Reducing bias in microbiome research: Comparing methods from sample collection to sequencing

Abstract

BackgroundMicrobiota profiles are strongly influenced by many technical aspects that impact the ability of researchers to compare results. To investigate and identify potential biases introduced by technical variations, we compared several approaches throughout the entire workflow of a microbiome study, from sample collection to sequencing, using commercially available mock communities (from bacterial strains as well as from DNA) and multiple human fecal samples, including a large set of positive controls created as a random mix of several participant samples.MethodsHuman fecal material was sampled, and aliquots were used to test two commercially available stabilization solutions (OMNIgene·GUT and Zymo Research) in comparison to samples frozen immediately upon collection. In addition, the methodology for DNA extraction, input of DNA, or the number of PCR cycles were analyzed. Furthermore, to investigate the potential batch effects in DNA extraction, sequencing, and barcoding, we included 139 positive controls.ResultsSamples preserved in both the stabilization buffers limited the overgrowth of Enterobacteriaceae when compared to unpreserved samples stored at room temperature (RT). These stabilized samples stored at RT were different from immediately frozen samples, where the relative abundance of Bacteroidota was higher and Actinobacteriota and Firmicutes were lower. As reported previously, the method used for cell disruption was a major contributor to variation in microbiota composition. In addition, a high number of cycles during PCR lead to an increase in contaminants detected in the negative controls. The DNA extraction had a significant impact on the microbial composition, also observed with the use of different Illumina barcodes during library preparation and sequencing, while no batch effect was observed in replicate runs.ConclusionOur study reaffirms the importance of the mechanical cell disruption method and immediate frozen storage as critical aspects in fecal microbiota studies. A comparison of storage conditions revealed that the bias was limited in RT samples preserved in stabilization systems, and these may be a suitable compromise when logistics are challenging due to the size or location of a study. Moreover, to reduce the effect of contaminants in fecal microbiota profiling studies, we suggest the use of ~125 pg input DNA and 25 PCR cycles as optimal parameters during library preparation.

Changes in the Gastrointestinal Microbiota Induced by Proton Pump Inhibitors—A Review of Findings from Experimental Trials

“…DNA extraction from fecal samples without mechanical and chemical lysis has resulted in the extraction of lower amounts of DNA from the Firmicutes, Bacteroidetes and Proteobacteria phyla [ 26 ]. It is also well recognized that the handling and preservation of fecal samples may affect the composition which is analyzed at a later stage [ 27 ], and these should ideally be standardized. In the reviewed studies, MoBio Powersoil or MoBio PowerFecal were used in three of the earlier studies and Qiagen DNA Stool or Qiagen DNease Powersoil were used in the three most recent studies, while for six studies, the DNA extraction was performed with a physical and chemical lysis step including bead beating but without a specific kit.…”

A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing

“…Notably, it is worth mentioning that the Comamonadaceae family and Ralstonia genus, which are among the top 10 phylotypes with significant effect sizes, have been previously reported as contaminants in some stool samples [ 13 , 14 ]. This finding adds further support to the notion that these phylotypes may contribute to the segregation observed between the ERP116736 and SMK datasets.…”

Comparative oncology using domesticated dogs and their microbiome

“…Differences in study design, and data generation and analysis are also factors that may influence the lack of consistency across comparative gut microbiome studies. For instance, while sample collection and storage, nucleic acid extraction, sequencing approach (amplicon vs. shotgun metagenomics), variable region (as in the case of 16S sequencing), and data processing and analysis are factors that influence human gut microbiome studies ( 26 ), it is unknown how these factors influence canine gut microbiome studies. Therefore, consistency across and between canine and human gut microbiome studies from sample collection to data analysis will be essential when performing comparative oncology studies to determine if inconsistencies are due to microbiome differences, or biases associated with the mentioned factors.…”