A guide to minimize contamination issues in microbiome restoration studies

Abstract

Many restoration ecology studies now incorporate an environmental microbiome component, made possible mainly via advanced DNA sequencing technologies. Environmental microbiomes are important for successful restoration as they support many ecosystem functions and services that are a target of restoration interventions. However, since microbes are ubiquitous in most environments, including laboratories and researchers, there are contamination risks. If these risks are not adequately managed, the conclusions drawn from these microbiome restoration studies could be compromised. Here, we provide a user guide for restoration ecologists on how to navigate microbiome contamination risks at each stage of a study, from planning and sampling to data analysis and publishing. The two main categories of contamination we discuss are cross-contaminationcontamination between samples-and external contamination-contamination from reagents and environmental sources. We also consider the impact of batch effects, where sampling and/or processing order could leave a signal in the data. Without adequate control, these contamination issues can undermine the results of microbiome restoration studies. We hope that this guide will help minimize the effect of contamination and improve the quality of data and studies going forward.

Does revegetation cause soil microbiota recovery? Evidence from revisiting a revegetation chronosequence 6 years after initial sampling

“…At each subsample point, soil was collected from two depths (0–10 cm and 20–30 cm), resulting in nine subsamples from each depth. Sampling implements were sterilized with Decon 90 and 5% bleach between quadrats and between depths to help minimize cross‐contamination (Cando‐Dumancela et al 2021). Collected soil was pooled by quadrat and sampling depth and homogenized in a sterile plastic bag in the field, resulting in a representative 500‐g sample from each depth in each quadrat.…”

“…While these amplicon sequencing methods have limitations (e.g. limited taxonomic resolution, susceptibility to contamination, amplification of non‐target DNA; Ji et al 2013; Thomsen & Willerslev 2015; Carini et al 2020; Cando‐Dumancela et al 2021), they do provide a cost‐effective method to generate detailed information on taxonomic diversity and community composition of soil microbiota (Berg et al 2020; Fierer et al 2021).…”

A practical guide for restoration ecologists to manage microbial contamination risks before laboratory processes during microbiota restoration studies

“…This is one of the most important questions to ask early as it will have serious impacts on the protocols employed to minimize contamination risks. Low biomass samples have a very high risk of contamination both from external sources (= external contamination) and between samples (= cross contamination) and need considerable effort to be employed to control, minimize, and account for this contamination (Minich et al 2019; Weyrich et al 2019; Cando‐Dumancela et al 2021). While the risks are lower for high biomass samples, effort is still required to ensure sample integrity from the outset and minimize the effect of cross and external contamination in fieldwork (discussed below).…”

“…contamination (Minich et al 2019;Weyrich et al 2019;Cando-Dumancela et al 2021). While the risks are lower for high biomass samples, effort is still required to ensure sample integrity from the outset and minimize the effect of cross and external contamination in fieldwork (discussed below).…”

“…Modern DNA sequencing methods allow for microbiota to be more accurately characterized than traditional approaches, which typically use field observations or microbial culturing, as most microbiota cannot be easily observed or cultured (Berg et al 2020). There is a vast literature exploring the contamination risks inherent in these DNA-based techniques in principle and practice (Minich et al 2019;Weyrich et al 2019;Cando-Dumancela et al 2021). These studies have largely focused on molecular laboratory and downstream data analysis methods (i.e.…”