Measuring the biodiversity of microbial communities by flow cytometry

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Measuring the biodiversity of microbial communities by flow cytometry. / Props, Ruben; Monsieurs, Pieter; Ahmed, Mohamed; Clement, Lieven; Boon, Nico.

In: Methods in Ecology and Evolution, Vol. 7, No. 11, 01.11.2016, p. 1376-1385.

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Props, R, Monsieurs, P, Ahmed, M, Clement, L & Boon, N 2016, 'Measuring the biodiversity of microbial communities by flow cytometry', Methods in Ecology and Evolution, vol. 7, no. 11, pp. 1376-1385. https://doi.org/10.1111/2041-210X.12607

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Props, Ruben ; Monsieurs, Pieter ; Ahmed, Mohamed ; Clement, Lieven ; Boon, Nico. / Measuring the biodiversity of microbial communities by flow cytometry. In: Methods in Ecology and Evolution. 2016 ; Vol. 7, No. 11. pp. 1376-1385.

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@article{4674eed6f8174eb19239f57477bf65af,
title = "Measuring the biodiversity of microbial communities by flow cytometry",
abstract = "Measuring the microbial diversity in natural and engineered environments is important for ecosystem characterization, ecosystem monitoring and hypothesis testing. Although the conventional assessment through single marker gene surveys has resulted in major advances, the complete procedure remains slow (i.e. weeks to months), labour-intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest, in highly resolved, temporal surveys of microbial diversity, necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort. Here, we demonstrate that sensitive single-cell measurements of phenotypic attributes, obtained via flow cytometry, can provide fast (i.e. within minutes) first-line assessments of microbial diversity dynamics, without demanding extensive sample preparation and downstream data processing. We developed a data processing pipeline that fits bivariate kernel density functions to phenotypic parameter combinations of an entire microbial community and concatenates them to a single one-dimensional phenotypic fingerprint. By calculating established diversity metrics from such phenotypic fingerprints, we construct an alternative interpretation of the microbial diversity that incorporates distinct phenotypic traits underlying cell-to-cell heterogeneity (i.e. morphology and nucleic acid content). Based on a detailed longitudinal study of a highly dynamic microbial ecosystem, our approach delivered temporal alpha diversity profiles that strongly correlated with the reference diversity, as estimated by 16S rRNA amplicon sequencing. This strongly suggests that the distribution of a limited amount of phenotypic features within a microbial community already provides sufficient resolving power for the measurement of diversity dynamics at the species level. We present a fast, robust analysis method for monitoring the microbial biodiversity of natural and engineered ecosystems that correlates well with the conventional marker gene surveys. Our work has both applied and fundamental implications that stretch from ecosystem monitoring and studies on microbial community dynamics, to supervised sampling strategies. Furthermore, our approach offers perspectives for the development of online and in situ monitoring systems for microbial ecosystems.",
keywords = "BR2, microbial ecology, flow cytometry, bioinformatics, 16S rRNA amplicon sequencing",
author = "Ruben Props and Pieter Monsieurs and Mohamed Ahmed and Lieven Clement and Nico Boon",
note = "Score=10",
year = "2016",
month = "11",
day = "1",
doi = "10.1111/2041-210X.12607",
language = "English",
volume = "7",
pages = "1376--1385",
journal = "Methods in Ecology and Evolution",
issn = "2041-210X",
publisher = "Wiley - John Wiley & Sons, Ltd",
number = "11",

}

RIS - Download

TY - JOUR

T1 - Measuring the biodiversity of microbial communities by flow cytometry

AU - Props, Ruben

AU - Monsieurs, Pieter

AU - Ahmed, Mohamed

AU - Clement, Lieven

AU - Boon, Nico

N1 - Score=10

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Measuring the microbial diversity in natural and engineered environments is important for ecosystem characterization, ecosystem monitoring and hypothesis testing. Although the conventional assessment through single marker gene surveys has resulted in major advances, the complete procedure remains slow (i.e. weeks to months), labour-intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest, in highly resolved, temporal surveys of microbial diversity, necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort. Here, we demonstrate that sensitive single-cell measurements of phenotypic attributes, obtained via flow cytometry, can provide fast (i.e. within minutes) first-line assessments of microbial diversity dynamics, without demanding extensive sample preparation and downstream data processing. We developed a data processing pipeline that fits bivariate kernel density functions to phenotypic parameter combinations of an entire microbial community and concatenates them to a single one-dimensional phenotypic fingerprint. By calculating established diversity metrics from such phenotypic fingerprints, we construct an alternative interpretation of the microbial diversity that incorporates distinct phenotypic traits underlying cell-to-cell heterogeneity (i.e. morphology and nucleic acid content). Based on a detailed longitudinal study of a highly dynamic microbial ecosystem, our approach delivered temporal alpha diversity profiles that strongly correlated with the reference diversity, as estimated by 16S rRNA amplicon sequencing. This strongly suggests that the distribution of a limited amount of phenotypic features within a microbial community already provides sufficient resolving power for the measurement of diversity dynamics at the species level. We present a fast, robust analysis method for monitoring the microbial biodiversity of natural and engineered ecosystems that correlates well with the conventional marker gene surveys. Our work has both applied and fundamental implications that stretch from ecosystem monitoring and studies on microbial community dynamics, to supervised sampling strategies. Furthermore, our approach offers perspectives for the development of online and in situ monitoring systems for microbial ecosystems.

AB - Measuring the microbial diversity in natural and engineered environments is important for ecosystem characterization, ecosystem monitoring and hypothesis testing. Although the conventional assessment through single marker gene surveys has resulted in major advances, the complete procedure remains slow (i.e. weeks to months), labour-intensive and susceptible to multiple sources of laboratory and data processing bias. Growing interest, in highly resolved, temporal surveys of microbial diversity, necessitates rapid, inexpensive and robust analytical platforms that require limited computational effort. Here, we demonstrate that sensitive single-cell measurements of phenotypic attributes, obtained via flow cytometry, can provide fast (i.e. within minutes) first-line assessments of microbial diversity dynamics, without demanding extensive sample preparation and downstream data processing. We developed a data processing pipeline that fits bivariate kernel density functions to phenotypic parameter combinations of an entire microbial community and concatenates them to a single one-dimensional phenotypic fingerprint. By calculating established diversity metrics from such phenotypic fingerprints, we construct an alternative interpretation of the microbial diversity that incorporates distinct phenotypic traits underlying cell-to-cell heterogeneity (i.e. morphology and nucleic acid content). Based on a detailed longitudinal study of a highly dynamic microbial ecosystem, our approach delivered temporal alpha diversity profiles that strongly correlated with the reference diversity, as estimated by 16S rRNA amplicon sequencing. This strongly suggests that the distribution of a limited amount of phenotypic features within a microbial community already provides sufficient resolving power for the measurement of diversity dynamics at the species level. We present a fast, robust analysis method for monitoring the microbial biodiversity of natural and engineered ecosystems that correlates well with the conventional marker gene surveys. Our work has both applied and fundamental implications that stretch from ecosystem monitoring and studies on microbial community dynamics, to supervised sampling strategies. Furthermore, our approach offers perspectives for the development of online and in situ monitoring systems for microbial ecosystems.

KW - BR2

KW - microbial ecology

KW - flow cytometry

KW - bioinformatics

KW - 16S rRNA amplicon sequencing

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/20771803

U2 - 10.1111/2041-210X.12607

DO - 10.1111/2041-210X.12607

M3 - Article

VL - 7

SP - 1376

EP - 1385

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

IS - 11

ER -

ID: 1892388