Unverified paper record
Development of a single-cell ICP-MS method for element analysis in Pisum sativum leaf protoplasts and chloroplasts.
Talanta · 9 Mar 2026 · 10.1016/j.talanta.2026.129645
Abstract
Heterogeneity of cellular and subcellular elemental distribution is poorly captured by conventional techniques due to limited sensitivity, throughput, and resolution. Single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS) enables quantitative single-cell element analysis but remains challenging in multicellular plants because of plant cell complexity and lack of protocols. Herein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts. An optimized fixation protocol (1% (v/v) glutaraldehyde for 15 min for protoplasts; 2.5% (v/v) for 30 min for chloroplasts) was applied to preserve structural integrity, with endogenous P and Mg identified as specific indicator elements for protoplasts and chloroplasts, respectively. To reduce interference from broken cells, a broken-signal correction method was employed during data processing. Following isolation, purification, and glutaraldehyde fixation, Pisum sativum leaf protoplasts and chloroplasts were subjected to SC-ICP-MS analysis. Quantitative analysis revealed significant elemental heterogeneity, with P as the most abundant in protoplasts (67.1-135 fg cell -1 ) and Mg as the most abundant in chloroplasts (33.6-41.5 fg cell -1 ). This technique advances single-cell element analysis in plants, enabling new insights into nutrient distribution, metal accumulation, and cellular responses to environmental stress beyond conventional techniques.
Plant phenotyping relevance
植物細胞・葉緑体の元素分布という生理状態を定量するSC-ICP-MS法の開発が研究の中心であり、固定条件や破損シグナル補正も技術的に検討している。
abstractHerein, this study established a robust SC-ICP-MS method for analyzing elemental heterogeneity in Pisum sativum leaf protoplasts and chloroplasts.
abstractTo reduce interference from broken cells, a broken-signal correction method was employed during data processing.
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