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

Abstract

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) similar to 10(8) CFU/mL for Escherichia coli, 10(2) similar to 10(7) CFU/mL for E. coli beta, 10(3) similar to 10(8) CFU/mL for Staphylococcus aureus, 10(3) similar to 10(7) CFU/mL for MRSA, 10(2) similar to 10(8) CFU/ mL for Pseudomonas aeruginosa, 10(3) similar to 10(8) CFU/mL for Enterococcus faecalis, 10(2) similar to 10(8) CFU/mL for Klebsiella pneumoniae, and 10(3) similar to 10(8) CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.