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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).
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
Microbiology, Flow Cytometry, Machine Learning
journal title
PLOS ONE
PLoS One
volume
12
issue
1
article number
e0169754
pages
19 pages
Web of Science type
Article
Web of Science id
000396167300067
ISSN
1932-6203
DOI
10.1371/journal.pone.0169754
project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
8505958
handle
http://hdl.handle.net/1854/LU-8505958
date created
2017-01-27 14:43:20
date last changed
2017-07-12 12:43:26
@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 {\textrangle}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},
  keyword      = {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},
}

Chicago
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).
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).
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).
MLA
Rubbens, Peter, Ruben Props, Nico Boon, et al. “Flow Cytometric Single-cell Identification of Populations in Synthetic Bacterial Communities.” PLOS ONE 12.1 (2017): n. pag. Print.