Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations - Eisenhofer, Raphael; Minich, Jeremiah J.; Marotz, Clarisse; Cooper, Alan; Knight, Rob; Weyrich, Laura S. (Trends in microbiology (Regular ed.), 2019)
Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the ‘RIDE’ checklist, to improve the validity of future low microbial biomass research. There is increasing interest in applying metagenomic techniques to find correlations between microorganisms and disease. Metagenomic techniques are highly sensitive and can detect contaminant DNA (DNA from sources other than the samples under study) and cross-contamination (DNA exchange between samples). Recent studies have shown that contaminant DNA and cross-contamination can confound metagenomic studies, especially for sample types that have low microbial biomass. There is an urgent need for the field to adopt authentication criteria to prevent future metagenomic studies from falling prey to the pitfalls of contaminant DNA and cross-contamination.