Mass Spectrometry Imaging Techniques Research Articles

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox.

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

Low-Temperature Plasma Pretreatment Enhanced Cholesterol Detection in Brain by Desorption Electrospray Ionization-Mass Spectrometry Imaging.

Cholesterol is a primary lipid molecule in the brain that contains one-fourth of the total body cholesterol. Abnormal cholesterol homeostasis is associated with neurodegenerative disorders. Mass spectrometry imaging (MSI) technique is a powerful tool for studying lipidomics and metabolomics. Among the MSI techniques, desorption electrospray ionization-MSI (DESI-MSI) has been used advantageously to study brain lipidomics due to its soft and ambient ionization nature. However, brain cholesterol is poorly ionized. To this end, we have developed a new method for detecting brain cholesterol by DESI-MSI using low-temperature plasma (LTP) pretreatment as an ionization enhancement. In this method, the brain sections were treated with LTP for 1 and 2 min prior to DESI-MSI analyses. Interestingly, the MS signal intensity of cholesterol (at m/z 369.35 [M + H - H2O]+) was more than 2-fold higher in the 1 min LTP-treated brain section compared to the untreated section. In addition, we detected cholesterol, more specifically excluding isomers by targeted-DESI-MSI in multiple reaction monitoring (MRM) mode and similar results were observed: the signal intensity of each cholesterol transition (m/z 369.4 → 95.1, 109.1, 135.1, 147.1, and 161.1) was increased by more than 2-fold due to 1 min LTP treatment. Cholesterol showed characteristic distributions in the fiber tract region, including the corpus callosum and anterior commissure, anterior part of the brain where LTP markedly (p < 0.001) enhanced the cholesterol intensity. In addition, the distributions of some unknown analytes were exclusively detected in the LTP-treated section. Our study revealed LTP pretreatment as a potential strategy to ionize molecules that show poor ionization efficiency in the MSI technique.

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice.

Depression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.

From molecules to visuals: Empowering drug discovery and development with mass spectrometry imaging.

Over the past three decades, mass spectrometry imaging (MSI) has emerged as a valuable tool for the spatial localization of drugs and metabolites directly from tissue surfaces without the need for labels. MSI offers molecular specificity, making it increasingly popular in the pharmaceutical industry compared to conventional imaging techniques like quantitative whole-body autoradiography (QWBA) and immunohistochemistry, which are unable to distinguish parent drugs from metabolites. Across the industry, there has been a consistent uptake in the utilization of MSI to investigate drug and metabolite distribution patterns, and the integration of MSI with omics technologies in preclinical investigations. To continue the further adoption of MSI in drug discovery and development, we believe there are two key areas that need to be addressed. First, there is a need for accurate quantification of analytes from MSI distribution studies. Second, there is a need for increased interactions with regulatory agencies for guidance on the utility and incorporation of MSI techniques in regulatory filings. Ongoing efforts are being made to address these areas, and it is hoped that MSI will gain broader utilization within the industry, thereby becoming a critical ingredient in driving drug discovery and development.

The discovery of regional neurotoxicity-associated metabolic alterations induced by carbon quantum dots in brain of mice using a spatial metabolomics analysis

BackgroundRecently, carbon quantum dots (CQDs) have been widely used in various fields, especially in the diagnosis and therapy of neurological disorders, due to their excellent prospects. However, the associated inevitable exposure of CQDs to the environment and the public could have serious severe consequences limiting their safe application and sustainable development.ResultsIn this study, we found that intranasal treatment of 5 mg/kg BW (20 µL/nose of 0.5 mg/mL) CQDs affected the distribution of multiple metabolites and associated pathways in the brain of mice through the airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique, which proved effective in discovery has proven to be significantly alerted and research into tissue-specific toxic biomarkers and molecular toxicity analysis. The neurotoxic biomarkers of CQDs identified by MSI analysis mainly contained aminos, lipids and lipid-like molecules which are involved in arginine and proline metabolism, biosynthesis of unsaturated fatty acids, and glutamine and glutamate metabolism, etc. as well as related metabolic enzymes. The levels or expressions of these metabolites and enzymes changed by CQDs in different brain regions would induce neuroinflammation, organelle damage, oxidative stress and multiple programmed cell deaths (PCDs), leading to neurodegeneration, such as Parkinson’s disease-like symptoms. This study enlightened risk assessments and interventions of QD-type or carbon-based nanoparticles on the nervous system based on toxic biomarkers regarding region-specific profiling of altered metabolic signatures.ConclusionThese findings provide information to advance knowledge of neurotoxic effects of CQDs and guide their further safety evaluation.

