Promise of spatially resolved omics for tumor research.

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy.

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy. However, metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity. Herein, choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types, and a choline-modified strategy for small molecule-drug conjugates (SMDCs) design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy, instead of directly inhibiting choline metabolism. As a proof-of-concept, choline-modified SMDCs were designed, screened, and investigated for their druggability in vitro and in vivo. This strategy improved tumor targeting, preserved tumor inhibition and reduced toxicity of paclitaxel, through targeted drug delivery to tumor by highly expressed choline transporters, and site-specific release by carboxylesterase. This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.

Identification of Diagnostic Metabolic Signatures in Thyroid Tumors Using Mass Spectrometry Imaging.

"Gray zone" thyroid follicular tumors are difficult to diagnose, especially when distinguishing between benign follicular thyroid adenoma (FTA) and malignant carcinoma (FTC). Thus, proper classification of thyroid follicular diseases may improve clinical prognosis. In this study, the diagnostic performance of metabolite enzymes was evaluated using imaging mass spectrometry to distinguish FTA from FTC and determine the association between metabolite enzyme expression with thyroid follicular borderline tumor diagnosis. Air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFAIDESI-MSI) was used to build a classification model for thyroid follicular tumor characteristics among 24 samples. We analyzed metabolic enzyme marker expression in an independent validation set of 133 cases and further evaluated the potential biological behavior of 19 thyroid borderline lesions. Phospholipids and fatty acids (FAs) were more abundant in FTA than FTC (p < 0.001). The metabolic enzyme panel, which included FA synthase and Ca2+-independent PLA2, was further validated in follicular thyroid tumors. The marker combination showed optimal performance in the validation group (area under the ROC, sensitivity, and specificity: 73.6%, 82.1%, and 60.6%, respectively). The findings indicate that AFAIDESI-MSI, in combination with low metabolic enzyme expression, could play a role in the diagnosis of thyroid follicular borderline tumors for strict follow-up.

Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids.

Three-dimensional (3D) cell spheroid models combined with mass spectrometry imaging (MSI) enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions. Herein, airflow-assisted desorption electrospray ionization-MSI (AFADESI-MSI) was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone (AMI). High-coverage imaging of >1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI. Following AMI treatment at different times, 15 metabolites of AMI involved in N-desethylation, hydroxylation, deiodination, and desaturation metabolic reactions were identified, and according to their spatiotemporal dynamics features, the metabolic pathways of AMI were proposed. Subsequently, the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis. The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism, providing considerable evidence for the mechanism of AMI hepatotoxicity. In addition, a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI. The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs, drug metabolites, and endogenous metabolites after AMI treatment, providing an effective tool for in vitro drug hepatotoxicity evaluation.

Spatiotemporally resolved metabolomics and isotope tracing reveal CNS drug targets.

Deconvolution of potential drug targets of the central nervous system (CNS) is particularly challenging because of the complicated structure and function of the brain. Here, a spatiotemporally resolved metabolomics and isotope tracing strategy was proposed and demonstrated to be powerful for deconvoluting and localizing potential targets of CNS drugs by using ambient mass spectrometry imaging. This strategy can map various substances including exogenous drugs, isotopically labeled metabolites, and various types of endogenous metabolites in the brain tissue sections to illustrate their microregional distribution pattern in the brain and locate drug action-related metabolic nodes and pathways. The strategy revealed that the sedative-hypnotic drug candidate YZG-331 was prominently distributed in the pineal gland and entered the thalamus and hypothalamus in relatively small amounts, and can increase glutamate decarboxylase activity to elevate γ-aminobutyric acid (GABA) levels in the hypothalamus, agonize organic cation transporter 3 to release extracellular histamine into peripheral circulation. These findings emphasize the promising capability of spatiotemporally resolved metabolomics and isotope tracing to help elucidate the multiple targets and the mechanisms of action of CNS drugs.

Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

Metabolic Perturbation Score-Based Mass Spectrometry Imaging Spatially Resolves a Functional Metabolic Response.

Metabolic perturbation score-based mass spectrometry imaging (MPS-MSI) is proposed to reveal the spatially resolved functional metabolic response associated with disease progression or drug action including metabolism pathways, species, biofunction, or biotransformation. The MPS-MSI enables the exploration of therapeutic or adverse effects, regional heterogeneous responses to drug treatment, possible molecular mechanisms, and even drug potential targets. MPS-MSI was demonstrated to be a promising molecular imaging tool not only for efficacy and safety evaluation but also for molecular mechanism investigation at the early stage of drug research and development.

