Friday, October, 1st, 2021, 11:00am – 12:30pm
Computational analysis of microbial flow cytometry data
Center for Microbial Ecology and Technology (CMET), Ghent University, Coupure Links 653a, B-9000 Ghent KYTOS, Technologiepark-Zwijnaarde 82, B-9052 Ghent
Flow cytometry (FCM) is an important technology for the study of microbial communities. It grants the ability to rapidly generate phenotypic single-cell data that are both quantitative, multivariate and of high temporal resolution. Microbial FCM data have a number of different characteristics and challenges compared to immunophenotyping FCM data. Most prokaryotic cells are much smaller in size and volume than human or mammalian cells, and although most cells are small, the size range within which microbial cells lie is larger than for mammalian cells, covering a range between 0.2 and 500µm. Microbial communities also comprise high levels of phenotypic and phylogenetic complexity (e.g. 1000s of taxa). In this talk, I will provide an overview of common pitfalls of traditional FCM computational techniques on these microbial data, and describe how we can move towards a tailored and reproducible approach for microbial ecology studies. Finally, I will list a number of open challenges to the field and offer further motivation for the use of standardized flow cytometry in microbial ecology research.
Ruben Props is a postdoctoral researcher at the Center for Microbial Ecology and Technology (CMET, Ghent University) and co-founder of KYTOS, a Ghent University spin-off company that develops microbial fingerprinting technologies to create actionable microbiome insights. He is specialized in the fields of single-cell and molecular microbiome analysis, and holds a PhD in Applied Bioscience Engineering from Ghent University (2014). He was a visiting research fellow at the department of Ecology and Evolutionary Biology at the University of Michigan (2016-2018). Currently, he is working on establishing the definition of a healthy microbiome in aquaculture, developing novel technologies to measure microbiome health, as well as understanding the importance of microbiome variation across aquatic ecosystems.