Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain

Comprehensive profiling and evaluation of free/conjugated Phytosterols in crops using chemical derivatization coupled with UHPLC-ESI-QTOF-MS/MS

Phytosterols are naturally existed in crops but their detection is constrained by sensitivity and accuracy due to the inefficient analytical approaches. This study hypothesizes that an untargeted analytical method combining chemical derivatization with ultrahigh performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry can identify the various composition and contents of phytosterols in different crops. The results showed that chemical derivatization significantly enhanced intensity of phytosterols compared with non-derivatized samples. Using precursor ion scanning (PIS) of m/z 252.0690, dansyl chloride-labeled phytosterols were identified, demonstrating that rapeseeds had the highest content of total phytosterol (3981.2 ± 95.3 mg/kg), followed by sunflower seeds, flaxseeds, corn and rice, respectively. Principal component analysis revealed significant variations in phytosterol distribution among 15 crop samples, suggesting the applicability of phytosterol profile as a marker for phytosterols-contained crops. Hence, the proposed analytic approach proves high efficiency and accuracy in determining phytosterols and advances the study for phytosterol-enriched crops.

Risk factors and predictive models in the progression from MCI to Alzheimer's disease

BACKGROUND

The conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is related to various factors. The causal relationships among these factors remain unclear. This study aims to investigate pathways of the progression by using causal analysis and build a predictive model with high accuracy.

METHODS

162 MCI patients were recruited from the Alzheimer's Disease Neuroimaging Initiative database. 68 patients progressed to AD. 94 patients did not convert to AD. We captured standard T1-weighted images, processed them for feature extraction, and selected relevant features using mRMR and LASSO to calculate cortical and nuclear scores. The computational causal structure discovery and regression analyses were adopted to analyze the intricate relationships among APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores. The individualized prediction nomogram was constructed.

RESULTS

Our results indicated that APOE ε4 alleles was the promoter that caused MCI to transform into AD. Three independent pathways were identified, including P-tau, Aβ1-42, and cortical atrophy. P-tau was the cause of nuclear atrophy. The APOE ε4 alleles, P-tau, Aβ1-42, cortical and nuclear scores all had good predictive value for the MCI conversion. The predictive accuracy of the combined model was the highest, with an AUC of 0.918 in the training cohort and 0.908 in the testing cohort. A multi-predictor nomogram was established.

CONCLUSION

Our study elucidated the initiating factors and three independent pathways involved in the conversion of MCI to AD. The predictive value of each factor was clarified and a multi-predictor nomogram was established with high accuracy.

High-coverage identification of hydroxyl compounds based on pyridine derivatization-assisted liquid chromatography mass spectrometry

Hydroxyl compounds are widely present in plants and play essential roles in plant growth and development. High-coverage detection of hydroxyl compounds is crucial for understanding the physiological processes of plants. Despite the prevalence of chemical derivatization-assisted liquid chromatography-high resolution mass spectrometry (CD-LC-HRMS) in high-coverage detection of compounds with diverse functional groups, the confident identification of these compounds after derivatization remains a significant challenge. Herein, a novel method was developed for the identification of pyridine (PY)-derivatized hydroxyl compounds by comparing the MS/MS similarity of derivatized and corresponding underivatized compounds. Fragmentation rules of standards were summarized, and theoretical calculations have demonstrated the MS/MS similarity of PY-derivatized hydroxyl compounds with their underivatized counterparts. The effectiveness of the developed method was demonstrated by identifying PY-derivatized authentic standards. A total of 90 hydroxyl compounds were putatively identified in maize using the proposed method. This method can significantly enhance ionization efficiency with minimal impact on the quality of the MS/MS spectra, enabling the effective utilization of mass spectra databases for the identification of hydroxyl compounds.

