Effects of sample treatments on genome recovery via single-cell genomics

Clingenpeel, Scott; Schwientek, Patrick; Hugenholtz, Philip; Woyke, Tanja

doi:10.1038/ismej.2014.92

Cited by 27 publications

(31 citation statements)

References 14 publications

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“…For ethanol-fixed biomass from sediment #3730, 76 out of 168 sorted events resulted in an MDA product. This efficiency (45%) is comparable with that of a recent study that reported a success rate of 50% when DNA from cells stored in 70% ethanol was used as PCR template (66). By avoiding any cell fixation before sorting of individual consortia (Fig.…”

Section: Identification Of Microbial Partners Within Activity-sortedsupporting

confidence: 85%

“…Given the observed specificity for active microbial aggregates of our sorting approach (Fig. 4B), preferential amplification of a single sequence type for this subset may be indicative of bias during cell lysis and/or the MDA reaction, a problem well discussed in the literature (66,69,70). Reactions that only recovered SRB sequences (n = 5) were not considered further in our analysis.…”

Section: Identification Of Microbial Partners Within Activity-sortedmentioning

confidence: 99%

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Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia

Hatzenpichler

Connon

Goudeau

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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170

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To understand the biogeochemical roles of microorganisms in the environment, it is important to determine when and under which conditions they are metabolically active. Bioorthogonal noncanonical amino acid tagging (BONCAT) can reveal active cells by tracking the incorporation of synthetic amino acids into newly synthesized proteins. The phylogenetic identity of translationally active cells can be determined by combining BONCAT with rRNAtargeted fluorescence in situ hybridization (BONCAT-FISH). In theory, BONCAT-labeled cells could be isolated with fluorescenceactivated cell sorting (BONCAT-FACS) for subsequent genetic analyses. Here, in the first application, to our knowledge, of BONCAT-FISH and BONCAT-FACS within an environmental context, we probe the translational activity of microbial consortia catalyzing the anaerobic oxidation of methane (AOM), a dominant sink of methane in the ocean. These consortia, which typically are composed of anaerobic methane-oxidizing archaea (ANME) and sulfatereducing bacteria, have been difficult to study due to their slow in situ growth rates, and fundamental questions remain about their ecology and diversity of interactions occurring between ANME and associated partners. Our activity-correlated analyses of >16,400 microbial aggregates provide the first evidence, to our knowledge, that AOM consortia affiliated with all five major ANME clades are concurrently active under controlled conditions. Surprisingly, sorting of individual BONCAT-labeled consortia followed by whole-genome amplification and 16S rRNA gene sequencing revealed previously unrecognized interactions of ANME with members of the poorly understood phylum Verrucomicrobia. This finding, together with our observation that ANME-associated Verrucomicrobia are found in a variety of geographically distinct methane seep environments, suggests a broader range of symbiotic relationships within AOM consortia than previously thought.activity-based cell sorting | BONCAT | click chemistry | ecophysiology | single-cell microbiology

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Section: Identification Of Microbial Partners Within Activity-sortedsupporting

confidence: 85%

Section: Identification Of Microbial Partners Within Activity-sortedmentioning

confidence: 99%

Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia

Hatzenpichler

Connon

Goudeau

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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170

209

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“…All samples are known to be contaminated, however, an exact quantification of contaminated sequences is missing due to the novelty of the data. A detailed description of these data can be found in the Additional file 1. benchmark : Sequence data from 30 single-cell genomes with low levels of contamination were obtained [41, 42]. Containing cross-contamination between 3 species ( Escherichia coli , Meiothermus ruber , Pedobacter heparinus ), the median per-sample contaminant proportion of 3% is very small (min=1 % , max=30 % ).…”

Section: Resultsmentioning

confidence: 99%

“…benchmark : Sequence data from 30 single-cell genomes with low levels of contamination were obtained [41, 42]. Containing cross-contamination between 3 species ( Escherichia coli , Meiothermus ruber , Pedobacter heparinus ), the median per-sample contaminant proportion of 3% is very small (min=1 % , max=30 % ).…”

Section: Resultsmentioning

confidence: 99%

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

et al. 2016

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BackgroundA major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data.ResultsWe present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows.ConclusionsAcdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1397-7) contains supplementary material, which is available to authorized users.

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“…In addition, Morono et al (2015) tried to optimize the preservation of intact environment sample using the cell alive system (CAS) freezing technique and found that the recovery of cells from the CAS-preserved sample was more efficient than preserved using conventional freezing methods [9]. In a similar study, Clingenpeel et al (2014) found that cryopreservation of samples of three bacterial strains allowed better recovery of single cell genomes as compared to ethanol and paraformaldehyde preserved cell samples [10]. Despite the fact that the strategies are still developing, the potential benefits of preserving environmental samples with their communities are immeasurable.…”

Section: Short Commentarymentioning

confidence: 99%

Preservation of Uncultivated Microbial Cells for Single Cell Genomics and Cultivation in Future

Prakash¹,

Jangid²

2015

Single Cell Biol

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Effects of sample treatments on genome recovery via single-cell genomics

Cited by 27 publications

References 14 publications

Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia

Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia

acdc – Automated Contamination Detection and Confidence estimation for single-cell genome data

Preservation of Uncultivated Microbial Cells for Single Cell Genomics and Cultivation in Future

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