Abstract
Plant–microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.
| Original language | English |
|---|---|
| Article number | 2045 |
| Journal | Microorganisms |
| Volume | 11 |
| Issue number | 8 |
| DOIs | |
| State | Published - Aug 2023 |
Funding
Gabriel Parker acknowledges the Department of Energy (DOE) Office of Science Graduate Student Research (SCGSR) Program fellowship for support. The manuscript preparation for Xiao-Ying Yu was supported partially by the Strategic Laboratory Directed Research and Development (LDRD) of the Physical Sciences Directorate of the Oak Ridge National Laboratory (ORNL). Luke Hanley is grateful for the support from the National Science Foundation by grant CHE-1904145, University of Illinois Chicago. ORNL is managed by UT-Battelle, LLC, for the U.S. Department of Energy (DOE) under contract number DE-AC05-00OR22725. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan , accessed on 4 June 2023).
Keywords
- MALDI
- SIMS
- machine learning
- mass spectrometry imaging
- metabolites
- metabolomics
- multivariant analysis
- plant–microbe interactions
- sample preparation