Qin Luyuan, Han Junshan, Wang Chuang, Xu Bin, Tan Deyun, He Song, Guo Lei, Bo Xiaochen, Xie Jianwei
State Key Laboratory of Toxicology and Medical Countermeasures, Institute of Pharmacology and Toxicology, Academy of Military Medical Sciences, Beijing, China.
Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing, China.
Front Plant Sci. 2022 Dec 16;13:1083901. doi: 10.3389/fpls.2022.1083901. eCollection 2022.
Castor bean or ricin-induced intoxication or terror events have threatened public security and social safety. Potential resources or materials include beans, raw extraction products, crude toxins, and purified ricin. The traceability of the origins of castor beans is thus essential for forensic and anti-terror investigations. As a new imaging technique with label-free, rapid, and high throughput features, matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) has been gradually stressed in plant research. However, sample preparation approaches for plant tissues still face severe challenges, especially for some lipid-rich, water-rich, or fragile tissues. Proper tissue washing procedures would be pivotal, but little information is known until now.
For castor beans containing plenty of lipids that were fragile when handled, we developed a comprehensive tissue pretreatment protocol. Eight washing procedures aimed at removing lipids were discussed in detail. We then constructed a robust MALDI-MSI method to enhance the detection sensitivity of RCBs in castor beans.
A modified six-step washing procedure was chosen as the most critical parameter regarding the MSI visualization of peptides. The method was further applied to visualize and quantify the defense peptides, Ricinus communis biomarkers (RCBs) in castor bean tissue sections from nine different geographic sources from China, Pakistan, and Ethiopia. Multivariate statistical models, including deep learning network, revealed a valuable classification clue concerning nationality and altitude.
蓖麻子或蓖麻毒素引起的中毒事件或恐怖活动威胁着公共安全和社会稳定。潜在的资源或材料包括蓖麻子、粗提物、粗毒素和纯化的蓖麻毒素。因此,蓖麻子来源的可追溯性对于法医和反恐调查至关重要。作为一种具有免标记、快速和高通量特点的新型成像技术,基质辅助激光解吸电离质谱成像(MALDI-MSI)在植物研究中逐渐受到重视。然而,植物组织的样品制备方法仍然面临严峻挑战,特别是对于一些富含脂质、富含水分或脆弱的组织。适当的组织清洗程序至关重要,但目前所知甚少。
对于含有大量脂质且处理时易碎的蓖麻子,我们开发了一种全面的组织预处理方案。详细讨论了旨在去除脂质的八种清洗程序。然后,我们构建了一种强大的MALDI-MSI方法,以提高蓖麻子中蓖麻生物标志物(RCBs)的检测灵敏度。
一种改良的六步清洗程序被选为肽段MSI可视化的最关键参数。该方法进一步应用于可视化和定量来自中国、巴基斯坦和埃塞俄比亚九个不同地理来源的蓖麻籽组织切片中的防御肽、蓖麻生物标志物(RCBs)。包括深度学习网络在内的多变量统计模型揭示了有关产地和海拔的有价值的分类线索。