Ginsenoside 3ß-O-Glc-DM (C3DM) suppressed glioma tumor growth by downregulating the EGFR/PI3K/AKT/mTOR signaling pathway and modulating the tumor microenvironment.

Ginsenosides are the main bioactive constituents of Panax ginseng, which have been broadly studied in cancer treatment. Our previous studies have demonstrated that 3ß-O-Glc-DM (C3DM), a biosynthetic ginsenoside, exhibited antitumor effects in several cancer cell lines with anti-colon cancer activity superior to ginsenoside 20(R)-Rg3 in vivo. However, the efficacy of C3DM on glioma has not been proved yet. In this study, the antitumor activities and underlying mechanisms of C3DM on glioma were investigated in vitro and in vivo. Cell viability, apoptosis, migration, FCM, IHC, RT-qPCR, quantitative proteomics, and western blotting were conducted to evaluate the effect of C3DM on glioma cells. ADP-Glo™ kinase assay was used to validate the interaction between C3DM and EGFR. Co-cultured assays, lactic acid kit, and spatially resolved metabolomics were performed to study the function of C3DM in regulating glioma microenvironment. Both subcutaneously transplanted syngeneic models and orthotopic models of glioma were used to determine the effect of C3DM on tumor growth in vivo. We found that C3DM dose-dependently induced apoptosis, and inhibited the proliferation, migration and angiogenesis of glioma cells. C3DM significantly inhibited tumor growth in both subcutaneous and orthotopic mouse glioma models. Moreover, C3DM attenuated the acidified glioma microenvironment and enhanced T-cell function. Additionally, C3DM inhibited the kinase activity of EGFR and influenced the EGFR/PI3K/AKT/mTOR signaling pathway in glioma. Overall, C3DM might be a promising candidate for glioma prevention and treatment.

Metabolic, genetic, and pharmacokinetic parameters for the prediction of olanzapine efficacy.

Clinical use of the a olanzapine has significantly different individual-to-individual outcomes. Accordingly, this study aimed to develop a means of predicting response to olanzapine using a combined approach based on pharmacokinetics, pharmacometabonomics, and genetic polymorphism. The olanzapine pharmacokinetics of 19 healthy volunteers treated with orally disintegrating tablets were determined using high-performance liquid chromatography-tandem mass spectrometry. Metabolic profiling and phenotyping were performed on the blood samples that remained after pharmacokinetic analysis using ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry. Uridine diphosphate-glucuronosyltransferase (UGT), tyrosine hydroxylase (TH), γ-aminobutyric acid transaminase (GABA-T), and succinic semialdehyde dehydrogenase (SSADH) were identified as key genes. The single nucleotide polymorphism genotypes most related to drug metabolism were investigated by polymerase chain reaction and Sanger sequencing. Forty-one metabolites (p < 0.05) are increased or decreased after treatment with olanzapine. Tryptophan metabolism, norepinephrine metabolism, and γ-aminobutyric acid metabolism were identified as being related to the effects of olanzapine. Subjects carrying rs1641031 AC and CC exhibited a 59.2% increase in the mean peak concentration (Cmax) value and a 25.33% decrease in the mean oral clearance rate (CL/F) value, compared to that in subjects with the GABA-T rs1641031 AA genotype (p < 0.05). Moreover, polymorphism of the GABA-T gene has an impact on the metabolism of 5-hydroxytryptamine. Lysophosphatidylethanolamine (0:0/18:3), lysophosphatidylethanolamine (0:0/22:5), and octadecatrienoic acid distinguish subjects with high and low olanzapine drug oral clearance and are thus identified as biomarkers for predicting its efficacy.

A temporo-spatial pharmacometabolomics method to characterize pharmacokinetics and pharmacodynamics in the brain microregions by using ambient mass spectrometry imaging.

