Quantitative light element profiling in plant tissues with monochromatic X-ray fluorescence analysis: a new frontier for abiotic stress studies.

Abstract

Determining elemental concentrations in plant tissues is essential for physiological studies on abiotic stress. However, high-throughput routine analysis of light elements (sodium to calcium) in plants is challenging due to the need for complete sample dissolution and expensive and time-consuming inductively coupled plasma-mass-spectrometry (ICP-MS). Ion chromatography and ion-selective electrodes are low-cost methods but suffer from major drawbacks, including limited throughput and time-consuming sample preparation. This study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis. We quantitatively assessed sodium and potassium uptake in Arabidopsis thaliana, Oryza sativa and Lactuca sativa in salinity treatments. The new method provides reliable results from samples as small as 1 mg, making it suitable for analysis at the seedling stage. This is enabled by the high sensitivity of the system and optimized sample preparation that ensures sufficient signal even at low sample masses. We tested the accuracy and precision of the technique for other light elements to demonstrate its broad applicability. The results show that the method delivers rapid, non-destructive, and extraction-free light element analysis on small samples highly correlating with ICP-MS. The monochromatic XRF method provides accurate measurements and reproducible results for studying salinity tolerance ideally suited for investigating elemental composition of early plant developmental stages, offering new possibilities for research into early stimuli responses.

Plant phenotyping relevance

植物組織中の元素濃度という生理形質を測定するMXRF法の開発と、ICP-MSとの相関、精度・再現性評価が研究の中心であるため。

Code and data availability

The supplied blocks describe MXRF phenotyping measurements (Na/K/light-element profiling in Arabidopsis, rice, lettuce) but contain no public phenotype dataset, image/sensor data, author analysis code, or trained model deposit. The only availability language is a Protocols.io protocol (dx.doi.org/10.17504/protocols.io.

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