Advancing Medical Diagnostics: Rapid, Label-Free Detection and Differentiation of Shiga Toxin Variants in Human Serum...

Shiga toxins-producing Escherichia coli (STEC) are zoonotic pathogens causing severe diseases such as hemorrhagic colitis (HC) and hemolytic uremic syndrome (HUS). Infections caused by STEC represent a public health concern due to the severity of the possible outcome and acute mortality. The early diagnosis of the infection is pivotal to driving a correct therapeutic protocol to limit the severity of the symptoms. The diagnosis is quite cumbersome, requires specialized approaches, and thus is rarely performed in the hospital, being managed by the relevant national reference laboratory, delaying the administration of the appropriate supportive care. In this context, the demand for affordable diagnostic tests to be carried out at the bedside is crucial for providing high-value healthcare. In this study, for the first time to the best of our knowledge, we developed and optimized a highly sensitive SERS-based platform that can detect and identify the two main Shiga toxin variants (Stx1 and Stx2a) as well as the cleaved form of Stx2a in human blood serum at extremely low concentrations with limits of detection reaching 0.007 ng/mL (0.1 pM). This method uses affordable, sensitive, and very efficient SERS substrates based on gold nanoparticle films, made with a cost-effective bottom-up approach, which are much cheaper than those typically found in the literature. Our results show that the platform works well in complex biological samples, offering high sensitivity and specificity. Moreover, integrating machine learning algorithms, such as principal component analysis (PCA), enables accurate identification of toxin types, overcoming the limitations of conventional diagnostic methods. This innovative approach represents a significant step toward accessible, rapid, and scalable clinical diagnostics, potentially transforming the early detection and management of STEC-related infections and preventing life-threatening complications.