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

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 Shimamura () and Kohei Miyazono ()
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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
Teppei Shimamura: Nagoya University
Kohei Miyazono: The University of Tokyo

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-32848-2

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