Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food.

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91-95%) strain-specific pathogen identification and quantification capabilities. The trained PCA-NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps.

Direct Metatranscriptome RNA-seq and Multiplex RT-PCR Amplicon Sequencing on Nanopore MinION - Promising Strategies for Multiplex Identification of Viable Pathogens in Food.

Viable pathogenic bacteria are major biohazards that pose a significant threat to food safety. Despite the recent developments in detection platforms, multiplex identification of viable pathogens in food remains a major challenge. A novel strategy is developed through direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing on Nanopore MinION to achieve real-time multiplex identification of viable pathogens in food. Specifically, this study reports an optimized universal Nanopore sample extraction and library preparation protocol applicable to both Gram-positive and Gram-negative pathogenic bacteria, demonstrated using a cocktail culture of E. coli O157:H7, Salmonella enteritidis, and Listeria monocytogenes, which were selected based on their impact on economic loss or prevalence in recent outbreaks. Further evaluation and validation confirmed the accuracy of direct metatranscriptome RNA-seq and multiplex RT-PCR amplicon sequencing using Sanger sequencing and selective media. The study also included a comparison of different bioinformatic pipelines for metatranscriptomic and amplicon genomic analysis. MEGAN without rRNA mapping showed the highest accuracy of multiplex identification using the metatranscriptomic data. EPI2ME also demonstrated high accuracy using multiplex RT-PCR amplicon sequencing. In addition, a systemic comparison was drawn between Nanopore sequencing of the direct metatranscriptome RNA-seq and RT-PCR amplicons. Both methods are comparable in accuracy and time. Nanopore sequencing of RT-PCR amplicons has higher sensitivity, but Nanopore metatranscriptome sequencing excels in read length and dealing with complex microbiome and non-bacterial transcriptome backgrounds.