Lipid-related ion suppression on the herbicide atrazine in earthworm samples in ToF-SIMS and matrix-assisted laser desorption ionization mass spectrometry imaging and the role of gas-phase basicity.

In mass spectrometry imaging (MSI), ion suppression can lead to a misinterpretation of results. Particularly phospholipids, most of which exhibit high gas-phase basicity (GB), are known to suppress the detection of metabolites and drugs. This study was initiated by the observation that the signal of an herbicide, i.e., atrazine, was suppressed in MSI investigations of earthworm tissue sections. Herbicide accumulation in earthworms was investigated by time-of-flight secondary ion mass spectrometry and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Additionally, earthworm tissue sections without accumulation of atrazine but with a homogeneous spray deposition of the herbicide were analyzed to highlight region-specific ion suppression. Furthermore, the relationship of signal intensity and GB in binary mixtures of lipids, amino acids, and atrazine was investigated in both MSI techniques. The GB of atrazine was determined experimentally through a linear plot of the obtained intensity ratios of the binary amino acid mixtures, as well as theoretically. The GBs values for atrazine of 896 and 906 kJ/mol in ToF-SIMS and 933 and 987 kJ/mol in MALDI-MSI were determined experimentally and that of 913 kJ/mol by quantum mechanical calculations. Compared with the GB of a major lipid component, phosphatidylcholine (GBPC = 1044.7 kJ/mol), atrazine's experimentally and computationally determined GBs in this work are significantly lower, making it prone to ion suppression in biological samples containing polar lipids.

Mass Spectrometry Imaging and Histology for the Analysis of Budding Intestinal Organoids.

Three-dimensional (3D) organoids have been at the forefront of regenerative medicine and cancer biology fields for the past decade. However, the fragile nature of organoids makes their spatial analysis challenging due to their budding structures and composition of single layer of cells. The standard sample preparation approaches can collapse the organoid morphology. Therefore, in this study, we evaluated several approaches to optimize a method compatible with both mass spectrometry imaging (MSI) and immunohistological techniques. Murine intestinal organoids were used to evaluate embedding in gelatin, carboxymethylcellulose (CMC)-gelatin-CMC-sucrose, or hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) solutions. Organoids were assessed with and without aldehyde fixation and analyzed for lipid distributions by MSI coupled with hematoxylin and eosin (H&E) staining and immunofluorescence (IF) in consecutive sections from the same sample. While chemical fixation preserves morphology for better histological outcomes, it can lead to suppression of the matrix-assisted laser desorption/ionization (MALDI) lipid signal. By contrast, leaving organoid samples unfixed enhanced MALDI lipid signal. The method that performed best for both MALDI and histological analysis was embedding unfixed samples in HPMC and PVP. This approach allowed assessment of cell proliferation by Ki67 while also identifying putative phosphatidylethanolamine (PE(18:0/18:1)), which was confirmed further by tandem MS approaches. Overall, these protocols will be amenable to multiplexing imaging mass spectrometry analysis with several histological assessments and help advance our understanding of the biological processes that take place in district subsets of cells in budding organoid structures.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Effect of optimal rapid thermal alloying of ohmic contacts to microwave pseudomorphic HEMTs

Gallium Arsenide (GaAs) pseudomorphic High Electron Mobility Transistors (pHEMTs) are currently used as active devices in manufacturing Monolithic Microwave Integrated Circuits (MMICs) for microwave and millimetre wave applications like Transmit-Receive (T/R) modules in Radars and communication. Low contact resistances (Rc) of pHEMT devices are necessary to obtain low access resistances to the device channel, for enhancement of transconductance and cut-off frequency. Devices formed by optimal and suboptimal conditions of rapid thermal alloying by infra-red optical radiation are investigated using cross-section transmission electron microscopy (XTEM), backside secondary ion mass spectrometry (backside SIMS) and thermal imaging techniques to understand the role of optimal alloy formation and its role in enhancement of device parameters.

Probing the stability of metal-organic frameworks by structure-responsive mass spectrometry imaging.

