Establishment of Liquid Chromatography Retention Index Based on Chemical Labeling for Metabolomic Analysis

An integrated strategy for in-depth profiling of N-acylethanolamines in biological samples by UHPLC-HRMS

BACKGROUND

N-acylethanolamines (NAEs) are a class of naturally occurring bioactive lipids that play crucial roles in various physiological processes, particularly exhibiting neuroprotective and anti-inflammatory properties. However, the comprehensive profiling of endogenous NAEs in complex biological matrices is challenging due to their low abundance, structural similarity and the limited availability of commercial standards. Here, we propose an integrated strategy for comprehensive profiling of NAEs that combines chemical derivatization and a three-dimensional (3D) prediction model based on quantitative structure-retention time relationship (QSRR) using liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS).

RESULTS

After acetyl chloride (ACC) derivatization, the detection sensitivity of NAEs was significantly improved. We developed a QSRR prediction model to construct an in-house database for 141 NAEs, encompassing information on RT, MS1 (m/z), and MS/MS spectra. Propargylamine-labeled fatty acids were synthesized as RT calibrants across various analytical conditions to enhance the robustness of the RT prediction model. NAEs in biological samples were then in-depth profiled using parallel reaction monitoring (PRM) acquisition. This integrated strategy identified and annotated a total of 50 NAEs across serum, hippocampus and cortex tissues from a 5xFAD mouse model of Alzheimer's disease (AD). Notably, the levels of polyunsaturated NAEs, particularly NAE 20:5 and NAE 22:6, were significantly decreased in 5xFAD mice compared to WT mice, as confirmed by accurate quantitation using ACC-d0/d3 derivatization.

SIGNIFICANCE

Our integrated strategy exhibits great potential for the in-depth profiling of NAEs in complex biological samples, facilitating the elucidation of NAE functions in diverse physiological and pathological processes.

Chemical Isotope Labeling and Dual-Filtering Strategy for Comprehensive Profiling of Urinary Glucuronide Conjugates

Glucuronidation, a crucial process in phase II metabolism, plays a vital role in the detoxification and elimination of endogenous substances and xenobiotics. A comprehensive and confident profiling of glucuronate-conjugated metabolites is imperative to understanding their roles in physiological and pathological processes. In this study, a chemical isotope labeling and dual-filtering strategy was developed for global profiling of glucuronide metabolites in biological samples. N,N-Dimethyl ethylenediamine (DMED-d0) and its deuterated counterpart DMED-d6 were used to label carboxylic acids through an amidation reaction. First, carboxyl-containing compounds were extracted based on a characteristic mass difference (Δm/z, 6.037 Da) observed in MS between light- and heavy-labeled metabolites (filter I). Subsequently, within the pool of carboxyl-containing compounds, glucuronides were identified using two pairs of diagnostic ions (m/z 247.1294/253.1665 and 229.1188/235.1559 for DMED-d0/DMED-d6-labeled glucuronides) originating from the fragmentation of the derivatized glucuronic acid group in MS/MS (filter II). Compared with non-derivatization, DEMD labeling significantly enhanced the detection sensitivity of glucuronides, as evidenced by a 3- to 55-fold decrease in limits of detection for representative standards. The strategy was applied to profiling glucuronide metabolites in urine samples from colorectal cancer (CRC) patients. A total of 685 features were screened as potential glucuronides, among which 181 were annotated, mainly including glucuronides derived from lipids, organic oxygen, and phenylpropanoids. Enzymatic biosynthesis was employed to accurately identify unknown glucuronides without standards, demonstrating the reliability of the dual-filtering strategy. Our strategy exhibits great potential for profiling the glucuronide metabolome with high coverage and confidence to reveal changes in CRC and other diseases.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Derivatization of carboxylic groups prior to their LC analysis - A review

Carboxylic acids (CAs) represent a large group of important molecules participating in various biologically significant processes. Analytical study of these compounds is typically performed by liquid chromatography (LC) combined with various types of detection. However, their analysis is often accompanied by a wide variety of problems depending on used separation system or detection method. The dominant ones are: i) poor chromatographic behavior of the CAs in reversed-phase LC; ii) absence of a chromophore (or fluorophore); iii) weak ionization in mass spectrometry (MS). To overcome these problems, targeted chemical modification, and derivatization, come into play. Therefore, derivatization still plays an important and, in many cases, irreplaceable role in sample preparation, and new derivatization methods of CAs are constantly being developed. The most commonly used type of reaction for CAs derivatization is amidation. In recent years, an increased interest in the isotopic labeling derivatization method has been observed. In this review, we comprehensively summarize the possibilities and actual trends in the derivatization of CAs that have been published over the past decade.

