Advancing Medical Diagnostics: Rapid, Label-Free Detection and Differentiation of Shiga Toxin Variants in Human Serum Using a Cost-Effective PCA-Assisted SERS Platform

Shiga toxins-producing <i>Escherichia coli</i> (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.

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Journal
ACS Applied Materials & Interfaces
Published
2025-11-07
DOI
https://doi.org/10.1021/acsami.5c18171
Primary Topic
Biosensors and Analytical Detection
Type
article
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article

Advancing Medical Diagnostics: Rapid, Label-Free Detection and Differentiation of Shiga Toxin Variants in Human Serum Using a Cost-Effective PCA-Assisted SERS Platform

Maurizio Brigotti, Amalia D’Avino, Domenico Sagnelli, Elisa Varrone et al.
ACS Applied Materials & Interfaces
Biosensors and Analytical Detection
article

Advancing Medical Diagnostics: Rapid, Label-Free Detection and Differentiation of Shiga Toxin Variants in Human Serum Using a Cost-Effective PCA-Assisted SERS Platform

Maurizio Brigotti, Amalia D’Avino, Domenico Sagnelli, Elisa Varrone, Stefano Morabito, Massimo Rippa, Valentina Marchesano, Lucia Petti, Gabriella Rossi, Gianluigi Ardissino, Lu Zhou, Bryan Guilcapi, Alessia Milano
article en

Abstract

Shiga toxins-producing <i>Escherichia coli</i> (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.

ACS Applied Materials & InterfacesVol. 17(46)
Academy of Medical Sciences (GB), Parthenope University of Naples (IT), Istituto Superiore di Sanità (IT), Office of the Assistant Secretary for Health (US), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT), Institute of Applied Science and Intelligent Systems (IT), Società Italiana di Medicina Generale (IT), Shandong First Medical University (CN), University of Bologna (IT)
Openalex Percentile: Top 68%
Biosensors and Analytical Detection
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