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

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

Integrative plasma and fecal metabolomics identify functional metabolites in adenoma-colorectal cancer progression and as early diagnostic biomarkers

Changes in plasma and fecal metabolomes in colorectal cancer (CRC) progression (normal-adenoma-CRC) remain unclear. Here, plasma and fecal samples were collected from four independent cohorts of 1,251 individuals (422 CRC, 399 colorectal adenoma [CRA], and 430 normal controls [NC]). By metabolomic profiling, signature plasma and fecal metabolites with consistent shift across NC, CRA, and CRC are identified, including CRC-enriched oleic acid and CRC-depleted allocholic acid. Oleic acid exhibits pro-tumorigenic effects in CRC cells, patient-derived organoids, and two murine CRC models, whereas allocholic acid has opposing effects. By integrative analysis, we found that oleic acid or allocholic acid directly binds to α-enolase or farnesoid X receptor-1 in CRC cells, respectively, to modulate cancer-associated pathways. Clinically, we establish a panel of 17 plasma metabolites that accurately diagnoses CRC in a discovery and three validation cohorts (AUC = 0.848-0.987). Overall, we characterize metabolite signatures, mechanistic significance, and diagnostic potential of plasma and fecal metabolomes in CRC.

Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics

Dysregulated glycerophospholipid (GP) metabolism in the brain is associated with the progression of neurodegenerative diseases including Alzheimer's disease (AD). Routine liquid chromatography-mass spectrometry (LC-MS)-based large-scale lipidomic methods often fail to elucidate subtle yet important structural features such as sn-position, hindering the precise interrogation of GP molecules. Leveraging high-resolution demultiplexing (HRdm) ion mobility spectrometry (IMS), we develop a four-dimensional (4D) lipidomic strategy to resolve GP sn-position isomers. We further construct a comprehensive experimental 4D GP database of 498 GPs identified from the mouse brain and an in-depth extended 4D library of 2500 GPs predicted by machine learning, enabling automated profiling of GPs with detailed acyl chain sn-position assignment. Analyzing three mouse brain regions (hippocampus, cerebellum, and cortex), we successfully identify a total of 592 GPs including 130 pairs of sn-position isomers. Further temporal GPs analysis in the three functional brain regions illustrates their metabolic alterations in AD progression.

Inhibition of DUSP18 impairs cholesterol biosynthesis and promotes anti-tumor immunity in colorectal cancer

Tumor cells reprogram their metabolism to produce specialized metabolites that both fuel their own growth and license tumor immune evasion. However, the relationships between these functions remain poorly understood. Here, we report CRISPR screens in a mouse model of colo-rectal cancer (CRC) that implicates the dual specificity phosphatase 18 (DUSP18) in the establishment of tumor-directed immune evasion. Dusp18 inhibition reduces CRC growth rates, which correlate with high levels of CD8+ T cell activation. Mechanistically, DUSP18 dephosphorylates and stabilizes the USF1 bHLH-ZIP transcription factor. In turn, USF1 induces the SREBF2 gene, which allows cells to accumulate the cholesterol biosynthesis intermediate lanosterol and release it into the tumor microenvironment (TME). There, lanosterol uptake by CD8+ T cells suppresses the mevalonate pathway and reduces KRAS protein prenylation and function, which in turn inhibits their activation and establishes a molecular basis for tumor cell immune escape. Finally, the combination of an anti-PD-1 antibody and Lumacaftor, an FDA-approved small molecule inhibitor of DUSP18, inhibits CRC growth in mice and synergistically enhances anti-tumor immunity. Collectively, our findings support the idea that a combination of immune checkpoint and metabolic blockade represents a rationally-designed, mechanistically-based and potential therapy for CRC.

Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

Using Data-Dependent and -Independent Hybrid Acquisitions for Fast Liquid Chromatography-Based Untargeted Lipidomics

Untargeted lipidomics using liquid chromatography (LC) coupled with tandem mass spectrometry (MS) is essential for large cohort studies. Using a fast LC gradient of less than 10 min for the rapid screening of lipids decreases the annotation rate, because of the lower coverage of the MS/MS spectra caused by the narrow peak width. A systematic procedure is proposed in this study to achieve a high annotation rate in fast LC-based untargeted lipidomics by integrating data-dependent acquisition (DDA) and sequential window acquisition of all-theoretical mass spectrometry data-independent acquisition (SWATH-DIA) techniques using the updated MS-DIAL program. This strategy uses variable SWATH-DIA methods for quality control (QC) samples, which are a mixture of biological samples that were analyzed multiple times to correct the MS signal drift. In contrast, biological samples are analyzed using DDA to facilitate the structural elucidation of lipids using the pure spectrum to the maximum extent. The workflow is demonstrated using an 8.6 min LC gradient, where the QC samples are analyzed using five different SWATH-DIA methods. The use of both DDA and SWATH-DIA achieves a 1.7-fold annotation coverage from publicly available benchmark data obtained using a fast LC-DDA-MS technique and offers 95.3% lipid coverage, as compared to the benchmark data set from a 25 min LC gradient. This study demonstrates that harmonized improvements in analytical conditions and informatics tools provide a comprehensive lipidome in fast LC-based untargeted lipidomics, not only for large-scale studies but also for small-scale experiments, contributing to both clinical applications and basic biology.

Revealing Structure and Localization of Steroid Regioisomers through Predictive Fragmentation Patterns in Mass Spectrometry Imaging

Identifying and mapping steroids in tissues can provide opportunities for biomarker discovery, the interrogation of disease progression, and new therapeutics. Although separation coupled to mass spectrometry (MS) has emerged as a powerful tool for studying steroids, imaging and annotating steroid isomers remains challenging. Herein, we present a new method based on the fragmentation of silver-cationized steroids in tandem MS, which produces distinctive and consistent fragmentation patterns conferring confidence in steroid annotation at the regioisomeric level without using prior derivatization, separation, or instrumental modification. In addition to predicting the structure of the steroid with isomeric specificity, the method is simple, flexible, and inexpensive, suggesting that the wider community will easily adapt to it. We demonstrate the utility of our approach by visualizing steroids and steroid isomer distributions in mouse brain tissue using silver-doped pneumatically assisted nanospray desorption electrospray ionization mass spectrometry imaging.

Reactive Matrices for MALDI-MS of Cholesterol

Cholesterol is a critical molecule whose dysregulation in certain brain regions is related to multiple neurological disorders. It is of pathological importance to map the distribution of cholesterol in brain. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been widely used in the molecular imaging of metabolites at a high spatial resolution. However, it is challenging to analyze cholesterol by MALDI-MS due to its difficulty in ionization. Herein, we present for the first time a type of reactive matrix for MALDI-MS of cholesterol. Methylpyridinium carboxaldehydes react with cholesterol and other hydroxyl-containing sterols, which greatly enhanced both desorption and ionization and improved the limits of detection to the low μg/mL range. Compared with previous methods, our reactive matrix requires only one step of chemical derivatization and avoids time-consuming enzymatic reaction, which simplified the sample pretreatment. The reactive matrix was successfully used in mapping the distribution of cholesterol in brain tissue sections using MALDI-MS imaging. In summary, this work has provided a sensitive and simple method for the MALDI-MS analysis of cholesterol, has proposed a novel solution to visualize the distribution of sterol metabolites, and has great potential for applications in neurological and pathological studies.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.

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.

The translational potential of cholesterol-based therapies for neurological disease

Cholesterol is an important metabolite and membrane component and is enriched in the brain owing to its role in neuronal maturation and function. In the adult brain, cholesterol is produced locally, predominantly by astrocytes. When cholesterol has been used, recycled and catabolized, the derivatives are excreted across the blood-brain barrier. Abnormalities in any of these steps can lead to neurological dysfunction. Here, we examine how precise interactions between cholesterol production and its use and catabolism in neurons ensures cholesterol homeostasis to support brain function. As an example of a neurological disease associated with cholesterol dyshomeostasis, we summarize evidence from animal models of Huntington disease (HD), which demonstrate a marked reduction in cholesterol biosynthesis with clinically relevant consequences for synaptic activity and cognition. In addition, we examine the relationship between cholesterol loss in the brain and cognitive decline in ageing. We then present emerging therapeutic strategies to restore cholesterol homeostasis, focusing on evidence from HD mouse models.

