Single probe-based fluorescence sensor array for bacteria identification by specific detection of reactive oxygen species alterations induced by nanomaterials

The detection and identification of bacteria in complex matrices remains challenging for conventional sensor arrays relying on nonspecific interactions between bacteria and sensing elements. Here, a fluorescence sensor array was developed for bacteria identification by specifically monitoring intracellular reactive oxygen species (ROS) level alterations induced by nanomaterials (NMs). Four common commercially available NMs (Co₃O₄, Fe₃O₄, SWCNTs, and GO) were employed as sensing elements to induce differential ROS levels in bacteria, and a universal ROS-responsive fluorescent probe was used to transduce these differences into unique response pattern for each bacteria species. Combined with machine-learning algorithms (LDA, RF and SVM), the array identified 12 bacteria species and 10 bacterial mixtures, as well as successfully classifying Gram-positive (G+) and Gram-negative (G−) bacteria. It also maintained robust performance by accurately quantifying bacteria concentrations in urine and plasma samples, exhibiting good linear relationships between factor 1 and S. aureus concentrations (OD 0.005–0.2). Correlation studies elucidated the complex relationships between the enzyme-like activity of NMs, antibacterial properties, and ROS generation. Different NMs induce differential ROS levels in different bacteria, forming the fundamental basis of our sensing strategy. Therefore, utilizing readily available commercial NMs as sensing elements and a single fluorescent dye as ROS detection probe offers advantages for bacteria detection and identification in complex environments and enables simple acquisition of large number of sensing elements. Integrating this nanomaterial-based ROS sensor array with machine learning presents a promising platform for simple, rapid, and accurate identification of bacteria in clinical diagnosis.