Label-free ultraviolet photoacoustic microscopy for multiscale structural phenotyping of epithelial morphogenesis in organoid-derived intestinal epithelial wrinkle tissue
- Journal
- Photoacoustics (Q1)
- Published
- 24 August 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Linbin Zha, Jangwon Yoon, Santanu Misra, Hyunjun Kye, Jeesu Kim, Jaesung Youn, et al.
- PMID
- 42765106
- DOI
- 10.1016/j.pacs.2026.100875
Why clinicians should know about it
- Picked for Histology (paper of the day, 22 September 2026): UV‑PAM label‑free imaging of organoid epithelial wrinkle morphology
Abstract
Mechanical wrinkling of epithelial tissues encodes key features of tissue morphogenesis and mechanotransductive responses, yet its quantitative, multiscale characterization remains challenging in organoid-based in vitro models due to reliance on fluorescence labelling and sectioning. Here, we develop label-free ultraviolet photoacoustic microscopy (UV-PAM) for multiscale imaging and quantitative analysis of human organoid-derived epithelial wrinkle tissues. By exploiting intrinsic nucleic-acid absorption at 266 nm, UV-PAM visualizes wrinkle structures and nuclear morphology without labeling or sectioning. Using morphomechanically defined wrinkle tissues, we identify multiscale signatures, including wrinkle wavelength, wrinkle index, and strain-induced nuclear elongation and redistribution. UV-PAM further captures chemically induced epithelial stress, revealing degradation of wrinkle structure and nuclear morphology. In a proof-of-concept dataset comprising two untreated and two DTT-treated specimens, integration of these intrinsic signatures with deep learning enabled patch-level discrimination between untreated and chemically perturbed wrinkle tissues on held-out field of views (FOVs), with training and test FOVs obtained from the same specimens. These results establish UV-PAM as a label-free platform for multiscale imaging of epithelial morphogenesis and preliminary computational phenotyping.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.