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

Zhijun Li, Yizhou Jiang, Shihuan Tang, Haixia Zou, Wentao Wang, Guangpei Qi, Hongbo Zhang*, Kun Jin, Yuhe Wang, Hong Chen, Liyuan Zhang, Xiangmeng Qu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
43 Downloads (Pure)


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. [Figure not available: see fulltext.]

Original languageEnglish
Article number273
Number of pages14
JournalMicrochimica Acta
Issue number8
Publication statusPublished - Aug 2022
MoE publication typeA1 Journal article-refereed


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