2026-03-19 ユニバーシティ・カレッジ・ロンドン（UCL）
＜関連情報＞
- https://www.ucl.ac.uk/news/2026/mar/new-x-ray-technique-could-transform-tissue-diagnosis
- https://www.pnas.org/doi/10.1073/pnas.2525239123
実験室用X線顕微鏡を用いた、染色されていない軟組織の細胞内解像度での3次元高コンテンツイメージング Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based X-ray microscope
Michela Esposito, Alberto Astolfo, Yang Zhou, +12 , and Alessandro Olivo
Proceedings of the National Academy of Sciences Published:March 17, 2026
DOI:https://doi.org/10.1073/pnas.2525239123
Significance
Three-dimensional analysis of biological tissue is crucial for linking tissue morphology to cellular function. While conventional histology remains the gold standard for visualizing tissue architecture at subcellular resolution, it is limited by its destructive, two-dimensional nature and anisotropic resolution, hindering its use in capturing full three-dimensional representations of tissue volumes. Here, we demonstrate that phase-contrast X-ray microscopy enables nondestructive, three-dimensional virtual histology of unstained liver tissue with subcellular resolution. By automatically detecting cells’ nuclei over three-dimensional tissue volumes and quantifying relevant parameters (e.g. electron density, nuclear morphology), this instrument has the potential to provide capabilities in biomedicine linking volumetric tissue architecture with cell-scale quantification.
Abstract
With increasing interest in studying biological systems across spatial scales—from centimeters down to nanometers—histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here, we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterization of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual Hematoxylin and Eosin (H&E) staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.
