Using Raman Spectroscopy, US-based scientists have developed a new less-invasive disgnostic tool to identify kidney diseases and assess its severity level. Otherwise, Raman spectroscopy was also being used in exploring Mars surface by geologists.
Professor Chandra Mohan of the University of Houston and his colleague Wei-Chuan Shih have found that there is a difference in signals obtained from a healthy Raman signal and a diseased kidney’s Raman signal, which helps diagnose the damage or status of a kidney in a patient.
Instead of looking for a specific molecule or biomarker, such as creatinine, both Shih and Mohan relied upon dofferentiating a healthy kidney and a diseased kidney using the Raman spectroscopy signals.
“There are some molecules that must be responsible for these different Raman signals, but we don’t need to know what those molecules may be. As long as there’s a difference in the signal, that’s good enough — you can easily differentiate between a diseased kidney’s Raman signal and a healthy kidney’s Raman signal,” saod Mohan.
Usually patients with kidney disease have to undergo renal biopsies but there is a limit to how many tests they can undergo to assess the tissue damage. Now with the help of the Raman Spectroscopy, the researchers are able to provide molecular fingerprints without the need for biopsies to identify the status of kidney’s damage, if any.
Being minimal invasive and often non-invasive, the new diagnostic method also enables a label-free detection for the quantification of subtle molecular changes, said Mohan.
Shih’s expertise in molecular sensing using light-based sensing technologies, such as optical probes and Mohan’s expert work in the genomics and proteomics of lupus and other autoimmune diseases, helped them to work together for applications ranging from non-invasive glucose monitoring to sensing environmental hazards such as oil spills, in addition to determine creatinine levels in a kidney disease.
“By adapting multivariate analysis to Raman spectroscopy, we have successfully differentiated between the diseased and the non-diseased with up to 100% accuracy, and among the severely diseased, the mildly diseased and the healthy with up to 98% accuracy,” said researchers Mohan and Shih in their paper published in Journal of Biophotonics.
Other authors of the paper include Jingting Li, Yong Du, Ji Qi and Ravikumar Sneha, all from the University of Houston, and Anthony Chang of the University of Chicago.