Recently, the team of Han Jinsong/Li Fei from the School of Engineering published the results of “A Dual Fluorescence Turn-On Sensor Array Formed by Poly(para-(aryleneethynylene) (PPE) and Aggregation-Induced Emission (AIE) Boosts Sensitivity in Multiplex Bacterial Recognition” in the international authoritative journal, Angewandte Chemie Internationl Edition. A Dual Fluorescence Turn-On Sensor Array Formed by Poly(para-aryleneethynylene) (PPE) and Aggregation-Induced Emission (AIE) Boosts Sensitivity in Multiplex Bacterial Recognition”. Our master's student Yang Yu and doctoral student Weiwei Ni are the co-first authors of this paper, and Prof. Jin-Song Han, Distinguished Associate Researcher Fei Li, and Lecturer Hui Huang are the co-corresponding authors of this paper, and China Pharmaceutical University is the first corresponding organization.
Bacterial infections have become a major cause of death and disease worldwide. Timely and accurate identification of bacterial classes can help select appropriate antibiotics and curb disease progression. Traditional bacterial detection methods, such as culturing and counting colonies, are complicated to perform and take several days to produce reliable results. In recent years, emerging technologies, including immunology-based methods and polymerase chain reaction (PCR), have improved the sensitivity and efficiency of detection. Although these methods show great potential for medical applications, they still require specific designs for each bacterium (e.g., specific primers and antibodies, etc.) and do not meet the demand for high-throughput bacterial analysis. Therefore, there is an urgent need to develop a method that can simultaneously characterize multiple types of bacterial strains.
To address this issue, the team developed an electrostatic complex sensor array based on the “signal amplification” effect of poly(p-phenylene acetylene) (PPE) and various positively charged aggregation-induced luminescence (AIE) dyes. The sensing system produces a dual-channel fluorescent “turn-on” signal response to bacteria, enabling parallel detection of 20 bacterial species at low concentrations (OD600 = 0.001), quantification of bacteria, and identification of bacterial mixtures without the need for any sample preprocessing. Combined with the optimization of multiple artificial intelligence machine learning algorithms, the sensing system enables rapid identification of clinical samples from different types of urinary tract infections in less than 30 seconds without sample preprocessing, which greatly improves detection efficiency. The sensor array combines two types of fluorescent dyes with opposite properties, realizes signal amplification, improves detection sensitivity, and breaks through the technical problems of “undetectable”, “inadmissible”, and “not widely detectable” in the diagnosis of clinical infectious diseases. It breaks through the technical bottleneck of “undetectable”, “inaccurate”, and “not widely detectable” in the diagnosis of clinical infectious diseases, and demonstrates great potential for clinical application. In the future, the team will further develop portable, intelligent and multi-functional parallel testing equipment to promote the clinical translation and application of this technology.
This work was supported by the National Natural Science Foundation of China (82072017, 32272415, 52003298), the Natural Science Foundation of Jiangsu Province (BK20200578), the “Double First-class” Discipline Innovation Team of China Pharmaceutical University (CPUQNJC22_04), the National High-level Young National High-level Young Talent Program, Xingyao Scholar Program of China Pharmaceutical University, etc.
