Three-dimensionally visualized rhizoid system of moss, Physcomitrium patens, by refraction-contrast X-ray micro-computed tomography. (22nd August 2022)

• Record Type: Journal Article
• Authors: Yamaura, Ryohei; Tamaoki, Daisuke; Kamachi, Hiroyuki; Yamauchi, Daisuke; Mineyuki, Yoshinobu; Uesugi, Kentaro; Hoshino, Masato; Suzuki, Tomomi; Shimazu, Toru; Kasahara, Haruo; Kamada, Motoshi; Hanba, Yuko T; Kume, Atsushi; Fujita, Tomomichi; Karahara, Ichirou
• Is Part Of: Microscopy. Volume 71:Number 6(2022)
• Page Start: 364
• Page End: 373
• Publication Date: 2022-08-22
• Subjects: Physcomitrium (Physcomitrella) -- rhizoid system -- SPring-8 -- 3D architecture -- X-ray micro-CT -- synchrotron radiation; Microscopy -- Periodicals; 502.825
• DOI: 10.1093/jmicro/dfac041
• Languages: English
• ISSNs: 2050-5698

Abstract

Abstract: Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems is different, how much they exploit common systems or distinct systems to architect their structures is largely unknown. To understand the regulatory mechanism of how bryophytes architect the rhizoid system responding to environmental factors, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens, in 3D. The rhizoids having a diameter of 21.3 µm on the average were visualized by refraction-contrast X-ray micro-computed tomography using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line and black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids, which appeared in three different form types in tomograms, was tested by a method using a Canny edge detector or machine learning. The accuracy of output images was evaluated by comparing with the manually segmented ground truth images using measures such as F1 score and Intersection over Union, revealing that the automatic segmentation using machine learning was more effective than that using the Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids. We successfully visualized the moss rhizoid system in 3D for the first time.