Essentials in the acquisition, interpretation, and reporting of plant metabolite profiles

Plant metabolite profiling reveals the diversity of secondary or specialized metabolites in the plant kingdom with its hundreds of thousands of species. Specialized plant metabolites constitute a vast class of chemicals posing significant challenges in analytical chemistry. In order to be of maximum scientific relevance, reports dealing with these compounds and their source species must be transparent, make use of standards and reference materials, and be based on correctly and traceably identified plant material. Essential aspects in qualitative plant metabolite profiling include: (i) critical review of previous literature and a reasoned sampling strategy; (ii) transparent plant sampling with wild material documented by vouchers in public herbaria and, optimally, seed banks; (iii) if possible, inclusion of generally available reference plant material; (iv) transparent, documented state-of-the art chemical analysis, ideally including chemical reference standards; (v) testing for artefacts during preparative extraction and isolation, using gentle analytical methods; (vi) careful chemical data interpretation, avoiding over- and misinterpretation and taking into account phytochemical complexity when assigning identification confidence levels, and (vii) taking all previous scientific knowledge into account in reporting the scientific data. From the current stage of the phytochemical literature, selected comments and suggestions are given. In the past, proposed revisions of botanical taxonomy were sometimes based on metabolite profiles, but this approach ("chemosystematics" or "chemotaxonomy") is outdated due to the advent of DNA sequence-based phylogenies. In contrast, systematic comparisons of plant metabolite profiles in a known phylogenetic framework remain relevant. This approach, known as chemophenetics, allows characterizing species and clades based on their array of specialized metabolites, aids in deducing the evolution of biosynthetic pathways and coevolution, and can serve in identifying new sources of rare and economically interesting natural products.

Sample preparation for fatty acid analysis in biological samples with mass spectrometry-based strategies

Fatty acids (FAs) have attracted many interests for their pivotal roles in many biological processes. Imbalance of FAs is related to a variety of diseases, which makes the measurement of them important in biological samples. Over the past two decades, mass spectrometry (MS) has become an indispensable technique for the analysis of FAs owing to its high sensitivity and precision. Due to complex matrix effect of biological samples and inherent poor ionization efficiency of FAs in MS, sample preparation including extraction and chemical derivatization prior to analysis are often employed. Here, we describe an updated overview of FA extraction techniques, as well as representative derivatization methods utilized in different MS platforms including gas chromatography-MS, liquid chromatography-MS, and mass spectrometry imaging based on different chain lengths of FAs. Derivatization strategies for the identification of double bond location in unsaturated FAs are also summarized and highlighted. The advantages, disadvantages, and prospects of these methods are compared and discussed. This review provides the development and valuable information for sample pretreatment approaches and qualitative and quantitative analysis of interested FAs using different MS-based platforms in complex biological matrices. Finally, the challenges of FA analysis are summarized and the future perspectives are prospected.

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry

Consistent retention time (tR) of metabolites is vital for identification in metabolomic analysis with ultrahigh-performance liquid-chromatography (UPLC). To minimize inter-experimental tR variations from the reversed-phase UPLC-MS, we developed an endogenous retention-index (endoRI) using in-sample straight-chain acylcarnitines with different chain-length (LC, C0-C26) without additives. The endoRI-corrections reduced the tR variations caused by the combined changes of mobile phases, gradients, flow-rates, elution time, columns and temperature from up to 5.1 min-0.2 min for most metabolites in a model metabolome consisting of 91 metabolites and multiple biological matrices including human serum, plasma, fecal, urine, A549 cells and rabbit liver extracts. The endoRI-corrections also reduced the inter-batch and inter-platform tR variations from 1.5 min to 0.15 min for 95 % of detected features in the above biological samples. We further established a quantitative model between tR and LC for predicting tR values of acylcarnitines when absent in samples. This makes it possible to compare metabolites' tR from different tR databases and the UPLC-based metabolomic data from different batches.

A chemical derivatization-based pseudotargeted liquid chromatography-tandem mass spectrometry method for sensitive and high coverage determination of bile acids in human serum

Global profiling of bile acids (BAs) is imperative for understand their function and disease pathogenesis. But it is still a challenging task, as the collision-induced dissociation (CID) fragment ions of unconjugated BAs showed low ion intensities to insufficient analysis. Herein, we developed a highly sensitive method for pseudotargeted profiling of BAs by chemical derivatization. In the developed method, a labeling reagent, 2-dimethylaminoethylamine (DMED), was adopted to label the carboxyl group of BAs. The results demonstrated that the detection sensitivities of unconjugated BAs were increased by 4-200 folds after DMED-labeling. Moreover, to profile other potential BAs not included in the 91 known targets, diverse survey experiments were performed on Qtrap-MS to search BAs for both precursor and fragment ion species, and retention index (RI) strategy was adopted to facilitate the identification of isomers. Finally, MRM-based LC-MS/MS method was validated for the pseudotargeted profiling of the BAs submetabolome with good linearity (r2 ≥ 0.990 for 89 known BAs) and high sensitivity (0.05-0.5 ng/mL for unconjugated BAs), covering unconjugated, glycine, taurine, sulfuric acid, glucuronic acid, and as well as those doubly-conjugated with above types. With this method, a total of 107 BAs, covering 54 BAs identified by authentic standards and 53 BAs candidates, were successfully determined in human serum of women with intrahepatic cholestasis of pregnancy (ICP). Multivariate analysis revealed deferentially expressed BAs. ICP disease altered the BAs profile with a reduced proportion of unconjugated, sulfate- and doubly-conjugated BAs and an increased proportion of glycine and taurine conjugates. Altered proportion and profile of BAs in ICP groups were gradually recovered during the ursodeoxycholic acid (UDCA) therapy. Overall, the strategy of DMED-labeling technique combined with diverse survey experiments is sufficiently sensitive and robust to comprehensively analysis of metabolic profiling of BAs in human serum.

