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Flow cytometric single-cell identification of populations in synthetic bacterial communities

Peter Rubbens (UGent) , Ruben Props (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)
(2017) PLOS ONE. 12(1).
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Abstract
Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.
Keywords
Microbiology, Flow Cytometry, Machine Learning

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Citation

Please use this url to cite or link to this publication:

MLA
Rubbens, Peter et al. “Flow Cytometric Single-cell Identification of Populations in Synthetic Bacterial Communities.” PLOS ONE 12.1 (2017): n. pag. Print.
APA
Rubbens, P., Props, R., Boon, N., & Waegeman, W. (2017). Flow cytometric single-cell identification of populations in synthetic bacterial communities. PLOS ONE, 12(1).
Chicago author-date
Rubbens, Peter, Ruben Props, Nico Boon, and Willem Waegeman. 2017. “Flow Cytometric Single-cell Identification of Populations in Synthetic Bacterial Communities.” Plos One 12 (1).
Chicago author-date (all authors)
Rubbens, Peter, Ruben Props, Nico Boon, and Willem Waegeman. 2017. “Flow Cytometric Single-cell Identification of Populations in Synthetic Bacterial Communities.” Plos One 12 (1).
Vancouver
1.
Rubbens P, Props R, Boon N, Waegeman W. Flow cytometric single-cell identification of populations in synthetic bacterial communities. PLOS ONE. 2017;12(1).
IEEE
[1]
P. Rubbens, R. Props, N. Boon, and W. Waegeman, “Flow cytometric single-cell identification of populations in synthetic bacterial communities,” PLOS ONE, vol. 12, no. 1, 2017.
@article{8505958,
  abstract     = {{Bacterial cells can be characterized in terms of their cell properties using flow cytometry. Flow cytometry is able to deliver multiparametric measurements of up to 50,000 cells per second. However, there has not yet been a thorough survey concerning the identification of the population to which bacterial single cells belong based on flow cytometry data. This paper not only aims to assess the quality of flow cytometry data when measuring bacterial populations, but also suggests an alternative approach for analyzing synthetic microbial communities. We created so-called in silico communities, which allow us to explore the possibilities of bacterial flow cytometry data using supervised machine learning techniques. We can identify single cells with an accuracy >90% for more than half of the communities consisting out of two bacterial populations. In order to assess to what extent an in silico community is representative for its synthetic counterpart, we created so-called abundance gradients, a combination of synthetic (i.e., in vitro) communities containing two bacterial populations in varying abundances. By showing that we are able to retrieve an abundance gradient using a combination of in silico communities and supervised machine learning techniques, we argue that in silico communities form a viable representation for synthetic bacterial communities, opening up new opportunities for the analysis of synthetic communities and bacterial flow cytometry data in general.}},
  articleno    = {{e0169754}},
  author       = {{Rubbens, Peter and Props, Ruben and Boon, Nico and Waegeman, Willem}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{Microbiology,Flow Cytometry,Machine Learning}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{19}},
  title        = {{Flow cytometric single-cell identification of populations in synthetic bacterial communities}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0169754}},
  volume       = {{12}},
  year         = {{2017}},
}
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