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De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee.

ABSTRACT: Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.

SUBMITTER: Liu Y

PROVIDER: S-EPMC9649624 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee.

Liu Yunxi Y Elworth R A Leo RAL Jochum Michael D MD Aagaard Kjersti M KM Treangen Todd J TJ

Nature communications 20221110 1

Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamin ...[more]

PMID: 36357382

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Project description:The bacterial microbiota of the mosquito influences numerous physiological processes of the host. As low-microbial-biomass ecosystems, mosquito tissues are prone to contamination from the laboratory environment and from reagents commonly used to isolate DNA from tissue samples. In this report, we analyzed nine 16S rRNA data sets, including new data obtained by us, to gain insight into the impact of potential contaminating sequences on the composition, diversity, and structure of the mosquito tissue microbial community. Using a clustering-free approach based on the relative abundance of amplicon sequence variants (ASVs) in tissue samples and negative controls, we identified candidate contaminating sequences that sometimes differed from, but were consistent with, results found using established methodologies. Some putative contaminating sequences belong to bacterial taxa previously identified as contaminants that are commonly found in metagenomic studies but that have also been identified as part of the mosquito core microbiota, with putative physiological relevance for the host. Using different relative abundance cutoffs, we show that contaminating sequences have a significant impact on tissue microbiota diversity and structure analysis. IMPORTANCE The study of tissue-associated microbiota from mosquitoes (primarily from the gut) has grown significantly in the last several years. Mosquito tissue samples represent a challenge for researchers given their low microbial biomass and similar taxonomic composition commonly found in the laboratory environment and in molecular reagents. Using new and published data sets that identified mosquito tissue microbiota from gut and reproductive tract tissues (and their respective negative controls), we developed a simple method to identify contamination microbiota. This approach uses an initial taxonomic identification without operational taxonomic unit (OTU) clustering and evaluates the relative abundance of control sample sequences, allowing the identification and removal of purported contaminating sequences in data sets obtained from low-microbial-biomass samples. While it was exemplified with the analysis of tissue microbiota from mosquitos, it can be extended to other data sets dealing with similar technical artifacts.

Project description:BackgroundSingle cell genomics has revolutionized microbial sequencing, but complete coverage of genomes in complex microbiomes is imperfect due to enormous variation in organismal abundance and amplification bias. Empirical methods that complement rapidly improving bioinformatic tools will improve characterization of microbiomes and facilitate better genome coverage for low abundance microbes.MethodsWe describe a new approach to sequencing individual species from microbiomes that combines antibody phage display against intact bacteria with fluorescence activated cell sorting (FACS). Single chain (scFv) antibodies are selected using phage display against a bacteria or microbial community, resulting in species-specific antibodies that can be used in FACS for relative quantification of an organism in a community, as well as enrichment or depletion prior to genome sequencing.ResultsWe selected antibodies against Lactobacillus acidophilus and demonstrate a FACS-based approach for identification and enrichment of the organism from both laboratory-cultured and commercially derived bacterial mixtures. The ability to selectively enrich for L. acidophilus when it is present at a very low abundance (<0.2%) leads to complete (>99.8%) de novo genome coverage whereas the standard single-cell sequencing approach is incomplete (<68%). We show that specific antibodies can be selected against L. acidophilus when the monoculture is used as antigen as well as when a community of 10 closely related species is used demonstrating that in principal antibodies can be generated against individual organisms within microbial communities.ConclusionsThe approach presented here demonstrates that phage-selected antibodies against bacteria enable identification, enrichment of rare species, and depletion of abundant organisms making it tractable to virtually any microbe or microbial community. Combining antibody specificity with FACS provides a new approach for characterizing and manipulating microbial communities prior to genome sequencing.

Project description:Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

Dataset Information

De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee.

Publications

De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee.

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