The brain is the most advanced organ with various complex structural and functional microregions. It is often challenging to understand what and where the molecular events would occur for a given drug treatment in the brain. Herein, a temporo-spatial pharmacometabolomics method was proposed based on ambient mass spectrometry imaging and was applied to evaluate the microregional effect of olanzapine (OLZ) on brain tissue and demonstrate its effectiveness in characterizing the microregional pharmacokinetics and pharmacodynamics of OLZ for improved understanding of the molecular mechanism of drugs acting on the microregions of the brain. It accurately and simultaneously illustrated the levels dynamics and microregional distribution of various substances, including exogenous drugs and its metabolites, as well as endogenous functional metabolites from complicated brain tissue. The targeted imaging analysis of the prototype drug and its metabolites presented the absorption, distribution, metabolism, and excretion characteristics of the drug itself. Moreover, the endogenous functional metabolites were identified along with the associated therapeutic and adverse effects of the drug, which can reflect the pharmacodynamics effect on the microregional brain. Therefore, this method is significant in elucidating and understanding the molecular mechanism of central nervous system drugs at the temporo and spatial metabolic level of system biology.

Integrated spatially resolved metabolomics and network toxicology to investigate the hepatotoxicity mechanisms of component D of Polygonum multiflorum Thunb.

ETHNOPHARMACOLOGICAL RELEVANCE: The liver toxicity of Reynoutria multiflora (Thunb.) Moldenke. (Polygonaceae) (Polygonum multiflorum Thunb, PM) has always attracted much attention, but the related toxicity materials and mechanisms have not been elucidated due to multi-component and multi-target characteristics. In previous hepatotoxicity screening, different components of PM were first evaluated and the hepatotoxicity of component D [95% ethanol (EtOH) elution] in a 70% EtOH extract of PM (PM-D) showed the highest hepatotoxicity. Furthermore, the main components of PM-D were identified and their hepatotoxicity was evaluated based on a zebrafish embryo model. However, the hepatotoxicity mechanism of PM-D is unknown. AIM OF THE STUDY: This work is to explore the hepatotoxicity mechanisms of PM-D by integrating network toxicology and spatially resolved metabolomics strategy. MATERIALS AND METHODS: A hepatotoxicity interaction network of PM-D was constructed based on toxicity target prediction for eight key toxic ingredients and a hepatotoxicity target collection. Then the key signaling pathways were enriched, and molecular docking verification was implemented to evaluate the ability of toxic ingredients to bind to the core targets. The pathological changes of liver tissues and serum biochemical assays of mice were used to evaluate the liver injury effect of mice with oral administration of PM-D. Furthermore, spatially resolved metabolomics was used to visualize significant differences in metabolic profiles in mice after drug administration, to screen hepatotoxicity-related biomarkers and analyze metabolic pathways. RESULTS: The contents of four key toxic compounds in PM-D were detected. Network toxicology identified 30 potential targets of liver toxicity of PM-D. GO and KEGG enrichment analyses indicated that the hepatotoxicity of PM-D involved multiple biological activities, including cellular response to endogenous stimulus, organonitrogen compound metabolic process, regulation of the apoptotic process, regulation of kinase, regulation of reactive oxygen species metabolic process and signaling pathways including PI3K-Akt, AMPK, MAPK, mTOR, Ras and HIF-1. The molecular docking confirmed the high binding activity of 8 key toxic ingredients with 10 core targets, including mTOR, PIK3CA, AKT1, and EGFR. The high distribution of metabolites of PM-D in the liver of administrated mice was recognized by mass spectrometry imaging. Spatially resolved metabolomics results revealed significant changes in metabolic profiles after PM-D administration, and metabolites such as taurine, taurocholic acid, adenosine, and acyl-carnitines were associated with PM-D-induced liver injury. Enrichment analyses of metabolic pathways revealed tht linolenic acid and linoleic acid metabolism, carnitine synthesis, oxidation of branched-chain fatty acids, and six other metabolic pathways were significantly changed. Comprehensive analysis revealed that the hepatotoxicity caused by PM-D was closely related to cholestasis, mitochondrial damage, oxidative stress and energy metabolism, and lipid metabolism disorders. CONCLUSIONS: In this study, the hepatotoxicity mechanisms of PM-D were comprehensively identified through an integrated spatially resolved metabolomics and network toxicology strategy, providing a theoretical foundation for the toxicity mechanisms of PM and its safe clinical application.

Norm ISWSVR: A Data Integration and Normalization Approach for Large-Scale Metabolomics.

Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body.

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

Development and validation of a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma using ultrahigh-performance liquid chromatography-tandem mass spectrometry.