A dataset of branched fatty acid esters of hydroxy fatty acids diversity in foods

Branched fatty acid esters of hydroxy fatty acids (FAHFAs) are a class of bioactive lipids that show therapeutic potential for diabetes, anti-cancer and inflammation. These FAHFAs can be obtained through dietary intake, potentially improving human health. However, there is currently inadequate knowledge regarding the presence and variety of FAHFAs in different foods. Herein, we profile FAHFAs from 12 typical food samples and 4 medicinal food samples with the aid of our previous established chemical isotope labeling-assisted liquid chromatography-mass spectrometry method and build a comprehensive dataset of FAHFA diversity. The dataset comprised a total of 1207 regioisomers belonging to 298 different families, with over 100 families being newly discovered for the first time. Therefore, our findings contribute valuable insights into the molecular diversity and presence of FAHFA in a range of foods. This dataset serves as a foundation for further exploration of the nutritional and medicinal functions of FAHFAs.

Analytical Strategy for Oxylipin Annotation by Combining Chemical Derivatization-Based Retention Index Algorithm and Feature Tandem Mass Spectrometric Fragmentation as a Biomarker Discovery Tool

Accurate oxylipin annotation is crucial for advancing our understanding of physiological processes in health and disease and identifying biomarkers. However, a full view of oxylipins for early diagnosis needs further attention due to the lack of proper analytical methods, which may be attributed to the wide dynamic range, poor sensitivity, extreme molecular complexity, and limited commercially available standards of oxylipins. Here, we devised a novel method by combining a chemical derivatization (CD)-based retention index (RI) algorithm and feature tandem mass spectrometric fragmentation annotation (CD-RI-LC-MS/MS) for identification and quantification of oxylipins. To this end, N,N-diethyl-1,3-diaminopropane (DEPA) was used for fast labeling of oxylipin (within 0.5 min at room temperature) to improve separation resolution and detection sensitivity. The RI algorithm was established to calibrate the retention time variances and assist the identification of oxylipins during liquid chromatography-tandem mass spectrometry (LC-MS) analysis. MS/MS analysis of in total 58 DEPA derivatives of authentic oxylipin standards was subsequently employed to obtain the tandem mass spectrometric feature fragmentation rules for further structure elucidation of the unknown regio-isomers. Finally, a method based upon CD-RI-LC-MS/MS was established for profiling oxylipins from Standard Reference Material (SRM) 1950 human plasma and nonalcoholic fatty liver disease (NAFLD) mouse liver tissue samples. A total of 87 and 96 potential oxylipins including 12 and 14 unreported oxylipins were detected and identified from human plasma and mouse liver tissues, respectively. The results showed that compared to the control group, in the liver samples of the NAFLD mouse, the content levels of prostaglandin (PG) E2, PGF2a, 8-hydroxy-eicosatrienoic acid (8-HETrE), and the newly discovered 2-hydroxy-octadecatrienoic acid (2-HOTrE) were remarkably increased, while the oxidation product of n-3 PUFA (p < 0.05) and all hydroperoxy oxylipins significantly decreased. On balance, this method contributes to future studies on oxylipin screening and application in other biological samples for facilitating the understanding of oxylipin roles in metabolic regulation of numerous diseases.

The Metabolomic Characteristics and Dysregulation of Fatty Acid Esters of Hydroxy Fatty Acids in Breast Cancer

Lipid reprogramming metabolism is crucial for supporting tumor growth in breast cancer and investigating potential tumor biomarkers. Fatty acid esters of hydroxy fatty acids (FAHFAs) are a class of endogenous lipid metabolites with anti-diabetic and anti-inflammatory properties that have been discovered in recent years. Our previous targeted analysis of sera from breast cancer patients revealed a significant down-regulation of several FAHFAs. In this study, we aimed to further explore the relationship between FAHFAs and breast cancer by employing chemical isotope labeling combined with liquid chromatography-mass spectrometry (CIL-LC-MS) for profiling of FAHFAs in tumors and adjacent normal tissues from breast cancer patients. Statistical analysis identified 13 altered isomers in breast cancer. These isomers showed the potential to distinguish breast cancer tissues with an area under the curve (AUC) value above 0.9 in a multivariate receiver operating curve model. Furthermore, the observation of up-regulated 9-oleic acid ester of hydroxy stearic acid (9-OAHSA) and down-regulated 9-hydroxystearic acid (9-HSA) in tumors suggests that breast cancer shares similarities with colorectal cancer, and their potential mechanism is to attenuate the effects of pro-apoptotic 9-HSA by enhancing the synthesis of FAHFAs, thereby promoting tumor survival and progression through this buffering system.

