Literature DB >> 33468704

Computational Analysis of Microbial Flow Cytometry Data.

Peter Rubbens1, Ruben Props2.   

Abstract

Flow cytometry is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. The complexity and amount of data necessitate an objective and streamlined data processing workflow that extends beyond commercial instrument software. No full overview of the necessary steps regarding the computational analysis of microbial flow cytometry data currently exists. In this review, we provide an overview of the full data analysis pipeline, ranging from measurement to data interpretation, tailored toward studies in microbial ecology. At every step, we highlight computational methods that are potentially useful, for which we provide a short nontechnical description. We place this overview in the context of a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
Copyright © 2021 Rubbens and Props.

Entities:  

Keywords:  bioinformatics; cytometry; data analysis; fingerprinting; microbial ecology; multivariate statistics; single cell

Year:  2021        PMID: 33468704      PMCID: PMC7820666          DOI: 10.1128/mSystems.00895-20

Source DB:  PubMed          Journal:  mSystems        ISSN: 2379-5077            Impact factor:   6.496


  104 in total

Review 1.  Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities.

Authors:  Susann Müller; Gerhard Nebe-von-Caron
Journal:  FEMS Microbiol Rev       Date:  2010-02-06       Impact factor: 16.408

2.  Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data.

Authors:  Ruben Props; Peter Rubbens; Michael Besmer; Benjamin Buysschaert; Jurg Sigrist; Hansueli Weilenmann; Willem Waegeman; Nico Boon; Frederik Hammes
Journal:  Water Res       Date:  2018-08-07       Impact factor: 11.236

3.  Absolute quantification of microbial taxon abundances.

Authors:  Ruben Props; Frederiek-Maarten Kerckhof; Peter Rubbens; Jo De Vrieze; Emma Hernandez Sanabria; Willem Waegeman; Pieter Monsieurs; Frederik Hammes; Nico Boon
Journal:  ISME J       Date:  2016-09-09       Impact factor: 10.302

4.  Stripping flow cytometry: How many detectors do we need for bacterial identification?

Authors:  Peter Rubbens; Ruben Props; Cristina Garcia-Timermans; Nico Boon; Willem Waegeman
Journal:  Cytometry A       Date:  2017-11-22       Impact factor: 4.355

5.  Monitoring biofilm function in new and matured full-scale slow sand filters using flow cytometric histogram image comparison (CHIC).

Authors:  Sandy Chan; Kristjan Pullerits; Janine Riechelmann; Kenneth M Persson; Peter Rådström; Catherine J Paul
Journal:  Water Res       Date:  2018-03-13       Impact factor: 11.236

6.  flowAI: automatic and interactive anomaly discerning tools for flow cytometry data.

Authors:  Gianni Monaco; Hao Chen; Michael Poidinger; Jinmiao Chen; João Pedro de Magalhães; Anis Larbi
Journal:  Bioinformatics       Date:  2016-04-10       Impact factor: 6.937

7.  flowDiv: a new pipeline for analyzing flow cytometric diversity.

Authors:  Bruno M S Wanderley; Daniel S A Araújo; María V Quiroga; André M Amado; Adrião D D Neto; Hugo Sarmento; Sebastián D Metz; Fernando Unrein
Journal:  BMC Bioinformatics       Date:  2019-05-28       Impact factor: 3.169

8.  Quantification and isolation of Bacillus subtilis spores using cell sorting and automated gating.

Authors:  Marianna Karava; Felix Bracharz; Johannes Kabisch
Journal:  PLoS One       Date:  2019-07-29       Impact factor: 3.240

9.  flowCore: a Bioconductor package for high throughput flow cytometry.

Authors:  Florian Hahne; Nolwenn LeMeur; Ryan R Brinkman; Byron Ellis; Perry Haaland; Deepayan Sarkar; Josef Spidlen; Errol Strain; Robert Gentleman
Journal:  BMC Bioinformatics       Date:  2009-04-09       Impact factor: 3.169

10.  Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics.

Authors:  Linn Sgier; Remo Freimann; Anze Zupanic; Alexandra Kroll
Journal:  Nat Commun       Date:  2016-05-18       Impact factor: 14.919

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  1 in total

1.  Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma.

Authors:  Maren Schmiester; René Maier; René Riedel; Pawel Durek; Marco Frentsch; Stefan Kolling; Mir-Farzin Mashreghi; Robert Jenq; Liangliang Zhang; Christine B Peterson; Lars Bullinger; Hyun-Dong Chang; Il-Kang Na
Journal:  Gut Microbes       Date:  2022 Jan-Dec
  1 in total

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