Advances and Challenges in Plant Sterol Research: Fundamentals, Analysis, Applications and Production

Plant sterols (PS) are cholesterol-like terpenoids widely spread in the kingdom Plantae. Being the target of extensive research for more than a century, PS have topped with evidence of having beneficial effects in healthy subjects and applications in food, cosmetic and pharmaceutical industries. However, many gaps in several fields of PS's research still hinder their widespread practical applications. In fact, many of the mechanisms associated with PS supplementation and their health benefits are still not fully elucidated. Furthermore, compared to cholesterol data, many complex PS chemical structures still need to be fully characterized, especially in oxidized PS. On the other hand, PS molecules have also been the focus of structural modifications for applications in diverse areas, including not only the above-mentioned but also in e.g., drug delivery systems or alternative matrixes for functional foods and fats. All the identified drawbacks are also superimposed by the need of new PS sources and technologies for their isolation and purification, taking into account increased environmental and sustainability concerns. Accordingly, current and future trends in PS research warrant discussion.

Coupled Lipidomics and Digital Pathology as an Effective Strategy to Identify Novel Adverse Outcome Pathways in Eisenia fetida Exposed to MoS2 Nanosheets and Ionic Mo

Molybdenum disulfide (MoS₂) nanosheets are increasingly applied in several fields, but effective and accurate strategies to fully characterize potential risks to soil ecosystems are lacking. We introduce a coelomocyte-based in vivo exposure strategy to identify novel adverse outcome pathways (AOPs) and molecular endpoints from nontransformed (NTMoS₂) and ultraviolet-transformed (UTMoS₂) MoS₂ nanosheets (10 and 100 mg Mo/L) on the earthworm Eisenia fetida using nontargeted lipidomics integrated with transcriptomics. Machine learning-based digital pathology analysis coupled with phenotypic monitoring was further used to establish the correlation between lipid profiling and whole organism effects. As an ionic control, Na₂MoO₄ exposure significantly reduced (61.2-79.5%) the cellular contents of membrane-associated lipids (glycerophospholipids) in earthworm coelomocytes. Downregulation of the unsaturated fatty acid synthesis pathway and leakage of lactate dehydrogenase (LDH) verified the Na₂MoO₄-induced membrane stress. Compared to conventional molybdate, NTMoS₂ inhibited genes related to transmembrane transport and caused the differential upregulation of phospholipid content. Unlike NTMoS₂, UTMoS₂ specifically upregulated the glyceride metabolism (10.3-179%) and lipid peroxidation degree (50.4-69.4%). Consequently, lipolytic pathways were activated to compensate for the potential energy deprivation. With pathology image quantification, we report that UTMoS₂ caused more severe epithelial damage and intestinal steatosis than NTMoS₂, which is attributed to the edge effect and higher Mo release upon UV irradiation. Our results reveal differential AOPs involving soil sentinel organisms exposed to different Mo forms, demonstrating the potential of liposome analysis to identify novel AOPs and furthermore accurate soil risk assessment strategies for emerging contaminants.