Mass Spectrometry Based on Chemical Derivatization Has Brought Novel Discoveries to Lipidomics: A Comprehensive Review

Lipids, as one of the most important organic compounds in organisms, are important components of cells and participate in energy storage and signal transduction of living organisms. As a rapidly rising field, lipidomics research involves the identification and quantification of multiple classes of lipid molecules, as well as the structure, function, dynamics, and interactions of lipids in living organisms. Due to its inherent high selectivity and high sensitivity, mass spectrometry (MS) is the "gold standard" analysis technique for small molecules in biological samples. The combination chemical derivatization with MS detection is a unique strategy that could improve MS ionization efficiency, facilitate structure identification and quantitative analysis. Herein, this review discusses derivatization-based MS strategies for lipidomic analysis over the past decade and focuses on all the reported lipid categories, including fatty acids and modified fatty acids, glycerolipids, glycerophospholipids, sterols and saccharolipids. The functional groups of lipids mainly involved in chemical derivatization include the C=C group, carboxyl group, hydroxyl group, amino group, carbonyl group. Furthermore, representative applications of these derivatization-based lipid profiling methods were summarized. Finally, challenges and countermeasures of lipid derivatization are mentioned and highlighted to guide future studies of derivatization-based MS strategy in lipidomics.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

Analysis of Phospholipids in Digestion Using Hybrid IDA and SWATH Acquisition: An Example for Krill Oil

The composition and digestion of phospholipid-rich foods have important effects on the health of the body. Herein, a model-assisted liquid chromatography coupling mass spectrometry (LC-MS) method was established to analyze the phosphatidylcholine (PC) and lyso-phosphatidylcholine (LPC) species in krill oil before and after digestion. According to the confirmed PC and LPC species in the IDA (information dependent acquisition) results, three categories of mathematical models were set up, involving the retention time (RT), carbon number and unsaturation degree of the fatty acyl chain. All of the regression coefficient values (R²) were greater than 0.90, showing satisfactory fitting results. On this basis, using the computationally created precursor ion mass of PC and LPC species, 12 extra PC species and 4 LPC species were found in the SWATH (sequential windowed acquisition of all theoretical fragment ions) results. The PC and LPC compositions in the final digestive products had obvious differences among the different krill oils with different phospholipid content. Furthermore, more than half of the LPC species in the final digestive products were newly generated, indicating that LPC was one of basic constituents in the digestive products of krill oil. In conclusion, model-assisted hybrid IDA and SWATH acquisition has excellent detection performance, contributing to deep studies of the formations and functions of phospholipids.

Screening of hepatocellular carcinoma via machine learning based on atmospheric pressure glow discharge mass spectrometry

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The diagnosis of HCC is currently based on alpha-fetoprotein detection, imaging examinations, and liver biopsy, which are expensive or invasive. Here, we developed a cost-effective, time-saving, and painless method for the screening of HCC via machine learning based on atmospheric pressure glow discharge mass spectrometry (APGD-MS). Ninety urine samples from HCC patients and healthy control (HC) participants were analyzed. The relative quantification data were utilized to train machine learning models. Neural network was chosen as the best classifier with a classification accuracy of 94%. Besides, the levels of eleven urinary carbonyl metabolites were found to be significantly different between HCC and HC, including glycolic acid, pyroglutamic acid, acetic acid, etc. The possible reasons for the regulation were tentatively proposed. This method realizes the screening of HCC via potential urine metabolic biomarkers based on APGD-MS, bringing a hopeful point-of-care diagnosis of HCC in a patient-friendly manner.

Chemical derivatization strategy for mass spectrometry-based lipidomics

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.

Critical assessment of chromatographic metadata in publicly available metabolomics data repositories

INTRODUCTION

The structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup.

OBJECTIVES

In contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories.

METHODS

We acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient.

RESULTS

We found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients.

CONCLUSION

Reporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.

Beneficial effect of the short-chain fatty acid propionate on vascular calcification through intestinal microbiota remodelling

BACKGROUND

Vascular calcification is a major cause of the high morbidity and mortality of cardiovascular diseases and is closely associated with the intestinal microbiota. Short-chain fatty acids (SCFAs) are derived from the intestinal microbiota and can also regulate intestinal microbiota homeostasis. However, it remains unclear whether exogenous supplementation with propionate, a SCFA, can ameliorate vascular calcification by regulating the intestinal microbiota. This study was conducted to explore the roles of propionate and the intestinal microbiota in the process of vascular calcification.

METHODS

In total, 92 patients were enrolled consecutively as the observational cohort to analyse the relationship between SCFAs and vascular calcification in both blood and faecal samples. A rat model of vascular calcification was induced by vitamin D3 and nicotine (VDN) to validate the effect of propionate. Differences in the intestinal microbiota were analysed by 16S ribosomal RNA gene sequencing. Faecal microbiota transplantation and Akkermansia muciniphila transplantation experiments were performed to evaluate the functions of the intestinal microbiota.

