1,135 ionomes reveal the global pattern of leaf and seed mineral nutrient and trace element diversity in Arabidopsis thaliana

Ana Carolina A. L. Campos, William F. A. vanDijk, Priya Ramakrishna, Tom Giles, Pamela Korte, Alex Douglas, Pete Smith, David E. Salt* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
5 Downloads (Pure)


Soil is a heterogeneous reservoir of essential elements needed for plant growth and development. Plants have evolved mechanisms to balance their nutritional needs based on availability of nutrients. This has led to genetically based variation in the elemental composition, the 'ionome', of plants, both within and between species. We explore this natural variation using a panel of wild-collected, geographically widespread Arabidopsis thaliana accessions from the 1001 Genomes Project including over 1,135 accessions, and the 19 parental accessions of the Multi-parent Advanced Generation Inter-Cross (MAGIC) panel, all with full-genome sequences available. We present an experimental design pipeline for high-throughput ionomic screenings and analyses with improved normalisation procedures to account for errors and variability in conditions often encountered in large-scale, high-throughput data collection. We report quantification of the complete leaf and seed ionome of the entire collection using this pipeline and a digital tool, , to interact with the dataset. We describe the pattern of natural ionomic variation across the A. thaliana species and identify several accessions with extreme ionomic profiles. It forms a valuable resource for exploratory genetic mapping studies to identify genes underlying natural variation in leaf and seed ionome and genetic adaptation of plants to soil conditions.

Original languageEnglish
Pages (from-to)536-554
Number of pages19
JournalThe Plant Journal
Issue number2
Early online date23 Mar 2021
Publication statusPublished - 29 Apr 2021


  • Arabidopsis thaliana
  • Ionomics
  • natural variation
  • data normalisation methods
  • high&#8208
  • throughput phenotyping
  • high-throughput phenotyping


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