期刊
PLANTS-BASEL
卷 11, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/plants11091234
关键词
mass spectrometry; plant; chemical imaging
资金
- U.K. Biotechnology and Biological Sciences Research Council (BBSRC)
- University of Manchester
- Syngenta Ltd.
The detection and spatial localisation of chemical species in plant tissues are important in plant science. Mass spectrometry imaging combines mass spectrometry with 2D analysis to detect and locate numerous chemical species. This article provides a detailed overview of mass spectrometry imaging methodologies, sample preparation, and data analysis methods. It also reviews the applications of this technique in spatial profiling of metabolites, detection of agrochemicals, and disease diagnosis in plants. The article discusses the challenges in analyzing plant tissues and suggests an integrated approach to maximize the information obtained from samples.
The detection of chemical species and understanding their respective localisations in tissues have important implications in plant science. The conventional methods for imaging spatial localisation of chemical species are often restricted by the number of species that can be identified and is mostly done in a targeted manner. Mass spectrometry imaging combines the ability of traditional mass spectrometry to detect numerous chemical species in a sample with their spatial localisation information by analysing the specimen in a 2D manner. This article details the popular mass spectrometry imaging methodologies which are widely pursued along with their respective sample preparation and the data analysis methods that are commonly used. We also review the advancements through the years in the usage of the technique for the spatial profiling of endogenous metabolites, detection of xenobiotic agrochemicals and disease detection in plants. As an actively pursued area of research, we also address the hurdles in the analysis of plant tissues, the future scopes and an integrated approach to analyse samples combining different mass spectrometry imaging methods to obtain the most information from a sample of interest.
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