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New Phytol. 2018 Jul;219(2):808-823. doi: 10.1111/nph.15129. Epub 2018 Apr 5.

The 'PhenoBox', a flexible, automated, open-source plant phenotyping solution.

Author information

1
Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), Dr. Bohr-Gasse 3, 1030, Vienna, Austria.
2
Department of Genome-oriented Bioinformatics, Technische Universität München, Wissenschaftszentrum Weihenstephan, Freising, Germany.
3
Vienna Biocenter Core Facilities (VBCF), Dr. Bohr-Gasse 3, 1030, Vienna, Austria.
4
Workshop, Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Campus-Vienna-Biocenter 1, 1030, Vienna, Austria.

Abstract

There is a need for flexible and affordable plant phenotyping solutions for basic research and plant breeding. We demonstrate our open source plant imaging and processing solution ('PhenoBox'/'PhenoPipe') and provide construction plans, source code and documentation to rebuild the system. Use of the PhenoBox is exemplified by studying infection of the model grass Brachypodium distachyon by the head smut fungus Ustilago bromivora, comparing phenotypic responses of maize to infection with a solopathogenic Ustilago maydis (corn smut) strain and effector deletion strains, and studying salt stress response in Nicotiana benthamiana. In U. bromivora-infected grass, phenotypic differences between infected and uninfected plants were detectable weeks before qualitative head smut symptoms. Based on this, we could predict the infection outcome for individual plants with high accuracy. Using a PhenoPipe module for calculation of multi-dimensional distances from phenotyping data, we observe a time after infection-dependent impact of U. maydis effector deletion strains on phenotypic response in maize. The PhenoBox/PhenoPipe system is able to detect established salt stress responses in N. benthamiana. We have developed an affordable, automated, open source imaging and data processing solution that can be adapted to various phenotyping applications in plant biology and beyond.

KEYWORDS:

PhenoBox; PhenoPipe; infection prediction; open source; plant pathogens; plant phenotyping; salt stress; smut fungi

PMID:
29621393
DOI:
10.1111/nph.15129

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