Reducing bias in microbiome research: Comparing methods from sample collection to sequencingOriginal paper
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease, four years before the first published case study.
Read MoreWhat was studied?
This study examined how technical choices made throughout a microbiome workflow, from sample collection through sequencing, can bias the resulting microbiota profiles. The researchers compared different sample preservation methods, DNA extraction approaches, DNA input amounts, and PCR cycle numbers. They also investigated potential batch effects introduced during DNA extraction, sequencing, and barcoding steps.
Who was studied?
The study used commercially available mock communities, including both bacterial-strain mock communities and DNA-based mock communities, rather than a human patient cohort. It also used multiple human fecal samples collected and processed under different conditions. A large set of 139 positive controls, created as a random mix of several participant samples, was included to assess batch effects.
What were the most important findings?
Samples preserved in either of two commercial stabilization buffers (OMNIgene GUT and Zymo Research) showed less overgrowth of Enterobacteriaceae compared to unpreserved samples stored at room temperature. However, these stabilized room-temperature samples still differed in composition from samples frozen immediately upon collection. This indicates that both preservation method and storage condition independently shape the observed microbiota profile.
What are the greatest implications of this study?
The findings show that technical variation at multiple stages of the microbiome workflow, including sample preservation, extraction, and processing batch, can introduce biases that affect comparability across studies. Researchers comparing microbiome results across studies or sites need to account for these methodological differences rather than assuming profiles reflect biology alone. Standardizing or at least reporting preservation and processing methods would improve the reliability of cross-study comparisons.