Dataset Information

Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM.

ABSTRACT: Background:Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. Results:We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. Conclusions:With material costs well below USD$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.

SUBMITTER: Schneider D

PROVIDER: S-EPMC6927185 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM.

Schneider Dominik D Lopez Laura S LS Li Meng M Crawford Joseph D JD Kirchhoff Helmut H Kunz Hans-Henning HH

Plant methods 20191223

<h4>Background</h4>Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis r ...[more]

PMID: 31889980

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Project description:BackgroundA commercial biotyping system (Taxa Profile™, Merlin Diagnostika) testing the metabolization of various substrates by bacteria was used to determine if a set of phenotypic features will allow the identification of members of the genus Brucella and their differentiation into species and biovars.ResultsA total of 191 different amines, amides, amino acids, other organic acids and heterocyclic and aromatic substrates (Taxa Profile™ A), 191 different mono-, di-, tri- and polysaccharides and sugar derivates (Taxa Profile™ C) and 95 amino peptidase- and protease-reactions, 76 glycosidase-, phosphatase- and other esterase-reactions, and 17 classic reactions (Taxa Profile™ E) were tested with the 23 reference strains representing the currently known species and biovars of Brucella and a collection of 60 field isolates. Based on specific and stable reactions a 96-well "Brucella identification and typing" plate (Micronaut™) was designed and re-tested in 113 Brucella isolates and a couple of closely related bacteria.Brucella species and biovars revealed characteristic metabolic profiles and each strain showed an individual pattern. Due to their typical metabolic profiles a differentiation of Brucella isolates to the species level could be achieved. The separation of B. canis from B. suis bv 3, however, failed. At the biovar level, B. abortus bv 4, 5, 7 and B. suis bv 1-5 could be discriminated with a specificity of 100%. B. melitensis isolates clustered in a very homogenous group and could not be resolved according to their assigned biovars.ConclusionsThe comprehensive testing of metabolic activity allows cluster analysis within the genus Brucella. The biotyping system developed for the identification of Brucella and differentiation of its species and biovars may replace or at least complement time-consuming tube testing especially in case of atypical strains. An easy to handle identification software facilitates the applicability of the Micronaut™ system for microbiology laboratories.

Dataset Information

Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM.

Publications

Fluctuating light experiments and semi-automated plant phenotyping enabled by self-built growth racks and simple upgrades to the IMAGING-PAM.

Similar Datasets

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