Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

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 co

SUBMITTER: Rubbens P 

PROVIDER: S-EPMC5266259 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature