qiita_sid_10401
Quantifying and understanding well-to-well contamination in microbiome research
Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using a set of different bacteria each assigned a particular well in a plate, we assess the frequency at which sequences from each source appears in other wells. We evaluate the effects of different DNA extraction methods performed in two labs using a consistent plate layout including blanks, low biomass samples, and high biomass samples. Well-to-well contamination occured primarily during DNA extraction and to a lesser extent during library preparation, while barcode leakage was negligible. Processing sites differed in the levels of contamination. DNA extraction methods differed in their occurrences and levels of well-to-well contamination, with robotic methods having more well-to-well contamination while manual methods having higher background contaminants. Well-to-well contamination was observed to occur primarily in neighboring samples, but with rare events occurring up to 10 wells apart. The effect of well-to-well was greatest in samples with lower biomass, and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of crosstalk from nearby wells and background contaminants. Microbiome research studies should account for and evaluate well-to-well contamination in study design and be extremely careful about removing taxa or OTUs appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants. To reduce well-to-well effects, samples should be randomized across plates, and samples of similar biomass processed together. These effects should be considered in the development of new assays including extraction methods, and special care should be taken when analyzing behavior of low frequency OTUs that are highly abundant in other samples in the same experiment. Clinical microbiome assays need to account for the impact of well-to-well contamination on alpha and beta diversity metrics.