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A tool named Iris for versatile high-throughput phenotyping in microorganisms

Nature Microbiology volume 2, Article number: 17014 (2017) Cite this article

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Abstract

Advances in our ability to systematically introduce and track controlled genetic variance in microorganisms have, in the past decade, fuelled high-throughput reverse genetics approaches. When coupled to quantitative readouts, such approaches are extremely powerful at elucidating gene function and providing insights into the underlying pathways and the overall cellular network organization. Yet, until now, all efforts to quantify microbial macroscopic phenotypes have been restricted to monitoring growth in a small number of model microorganisms. We have developed an image analysis software named Iris, which allows for systematic exploration of a number of orthogonal-to-growth processes, including biofilm formation, colony morphogenesis, envelope biogenesis, sporulation and reporter activity. In addition, Iris provides more sensitive growth measurements than currently available software and is compatible with a variety of different microorganisms, as well as with endpoint or kinetic data. We used Iris to reanalyse existing chemical genomics data in Escherichia coli and to perform proof-of-principle screens on colony biofilm formation and morphogenesis of different bacterial species and the pathogenic fungus Candida albicans. We thereby recapitulated existing knowledge but also identified a plethora of additional genes and pathways involved in both processes.

High-throughput reverse genetics can provide unprecedentedly rich information on gene function and cellular network organization1. Both gene–gene and gene–drug interactions rely on accurately measuring the phenotypic change between perturbed and unperturbed states. Until now, fitness-related measures have been exclusively used for quantifying such changes. For pooled barcoded libraries, this is a necessity, as sequencing reports on the relative abundance of each mutant in the experiment2. While options increase when using ordered libraries1, growth has dominated as the simplest phenotypic readout, reporting on effects at multiple levels and scales3. Concordantly, currently available software tools4–9 are tailored to measure colony size in high-density arrayed plates as a proxy of growth. Yet many bacterial core programs, such as biofilm, motility, competence and sporulation occur at stages when cells slow down or stop growing completely. Other processes impact cellular physiology but do not have a measureable effect on growth. Unfortunately, current tools cannot quantify such orthogonal-to-growth readouts, and even large-scale efforts to map their underlying genetic determinants have relied on qualitative or semi-quantitative metrics10–12.

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Figure 1: Schematic overview of software design.
Figure 2: Colony integral opacity is a sensitive metric of growth fitness.
Figure 3: High-throughput quantification of macrocolony biofilm formation.
Figure 4: Quantification of colony morphology and invasive filamentation.

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Acknowledgements

The authors thank K.C. Huang for advice on colony boundary detection algorithms. The authors also thank him, L.E. Dietrich and A. Price-Whelan for critically reading the manuscript and providing feedback. The authors thank L. Burrows (McMaster, Canada) for providing an antibody against PilC. This work was supported by the Sofja Kovalevskaja Award of the Alexander von Humboldt Foundation to A.T.

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  1. Manuel Banzhaf, Lucia Herrera-Dominguez and Alexandra Koumoutsi: These authors contributed equally to this work.

Authors and Affiliations

  1. European Molecular Biology Laboratory, Genome Biology Unit, Meyerhofstr. 1, 69117 Heidelberg, Germany

    George Kritikos, Manuel Banzhaf, Lucia Herrera-Dominguez, Alexandra Koumoutsi, Morgane Wartel, Matylda Zietek & Athanasios Typas

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  1. George Kritikos
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Contributions

G.K., M.B., L.H.-D., A.K., M.W., M.Z. and A.T. conceived and designed the experiments. M.B., L.H.-D., A.K., M.W. and M.Z. performed the experiments. G.K. designed and implemented the software used in the analysis. G.K. analysed the data. G.K. and A.T. wrote the paper.

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Correspondence to Athanasios Typas.

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Kritikos, G., Banzhaf, M., Herrera-Dominguez, L. et al. A tool named Iris for versatile high-throughput phenotyping in microorganisms. Nat Microbiol 2, 17014 (2017). https://doi.org/10.1038/nmicrobiol.2017.14

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