada, Y. Kuroda, F. Ogura, T. Murakami, A. Niwa, and M. Dezawa, Morphologic and gene expression criteria for identifying human induced pluripotent stem cells, PLoS One 7, e48677 (2012).

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Phys. Rev. X 15, 021015– Published 15 April, 2025
  • Received 7 June 2024
  • Revised 10 December 2024
  • Accepted 13 March 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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