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Cannoot, B.

Publications and source records attributed to Cannoot, B..

2 recordsLinked to original sources

High-resolution transcriptional atlas of growing maize shoot organs throughout plant development under well-watered and drought conditions

Crop improvement goals for maize (Zea mays L.) involve the targeted optimization of various organs, making it crucial to understand the developmental characteristics and gene expression patterns during organ development and in response to environmental stresses such as drought. In this study, we investigated the development of maize leaves and internodes at both macroscopic and cellular level, and identified a shared fundamental growth design with distinct timing between the two organs. By transcriptome profiling developmental zones of leaves and internodes of different ranks, and of the ear, at different growth stages under both well-watered and drought conditions, we generated a high-resolution spatiotemporal transcriptome dataset on 272 different tissues and conditions, which we make available as a searchable database. While the gene regulatory networks governing cell division and cell elongation were highly conserved across organs, precise expression regulation of particular gene families was observed across organs and within the same organ. Additionally, we highlight the expression of key genes involved in regulating leaf angle and vascular development, showing spatiotemporal regulation of differentiation parallel to growth. This comprehensive expression atlas, combined with phenotypic data, offers a deeper understanding of the similarities and differences among shoot organs and tissues during development and drought response, and provides a valuable resource for engineering organ-specific traits in maize.

plant biology↗

Monitoring of drought stress and transpiration rate using proximal thermal and hyperspectral imaging in an indoor automated plant phenotyping platform

BackgroundThermography is a popular tool to assess plant water use behavior, as plant temperature is influenced by transpiration rate, and is commonly used in field experiments to detect drought stress. Its application in indoor automated phenotyping platforms is still limited and mainly focuses on differences in plant temperature between genotypes or treatments, instead of estimating stomatal conductance or transpiration rate. In this study, the transferability of commonly used thermography analysis protocols from the field to greenhouse phenotyping platforms was evaluated. In addition, the added value of combining thermal infrared (TIR) with hyperspectral imaging to monitor drought effects on plant transpiration rate (E) was evaluated. ResultsThe sensitivity of commonly used TIR indices to detect drought-induced and genotypic differences in water status was investigated in eight maize inbred lines in the automated phenotyping platform PHENOVISION. Indices that normalized plant temperature for vapor pressure deficit and/or air temperature at the time of imaging were most sensitive to drought and could detect genotypic difference in the plants water use behavior. However, these indices were not strongly correlated to stomatal conductance and E. The canopy temperature depression index, the crop water stress index and the simplified stomatal conductance index were more suitable to monitor these traits, and were consequently used to develop empirical E prediction models by combining them with hyperspectral indices and/or environmental variables. Different modeling strategies were evaluated including single index-based, machine learning and mechanistic models. Model comparison showed that combining multiple thermal infrared indices in a random forest model can improve E prediction accuracy, and that the contribution of the hyperspectral data is limited when multiple indices are used. However, the empirical models trained on one genotype were not transferable to all eight inbred lines. ConclusionOverall, this study demonstrates that existing TIR indices can be used to monitor drought stress and develop E prediction models in an indoor setup, as long as the indices normalize plant temperature for ambient air temperature or relative humidity.

plant biology↗