Event Details

Biophotonics for Early Disease Detection, Stratification and Personalised Interventions

  • 2023-12-18
  • Prof. Sumeet Mahajan, Professor, Faculty of Engineering and Physical Sciences

In this talk I will give an overview of our biophotonics research wherein we apply both spectroscopic and imaging techniques to understand disease pathology and to develop early and accurate diagnostics and stratification methods for personalised treatments. Raman spectroscopy, a vibrational finger-printing technique, along with non-linear multimodal techniques such as coherent anti-Stokes Raman, harmonic generation and two-photon fluorescence microscopy form the main palette of label-free biophotonic tools used by us for various applications. The combination of chemical, structural and molecular information at high sensitivity and selectivity provided by these techniques offers improved classification, identification and hence, disease diagnosis and prognosis. We have developed new ways to improve specificity of Raman spectroscopy using multiple excitation s. I will further describe the application of our novel multi-excitation Raman spectroscopy (MX-Raman) method for detection of anti-microbial resistant (AMR) bacterial strains and in the stratification of patients with neurodegenerative diseases such as Alzheimer’s. MX-Raman provides robust and repeatable strain-level bacterial detection without additional reagents or sample preparation for classification with accuracies up to 99.75% [1,2], and thus enabling differentiation between AMR and sensitive strains of the same pathogenic bacteria species within minutes. Our studies on Alzheimer’s disease (AD) samples shows that different conformational strains can be distinguished [3]. New results show that neurodegenerative diseases caused by the misfolding of the same protein (Tau) can be distinguished from AD and accurately diagnosed with near 100% accuracy in patient cerebrospinal fluid (CSF) samples. The results show the ability to stratify patients between and within a disease c lass which can lead to personalised treatment. Our methodologies based on MX-Raman and non-linear imaging techniques provide rich structural and chemical information conferring highly specific detection and their non-invasive, non-destructive nature allows temporal monitoring. Thus they can be used for developing in vitro assays for testing/screening potential treatments (such as those based on ancient medicine) and eventually also allow monitoring of patients by the bedside. Thus while our work establishes the utility of label-free MX-Raman and non-linear imaging techniques, their huge potential for improving diagnostics and personalised interventions can only be realised through wider collaborations.

Professor, Faculty of Engineering and Physical Sciences, University of Southampton, SO17 1BJ, Southampton, United Kingdom