The widespread application of metal-organic frameworks (MOFs) is seriously hindered by their structural instability and it is still very challenging to probe the stability of MOFs during application by current techniques. Here, we report a novel structure-responsive mass spectrometry (SRMS) imaging technique to probe the stability of MOFs. We discovered that intact CuBTC (as a model of MOFs) could generate the characteristic peaks of organic ligands and carbon cluster anions in laser desorption/ionization mass spectrometry, but these peaks were significantly changed when the structure of CuBTC was dissociated, thus enabling a label-free probing of the stability. Furthermore, SRMS can be performed in imaging mode to visualize the degradation kinetics and reveal the spatial heterogeneity of the stability of CuBTC. This technique was successfully applied in different application scenarios (in water, moist air, and CO2) and also validated with different MOFs. It thus provides a versatile new tool for better design and application of environment-sensitive materials.

Database-Driven Spatially Resolved Lipidomics Highlights Heterogeneous Metabolic Alterations in Type 2 Diabetic Mice.

Spatially resolved lipidomics is pivotal for detecting and interpreting lipidomes within spatial contexts using the mass spectrometry imaging (MSI) technique. However, comprehensive and efficient lipid identification in MSI remains challenging. Herein, we introduce a high-coverage, database-driven approach combined with air-flow-assisted desorption electrospray ionization (AFADESI)-MSI to generate spatial lipid profiles across whole-body mice. Using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), we identified 2868 unique lipids in the serum and various organs of mice. Subsequently, we systematically evaluated the distinct ionization properties of the lipids between LC-MS and MSI and created a detailed MSI database containing 14 123 ions. This method enabled the visualization of aberrant fatty acid and phospholipid metabolism across organs in a diabetic mouse model. As a powerful extension incorporated into the MSIannotator tool, our strategy facilitates the rapid and accurate annotation of lipids, providing new research avenues for probing spatially resolved heterogeneous metabolic changes in response to diseases.

Improving the Signal Intensity of Cryosections Using a Conductive Adhesive Film in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

The matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to obtain the molecular images of cryosections without labeling. Although MALDI-MSI has been widely used to detect small molecules from biological tissues, issues remain due to the technical process of cryosectioning and limited mass spectrometry parameters. The use of a conductive adhesive film is a unique method to obtain high-quality sections from cutting tissue, such as bone, muscle, adipose tissue, and whole body of mice or fish, and we have reported the utilization of the film for MALDI-MSI in previous. However, some signal of the small molecules using the conductive adhesive films was still lower than on the indium tin oxide (ITO) glass slide. Here, the sample preparation and analytical conditions for MALDI-MSI using an advanced conductive adhesive film were optimized to obtain strong signals from whole mice heads. The effects of tissue thickness and laser ionization power on signal intensity were verified using MALDI-MSI. The phospholipid signal intensity was measured for samples with three tissue thicknesses (5, 10, and 20 μm); compared to the signals from the samples on the ITO glass slides, the signals with conductive adhesive films exhibited significantly higher intensities when a laser with a higher range of power was used to ionize the small molecules. Thus, the technique using the advanced conductive adhesive film showed an improvement in MALDI-MSI analysis.

Imaging of N-Linked Glycans in Biological Tissue Sections Using Nanospray Desorption Electrospray Ionization (nano-DESI) Mass Spectrometry.

N-linked glycans are complex biomolecules vital to cellular functions that have been linked to a wide range of pathological conditions. Mass spectrometry imaging (MSI) has been used to study the localization of N-linked glycans in cells and tissues. However, their structural diversity presents a challenge for MSI techniques, which stimulates the development of new approaches. In this study, we demonstrate for the first time spatial mapping of N-linked glycans in biological tissues using nanospray desorption electrospray ionization mass spectrometry imaging (nano-DESI MSI). Nano-DESI MSI is an ambient ionization technique that has been previously used for imaging of metabolites, lipids, and proteins in biological tissue samples without special sample pretreatment. N-linked glycans are released from glycoproteins using an established enzymatic digestion with peptide N-glycosidase F, and their spatial localization is examined using nano-DESI MSI. We demonstrate imaging of N-linked glycans in formalin-fixed paraffin-embedded human hepatocellular carcinoma and human prostate tissues in both positive and negative ionization modes. We examine the localization of 38 N-linked glycans consisting of high mannose, hybrid fucosylated, and sialyated glycans. We demonstrate that negative mode nano-DESI MSI is well-suited for imaging of underivatized sialylated N-linked glycans. On-tissue MS/MS of different adducts of N-linked glycans proves advantageous for elucidation of the glycan sequence. This study demonstrates the applicability of liquid extraction techniques for spatial mapping of N-linked glycans in biological samples, providing an additional tool for glycobiology research.

