High resolution single cell analytics to follow microbial community dynamics in anaerobic ecosystems

Müller, Susann; Hübschmann, Thomas; Kleinsteuber, Sabine; Vogt, Carsten

doi:10.1016/j.ymeth.2012.04.001

Cited by 22 publications

(12 citation statements)

References 65 publications

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“…Anaerobes are the major bacterial group in the normal microbiota in well oxygenated humans, and the role of the normal anaerobic human microbiota in host defence, dietary factors and pathogenesis is not fully understood yet [1,2,4e7]. Recent studies of the human microbiota of various anatomic sites together with improved anaerobic methods have given more knowledge about it, quantifying the microbiota at different body sites and studying their function [4,8,9]. The major sites with a complex anaerobic flora are the oral cavity, upper respiratory tract, gastrointestinal tract, female genital tract and skin [1].…”

Section: Introductionmentioning

confidence: 99%

Development of antimicrobial resistance in the normal anaerobic microbiota during one year after administration of clindamycin or ciprofloxacin

2015

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Section: Introductionmentioning

confidence: 99%

Development of antimicrobial resistance in the normal anaerobic microbiota during one year after administration of clindamycin or ciprofloxacin

2015

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“…Noteworthy, the relative abundance information of the individual clusters is lost when only the black-and-white combination is used, thus representing presence/ absence information. Higher resolved information can be obtained if the cell number is integrated as gray level of the blots (Bombach et al, 2011; Müller et al, 2012). In the second step, the simplified images are processed with image analysis software.…”

Section: Methodsmentioning

confidence: 99%

Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

et al. 2014

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Optical characteristics of individual bacterial cells of natural communities can be measured with flow cytometry (FCM) in high throughput. The resulting data are visualized in cytometric histograms. These histograms represent individual cytometric fingerprints of microbial communities, e.g., at certain time points or microenvironmental conditions. Up to now four tools for analyzing the variation in these cytometric fingerprints are available but have not yet been systematically compared regarding application: Dalmatian Plot, Cytometric Histogram Image Comparison (CHIC), Cytometric Barcoding (CyBar), and FlowFP. In this article these tools were evaluated concerning (i) the required experience of the operator in handling cytometric data sets, (ii) the detection level of changes, (iii) time demand for analysis, and (iv) software requirements. As an illustrative example, FCM was used to characterize the microbial community structure of electroactive microbial biofilms. Their cytometric fingerprints were determined, analyzed with all four tools, and correlated to experimental and functional parameters. The source of inoculum (four different types of wastewater samples) showed the strongest influence on the microbial community structure and biofilm performance while the choice of substrate (acetate or lactate) had no significant effect in the present study. All four evaluation tools were found suitable to monitor structural changes of natural microbial communities. The Dalmatian Plot was shown to be most sensitive to operator impact but nevertheless provided an overview on community shifts. CHIC, CyBar, and FlowFP showed less operator dependence and gave highly resolved information on community structure variation on different detection levels. In conclusion, experimental and productivity parameters correlated with the biofilm structures and practical process integration details were available from cytometric fingerprint analysis.

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“…The CHIC tool rapidly determines changes in natural community structures using flow cytometric 2D histograms that are converted into images and then processed on a standard computer. Another approach based on image comparison was recently developed by our group; however, it still needs gate information (11,12). In contrast, the CHIC tool allows a comparison of cytometric datasets without gating decisions and any cytometric pre‐experience due to an automated image comparison procedure.…”

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confidence: 99%

CHIC—an automated approach for the detection of dynamic variations in complex microbial communities

et al. 2013

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Altering environmental conditions change structures of microbial communities. These effects have an impact on the single-cell level and can be sensitively detected using community flow cytometry. However, although highly accurate, microbial monitoring campaigns are still rarely performed applying this technique. One reason is the limited access to pattern analysis approaches for the evaluation of microbial cytometric data. In this article, a new analyzing tool, Cytometric Histogram Image Comparison (CHIC), is presented, which realizes trend interpretation of variations in microbial community structures (i) without any previous definition of gates, by working (ii) person independent, and (iii) with low computational demand. Various factors influencing a sensitive determination of changes in community structures were tested. The sensitivity of this technique was found to discriminate down to 0.5% internal variation. The final protocol was exemplarily applied to a complex microbial community dataset, and correlations to experimental variation were successfully shown. ' 2013 International Society for Advancement of Cytometry

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High resolution single cell analytics to follow microbial community dynamics in anaerobic ecosystems

Cited by 22 publications

References 65 publications

Development of antimicrobial resistance in the normal anaerobic microbiota during one year after administration of clindamycin or ciprofloxacin

Development of antimicrobial resistance in the normal anaerobic microbiota during one year after administration of clindamycin or ciprofloxacin

Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

CHIC—an automated approach for the detection of dynamic variations in complex microbial communities

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