IMIS | Flanders Marine Institute
 

Flanders Marine Institute

Platform for marine research

IMIS

Publications | Institutes | Persons | Datasets | Projects | Maps
[ report an error in this record ]basket (0): add | show Print this page

Flow cytometric single-cell identification of populations in synthetic bacterial communities
Rubbens, P.; Props, R.; Boon, N.; Waegeman, W. (2017). Flow cytometric single-cell identification of populations in synthetic bacterial communities. PLoS One 12(1): e0169754. https://hdl.handle.net/10.1371/journal.pone.0169754
In: PLoS One. Public Library of Science: San Francisco. ISSN 1932-6203; e-ISSN 1932-6203, more
Peer reviewed article  

Available in  Authors 

Authors  Top 
  • Rubbens, P., more
  • Props, R., more
  • Boon, N., more
  • Waegeman, W.

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.

All data in the Integrated Marine Information System (IMIS) is subject to the VLIZ privacy policy Top | Authors