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An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis

Author

Listed:
  • Kei Takahashi

    (The University of Tokyo)

  • Ko Abe

    (Kobe Pharmaceutical University)

  • Shimpei I. Kubota

    (The University of Tokyo)

  • Noriaki Fukatsu

    (Nagoya University)

  • Yasuyuki Morishita

    (The University of Tokyo)

  • Yasuhiro Yoshimatsu

    (Niigata University)

  • Satoshi Hirakawa

    (Hamamatsu University School of Medicine)

  • Yoshiaki Kubota

    (Keio University School of Medicine)

  • Tetsuro Watabe

    (Tokyo Medical and Dental University (TMDU))

  • Shogo Ehata

    (The University of Tokyo)

  • Hiroki R. Ueda

    (The University of Tokyo
    Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research)

  • Teppei Shimamura

    (Nagoya University)

  • Kohei Miyazono

    (The University of Tokyo)

Abstract

The blood and lymphatic vasculature networks are not yet fully understood even in mouse because of the inherent limitations of imaging systems and quantification methods. This study aims to evaluate the usefulness of the tissue-clearing technology for visualizing blood and lymphatic vessels in adult mouse. Clear, unobstructed brain/body imaging cocktails and computational analysis (CUBIC) enables us to capture the high-resolution 3D images of organ- or area-specific vascular structures. To evaluate these 3D structural images, signals are first classified from the original captured images by machine learning at pixel base. Then, these classified target signals are subjected to topological data analysis and non-homogeneous Poisson process model to extract geometric features. Consequently, the structural difference of vasculatures is successfully evaluated in mouse disease models. In conclusion, this study demonstrates the utility of CUBIC for analysis of vascular structures and presents its feasibility as an analysis modality in combination with 3D images and mathematical frameworks.

Suggested Citation

  • Kei Takahashi & Ko Abe & Shimpei I. Kubota & Noriaki Fukatsu & Yasuyuki Morishita & Yasuhiro Yoshimatsu & Satoshi Hirakawa & Yoshiaki Kubota & Tetsuro Watabe & Shogo Ehata & Hiroki R. Ueda & Teppei Sh, 2022. "An analysis modality for vascular structures combining tissue-clearing technology and topological data analysis," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32848-2
    DOI: 10.1038/s41467-022-32848-2
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    References listed on IDEAS

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    1. Takeyuki Miyawaki & Shota Morikawa & Etsuo A. Susaki & Ai Nakashima & Haruki Takeuchi & Shun Yamaguchi & Hiroki R. Ueda & Yuji Ikegaya, 2020. "Visualization and molecular characterization of whole-brain vascular networks with capillary resolution," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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    Cited by:

    1. Lin Yang & Qiongliang Liu & Pramod Kumar & Arunima Sengupta & Ali Farnoud & Ruolin Shen & Darya Trofimova & Sebastian Ziegler & Neda Davoudi & Ali Doryab & Ali Önder Yildirim & Markus E. Diefenbacher , 2024. "LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages," Nature Communications, Nature, vol. 15(1), pages 1-22, December.

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