Numerous important environments harbour low levels of microbial biomass, including certain human tissues, the atmosphere, plant seeds, treated drinking water, hyper-arid soils and the deep subsurface, with some environments lacking resident microbes altogether. These low microbial biomass environments pose unique challenges for standard DNA-based sequencing approaches, as the inevitability of contamination from external sources becomes a critical concern when working near the limits of detection. Likewise, lower-biomass samples can be disproportionately impacted by cross-contamination and practices suitable for handling higher-biomass samples may produce misleading results when applied to lower microbial biomass samples. This Consensus Statement outlines strategies to reduce contamination and cross-contamination, focusing on marker gene and metagenomic analyses. We also provide minimal standards for reporting contamination information and removal workflows. Considerations must be made at every study stage, from sample collection and handling through data analysis and reporting to reduce and identify contaminants. We urge researchers to adopt these recommendations when designing, implementing and reporting microbiome studies, especially those conducted in low-biomass systems.

The human microbiome can play key roles in disease, and diagnostic testing will soon have the ability to examine these roles in the context of clinical applications. Currently, most diagnostic testing in pathology applications focuses on a small number of disease‐causing microbes and dismisses the whole microbial community that causes or is modulated by disease. Microbiome modifications have already provided clinically relevant insights in gut and oral diseases, such as irritable bowel disease, but there are currently limitations when clinically examining microbiomes outside of these body sites. This is critical, as the majority of microbial samples used in pathology originate from body sites that contain low concentrations of microbial DNA, including skin, tissue, blood, and urine. These samples, also known as low microbial biomass samples, are difficult to examine without careful consideration and precautions to mitigate contamination and biases. Here, we present the limitations when analysing low microbial biomass samples using current protocols and techniques and highlight the advantages that microbiome testing can offer diagnostics in the future, if the proper precautions are implemented. Specifically, we discuss the sources of contamination and biases that may result in false assessments for these sample types. Finally, we provide recommendations to mitigate contamination and biases from low microbial biomass samples during diagnostic testing, which will be especially important to effectively diagnose and treat patients using microbiome analyses.

Shotgun metagenomics enables the reconstruction of complex microbial communities at a high level of detail. Such an approach can be conducted using both short-read and long-read sequencing data, as well as a combination of both. To assess the pros and cons of these different approaches, we used 22 fecal DNA extracts collected weekly for 11 weeks from two respective lab mice to study seven performance metrics over four combinations of sequencing depth and technology: (i) 20 Gbp of Illumina short-read data, (ii) 40 Gbp of short-read data, (iii) 20 Gbp of PacBio HiFi long-read data, and (iv) 40 Gbp of hybrid (20 Gbp of short-read +20 Gbp of long-read) data. No strategy was best for all metrics; instead, each one excelled across different metrics. The long-read approach yielded the best assembly statistics, with the highest N50 and lowest number of contigs. The 40 Gbp short-read approach yielded the highest number of refined bins. Finally, the hybrid approach yielded the longest assemblies and the highest mapping rate to the bacterial genomes. Our results suggest that while long-read sequencing significantly improves the quality of reconstructed bacterial genomes, it is more expensive and requires deeper sequencing than short-read approaches to recover a comparable amount of reconstructed genomes. The most optimal strategy is study-specific and depends on how researchers assess the trade-off between the quantity and quality of recovered genomes. IMPORTANCE: Mice are an important model organism for understanding the gut microbiome. When studying these gut microbiomes using DNA techniques, researchers can choose from technologies that use short or long DNA reads. In this study, we perform an extensive benchmark between short- and long-read DNA sequencing for studying mice gut microbiomes. We find that no one approach was best for all metrics and provide information that can help guide researchers in planning their experiments.

Ancient human migrations provide the critical genetic background to historical and contemporary human demographic patterns. However, our ability to infer past human migration events, especially those that occurred over rapid timescales, is often limited. A key example is the peopling of Polynesia, where the timing is relatively well defined, but the exact routes taken during the final stages and the source populations are not. Here, we discuss the technical limitations of current methods for inferring rapid human migration events, using the final stages of Polynesian migration as an example. We also introduce a promising new proxy method to infer human migrations—patterns of bacterial evolution within ancient dental calculus (calcified plaque). While we focus on Polynesia, this method should be applicable to other past migrations, enhancing our understanding of human prehistory and revealing the crucial events that shaped it.