Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis

1.
State Key Laboratory of Extreme Photonics and Instrumentation, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zhejiang University, Hangzhou, 310058, China
2.
Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Haining, 314400, China
3.
Tandon School of Engineering, New York University, New York, 11201, USA
4.
The Second Affiliated Hospital, School of Medicine, Interdisciplinary Institute of Neuroscience and Technology (ZIINT), Zhejiang University, Hangzhou, 310020, China
5.
Britton Chance Center for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China
6.
The School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China
7.
Center for Genetic Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
8.
School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei, 230009, China

Funds: The authors acknowledge Live Multiphoton Microscopy of Zhejiang University 7T Magnetic Resonance Imaging Platform and Fen Yang for technical assistance, Shuangshuang Liu from the Core Facilities, Zhejiang University School of Medicine for her technical support, and Prof. Dongyu Li from Huazhong University of Science and Technology, for his constructive suggestion.

摘要

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Abstract: Three-photon microscopy (3PM) enables high-resolution three-dimensional (3D) imaging in deeply situated and highly scattering biological specimens, facilitating precise characterization of biological morphology and cellular-level physiology in vivo. However, the use of fluorescent probes with relatively low three-photon absorption cross-sections necessitates high-peak-power lasers for excitation, which poses inherent risks of light-induced damage. Additionally, the low repetition frequency of these lasers prolongs scanning time per pixel, hampering imaging speed and exacerbating the potential for photodamage. Such limitations hinder the application of 3PM in studying vulnerable tissues, including muscle regeneration. To address this critical issue, we developed the Multi-Scale Attention Denoising Network (MSAD-Net), a precise and versatile denoising network suitable for diverse structures and varying noise levels. Our network enables the use of lower excitation power (1/4–1/2 of the common power: 1.0–1.5 mW vs 4–6 mW) and shorter scanning time (1/6–1/4 of the common time: 2–3 μs/pixel vs 12 μs/pixel) in 3PM while preserving image quality and tissue integrity. It achieves a structural similarity index (SSIM) of with an average of 0.9932 and a fast inference time of just 80 ms per frame which ensured both high fidelity and practicality for downstream applications. By utilizing MSAD-Net-assisted imaging, we characterize the biological morphology and functionality of muscle regeneration processes through deep in vivo five-channel imaging under low excitation power and short scanning time, while maintaining a high signal-to-noise ratio (SNR) and excellent axial spatial resolution. Furthermore, we conducted high axial-resolution dynamic imaging of vascular microcirculation, macrophages, and ghost fibers. Our findings provide a deeper understanding of the mechanisms underlying muscle regeneration at the cellular and tissue levels.

