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Evaluation of procedures for the collection, processing, and analysis of biomolecules from low-biomass surfaces.

ABSTRACT: To comprehensively assess microbial diversity and abundance via molecular-analysis-based methods, procedures for sample collection, processing, and analysis were evaluated in depth. A model microbial community (MMC) of known composition, representative of a typical low-biomass surface sample, was used to examine the effects of variables in sampling matrices, target cell density/molecule concentration, and cryogenic storage on the overall efficacy of the sampling regimen. The MMC used in this study comprised 11 distinct species of bacterial, archaeal, and fungal lineages associated with either spacecraft or clean-room surfaces. A known cellular density of MMC was deposited onto stainless steel coupons, and after drying, a variety of sampling devices were used to recover cells and biomolecules. The biomolecules and cells/spores recovered from each collection device were assessed by cultivable and microscopic enumeration, and quantitative and species-specific PCR assays. rRNA gene-based quantitative PCR analysis showed that cotton swabs were superior to nylon-flocked swabs for sampling of small surface areas, and for larger surfaces, biological sampling kits significantly outperformed polyester wipes. Species-specific PCR revealed differential recovery of certain species dependent upon the sampling device employed. The results of this study empower current and future molecular-analysis-based microbial sampling and processing methodologies.

SUBMITTER: Kwan K

PROVIDER: S-EPMC3126404 | biostudies-other | 2011 May

REPOSITORIES: biostudies-other

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Evaluation of procedures for the collection, processing, and analysis of biomolecules from low-biomass surfaces.

Kwan K K Cooper M M La Duc M T MT Vaishampayan P P Stam C C Benardini J N JN Scalzi G G Moissl-Eichinger C C Venkateswaran K K

Applied and environmental microbiology 20110311 9

To comprehensively assess microbial diversity and abundance via molecular-analysis-based methods, procedures for sample collection, processing, and analysis were evaluated in depth. A model microbial community (MMC) of known composition, representative of a typical low-biomass surface sample, was used to examine the effects of variables in sampling matrices, target cell density/molecule concentration, and cryogenic storage on the overall efficacy of the sampling regimen. The MMC used in this stu ...[more]

PMID: 21398492

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Project description:Microorganisms in glacier ice provide tens to hundreds of thousands of years archive for a changing climate and microbial responses to it. Analyzing ancient ice is impeded by technical issues, including limited ice, low biomass, and contamination. While many approaches have been evaluated and advanced to remove contaminants on ice core surfaces, few studies leverage modern sequencing to establish in silico decontamination protocols for glacier ice. Here we sought to apply such "clean" sampling techniques with in silico decontamination approaches used elsewhere to investigate microorganisms archived in ice at ∼41 (D41, ∼20,000 years) and ∼49 m (D49, ∼30,000 years) depth in an ice core (GS3) from the summit of the Guliya ice cap in the northwestern Tibetan Plateau. Four "background" controls were established - a co-processed sterile water artificial ice core, two air samples collected from the ice processing laboratories, and a blank, sterile water sample - and used to assess contaminant microbial diversity and abundances. Amplicon sequencing revealed 29 microbial genera in these controls, but quantitative PCR showed that the controls contained about 50-100-times less 16S DNA than the glacial ice samples. As in prior work, we interpreted these low-abundance taxa in controls as "contaminants" and proportionally removed them in silico from the GS3 ice amplicon data. Because of the low biomass in the controls, we also compared prokaryotic 16S DNA amplicons from pre-amplified (by re-conditioning PCR) and standard amplicon sequencing, and found the resulting microbial profiles to be repeatable and nearly identical. Ecologically, the contaminant-controlled ice microbial profiles revealed significantly different microorganisms across the two depths in the GS3 ice core, which is consistent with changing climate, as reported for other glacier ice samples. Many GS3 ice core genera, including Methylobacterium, Sphingomonas, Flavobacterium, Janthinobacterium, Polaromonas, and Rhodobacter, were also abundant in previously studied ice cores, which suggests wide distribution across glacier environments. Together these findings help further establish "clean" procedures for studying low-biomass ice microbial communities and contribute to a baseline understanding of microorganisms archived in glacier ice.

Project description:High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (108, 107, 106, 105 and 104 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 106 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 106 bacteria per sample for robust and reproducible microbiota analysis.

Dataset Information

Evaluation of procedures for the collection, processing, and analysis of biomolecules from low-biomass surfaces.

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Evaluation of procedures for the collection, processing, and analysis of biomolecules from low-biomass surfaces.

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