RATIONALE: Cardiovascular disease, as a multifactorial disease caused by genetics and environment, has emerged as a leading cause of mortality. The discovery of metabolic biomarkers for the clinical diagnosis, early warning and elucidation of the molecular pathogenesis of cardiovascular disease, using metabolomics, has attracted broad interest. Therefore, this work aimed to develop a sensitive and reliable targeted metabolomics method for the quantification of cardiovascular disease-related biomarkers in plasma. METHODS: The method was developed and validated using ultrahigh-performance liquid chromatography augmented with tandem mass spectrometry (UHPLC/MS/MS). The LC conditions and MS parameters were optimized using selected reaction monitoring scanning mode to high-throughput and sensitive separation, and could detect 20 metabolic biomarkers in a single experiment. And the linearity, selectivity, accuracy, precision, stability and recovery of the developed method were assessed according to the Bioanalytical Method Validation guidelines of the United States Food and Drug Administration. RESULTS: These quantified metabolic biomarkers are involved in pathways such as aromatic amino acid catabolism (e.g. phenylalanine, tryptophan, tyrosine), trimethylamine N-oxide (TMAO) biosynthesis (e.g. TMAO, choline, carnitine, betaine) and histidine metabolism (e.g. histidine), among others. All analytes exhibited excellent linearities with coefficients of determination greater than 0.99. Accuracies deviated by less than 15% for medium- and high-concentration samples and less than 20% for low-concentration samples, with intra- and inter-day precisions of 1.12-14.12% and 0.30-13.74%, respectively. Recoveries and stabilities also met the analysis requirements of biological samples. CONCLUSIONS: The targeted metabolomics method was shown to have a powerful ability to accurately analyze metabolic biomarkers, thereby providing valuable information for large-scale biomarker validation and clarifying the potential material basis of cardiovascular disease for clinical diagnosis or early warning.

Molecular Pathological Diagnosis of Thyroid Tumors Using Spatially Resolved Metabolomics.

The pathological diagnosis of benign and malignant follicular thyroid tumors remains a major challenge using the current histopathological technique. To improve diagnosis accuracy, spatially resolved metabolomics analysis based on air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique was used to establish a molecular diagnostic strategy for discriminating four pathological types of thyroid tumor. Without any specific labels, numerous metabolite features with their spatial distribution information can be acquired by AFADESI-MSI. The underlying metabolic heterogeneity can be visualized in line with the cellular heterogeneity in native tumor tissue. Through micro-regional feature extraction and in situ metabolomics analysis, three sets of metabolic biomarkers for the visual discrimination of benign follicular adenoma and differentiated thyroid carcinomas were discovered. Additionally, the automated prediction of tumor foci was supported by a diagnostic model based on the metabolic profile of 65 thyroid nodules. The model prediction accuracy was 83.3% when a test set of 12 independent samples was used. This diagnostic strategy presents a new way of performing in situ pathological examinations using small molecular biomarkers and provides a model diagnosis for clinically indeterminate thyroid tumor cases.

Integrated untargeted and targeted metabolomics to reveal therapeutic effect and mechanism of Alpiniae oxyphyllae fructus on Alzheimer's disease in APP/PS1 mice.

Introduction: Alpiniae oxyphyllae Fructus (AOF) has been abundantly utilized for the treatment of diarrhea, dyspepsia, kidney asthenia, and abdominal pain in China. AOF is effective for treating AD in clinical trials, but its exact mode of action is yet unknown. Methods: In this study, metabolomics was combined to ascertain the alterations in plasma metabolism in APP/PS1 transgenic mice, the therapy of AOF on model mice, and the dynamic variations in 15 bile acids (BAs) concentration. Results: 31 differential biomarkers were finally identified in APP/PS1 group vs. the WT group. The levels of 16 metabolites like sphinganine (Sa), lyso PE (20:2), lysoPC (17:0), glycocholic acid (GCA), deoxycholicacid (DCA) were increased in APP/PS1 group, and those of 15 metabolites like phytosphingosine, cer (d18:0/14:0), and fumaric acid were reduced in APP/PS1 group. After AOF treatment, 29 of the 31 differential metabolites showed a tendency to be back-regulated, and 15 metabolites were significantly back-regulated, including sphinganine (Sa), lyso PE (20:2), glycocholic acid (GCA), deoxycholic acid (DCA). The relationship between BAs level and AD had been received increasing attention in recent years, and we also found notable differences between DCA and GCA in different groups. Therefore, a BAs-targeted metabonomic way was established to determine the level of 15 bile acids in different groups. The consequence demonstrated that primary BAs (CA, CDCA) declined in APP/PS1 model mice. After 3 months of AOF administration, CA and CDCA levels showed an upward trend. Conjugated primary bile acids (TCA, GCA, TCDCA, GCDCA), and secondary bile acids (DCA, LCA, GDCA, TDCA, TLCA GLCA) ascended in APP/PS1 group. After 3 months of AOF treatment, the levels of most BAs decreased to varying degrees. Notably, the metabolic performance of DCA and GCA in different groups was consistent with the predictions of untargeted metabolomics, validating the correctness of untargeted metabolomics. Discussion: According to metabolic pathways of regulated metabolites, it was prompted that AOF ameliorated the symptom of AD mice probably by regulating bile acids metabolism. This study offers a solid foundation for further research into the AOF mechanism for the therapy of AD.