RESULTS

The results of the observational cohort study revealed that the levels of SCFAs (particularly propionate) in both blood and faecal samples independently correlated negatively with calcification scores (P < 0.01). To verify the activities of propionate, it was provided to VDN-treated rats, and oral or rectal propionate delivery reshaped the intestinal microbiota, resulted in elevated SCFA production, improved intestinal barrier function and alleviated inflammation, ultimately ameliorating vascular calcification. Furthermore, we demonstrated that transplantation of the propionate-modulated intestinal microbiota induced beneficial outcomes similar to those with oral or rectal propionate administration. Interestingly, linear discriminant analysis (LDA) effect size (LEfSe) revealed that oral or rectal propionate administration and propionate-modulated intestinal microbiota transplantation both enriched primarily Akkermansia. Subsequently, we demonstrated that Akkermansia supplementation could ameliorate VDN-induced vascular calcification in rats.

CONCLUSIONS

Propionate can significantly ameliorate vascular calcification in VDN-treated rats, and this effect is mediated by intestinal microbiota remodelling. The findings in our study indicate that the intestinal tract-vessel axis is a promising target for alleviating vascular calcification. Video Abstract.

N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics

Chromatographic retention time information is valuable, orthogonal information to MS and MS/MS data that can be used in metabolite identification. However, while comparison of MS data between different instruments is possible to a certain degree, retention times (RTs) can vary extensively, even when nominally the same phase system is used. Different factors such as column dead volumes, system extra column volume, and gradient dwell volume can influence absolute retention times. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for deviations in experimental conditions. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics to the same extent as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinium sulfonates (NAPS). These reference standards ionize in both positive and negative ionization modes and are UV-active. We demonstrate the NAPS can be used for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry (RP-LC–MS)–based metabolomics. Having measured >500 metabolite standards and varying flow rate and column dimension, we show that conversion of RT to retention indices (RI) substantially improves comparability of retention information and enables to use of RI for metabolite annotation and identification.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.

Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Development and Application of Liquid Chromatographic Retention Time Indices in HRMS-Based Suspect and Nontarget Screening

There is an increasing need for comparable and harmonized retention times (t_R) in liquid chromatography (LC) among different laboratories, to provide supplementary evidence for the identity of compounds in high-resolution mass spectrometry (HRMS)-based suspect and nontarget screening investigations. In this study, a rigorously tested, flexible, and less system-dependent unified retention time index (RTI) approach for LC is presented, based on the calibration of the elution pattern. Two sets of 18 calibrants were selected for each of ESI+ and ESI-based on the maximum overlap with the retention times and chemical similarity indices from a total set of 2123 compounds. The resulting calibration set, with RTI set to range between 1 and 1000, was proposed as the most appropriate RTI system after rigorous evaluation, coordinated by the NORMAN network. The validation of the proposed RTI system was done externally on different instrumentation and LC conditions. The RTI can also be used to check the reproducibility and quality of LC conditions. Two quantitative structure-retention relationship (QSRR)-based models were built based on the developed RTI systems, which assist in the removal of false-positive annotations. The applicability domains of the QSRR models allowed completing the identification process with higher confidence for substances within the domain, while indicating those substances for which results should be treated with caution. The proposed RTI system was used to improve confidence in suspect and nontarget screening and increase the comparability between laboratories as demonstrated for two examples. All RTI-related calculations can be performed online at http://rti.chem.uoa.gr/.

Integration of Chemical Derivatization and in-Source Fragmentation Mass Spectrometry for High-Coverage Profiling of Submetabolomes

In-source fragmentation-based high-resolution mass spectrometry (ISF-HRMS) is a potential analytical technique, which is usually used to profile some specific compounds that can generate diagnostic neutral loss (NL) or fragment ion (FI) in ion source inherently. However, the ISF-HRMS method does not work for those compounds that cannot inherently produce diagnostic NL or FI in ion source. In this study, a derivatization-based in-source fragmentation-information-dependent acquisition (DISF-IDA) strategy was proposed for profiling the metabolites with easily labeled functional groups (submetabolomes) by liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry (LC-ESI-Q-TOF MS). As a proof-of-concept study, 36 carboxylated compounds labeled with N,N-dimethylethylenediamine (DMED) were selected as model compounds to examine performance of DISF-IDA strategy in screening the carboxylated metabolites and acquiring their MSⁿ spectra. In ESI source, the DEMD-derived carboxylated compounds were fragmented to produce characteristic neutral losses of 45.0578, 63.0684, and/or 88.1000 Da that were further used as diagnostic features for screening the carboxylated metabolites by DISF-IDA-based LC-Q-TOF MS. Furthermore, high-resolution MSⁿ spectra of the model compounds were also obtained within a single run of DISF-IDA-based LC-Q-TOF MS analysis, which contributed to the improvement of the annotation confidence. To further verify its applicability, DISF-IDA strategy was used for profiling carboxylated submetabolome in mice feces. Using this strategy, a total of 351 carboxylated metabolites were detected from mice feces, of which 178 metabolites (51% of the total) were positively or putatively identified. Moreover, DISF-IDA strategy was also demonstrated to be applicable for profiling other submetabolomes with easily labeled functional groups such as amino, carbonyl, and cis-diol groups. Overall, our proposed DISF-IDA strategy is a promising technique for high-coverage profiling of submetabolomes with easily labeled functional groups in biological samples.

