An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions

Diego, Nuria De; Fürst, Tomáš; Humplík, Jan; Ugena, Lydia; Podlešáková, Kateřina; Spíchal, Lukáš

doi:10.3389/fpls.2017.01702

Cited by 49 publications

(56 citation statements)

References 37 publications

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“…Thus 16 trays were evaluated, each containing 110 seeds. The trays were placed into a controlled condition chamber with a robotically driven arm holding an RGB camera [17]. Each tray was photographed every 2 h for 12 days.…”

Section: Methodsmentioning

confidence: 99%

Bayesian approach for analysis of time-to-event data in plant biology

et al. 2020

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Background: Plants, like all living organisms, metamorphose their bodies during their lifetime. All the developmental and growth events in a plant's life are connected to specific points in time, be it seed germination, seedling emergence, the appearance of the first leaf, heading, flowering, fruit ripening, wilting, or death. The onset of automated phenotyping methods has brought an explosion of such time-to-event data. Unfortunately, it has not been matched by an explosion of adequate data analysis methods. Results and discussion:In this paper, we introduce the Bayesian approach towards time-to-event data in plant biology. As a model example, we use seedling emergence data of maize under control and stress conditions but the Bayesian approach is suitable for any time-to-event data (see the examples above). In the proposed framework, we are able to answer key questions regarding plant emergence such as these: (1) Do seedlings treated with compound A emerge earlier than the control seedlings? (2) What is the probability of compound A increasing seedling emergence by at least 5 percent? Conclusion:Proper data analysis is a fundamental task of general interest in life sciences. Here, we present a novel method for the analysis of time-to-event data which is applicable to many plant developmental parameters measured in field or in laboratory conditions. In contrast to recent and classical approaches, our Bayesian computational method properly handles uncertainty in time-to-event data and it is capable to reliably answer questions that are difficult to address by classical methods.

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Section: Methodsmentioning

confidence: 99%

Bayesian approach for analysis of time-to-event data in plant biology

et al. 2020

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“…The protocol for analysis of Arabidopsis rosette growth described by De Diego et al (2017) was modified as follows. Seeds of A. thaliana (ecotype Col-0) were surface- sterilized and sown on 12 cm × 12 cm square plates containing a 0.5× MS medium ( Murashige and Skoog, 1962 ) (pH 5.7) supplemented with a gelling agent (0.6% Phytagel; Sigma-Aldrich, Germany).…”

Section: Methodsmentioning

confidence: 99%

“…For non-invasive estimation of the changes in leaf color, we calculated three vegetative indices (NGRDI, GLI, and VARI) which have exhibited correlation with the plant biomass, nutrient status or tolerance to abiotic stress ( Gitelson et al, 2002 ; Perry and Roberts, 2008 ; Hunt et al, 2013 ). The images captured on the seventh day of an Arabidopsis rosette growth assay subjected to HTS were segmented for the extraction of leaf rosettes using software described in our previous report ( De Diego et al, 2017 ). Afterward, the values corresponding to particular color channels (red = R, green = G, and blue = B) were extracted for each pixel within the plant mask, and the vegetative indices were calculated as follows:Normalized green red difference index…”

Section: Methodsmentioning

confidence: 99%

“…In addition, we optimized the principles of two previously described protocols for implementation into the MTHTS approach. These included (i) the fast scoring of the germination rate based on a standardized 96-well plate test coupled with spectrophotometric reading of tetrazolium salt reduction ( Pouvreau et al, 2013 ) and (ii) an automated method for high-throughput screening of Arabidopsis rosette growth in multi-well plates ( De Diego et al, 2017 ). A highly efficient and reliable method for characterizing biostimulant efficacy at various salt stress levels was realized by developing and combining a high-throughput seed germination assay in Arabidopsis with the improved Arabidopsis rosette growth assay.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening of Arabidopsis Germination and Rosette Growth

et al. 2018

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Environmental stresses have a significant effect on agricultural crop productivity worldwide. Exposure of seeds to abiotic stresses, such as salinity among others, results in lower seed viability, reduced germination, and poor seedling establishment. Alternative agronomic practices, e.g., the use of plant biostimulants, have attracted considerable interest from the scientific community and commercial enterprises. Biostimulants, i.e., products of biological origin (including bacteria, fungi, seaweeds, higher plants, or animals) have significant potential for (i) improving physiological processes in plants and (ii) stimulating germination, growth and stress tolerance. However, biostimulants are diverse, and can range from single compounds to complex matrices with different groups of bioactive components that have only been partly characterized. Due to the complex mixtures of biologically active compounds present in biostimulants, efficient methods for characterizing their potential mode of action are needed. In this study, we report the development of a novel complex approach to biological activity testing, based on multi-trait high-throughput screening (MTHTS) of Arabidopsis characteristics. These include the in vitro germination rate, early seedling establishment capacity, growth capacity under stress and stress response. The method is suitable for identifying new biostimulants and characterizing their mode of action. Representatives of compatible solutes such as amino acids and polyamines known to be present in many of the biostimulant irrespective of their origin, i.e., well-established biostimulants that enhance stress tolerance and crop productivity, were used for the assay optimization and validation. The selected compounds were applied through seed priming over a broad concentration range and the effect was investigated simultaneously under control, moderate stress and severe salt stress conditions. The new MTHTS approach represents a powerful tool in the field of biostimulant research and development and offers direct classification of the biostimulants mode of action into three categories: (1) plant growth promotors/inhibitors, (2) stress alleviators, and (3) combined action.

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“…The recent alternatives to airplanes in aerial HTPPs include 'phenotowers' [122] and 'blimps' [123]. Few of such HTPPs have been deployed for phenotyping several crop plants such as Arabidopsis [114,124], cotton [121], barley [8], maize [125], wheat [126,127], rice [128], sorghum [8,129], etc. and most of these are run by large seed companies and advanced crop research institutes around the world.…”

Section: High Throughput Plant Phenotyping Platforms (Htpps)mentioning

confidence: 99%

Using Plant Phenomics to Exploit the Gains of Genomics

et al. 2019

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Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.

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An Automated Method for High-Throughput Screening of Arabidopsis Rosette Growth in Multi-Well Plates and Its Validation in Stress Conditions

Cited by 49 publications

References 37 publications

Bayesian approach for analysis of time-to-event data in plant biology

Bayesian approach for analysis of time-to-event data in plant biology

Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening of Arabidopsis Germination and Rosette Growth

Using Plant Phenomics to Exploit the Gains of Genomics

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