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[1]	Shadrin IY, Khodabukus A, Bursac N. Striated muscle function, regeneration, and repair. Cell Mol Life Sci. 2016;73:4175–202.
[2]	Sousa-Victor P, García-Prat L, Muñoz-Cánoves P. Control of satellite cell function in muscle regeneration and its disruption in ageing. Nat Rev Mol Cell Biol. 2022;23:204–26.
[3]	Wang YX, Rudnicki MA. Satellite cells, the engines of muscle repair. Nat Rev Mol Cell Biol. 2012;13:127–33.
[4]	Yin H, Price F, Rudnicki MA. Satellite cells and the muscle stem cell niche. Physiol Rev. 2013;93:23–67.
[5]	Dumont NA, Bentzinger CF, Sincennes MC, Rudnicki MA. Satellite cells and skeletal muscle regeneration. In: Comprehensive physiology. 2015.
[6]	Schmidt M, Schuler SC, Huttner SS, von Eyss B, von Maltzahn J. Adult stem cells at work: regenerating skeletal muscle. Cell Mol Life Sci. 2019;76:2559–70.
[7]	Hicks MR, Pyle AD. The emergence of the stem cell niche. Trends Cell Biol. 2023;33:112–23.
[8]	Johnson AL, Kamal M, Parise G. The role of supporting cell populations in satellite cell mediated muscle repair. Cells. 2023;12:1968.
[9]	Horton NG, et al. In vivo three-photon microscopy of subcortical structures within an intact mouse brain. Nat Photonics. 2013;7:205–9.
[10]	Ouzounov DG, et al. In vivo three-photon imaging of activity of GCaMP6-labeled neurons deep in intact mouse brain. Nat Methods. 2017;14:388–90.
[11]	Wang T, et al. Three-photon imaging of mouse brain structure and function through the intact skull. Nat Methods. 2018;15:789–92.
[12]	He M, et al. Aggregation-induced emission nanoprobe assisted ultra-deep through-skull three-photon mouse brain imaging. Nano Today. 2022;45:101536.
[13]	Zhang H, et al. Large-depth three-photon fluorescence microscopy imaging of cortical microvasculature on nonhuman primates with bright AIE probe in vivo. Biomaterials. 2022;289:121809.
[14]	Chow DM, et al. Deep three-photon imaging of the brain in intact adult zebrafish. Nat Methods. 2020;17:605–8.
[15]	Choe K, et al. Intravital three-photon microscopy allows visualization over the entire depth of mouse lymph nodes. Nat Immunol. 2022;23:330–40.
[16]	Bakker GJ, et al. Intravital deep-tumor single-beam 3-photon, 4-photon, and harmonic microscopy. Elife. 2022;11:e63776.
[17]	He M, et al. Lipid droplets imaging with three-photon microscopy. J Innov Opt Health Sci. 2022;16:2250033.
[18]	Li S, et al. Extending the stokes shifts of donor-acceptor fluorophores by regulating the donor configuration for in vivo three-photon fluorescence imaging. Chem Mater. 2022;34:5999–6008.
[19]	Wang S, et al. In vivo three-photon imaging of lipids using ultrabright fluorogens with aggregation-induced emission. Adv Mater. 2021;33:2007490.
[20]	Wang T, Xu C. Three-photon neuronal imaging in deep mouse brain. Optica. 2020;7:947–60.
[21]	Cheng P, et al. Direct control of store-operated calcium channels by ultrafast laser. Cell Res. 2021;31:758–72.
[22]	Galli R, et al. Intrinsic indicator of photodamage during label-free multiphoton microscopy of cells and tissues. PLoS ONE. 2014;9:e110295.
[23]	König K. Cell damage during multi-photon microscopy. In: Handbook Of biological confocal microscopy (ed Pawley JB). Springer US. 2006.
[24]	Li B, Wu C, Wang M, Charan K, Xu C. An adaptive excitation source for high-speed multiphoton microscopy. Nat Methods. 2020;17:163–6.
[25]	Zhong Y, et al. Terahertz photons promote neuron growth and synapse formation through cAMP signaling pathway. PhotoniX. 2025;6:9.
[26]	Yildirim M, Sugihara H, So PTC, Sur M. Functional imaging of visual cortical layers and subplate in awake mice with optimized three-photon microscopy. Nat Commun. 2019;10:177.
[27]	Shen B, et al. Surmounting photon limits and motion artifacts for biological dynamics imaging via dual-perspective self-supervised learning. PhotoniX. 2024;5:1.
[28]	Chen X, et al. Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging. PhotoniX. 2024;5:4.
[29]	Chaudhary S, Moon S, Lu H. Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun. 2022;13:5165.
[30]	Li XY, et al. Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat Methods. 2021;18:1395–400.
[31]	Lecoq J, Oliver M, Siegle JH, Orlova N, Ledochowitsch P, Koch C. Removing independent noise in systems neuroscience data using deepinterpolation. Nat Methods. 2021;18:1401–8.
[32]	Zhang G, et al. Bio-friendly long-term subcellular dynamic recording by self-supervised image enhancement microscopy. Nat Methods. 2023;20:1957–70.
[33]	Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods. 2024;21:365–7.
[34]	Huang B, et al. Enhancing image resolution of confocal fluorescence microscopy with deep learning. PhotoniX. 2023;4:2.
[35]	Qian J, Wang C, Wu H, Chen Q, Zuo C. Ensemble deep learning-enabled single-shot composite structured illumination microscopy (eDL-cSIM). PhotoniX. 2025. https://doi.org/10.1186/s43074-025-00171-w.
[36]	Zhong S, et al. Three-dimensional dipole orientation mapping with high temporal-spatial resolution using polarization modulation. PhotoniX. 2024;5:12.
[37]	Zheng Z, et al. Intra- and intermolecular synergistic engineering of aggregation-induced emission luminogens to boost three-photon absorption for through-skull brain imaging. ACS Nano. 2022;16:6444–54.
[38]	Hennessy N, Simms C. Skeletal muscle extracellular matrix structure under applied deformation observed using second harmonic generation microscopy. Acta Biomater. 2023;172:135–46.
[39]	Webster MT, Manor U, Lippincott-Schwartz J, Fan CM. Intravital imaging reveals ghost fibers as architectural units guiding myogenic progenitors during regeneration. Cell Stem Cell. 2016;18:243–52.
[40]	He Y, Heng Y, Qin Z, Wei X, Wu Z, Qu J. Intravital microscopy of satellite cell dynamics and their interaction with myeloid cells during skeletal muscle regeneration. Sci Adv. 2023;9:eadi1891.

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doi: 10.1186/s43074-025-00191-6

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Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis

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doi: 10.1186/s43074-025-00191-6

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Ultra-low photodamage three-photon microscopy assisted by neural network for monitoring regenerative myogenesis

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PhotoniX

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