2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

• Published in: Mikrochimica acta (1966) Vol. 189; no. 8; p. 273
• Main Authors: Li, Zhijun, Jiang, Yizhou, Tang, Shihuan, Zou, Haixia, Wang, Wentao, Qi, Guangpei, Zhang, Hongbo, Jin, Kun, Wang, Yuhe, Chen, Hong, Zhang, Liyuan, Qu, Xiangmeng
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.08.2022; Springer; Springer Nature B.V
• Subjects: Algorithms; Analytical Chemistry; Big Data; Characterization and Evaluation of Materials; Chemistry; Chemistry and Materials Science; Drug resistance; E coli; Escherichia coli; Fluorescence; Klebsiella; Machine learning; Microengineering; Microorganisms; Nanochemistry; Nanomaterials; Nanotechnology; Original Paper; Pseudomonas aeruginosa; Recognition; Sensor arrays; Signal processing; Taxonomy; Pathogenic microbial taxonomic; Molecular response differential profiling; Machine learning approach; Accurate recognition
• ISSN: 0026-3672; 1436-5073; 1436-5073
• DOI: 10.1007/s00604-022-05368-5

An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n  = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10 5  ~ 10 8  CFU/mL for Escherichia coli , 10 2  ~ 10 7  CFU/mL for E. coli-β , 10 3  ~ 10 8  CFU/mL for Staphylococcus aureus , 10 3  ~ 10 7  CFU/mL for MRSA, 10 2  ~ 10 8  CFU/mL for Pseudomonas aeruginosa , 10 3  ~ 10 8  CFU/mL for Enterococcus faecalis , 10 2  ~ 10 8  CFU/mL for Klebsiella pneumoniae , and 10 3  ~ 10 8  CFU/mL for Candida albicans . Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification. Graphical abstract • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements’ performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.