Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, Nebraska 68198-6858, United States.
Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198-5900, United States.
Anal Chem. 2022 Feb 8;94(5):2615-2624. doi: 10.1021/acs.analchem.1c05021. Epub 2022 Jan 24.
Bacterial infections are the leading cause of morbidity and mortality in the world, particularly due to a delay in treatment and misidentification of the bacterial species causing the infection. Therefore, rapid and accurate identification of these pathogens has been of prime importance. The conventional diagnostic techniques include microbiological, biochemical, and genetic analyses, which are time-consuming, require large sample volumes, expensive equipment, reagents, and trained personnel. In response, we have now developed a paper-based ratiometric fluorescent sensor array. Environment-sensitive fluorescent dyes (3-hydroxyflavone derivatives) pre-adsorbed on paper microzone plates fabricated using photolithography, upon interaction with bacterial cell envelopes, generate unique fluorescence response patterns. The stability and reproducibility of the sensor array response were thoroughly investigated, and the analysis procedure was refined for optimal performance. Using neural networks for response pattern analysis, the sensor was able to identify 16 bacterial species and recognize their Gram status with an accuracy rate greater than 90%. The paper-based sensor was stable for up to 6 months after fabrication and required 30 times lower dye and sample volumes as compared to the analogous solution-based sensor. Therefore, this approach opens avenues to a state-of-the-art diagnostic tool that can be potentially translated into clinical applications in low-resource environments.
细菌感染是世界范围内发病率和死亡率的主要原因，特别是由于治疗延误和感染细菌种类的错误识别。因此，快速准确地识别这些病原体一直是至关重要的。传统的诊断技术包括微生物学、生化和遗传学分析，这些方法耗时、需要大量样本体积、昂贵的设备、试剂和经过培训的人员。有鉴于此，我们现在开发了一种基于纸张的比率荧光传感器阵列。预吸附在使用光刻技术制造的纸微区板上的环境敏感荧光染料（3-羟基黄酮衍生物）与细菌细胞包膜相互作用后，会产生独特的荧光响应模式。我们彻底研究了传感器阵列响应的稳定性和可重复性，并优化了分析程序以实现最佳性能。通过神经网络对响应模式进行分析，该传感器能够识别 16 种细菌，并以超过 90%的准确率识别其革兰氏状态。该基于纸张的传感器在制造后稳定长达 6 个月，并且与类似的基于溶液的传感器相比，所需的染料和样本体积低 30 倍。因此，这种方法为一种先进的诊断工具开辟了道路，该工具有可能转化为资源有限环境中的临床应用。
