Literature DB >> 29165907

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

Peter Rubbens1, Ruben Props2, Cristina Garcia-Timermans2, Nico Boon2, Willem Waegeman1.   

Abstract

Multicolor approaches are challenging for microbial flow cytometry; as flow cytometers are mainly developed for biomedical applications, modern instruments contain more detectors than needed. Some of these additional fluorescence detectors measure biological information due to spectral overlap, yet the extent to which this information is relevant for the identification of bacterial populations is ambiguous. In this paper we characterize the usefulness of these additional detectors. We propose a data-driven detector selection method to select the smallest subset of detectors that will optimally discriminate between bacterial populations. Using a detector elimination strategy, we show that one or more detectors can be removed without loss of resolving power. A number of additional detectors are included in the final subset, which help to improve the identification of bacterial populations. Experimental data were retrieved from two types of modern cytometers with different configurations. The method reveals a clear ordering of detector importances, which depends on the instrument from which the data were retrieved. In addition, we were able to pinpoint unexpected behavior of SYBR Green I in the red spectrum. As the field of microbial flow cytometry is maturing, these results motivate the construction of a different kind of cytometric instruments for microbiologists, for which the number of detectors is reduced, but tailored toward the characteristics of microbial experiments.
© 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Keywords:  automated identification of bacterial populations; bacterial communities; detector elimination; flow cytometry; microbiology; single-cell analysis; synthetic microbiology; variable selection

Mesh:

Year:  2017        PMID: 29165907     DOI: 10.1002/cyto.a.23284

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


  2 in total

1.  Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities.

Authors:  Jasmine Heyse; Benjamin Buysschaert; Ruben Props; Peter Rubbens; Andre G Skirtach; Willem Waegeman; Nico Boon
Journal:  Appl Environ Microbiol       Date:  2019-04-04       Impact factor: 4.792

Review 2.  Computational Analysis of Microbial Flow Cytometry Data.

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

  2 in total

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