Literature DB >> 29266796

Flow cytometric fingerprinting for microbial strain discrimination and physiological characterization.

Benjamin Buysschaert1, Frederiek-Maarten Kerckhof1, Peter Vandamme2, Bernard De Baets3, Nico Boon1.   

Abstract

The analysis of microbial populations is fundamental, not only for developing a deeper understanding of microbial communities but also for their engineering in biotechnological applications. Many methods have been developed to study their characteristics and over the last few decades, molecular analysis tools, such as DNA sequencing, have been used with considerable success to identify the composition of microbial populations. Recently, flow cytometric fingerprinting is emerging as a promising and powerful method to analyze bacterial populations. So far, these methods have primarily been used to observe shifts in the composition of microbial communities of natural samples. In this article, we apply a flow cytometric fingerprinting method to discriminate among 29 Lactobacillus strains. Our results indicate that it is possible to discriminate among 27 Lactobacillus strains by staining with SYBR green I and that the discriminatory power can be increased by combined SYBR green I and propidium iodide staining. Furthermore, we illustrate the impact of physiological changes on the fingerprinting method by demonstrating how flow cytometric fingerprinting is able to discriminate the different growth phases of a microbial culture. The sensitivity of the method is assessed by its ability to detect changes in the relative abundance of a mix of polystyrene beads down to 1.2%. When a mix of bacteria was used, the sensitivity was as between 1.2% and 5%. The presented data demonstrate that flow cytometric fingerprinting is a sensitive and reproducible technique with the potential to be applied as a method for the dereplication of bacterial isolates.
© 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Entities:  

Keywords:  Lactobacillus; SYBR green; clustering; dereplication; microbiology

Mesh:

Year:  2017        PMID: 29266796     DOI: 10.1002/cyto.a.23302

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  6 in total

1.  Characterizing Microbiome Dynamics - Flow Cytometry Based Workflows from Pure Cultures to Natural Communities.

Authors:  Johannes Lambrecht; Florian Schattenberg; Hauke Harms; Susann Mueller
Journal:  J Vis Exp       Date:  2018-07-12       Impact factor: 1.355

2.  flowEMMi: an automated model-based clustering tool for microbial cytometric data.

Authors:  Joachim Ludwig; Christian Höner Zu Siederdissen; Zishu Liu; Peter F Stadler; Susann Müller
Journal:  BMC Bioinformatics       Date:  2019-12-09       Impact factor: 3.169

Review 3.  Computational Analysis of Microbial Flow Cytometry Data.

Authors:  Peter Rubbens; Ruben Props
Journal:  mSystems       Date:  2021-01-19       Impact factor: 6.496

Review 4.  Advances in automated real-time flow cytometry for monitoring of bioreactor processes.

Authors:  Anna-Lena Heins; Manh Dat Hoang; Dirk Weuster-Botz
Journal:  Eng Life Sci       Date:  2021-11-12       Impact factor: 2.678

5.  Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes.

Authors:  Abhishek S Dhoble; Pratik Lahiri; Kaustubh D Bhalerao
Journal:  J Biol Eng       Date:  2018-09-12       Impact factor: 4.355

6.  Flow cytometric analysis reveals culture condition dependent variations in phenotypic heterogeneity of Limosilactobacillus reuteri.

Authors:  Nikhil Seshagiri Rao; Ludwig Lundberg; Shuai Palmkron; Sebastian Håkansson; Björn Bergenståhl; Magnus Carlquist
Journal:  Sci Rep       Date:  2021-12-07       Impact factor: 4.379

  6 in total

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