A mathematical method for calibrating the signal drift in liquid chromatography - mass spectrometry analysis

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become the most versatile analytical tool for profiling small-molecule compounds and increasingly been applied in many fields. Nevertheless, LC-MS based quantification still face some challenges, such as signal drift in LC-MS, which may affect the validity of the obtained data and lead to misinterpretation of biological results. Here, we established a calibration method known as "RIM" to compensate the signal drift of LC-MS. To this end, a mixture of d₄-2-dimethylaminoethylamine (d₄-DMED)-coded normal fatty acids (C5-C23) was used as calibrants to construct RIM calibration. With the addition of calibrants, not only the MS signal drift, but also the mass accuracy and LC retention time can be calibrated, thereby improving the reliability of quantitative data. The effectiveness of RIM was carefully validated using a human serum extract spiked with 34 standards and then RIM was applied for rat brain untargeted metabolome research. In addition, to expand the functionality and flexibility of RIM for data handling, we generated a MATLAB-based RIM program, which implements the above concepts and allows automatic data process. Taken together, the proposed RIM method has potential application in large-scale quantitative study of complex samples.

A structure-guided screening strategy for the discovery and identification of potential gibberellins from plant samples using liquid chromatography-mass spectrometry assisted by chemical isotope labeling

Gibberellins (GAs) play crucial roles in plant growth and development, and their regulatory functions rely on complex metabolic networks and signaling pathways. Therefore, the exploration of GAs metabolic network is of great importance. However, limited GAs have been found in given plant species, which makes it difficult to comprehensively study the GAs metabolic network. Herein, a structure-guided strategy for GAs screening based on liquid chromatography-mass spectrometry analysis assisted by chemical isotope labeling (CIL-LC-MS) was developed. In the proposed strategy, N,N-dimethyl ethylenediamine (DMED) and its isotopologue d₄-DMED were used to label GAs. In light of the characteristic fragmentation patterns exhibited by the labeled GAs, four principles were summarized to screen the potential GAs from plant tissues. Subsequently, the MS/MS fragmentation behavior and quantitative structure-retention relationship (QSRR) model were employed to assist in deciphering structures of GA candidates. With this strategy, thirty potential GAs were screened out and identified from five plant species. Seven of them were confirmed by the authentic standards. Twenty-two of them have not been reported before in the five plant species used in this study, including thirteen that have been reported in other plant species and nine that have never been reported in any plant species. Noteworthily, a total of nine potential GAs were speculated to be novel 16, 17-double hydrated GAs, which indicated that the 16, 17-double hydration may be a ubiquitous metabolic pathway of GAs in plants. This study was the first attempt to establish a structure-guided screening strategy for GAs. Our findings have enriched the GA species in plants and expanded the GAs family, which may be helpful for study of the metabolic pathway and physiological function of GAs.

High Coverage Profiling of Carboxylated Metabolites in HepG2 Cells Using Secondary Amine-Assisted Ultrahigh-Performance Liquid Chromatography Coupled to High-Resolution Mass Spectrometry

Carboxylic metabolites are an important class of metabolites, which widely exist in mammals with various types. Chemical isotope labeling liquid chromatography-mass spectrometry (CIL-LC-MS) has been widely used for the detection of carboxylated metabolites. However, high coverage analysis of carboxylated metabolites in biological samples is still challenging due to improper reactivity and selectivity of labeling reagents to carboxylated metabolites. In this study, we used N-methylphenylethylamine (MPEA) to label various types of carboxylated metabolites including short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), long-chain fatty acids (LCFAs), polycarboxylic acids (polyCAs), amino acids (AAs), and aromatic acids. Additionally, metabolites containing other functional groups, such as phenol, sulfhydryl, and phosphate groups, could not be labeled under the conditions of MPEA labeling. After MPEA labeling, the detection sensitivity of carboxylic acids was increased by 1-2 orders of magnitude, and their chromatographic retention on a reversed-phase (RP) column was enhanced (RT > 3 min). Under optimized labeling conditions, we used MPEA and d₃-N-methylphenylethylamine (d₃-MPEA) for high coverage screening of carboxylated metabolites in HepG2 cells by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). As a result, a total of 403 potential carboxylated metabolites were obtained of which 68 were confirmed based on our established in-house chemically labeled metabolite database (CLMD). SCFAs, MCFAs, LCFAs, polyCAs, AAs, and aromatic acids were all detected in HepG2 cell extracts. Due to the successful identification of AAs, the current method increased the coverage of carboxylated metabolites compared with our previous work. Moreover, 133 and 109 carboxylated metabolites with changed contents were obtained in HepG2 cells incubated with curcumin and R-3-hydroxybutyric acid, respectively. In general, our established method realized high coverage analysis of carboxylated metabolites in HepG2 cells.

