Imaging flow cytometry analysis of intracellular pathogens
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Date
2017-01-01
Authors
Haridas, Viraga
Ranjbar, Shahin
Vorobjev, Ivan A.
Goldfeld, Anne E.
Barteneva, Natasha S.
Journal Title
Journal ISSN
Volume Title
Publisher
Methods
Abstract
Abstract Imaging flow cytometry has been applied to address questions in infection biology, in particular, infections induced by intracellular pathogens. This methodology, which utilizes specialized analytic software makes it possible to analyze hundreds of quantified features for hundreds of thousands of individual cellular or subcellular events in a single experiment. Imaging flow cytometry analysis of host cell-pathogen interaction can thus quantitatively addresses a variety of biological questions related to intracellular infection, including cell counting, internalization score, and subcellular patterns of co-localization. Here, we provide an overview of recent achievements in the use of fluorescently labeled prokaryotic or eukaryotic pathogens in human cellular infections in analysis of host-pathogen interactions. Specifically, we give examples of Imagestream-based analysis of cell lines infected with Toxoplasma gondii or Mycobacterium tuberculosis. Furthermore, we illustrate the capabilities of imaging flow cytometry using a combination of standard IDEAS™ software and the more recently developed Feature Finder algorithm, which is capable of identifying statistically significant differences between researcher-defined image galleries. We argue that the combination of imaging flow cytometry with these software platforms provides a powerful new approach to understanding host control of intracellular pathogens.
Description
Keywords
Imaging flow cytometry, Fluorescent protein, Intracellular pathogen, Mycobacteria tuberculosis, Toxoplasma gondii, Feature Finder, Cellular heterogeneity, Colocalization, Phagosome maturation, Rab5, Rab7
Citation
Viraga Haridas, Shahin Ranjbar, Ivan A. Vorobjev, Anne E. Goldfeld, Natasha S. Barteneva, Imaging flow cytometry analysis of intracellular pathogens, In Methods, Volume 112, 2017, Pages 91-104