Label- and Slide-Free Multispectral Quantitative Epi-Illumination Deep-Ultraviolet Microscopy
- Journal
- BME frontiers (Q1)
- Published
- 30 October 2026
- Study design
- Unclassified
- Evidence level
- Level 5, Expert Opinion (CEBM 5)
- Authors
- Mingxuan Si, Viswanath Gorti, Aaron D Silva Trenkle, Arjun Renjith, Brienna E Heinsz, Kyan Nguyen, et al.
- PMID
- 42964509
- DOI
- 10.34133/bmef.0337
Why clinicians should know about it
- Picked for Histology (paper of the day, 2 November 2026): Novel label-free microscopy with quantitative imaging of fresh tissue
Abstract
Objective and Impact Statement: We present a low-cost, LED-based, epi-illumination deep-UV microscope (epi-DUV) for rapid, label- and slide-free quantitative imaging of fresh bulk tissues, offering a powerful tool for point-of-care and intraoperative histological assessment. Introduction: Label- and slide-free imaging is highly desired in clinical pathology to avoid time-consuming tissue processing while preserving molecular information. Deep-UV microscopy offers high-resolution molecular contrast via intrinsic biomolecular absorption, but prior implementations were limited to thin sections and cell monolayers. Methods: We use an epi-DUV system to capture 255- and 280-nm absorption images, alongside tryptophan autofluorescence, from fresh, unprocessed bulk tissue. A deterministic transformation is applied to the 255-nm absorption images to generate virtual histological staining computationally. Results: This method yields quantitative maps of nucleic acid mass, protein mass, and quantum yield, while the transformation produces accurate H&E-like contrast. The system achieves 0.5-μm lateral resolution and ~6-μm effective slice thickness across a 707 × 707-μm field of view with ~330-ms exposure. This is the first demonstration of quantitative deep-UV molecular imaging of fresh, unlabeled bulk tissue. Conclusion: These results establish a simple, low-cost platform for rapid histological assessment of unprocessed tissue. By providing complementary molecular readouts alongside morphological contrast, this approach overcomes conventional processing limitations for rapid clinical applications.
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.