Advanced applications of mass spectrometry imaging technology in quality control and safety assessments of traditional Chinese medicines.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicines (TCMs) have made great contributions to the prevention and treatment of human diseases in China, and especially in cases of COVID-19. However, due to quality problems, the lack of standards, and the diversity of dosage forms, adverse reactions to TCMs often occur. Moreover, the composition of TCMs makes them extremely challenging to extract and isolate, complicating studies of toxicity mechanisms. AIM OF THE REVIEW: The aim of this paper is therefore to summarize the advanced applications of mass spectrometry imaging (MSI) technology in the quality control, safety evaluations, and determination of toxicity mechanisms of TCMs. MATERIALS AND METHODS: Relevant studies from the literature have been collected from scientific databases, such as "PubMed", "Scifinder", "Elsevier", "Google Scholar" using the keywords "MSI", "traditional Chinese medicines", "quality control", "metabolomics", and "mechanism". RESULTS: MSI is a new analytical imaging technology that can detect and image the metabolic changes of multiple components of TCMs in plants and animals in a high throughput manner. Compared to other chemical analysis methods, such as liquid chromatography-mass spectrometry (LC-MS), this method does not require the complex extraction and separation of TCMs, and is fast, has high sensitivity, is label-free, and can be performed in high-throughput. Combined with chemometrics methods, MSI can be quickly and easily used for quality screening of TCMs. In addition, this technology can be used to further focus on potential biomarkers and explore the therapeutic/toxic mechanisms of TCMs. CONCLUSIONS: As a new type of analysis method, MSI has unique advantages to metabolic analysis, quality control, and mechanisms of action explorations of TCMs, and contributes to the establishment of quality standards to explore the safety and toxicology of TCMs.

Enhanced On-Tissue Chemical Derivatization with Hydrogel Assistance for Mass Spectrometry Imaging.

The improvement of on-tissue chemical derivatization for mass spectrometry imaging (MSI) of low-abundance and/or poorly ionizable functional molecules in biological tissue without delocalization is challenging. Here, we developed a novel hydrogel-assisted chemical derivatization (HCD) approach coupled with airflow-assisted desorption electrospray ionization (AFADESI)-MSI, allowing for enhanced visualization of inaccessible molecules in biological tissues. The derivatization reagent Girard's P (GP) reagent was creatively packaged into a hydrogel to form HCD blocks that have reactivity to carbonyl compounds as well as the feasibility of "cover/uncover" contact mode with tissue sections. The HCD blocks provided a favorable liquid microenvironment for the derivatization reaction and reduced matrix effects from derivatization reagents and tissue without obvious molecular migration, thus improving the derivatization efficiency. With this methodology, unusual carbonyl metabolites, including 166 fatty aldehydes (FALs) and 100 oxo fatty acids (FAs), were detected and visualized in rat brain, kidney, and liver tissue. This study provides a new approach to enhance chemical labeling for in situ tissue submetabolome profiling and improves our knowledge of the molecular histology and complex metabolism of biological tissues.

Innovation in drug toxicology: Application of mass spectrometry imaging technology.

Mass spectrometry imaging (MSI) is a powerful molecular imaging technology that can obtain qualitative, quantitative, and location information by simultaneously detecting and mapping endogenous or exogenous molecules in biological tissue slices without specific chemical labeling or complex sample pretreatment. This article reviews the progress made in MSI and its application in drug toxicology research, including the tissue distribution of toxic drugs and their metabolites, the target organs (liver, kidney, lung, eye, and central nervous system) of toxic drugs, the discovery of toxicity-associated biomarkers, and explanations of the mechanisms of drug toxicity when MSI is combined with the cutting-edge omics methodologies. The unique advantages and broad prospects of this technology have been fully demonstrated to further promote its wider use in the field of pharmaceutical toxicology.