Rapid enrichment and SERS differentiation of various bacteria in skin interstitial fluid by 4-MPBA-AuNPs-functionalized hydrogel microneedles
Ying Yang, Xingyu Wang, Yexin Hu, Zhongyao Liu, Xiao Ma, Feng Feng, Feng Zheng, Xinlin Guo, Wenyuan Liu, Wenting Liao, Lingfei Han
Abstract
Bacterial infection is a major threat to global public health, and can cause serious diseases such as bacterial skin infection and foodborne diseases. It is essential to develop a new method to rapidly diagnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time. In this work, we developed a 4-mercaptophenylboronic acid gold nanoparticles (4-MPBA-AuNPs)-functionalized hydrogel microneedle (MPBA-H-MN) for bacteria detection in skin interstitial fluid. MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min. Surface enhanced Raman spectroscopy (SERS) detection was then performed and combined with machine learning technology to distinguish and identify a variety of bacteria. Overall, the capture efficiency of this method exceeded 50%. In the concentration range of 1 × 10 7 to 1 × 10 10 colony-forming units/mL (CFU/mL), the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration. Using random forest (RF)-based machine learning, bacteria were effectively distinguished with an accuracy of 97.87%. In addition, the harmless disposal of used MNs by photothermal ablation was convenient, environmentally friendly, and inexpensive. This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites. • MPBA-H-MN was designed as a polyacrylic acid hydrogel microneedle with 4-MPBA-AuNPs. • MPBA-H-MN diagnosed clinical and foodborne bacterial infections in interstitial fluid. • MPBA-H-MN enriched over 50 % bacteria in 5 min by hydrogel swelling and 4-MPBA capture. • MPBA-H-MN can be directly used for SERS detection of bacteria on the surface of AuNPs. • Through RF machine learning, bacteria could be efficiently distinguished.