Journal Article Epublish
PubMed: 40374604 DOI: 10.1038/s41467-025-59163-w
Label-free multimodal optical biopsy reveals biomolecular and morphological features of diabetic kidney tissue in 2D and 3D.
Nat Commun 16(1):4509
Authors: Anthony A Fung, Zhi Li, Craig Boote, Petar Markov, Joseph P Gaut, Sanjay Jain, Lingyan Shi
Affiliations
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- School of Optometry & Vision Sciences, Cardiff University, Cardiff, UK.
- EMBL c/o DESY, Hamburg, Germany.
- Washington University School of Medicine, Department of Pathology and Immunology, St. Louis, MO, USA.
- Division of Nephrology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
Abstract
Kidney disease, the ninth leading cause of death in the United States, suffers from poor diagnostic efficiency (10%). Traditional biopsies use molecular reagents to enhance diagnostic power but are limited by overlapping spatial and chromatic signals, product quality variability, and additional processing. To address these challenges without disrupting routine diagnostics, we implement label-free imaging modalities-stimulated Raman scattering (SRS), second harmonic generation (SHG), and two-photon fluorescence (TPF)-within a single setup. We identify morphological, lipidomic, and metabolic biomarkers in control and diabetic kidney samples at subcellular resolution. Label-free Stimulated Raman Histology (SRH) reveals distinct collagen morphology, mesangial-glomerular volumes, lipid saturation, redox status, and lipid-protein concentrations previously unrecognized in kidney diseases. Using the same tissue section enhances diagnostic value without compromising limited tissue. These multimodal biomarkers broadly deepen the understanding of kidney disease progression by integrating lipidomic, fibrotic, and metabolic data.
MeSH
Kidney Diseases, Biological Science Disciplines, Kidney, Biopsy, Biomarkers, Tissues, Lipidomics