Plant Sample Preparation for Metabolomics, Lipidomics, Ionomics, Fluxomics, and Peptidomics

被引：0

作者：

da Silva, Walace Breno ^{[1
]}

Hispagnol, Gabriel Felipe ^{[1
]}

Nunes, Emanuel Victor dos Santos ^{[1
]}

Castro-Gamboa, Ian ^{[1
]}

Pilon, Alan Cesar ^{[1
]}

机构：

[1] Sao Paulo State Univ UNESP, Inst Chem, Dept Biochem & Organ Chem, BR-14800060 Araraquara, SP, Brazil

来源：

SEPARATIONS | 2025年 / 12卷 / 02期

基金：

巴西圣保罗研究基金会;

关键词：

plants; sample preparation; metabolomics; lipidomics; ionomics; fluxomics; peptidomics; BIOLOGICAL SAMPLES; QUALITY-ASSURANCE; FLUX ANALYSIS; RAPID METHOD; EXTRACTION; METABOLITE; DESIGN; PROTOCOL; OPTIMIZATION; DERIVATIZATION;

D O I：

10.3390/separations12020021

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Plant metabolomics, lipidomics, ionomics, fluxomics, and peptidomics are essential approaches for exploring how plants respond to epigenetic, pathological, and environmental stimuli through comprehensive chemical profiling. Over the past decades, significant progress has been made in protocols and methodologies to address the challenges in sample collection and extraction. Despite these advancements, sample preparation remains intricate, with ongoing debates about the most effective strategies. This review emphasizes the importance of clear research questions and well-designed experiments to minimize complexity, save time, and enhance reproducibility. It provides an overview of the key steps in these fields, including harvesting, drying, extraction, and data pre-acquisition for major analytical platforms. By discussing best practices and common challenges, this review aims to streamline methods and promote more consistent and reliable research outcomes.

引用

页数：21

共 145 条

[1]

Rakusanova S., Cajka T., Tips and Tricks for LC–MS-Based Metabolomics and Lipidomics Analysis, TrAC Trends Anal. Chem, 180, (2024)

[2]

Jacyna J., Kordalewska M., Markuszewski M.J., Design of Experiments in Metabolomics-Related Studies: An Overview, J. Pharm. Biomed. Anal, 164, pp. 598-606, (2019)

[3]

Pilon A.C., Carneiro R.L., Carnevale Neto F., Da S., Bolzani V., Castro-Gamboa I., Interval Multivariate Curve Resolution in the Dereplication of HPLC-DAD Data from Jatropha Gossypifolia, Phytochem. Anal, 24, pp. 401-406, (2013)

[4]

Kofeler H.C., Ahrends R., Baker E.S., Ekroos K., Han X., Hoffmann N., Holcapek M., Wenk M.R., Liebisch G., Recommendations for Good Practice in MS-Based Lipidomics, J. Lipid Res, 62, (2021)

[5]

Zullig T., Trotzmuller M., Kofeler H.C., Lipidomics from Sample Preparation to Data Analysis: A Primer, Anal. Bioanal. Chem, 412, pp. 2191-2209, (2020)

[6]

Zhang Y., Xu Y., Zheng L., Disease Ionomics: Understanding the Role of Ions in Complex Disease, Int. J. Mol. Sci, 21, (2020)

[7]

Filipiak M., Filipiak Z.M., Application of Ionomics and Ecological Stoichiometry in Conservation Biology: Nutrient Demand and Supply in a Changing Environment, Biol. Conserv, 272, (2022)

[8]

Lin G., Ma L., He X., Tang J., Wang L., Gene Regulation and Ionome Homeostasis in Rice Plants in Response to Arsenite Stress: Potential Connection between Transcriptomics and Ionomics, BioMetals, 36, pp. 1157-1169, (2023)

[9]

Schrader M., Origins, Technological Development, and Applications of Peptidomics, pp. 3-39, (2018)

[10]

Schrader M., Fricker L.D., Current Challenges and Future Directions in Peptidomics, pp. 485-498, (2024)

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