How Advances in Food Testing Technology Help Fight SARS-CoV-2

Over my two-decade career as a food microbiologist, I have seen a dramatic shift in the methods used in the detection of foodborne pathogens—from conventional microbiology techniques to molecular methods. Developments in these molecular technologies have led to wider access and shorter testing times that have allowed public health agencies and food manufacturers to ensure a safer food supply.

Having started my career at FDA, I have seen firsthand the significance of rapid methods in minimizing the scale and impact of foodborne outbreaks. Access to in-house molecular testing has helped manufacturers to monitor the bacterial load in their facilities and allowed them to perform environmental mapping to identify areas to focus sanitation efforts. As part of a quality assurance program, increased testing for microorganisms has helped companies spot trends and intervene before issues grow out of control. The passage of the Food Safety Modernization Act (FSMA), which declared that environmental monitoring and finished product testing are a part of a robust food safety system, is an open endorsement and recognition of the benefits of testing by public health agencies.

With the recognition of the importance of pathogen detection, molecular methods have continued to be developed to improve testing. Initially, immunological and nucleic acid amplification testing methods were developed for more rapid, sensitive, and specific results, but these methods were mostly used for screening for the presence or absence of pathogens. Additional tests were needed to confirm the presence of potential pathogens and to more deeply characterize the microorganisms.

Technologies such as pulsed field gel electrophoresis (PFGE), multi-locus variable-number tandem repeat analysis (MLVA), multi-locus sequence typing (MLST), and whole genome sequencing (WGS) provide higher resolution in distinguishing strains of bacteria to match food, clinical, and environmental isolates in outbreak investigations, to track pathogens within a facility, and to identify sources of contamination. Taken together, there are many techniques that have been useful in ensuring food safety, but biotech companies that develop tests still strive for the holy grail: a rapid, low-cost, sensitive, and specific method for detection with strain level resolution that is easy to use and does not require extensive expertise and training in performing the test or analyzing data.

Next Generation Sequencing

One promising candidate to achieve these goals is next generation sequencing (NGS). With the establishment of GenomeTrakr in 2014 and Pulsenet’s adoption of WGS in 2016, genomic data has been widely adopted as a common language across a network of government, academic, and private industry laboratories. The availability and accessibility of high-quality, genome-wide data makes NGS incredibly powerful in strain level resolution of microorganisms for many users. An additional advantage of NGS is the generation of millions of sequencing reads to provide redundancy in detection of multiple targets to increase accuracy and minimize false positive and false negative results.

While NGS can provide these benefits, sequencing workflows can be complex and laborious, requiring many hands-on steps in sample preparation, amplification, library preparation, and loading sequencing flow cells. These workflows are not easily performed by novices, requiring expertise and training in both performing assays and interpreting data. Many experts had been skeptical that NGS could ever be used for routine detection and characterization of pathogens in foods because of these challenges. These predictions may have proven true if not for the application of automated robotic systems and data analysis pipelines to simplify NGS. Minimally trained technicians can load an automated NGS platform, walk away, and view curated results after the analyses are done. Analytical software can interpret NGS data to simply provide answers to the questions asked, without requiring the user to have a deep understanding of bioinformatics and genome assemblies.