Phenoliner: A New Field Phenotyping Platform for Grapevine Research.
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ABSTRACT:
SUBMITTER: Kicherer A
PROVIDER: S-EPMC5539483 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:We have developed a multi-analyte fluid-phase protein array technology termed high-throughput immunophenotyping using transcription (HIT). This method employs a panel of monoclonal antibodies, each tagged with a unique oligonucleotide sequence that serves as a molecular barcode. After staining a sample, T7 polymerase amplifies the tags which are then hybridized to a DNA microarray for indirect measurement of each analyte. Here we screened 90 antibodies directed against a panel of cell surface markers as well as 4 isotype controls to compare resting human naive CD4+ T cells versus CD4+ T cells activated for 48 h with anti-CD3/anti-CD28 coated beads. Keywords: protein profiling, response to stimulus Two-condition experiment, activated versus resting cells. Biological replicates: 3 normal human donors, each donor served as an internal control. One replicate per array. One of the biological replicates was performed as a dye-swap to monitor dye-bias. We also performed a self-self comparison between activated cells from two different donors.
2010-05-26 | E-GEOD-10665 | biostudies-arrayexpress
Project description:We have developed a multi-analyte fluid-phase protein array technology termed high-throughput immunophenotyping using transcription (HIT). This method employs a panel of monoclonal antibodies, each tagged with a unique oligonucleotide sequence that serves as a molecular barcode. After staining a sample, T7 polymerase amplifies the tags which are then hybridized to a DNA microarray for indirect measurement of each analyte. Here we screened 90 antibodies directed against a panel of cell surface markers as well as 4 isotype controls to compare resting human naive CD4+ T cells versus CD4+ T cells activated for 48 h with anti-CD3/anti-CD28 coated beads. Keywords: protein profiling, response to stimulus
2008-03-03 | GSE10665 | GEO
Project description:BackgroundPreclinical radiation biology has become increasingly sophisticated due to the implementation of advanced small animal image guided radiation platforms into laboratory investigation. These small animal radiotherapy devices enable state-of-the-art image guided therapy (IGRT) research to be performed by combining high-resolution cone beam computed tomography (CBCT) imaging with an isocentric irradiation system. Such platforms are capable of replicating modern clinical systems similar to those that integrate a linear accelerator with on-board CBCT image guidance.MethodsIn this study, we present a dosimetric evaluation of the small animal radiotherapy research platform (SARRP, Xstrahl Inc.) focusing on small field dosimetry. Physical dosimetry was assessed using ion chamber for calibration and radiochromic film, investigating the impact of beam focus size on the dose rate output as well as beam characteristics (beam shape and penumbra). Two film analysis tools) have been used to assess the dose output using the 0.5 mm diameter aperture.ResultsGood agreement (between 1.7-3%) was found between the measured physical doses and the data provided by Xstrahl for all apertures used. Furthermore, all small field dosimetry data are in good agreement for both film reading methods and with our Monte Carlo simulations for both focal spot sizes. Furthermore, the small focal spot has been shown to produce a more homogenous beam with more stable penumbra over time.ConclusionsFilmQA Pro is a suitable tool for small field dosimetry, with a sufficiently small sampling area (0.1 mm) to ensure an accurate measurement. The electron beam focus should be chosen with care as this can potentially impact on beam stability and reproducibility.
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Project description:Background:Almond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates. Results:Our method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield. Conclusions:Our findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders.
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Project description:In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField® platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops' phenotype. In this paper, we will highlight the PhénoField® production capability using data from 2017-wheat trial. The innovative approach of the PhénoField® platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField® platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.
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Project description:Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Project description:Development of a phenotyping platform for the characterization of zebrafish autism models.
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Project description:Conventional field phenotyping for drought tolerance, the most important factor limiting yield at a global scale, is labor-intensive and time-consuming. Automated greenhouse platforms can increase the precision and throughput of plant phenotyping and contribute to a faster release of drought tolerant varieties. The aim of this work was to establish a framework of analysis to identify early traits which could be efficiently measured in a greenhouse automated phenotyping platform, for predicting the drought tolerance of field grown soybean genotypes. A group of genotypes was evaluated, which showed variation in their drought susceptibility index (DSI) for final biomass and leaf area. A large number of traits were measured before and after the onset of a water deficit treatment, which were analyzed under several criteria: the significance of the regression with the DSI, phenotyping cost, earliness, and repeatability. The most efficient trait was found to be transpiration efficiency measured at 13 days after emergence. This trait was further tested in a second experiment with different water deficit intensities, and validated using a different set of genotypes against field data from a trial network in a third experiment. The framework applied in this work for assessing traits under different criteria could be helpful for selecting those most efficient for automated phenotyping.
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