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Three-dimensional high-content imaging of unstained soft tissue with subcellular resolution using a laboratory-based X-ray microscope.

March 24, 2026pubmed logopapers

Authors

Esposito M,Astolfo A,Zhou Y,Buchanan I,Teplov A,Hutchinson JC,Endrizzi M,Vinogradova AE,Makarova O,Divan R,Tang CM,Yagi Y,Lee PD,Walsh CL,Ferrara JD,Olivo A

Affiliations (9)

  • Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.
  • Department of Mechanical Engineering, University College London, London WC1E 6BT, United Kingdom.
  • Department of Pathology and Lab Medicine, Memorial Sloan Kettering Cancer Center, New York 10065, NY.
  • Department of Histopathology, Great Ormond Street Hospital for Children National Health Service Foundation Trust, London WC1N 1EH, United Kingdom.
  • X-ray microscopy and tomography lab, The Francis Crick Institute, London NW1 1AT, United Kingdom.
  • Rigaku Americas, The Woodlands, TX 77381.
  • Creatv MicroTech Inc., Chicago, IL 60612.
  • Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439.
  • Creatv MicroTech Inc., Potomac, MD 20854.

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.

Topics

Imaging, Three-DimensionalJournal Article

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