Mass Spectrometry Imaging of Biomaterials.

The science related to biomaterials and tissue engineering accounts for a growing part of our knowledge. Surface modifications of biomaterials, their performance in vitro, and the interaction between them and surrounding tissues are gaining more and more attention. It is because we are interested in finding sophisticated materials that help us to treat or mitigate different disorders. Therefore, efficient methods for surface analysis are needed. Several methods are routinely applied to characterize the physical and chemical properties of the biomaterial surface. Mass Spectrometry Imaging (MSI) techniques are able to measure the information about molecular composition simultaneously from biomaterial and adjacent tissue. That is why it can answer the questions connected with biomaterial characteristics and their biological influence. Moreover, this kind of analysis does not demand any antibodies or dyes that may influence the studied items. It means that we can correlate surface chemistry with a biological response without any modification that could distort the image. In our review, we presented examples of biomaterials analyzed by MSI techniques to indicate the utility of SIMS, MALDI, and DESI-three major ones in the field of biomaterials applications. Examples include biomaterials used to treat vascular system diseases, bone implants with the effects of implanted material on adjacent tissues, nanofibers and membranes monitored by mass spectrometry-related techniques, analyses of drug-eluting long-acting parenteral (LAPs) implants and microspheres where MSI serves as a quality control system.

Sulfur Impurities in COx Feed Gases of Solid Oxide Cells: Effect on Degradation and Removal Strategies

High-temperature solid oxide cells (SOC) offer unrivaled Faradaic and energetic efficiencies for carbon dioxide electrolysis. However, it is yet unclear which contaminant level in carbon monoxide and carbon dioxide feed gases are tolerable to achieve low degradation rates in commercial applications. In the present work, electrolyte-supported cells (ESC) with Ni/Gadolinia-doped ceria (Ni/CGO) fuel electrodes were exposed to CO/CO2 gas mixtures at open circuit voltage and in electrolysis operation. The resistance evolution over time was monitored by means of electrochemical impedance spectroscopy. With this approach it is shown that a severe reversible degradation over time periods of up to 72 h occurs. X-ray photoemission spectroscopy (XPS) and high-resolution secondary ion mass spectrometry (SIMS) imaging techniques were used to show that this degradation effect is related to sulfur impurities on the Ni surface. Inlet gas composition variations were carried out to show that sulfur impurities most likely in the low ppb level are present in both industrial CO and CO2 feed gases. The efficacy of different gas cleaning units at room temperature was investigated to identify a suitable desulfurization solution. The results demonstrate the importance of feed gas purity for the commercialization of high-temperature CO2 electrolysis.

Ion-to-Image, i2i, a Mass Spectrometry Imaging Data Analysis Platform for Continuous Ionization Techniques.

Mass spectrometry imaging (MSI) techniques generate data that reveal spatial distributions of molecules on a surface with high sensitivity and selectivity. However, processing large volumes of mass spectrometry data into useful ion images is not trivial. Furthermore, data from MSI techniques using continuous ionization sources where data are acquired in line scans require different data handling strategies compared to data collected from pulsed ionization sources where data are acquired in grids. In addition, for continuous ionization sources, the pixel dimensions are influenced by the mass spectrometer duty cycle, which, in turn, can be controlled by the automatic gain control (AGC) for each spectrum (pixel). Currently, there is a lack of data-handling software for MSI data generated with continuous ionization sources and AGC. Here, we present ion-to-image (i2i), which is a MATLAB-based application for MSI data acquired with continuous ionization sources, AGC, high resolution, and one or several scan filters. The source code and a compiled installer are available at https://github.com/LanekoffLab/i2i. The application includes both quantitative, targeted, and nontargeted data processing strategies and enables complex data sets to be processed in minutes. The i2i application has high flexibility for generating, processing, and exporting MSI data both from simple full scans and more complex scan functions interlacing MSn and SIM scan data sets, and we anticipate that it will become a valuable addition to the existing MSI software toolbox.