Boron Isotope Tag-Assisted Ultrahigh-Performance Liquid Chromatography Coupled with High-Resolution Mass Spectrometry for Discovery and Annotation of cis-Diol-Containing Metabolites

cis-Diol-containing metabolites are widely distributed in living organisms, and they participate in the regulation of various important biological activities. The profiling of cis-diol-containing metabolites could help us in fully understanding their functions. In this work, based on the characteristic isotope pattern of boron, we employed a boronic acid reagent as the isotope tag to establish a sensitive and selective liquid chromatography-high-resolution mass spectrometry method for the screening and annotation of cis-diol-containing metabolites in biological samples. Boronic acid reagent 2-methyl-4-phenylaminomethylphenylboronic acid was used to label the cis-diol-containing metabolites in biological samples to improve the selectivity and MS sensitivity of cis-diol-containing metabolites. Based on the characteristic 0.996 Da mass difference of precursor ions and the peak intensity ratio of 1:4 originating from ¹⁰B and ¹¹B natural isotopes, the potential cis-diol-containing metabolites were rapidly screened from biological samples. Potential cis-diol-containing metabolites were annotated by database searching and analysis of fragmentation patterns obtained by multistage MS (MSⁿ) via collision-induced dissociation. Importantly, the cis-diol position could be readily resolved by the MS³ spectrum. With this method, a total of 45 cis-diol-containing metabolites were discovered in rice, including monoglycerides, polyhydroxy fatty acids, fatty alcohols, and so forth. Furthermore, the established method showed superiority in avoiding false-positive results in profiling cis-diol-containing metabolites.

Ozone Exposure Induces Metabolic Disorders and NAD+ Depletion Through PARP1 Activation in Spinal Cord Neurons

Background and Objective: Ozone therapy has shown therapeutic efficacy in different disorders particularly low back pain (LBP). However, ozone therapy has been associated with toxic effects on the respiratory, endocrine, cardiovascular systems as well as nervous system because of its strong oxidizing capacity. Recent studies have reported possible associations between ozone exposure and metabolic disorders, but the findings are controversial and little is known on the mechanisms of action. This study aims to investigate the cytotoxic effects of ozone exposure and possible mechanism of action in the animal model.

Methods: Wistar neonate rats with the age of 24 h after birth were sacrificed by cervical dislocation under general anesthesia, then immersed in 75% alcohol and iodophor for 5 min, respectively. The spinal cord was isolated and cut to samples of ~1 mm³ and prepared for further experiments. The spinal cord neurons (SCNs) were exposed to ozone at different concentrations and then cultured in 96-well plates with glass bottom for 7 days. The cell viability, ATP levels and the NAD+ concentration were determined and compared between the different experimental groups and the control group.

Results: Analyses of the data by non-targeted liquid chromatography-mass spectrometry (LC-MS) analysis determined the metabolic disorder in SCNs following the ozone exposure. Moreover, our assessments showed that ozone exposure resulted in DNA damage, poly (ADP)-ribose polymerase-1 (PARP1) excessive activation, nicotinamide adenine dinucleotide (NAD+) depletion and decrease of ATP level in SCNs. The PARP1 inhibitor can inhibit the cytotoxic effect of ozone to SCNs without inhibiting the activation of AMP-activated protein kinase (AMPK). Our findings revealed that the cytotoxic effects of ozone to SCNs might be mediated by excessive PARP1 activation and subsequent NAD+ depletion. Moreover, using PARP1 inhibitor can protect SCNs from cytotoxic effects of ozone by preventing NAD+ depletion during ozone exposure.

Conclusion: Ozone exposure seems to induce metabolic disorders and NAD+ depletion through excessive PARP1 activation in SCNs.

Towards a More Reliable Identification of Isomeric Metabolites Using Pattern Guided Retention Validation

Reliable analyte identification is critical in metabolomics experiments to ensure proper interpretation of data. Due to chemical similarity of metabolites (as isobars and isomers) identification by mass spectrometry or chromatography alone can be difficult. Here we show that isomeric compounds are quite common in the metabolic space as given in common metabolite databases. Further, we show that retention information can shift dramatically between different experiments decreasing the value of external or even in-house compound databases. As a consequence the retention information in compound databases should be updated regularly, to allow a reliable identification. To do so we present a feasible and budget conscious method to guarantee updates of retention information on a regular basis using well designed compound mixtures. For this we combine compounds in "Ident-Mixes", showing a way to distinctly identify chemically similar compounds through combinatorics and principle of exclusion. We illustrate the feasibility of this approach by comparing Gas chromatography (GC)-columns with identical properties from three different vendors and by creating a compound database from measuring these mixtures by Liquid chromatography-mass spectrometry (LC-MS). The results show the high influence of used materials on retention behavior and the ability of our approach to generate high quality identifications in a short time.

