[Image courtesy of UC San Diego]
Researchers at the University of California San Diego and Washington University School of Medicine in St. Louis have developed a new imaging technology that provides detailed, three-dimensional views of kidney tissue without the need for stains, dyes or physical sectioning.
The technique, described as a label-free multimodal optical biopsy, could improve how diabetic kidney disease is diagnosed and studied, particularly in its early stages.
The study’s first author, Anthony Fung, led the development and integration of the imaging and analysis platform during his time as a doctoral and postdoctoral researcher at UC San Diego. He is currently a postdoctoral researcher at Yale University.
The research was published in Nature Communications, and outlined a method that uses lasers to capture high-resolution images showing structural and chemical changes in kidney tissue. These include fat buildup, protein alterations and scarring, all of which are early indicators of diabetic kidney disease.
“Think of it as a high-tech camera that can ‘see’ inside tissue without disrupting cells or requiring stains,” said Lingyan Shi, co-corresponding author and professor of bioengineering at UC San Diego. “It gives us a more complete picture of what’s happening inside the kidney—and does it in 3D.”
Unlike traditional biopsy techniques, which the researchers say require physical slicing and chemical staining of tissue samples, the new system captures intact tissue architecture while maintaining the flexibility for downstream analysis. This non-destructive approach could be particularly valuable when sample availability is limited.
“This technology has enormous potential to improve how we diagnose and monitor kidney disease, especially in its early stages when intervention can be most effective. It provides us clues to what adjustments or adaptations are happening in the cells in diabetes and related metabolic disorders in the kidney,” said Sanjay Jain, co-corresponding author and professor at Washington University School of Medicine.
To support clinical application, the team also used artificial intelligence to analyze the imaging data and identify potential signs of disease. This AI-enhanced analysis could eventually help clinicians make faster, more accurate diagnoses and tailor treatment plans to individual patients.
The researchers believe the platform could be adapted to study other diseases in which metabolism and tissue architecture play a critical role.
