Laboratory contamination over time during low‐biomass sample analysis

Abstract

Bacteria are not only ubiquitous on earth but can also be incredibly diverse within clean laboratories and reagents. The presence of both living and dead bacteria in laboratory environments and reagents is especially problematic when examining samples with low endogenous content (e.g., skin swabs, tissue biopsies, ice, water, degraded forensic samples or ancient material), where contaminants can outnumber endogenous microorganisms within samples. The contribution of contaminants within high‐throughput studies remains poorly understood because of the relatively low number of contaminant surveys. Here, we examined 144 negative control samples (extraction blank and no‐template amplification controls) collected in both typical molecular laboratories and an ultraclean ancient DNA laboratory over 5 years to characterize long‐term contaminant diversity. We additionally compared the contaminant content within a home‐made silica‐based extraction method, commonly used to analyse low endogenous content samples, with a widely used commercial DNA extraction kit. The contaminant taxonomic profile of the ultraclean ancient DNA laboratory was unique compared to modern molecular biology laboratories, and changed over time according to researcher, month and season. The commercial kit also contained higher microbial diversity and several human‐associated taxa in comparison to the home‐made silica extraction protocol. We recommend a minimum of two strategies to reduce the impacts of laboratory contaminants within low‐biomass metagenomic studies: (a) extraction blank controls should be included and sequenced with every batch of extractions and (b) the contributions of laboratory contamination should be assessed and reported in each high‐throughput metagenomic study.

Targeting the 16S rRNA Gene for Bacterial Identification in Complex Mixed Samples: Comparative Evaluation of Second (Illumina) and Third (Oxford Nanopore Technologies) Generation Sequencing Technologies

“…Nevertheless, the maximum fraction of N. meningitidis reads in any sample was limited to 0.06%, suggesting that when multiple samples are sequenced in the same run, adjusting read thresholds for OTU detection to higher levels (e.g., 0.1%) can help with reducing false positive identifications. Additionally, several Illumina sequencing reagents contaminants have been described previously [65][66][67], some of which were also identified here in NTCs, such as Delftia, Bradyrhizobium, Sphingomonas, Actinomyces, Corynebacterium, Devosia, Enhydrobacter, Mesorhizobium, Methylobacterium, Micrococcus, Stenotrophomonas, Streptococcus, Staphylococcus, and Pseudomonas (an extended description is available in the Supplementary Information). These genera are typically filtered out by the bioinformatics workflow in actual samples during pre-processing due to their very low abundances, and did not interfere with identification, but could, nevertheless, present issues if they belong to genera such as Pseudomonas and Staphylococcus that also contain pathogenic bacteria of interest present in the reference sample (i.e., P. aeruginosa and S. aureus).…”

Differential Response of Digesta- and Mucosa-Associated Intestinal Microbiota to Dietary Black Soldier Fly (Hermetia illucens) Larvae Meal in Seawater Phase Atlantic Salmon (Salmo salar)

“…The inclusion of negative controls, coupled with quantifications of microbial DNA concentration in the samples, has enabled fast and reliable identification of contaminating taxa in this study. Besides Pseudomonas, other common reagent contaminants, including Bradyrhizobium, Burkholderia, Comamonas, Methylobacterium, Propionibacterium, Ralstonia, Sphingomonas and Stenotrophomonas (83,85,87,(92)(93)(94)(95)(96), have also been frequently reported as members of Atlantic salmon intestinal microbiota, indicating that existing studies of Atlantic salmon intestinal microbiota may have been plagued with reagent contamination that is hard to ascertain due to lack of negative controls. As reagent contamination is unavoidable, study-specific and can critically influence sequencing-based microbiome analyses (85,97,98), negative controls should always be included and sequenced in microbiome studies especially when dealing with low microbial biomass samples like intestinal mucosa.…”

High‐resolution biomonitoring of plant pathogens and plant species using metabarcoding of pollen pellet contents collected from a honey bee hive

“…Another limitation with this metabarcoding study is that there were no blank samples included in the metagenomics workflow, which prevented us to discard commonly found contaminants from the extraction kits and other laboratory contaminants prior to sequencing (Weyrich et al, ). Given the high sensitivity aspect of HTS, there is also a risk of cross‐contamination with positive controls used for PCR, in which case renders the interpretation of “true” versus “false” signals ambiguous.…”