In-Depth Annotation Strategy of Saturated Hydroxy Fatty Acids Based on Their Chromatographic Retention Behaviors and MS Fragmentation Patterns

Hydroxy fatty acids are a class of bioactive compounds in a variety of organisms. The identification of hydroxy fatty acids in biological samples has still been a challenge because of their low abundance, high structural similarity, and limited availability of authentic hydroxy fatty acid standards. Here, we present a strategy for the annotation of saturated monohydroxyl fatty acids (OH-FAs) based on the integration of chromatographic retention rules and MS² fragmentation patterns. Thirty-nine authentic OH-FA standards were used to investigate their retention behavior on a reversed-phase stationary phase (C18) of liquid chromatography, and we found that their retention simultaneously follows two kinds of "carbon number rules". Using the "carbon number rules", the retention index (RI) of all OH-FAs that contain carbon numbers from 8 to 18 (C_8-18) can be predicted. Additionally, by studying the MS² fragmentation of OH-FAs under collision-induced dissociation, we found that the intensity ratio (IR) of the characteristic fragment ions ([M + H]⁺-63 and [M + H]⁺-45) is closely related to the position of the hydroxyl group on the OH-FA structure, which is helpful to further identify and confirm the OH-FA isomers. As a result, 97 of 107 potential OH-FAs detected in honey, human serum, and rice seedling by chemical isotope labeling-assisted liquid chromatography-mass spectrometry were annotated upon the RI matching and IR confirming. Furthermore, in order to simplify the annotation process of OH-FAs, we constructed an OH-FA library to facilitate the annotation of OH-FAs. Overall, this study provides a new and promising tool for the in-depth annotation of OH-FA isomers.

Describing the fecal metabolome in cryogenically collected samples from healthy participants

The chemical composition of feces plays an important role in human metabolism. Metabolomics and lipidomics are valuable tools for screening the metabolite composition in feces. Here we set out to describe fecal metabolite composition in healthy participants in frozen stools. Frozen stool samples were collected from 10 healthy volunteers and cryogenically drilled in four areas along the specimen. Polar metabolites were analyzed using derivatization followed by two-dimensional gas chromatography and time of flight mass spectrometry. Lipids were detected using ultra high-performance liquid chromatography coupled with quadruple time-of-flight mass spectrometry. 2326 metabolic features were detected. Out of a total of 298 metabolites that were annotated we report here 185 that showed a technical variation of x < 30%. These metabolites included amino acids, fatty acid derivatives, carboxylic acids and phenolic compounds. Lipids predominantly belonged to the groups of diacylglycerols, triacylglycerols and ceramides. Metabolites varied between sampling areas, some were broadly homogeneous, others varied 80%. A LASSO-computed network using metabolites present in all areas showed two main clusters describing the system, DAG lipids and phenyllactic acid. In feces from healthy participants, the main groups detected were phenolic compounds, ceramides, diacylglycerols and triacylglycerols.

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.

Stable isotope labeling combined with liquid chromatography-tandem mass spectrometry for comprehensive analysis of short-chain fatty acids

Short-chain fatty acids (SCFAs) are one class of bacterial metabolites mainly formed by gut microbiota from undigested fibers and proteins. These molecules are able to mediate signal conduction processes of cells, acting as G protein-coupled receptors (GPR) activators and histone deacetylases (HDAC) inhibitors. It was reported that SCFAs were closely associated with various human diseases. However, it is still challenging to analyze SCFAs because of their diverse structures and broad range of concentrations. In this study, we developed a highly sensitive method for simultaneous detection of 34 SCFAs by stable isotope labeling coupled with ultra-high performance liquid chromatography-electrospray ionization-mass spectrometry (UHPLC-ESI-MS/MS) analysis. In this respect, a pair of isotope labeling reagents, N-(4-(aminomethyl)benzyl)aniline (4-AMBA) and N-(4-(aminomethyl)benzyl)aniline-d₅ (4-AMBA-d₅), were synthesized to label SCFAs from the feces of mice and SCFA standards, respectively. The 4-AMBA-d₅ labeled SCFAs were used as internal standards to compensate the ionization variances resulting from matrix effect and thus minimize quantitation deviation in MS detection. After 4-AMBA labeling, the retention of SCFAs on the reversed-phase column increased and the separation resolution of isomers were improved. In addition, the MS responses of most SCFAs were enhanced by up to three orders of magnitude compared to unlabeled SCFAs. The limits of detection (LODs) of SCFAs were as low as 0.005 ng/mL. Moreover, good linearity for 34 SCFAs was obtained with the coefficient of determination (R²) ranging from 0.9846 to 0.9999 and the intra- and inter-day relative standard deviations (RSDs) were <17.8% and 15.4%, respectively, indicating the acceptable reproducibility of the developed method. Using the developed method, we successfully quantified 21 SCFAs from the feces of mice. Partial least squares discriminant analysis (PLS-DA) and t-test analysis showed that the contents of 9 SCFAs were significantly different between Alzheimer's disease (AD) and wide type (WT) mice fecal samples. Compared to WT mice, the contents of propionic acid, isobutyric acid, 3-hydroxybutyric acid, and 3-hydroxyisocaleric acid were decreased in AD mice, while lactic acid, 2-hydroxybutyric acid, 2-hydroxyisobutyric acid, levulinic acid, and valpronic acid were increased in AD mice. These significantly changed SCFAs in the feces of AD mice may afford to a better understanding of the pathogenesis of AD. Taken together, the developed UHPLC-ESI-MS/MS method could be applied for the sensitive and comprehensive determination of SCFAs from complex biological samples.