Dataset Information

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration.

ABSTRACT: Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

SUBMITTER: Tolleter D

PROVIDER: S-EPMC10988163 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration.

Tolleter Dimitri D Smith Edward N EN Dupont-Thibert Clémence C Uwizeye Clarisse C Vile Denis D Gloaguen Pauline P Falconet Denis D Finazzi Giovanni G Vandenbrouck Yves Y Curien Gilles G

Quantitative plant biology 20240229

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify ...[more]

PMID: 38572078

Similar Datasets

Project description:Glutathione is an important antioxidant and redox buffer in plants. It fulfills many important roles during plant development, defense and is essential for plant metabolism. Even though the compartment specific roles of glutathione during abiotic and biotic stress situations have been studied in detail there is still great lack of knowledge about subcellular glutathione concentrations within the different leaf areas at different stages of development. In this study a method is described that allows the calculation of compartment specific glutathione concentrations in all cell compartments simultaneously in one experiment by using quantitative immunogold electron microscopy combined with biochemical methods in different leaf areas of Arabidopsis thaliana Col-0 (center of the leaf, leaf apex, leaf base and leaf edge). The volume of subcellular compartments in the mesophyll of Arabidopsis was found to be similar to other plants. Vacuoles covered the largest volume within a mesophyll cell and increased with leaf age (up to 80% in the leaf apex of older leaves). Behind vacuoles, chloroplasts covered the second largest volume (up to 20% in the leaf edge of the younger leaves) followed by nuclei (up to 2.3% in the leaf edge of the younger leaves), mitochondria (up to 1.6% in the leaf apex of the younger leaves), and peroxisomes (up to 0.3% in the leaf apex of the younger leaves). These values together with volumes of the mesophyll determined by stereological methods from light and electron micrographs and global glutathione contents measured with biochemical methods enabled the determination of subcellular glutathione contents in mM. Even though biochemical investigations did not reveal differences in global glutathione contents, compartment specific differences could be observed in some cell compartments within the different leaf areas. Highest concentrations of glutathione were always found in mitochondria, where values in a range between 8.7mM (in the apex of younger leaves) and 15.1mM (in the apex of older leaves) were found. The second highest amount of glutathione was found in nuclei (between 5.5mM and 9.7mM in the base and the center of younger leaves, respectively) followed by peroxisomes (between 2.6mM in the edge of younger leaves and 4.8mM in the base of older leaves, respectively) and the cytosol (2.8mM in the edge of younger and 4.5mM in the center of older leaves, respectively). Chloroplasts contained rather low amounts of glutathione (between 1mM and 1.4mM). Vacuoles had the lowest concentrations of glutathione (0.01mM and 0.14mM) but showed large differences between the different leaf areas. Clear differences in glutathione contents between the different leaf areas could only be found in vacuoles and mitochondria revealing that glutathione in the later cell organelle accumulated with leaf age to concentrations of up to 15mM and that concentrations of glutathione in vacuoles are quite low in comparison to the other cell compartments.

Project description:High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

Dataset Information

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration.

Publications

The Arabidopsis leaf quantitative atlas: a cellular and subcellular mapping through unified data integration.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets