News & Events

Sensitive and Selective Bioanalysis using SERS and SESORS

Date: 
Tuesday, March 21, 2023 - 12:45 to 14:00
Speaker: 
Dr. Karen Faulds
Affiliation: 
Department of Pure and Applied Chemistry, University of Strathclyde, Glasgow
Event Category: 
LMC - Lectures in Modern Chemistry
Host: 
Dr. Russ Algar
Location: 
Chemistry B250

Abstract:

Surface enhanced Raman scattering (SERS) is an analytical technique with several advantages over competitive techniques in terms of improved sensitivity and multiplexing. We have made great progress in the development of SERS as a quantitative analytical method. Many bioanalytical detection methods exist, with fluorescence spectroscopy tending to dominate, however SERS has the advantage that it is both sensitive and has the ability to multiplex which is limited when using techniques such as fluorescence. We have developed approaches to both identify and quantify the presence of multiple analytes within a mixture e.g. pathogenic DNA sequences, bacteria using SERS combined with data analysis techniques. 

Here we demonstrate the development of new bioanalytical assays based upon SERS which have been used successfully for the detection of bacterial pathogens using modified SERS active probes. Biomolecule functionalised nanoparticles have been designed to give a specific SERS response resulting in discernible differences in the SERS which can be correlated to the presence of specific pathogens. In this presentation the simultaneous detection and quantitation of 3 pathogens within a multiplex sample will be demonstrated. We also explore the use of functionalized nanoparticles for the phenotypic screening of breast cancer cells and to study the effect of drug treatment on receptor status. The uptake of targeted versus non-targeted nanoparticles in breast cancer spheroids using a microfluidics approach will also be discussed. We have also recently published the use of nanoparticles functionalised with resonant Raman reporter molecule for the visualization of a 3D breast cancer tumour models at depth using Spatially Offset Raman combined with SERRS (SESORRS).