DeepS: Accelerating 3D Mass Spectrometry Imaging via a Deep Neural Network.

Mass spectrometry imaging (MSI) is a powerful methodology that enables the visualization of the spatial distribution of biomolecules, including lipids, peptides, and proteins, from biological tissue sections. While two-dimensional (2D) MSI has been widely reported in various applications, three-dimensional (3D) MSI can enable the mapping of biomolecule distribution in complex biological structures (e.g., organs) with an added dimension. However, traditional 3D MSI techniques are time-consuming since 3D MS images are constructed from 2D MSI analyses of a series of tissue sections. In this study, we propose a 3D MSI workflow, termed DeepS, which uses a 3D sparse sampling network (3D-SSNet) and a sparse sampling strategy to significantly accelerate 3D MSI analyses. Sparsely sampled tissue sections are reconstructed using 3D-SSNet, yielding results comparable to those using full sampling MSI, even at a sampling ratio of 20-30%. The workflow performed well when applied to 3D imaging of a mouse brain with Alzheimer's disease, and combined with transfer learning, it is successfully used for the 3D MSI analyses of more heterogeneous samples, e.g., a mouse brain with glioblastoma and a mouse kidney.

Photodissociation Dynamics of the [O2-H2O]+ Ionic Complex.

We present an experimental study on the photodissociation dynamics of [O2-H2O]+ in the 580-266 nm wavelength range using a cryogenic ion trap velocity map imaging spectrometer. The cryogenic ion trap produces mass selected and internally cold ions for photodissociation. By detecting both the O2+ and H2O+ photofragments using the time-of-flight mass spectrometry and velocity map imaging techniques, branching ratios and total kinetic energy release distributions of the O2+ + H2O and H2O+ + O2 product channels are experimentally measured at 16 different excitation energies. State-resolved photodissociation mechanisms of the parent [O2-H2O]+ are interpreted as (1) the O2(X3Σg-) + H2O+, O2(a1Δg) + H2O+, and O2(X3Σg-) + H2O+ channels are produced from direct dissociation of [O2-H2O]+ in its excited , , and states, respectively; (2) the O2+(X2Πg) + channel is produced from nonadiabatic relaxations of the excited , , and states to the ground state with subsequent dissociation. The latter nonadiabatic processes involve charge-transfer on the potential energy surfaces, and the charge-transfer probabilities are determined from experimental results. The dissociation energy of the ground state to the lowest dissociation limit is experimentally refined as D0 = 1.05 ± 0.05 eV. This work provides important information to understand the charge-transfer dynamics in the photochemistry of [O2-H2O]+ and in the ion-molecule reaction O2 + H2O+ → O2+ + H2O.

Unveiling the spatial distribution and molecular mechanisms of terpenoid biosynthesis in Salvia miltiorrhiza and S. grandifolia using multi-omics and DESI-MSI.

Salvia miltiorrhiza and S. grandifolia are rich in diterpenoids and have therapeutic effects on cardiovascular diseases. In this study, the spatial distribution of diterpenoids in both species was analyzed by a combination of metabolomics and mass spectrometry imaging techniques. The results indicated that diterpenoids in S. miltiorrhiza were mainly abietane-type norditerpenoid quinones with a furan or dihydrofuran D-ring and were mainly distributed in the periderm of the roots, e.g. cryptotanshinone and tanshinone IIA. The compounds in S. grandifolia were mainly phenolic abietane-type tricyclic diterpenoids with six- or seven-membered C-rings, and were widely distributed in the periderm, phloem, and xylem of the roots, e.g. 11-hydroxy-sugiol, 11,20-dihydroxy-sugiol, and 11,20-dihydroxy-ferruginol. In addition, the leaves of S. grandifolia were rich in tanshinone biosynthesis precursors, such as 11-hydroxy-sugiol, while those of S. miltiorrhiza were rich in phenolic acids. Genes in the upstream pathway of tanshinone biosynthesis were highly expressed in the root of S. grandifolia, and genes in the downstream pathway were highly expressed in the root of S. miltiorrhiza. Here, we describe the specific tissue distributions and mechanisms of diterpenoids in two Salvia species, which will facilitate further investigations of the biosynthesis of diterpenoids in plant synthetic biology.