Deep-profiling of phospholipidome via rapid orthogonal separations and isomer-resolved mass spectrometry

A lipidome comprises thousands of lipid species, many of which are isomers and isobars. Liquid chromatography-tandem mass spectrometry (LC-MS/MS), although widely used for lipidomic profiling, faces challenges in differentiating lipid isomers. Herein, we address this issue by leveraging the orthogonal separation capabilities of hydrophilic interaction liquid chromatography (HILIC) and trapped ion mobility spectrometry (TIMS). We further integrate isomer-resolved MS/MS methods onto HILIC-TIMS, which enable pinpointing double bond locations in phospholipids and sn-positions in phosphatidylcholine. This system profiles phospholipids at multiple structural levels with short analysis time (<10 min per LC run), high sensitivity (nM detection limit), and wide coverage, while data analysis is streamlined using a home-developed software, LipidNovelist. Notably, compared to our previous report, the system doubles the coverage of phospholipids in bovine liver and reveals uncanonical desaturation pathways in RAW 264.7 macrophages. Relative quantitation of the double bond location isomers of phospholipids and the sn-position isomers of phosphatidylcholine enables the phenotyping of human bladder cancer tissue relative to normal control, which would be otherwise indistinguishable by traditional profiling methods. Our research offers a comprehensive solution for lipidomic profiling and highlights the critical role of isomer analysis in studying lipid metabolism in both healthy and diseased states.

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

Exhaustion-associated cholesterol deficiency dampens the cytotoxic arm of antitumor immunity

The concept of targeting cholesterol metabolism to treat cancer has been widely tested in clinics, but the benefits are modest, calling for a complete understanding of cholesterol metabolism in intratumoral cells. We analyze the cholesterol atlas in the tumor microenvironment and find that intratumoral T cells have cholesterol deficiency, while immunosuppressive myeloid cells and tumor cells display cholesterol abundance. Low cholesterol levels inhibit T cell proliferation and cause autophagy-mediated apoptosis, particularly for cytotoxic T cells. In the tumor microenvironment, oxysterols mediate reciprocal alterations in the LXR and SREBP2 pathways to cause cholesterol deficiency of T cells, subsequently leading to aberrant metabolic and signaling pathways that drive T cell exhaustion/dysfunction. LXRβ depletion in chimeric antigen receptor T (CAR-T) cells leads to improved antitumor function against solid tumors. Since T cell cholesterol metabolism and oxysterols are generally linked to other diseases, the new mechanism and cholesterol-normalization strategy might have potential applications elsewhere.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

De novo phytosterol synthesis in animals

Sterols are vital for nearly all eukaryotes. Their distribution differs in plants and animals, with phytosterols commonly found in plants whereas most animals are dominated by cholesterol. We show that sitosterol, a common sterol of plants, is the most abundant sterol in gutless marine annelids. Using multiomics, metabolite imaging, heterologous gene expression, and enzyme assays, we show that these animals synthesize sitosterol de novo using a noncanonical C-24 sterol methyltransferase (C24-SMT). This enzyme is essential for sitosterol synthesis in plants, but not known from most bilaterian animals. Our phylogenetic analyses revealed that C24-SMTs are present in representatives of at least five animal phyla, indicating that the synthesis of sterols common to plants is more widespread in animals than currently known.

Development of a simultaneous quantification method for the gut microbiota-derived core nutrient metabolome in mice and its application in studying host-microbiota interaction

The gut microbiota interacts with the host via production of various metabolites of dietary nutrients. Herein, we proposed the concept of the gut microbiota-derived core nutrient metabolome, which covers 43 metabolites in carbohydrate metabolism, glycolysis, tricarboxylic acid cycle and amino acid metabolism, and established a quantitative UPLC-Q/TOF-MS method through 3-nitrophenylhydrazine derivatization to investigate the influence of obesity on the gut microbiota in mice. All metabolites could be simultaneously analyzed via separation on a BEH C18 column within 18 min. The lower limits of quantification of most analytes were less than 1 μM. Validation results demonstrated suitability for the analysis of mouse fecal samples. The method was then applied to detect the gut microbiota-derived nutrient metabolome in the feces of high-fat diet induced obese (DIO) and ob/ob (leptin-deficient) mice, as well as obesity-prone (OP) and obesity-resistant (OR) mice. Compared to the control groups, there were 13, 23 and 10 differentially abundant metabolites detected in ob/ob, DIO and OP groups, respectively. Among them, amino acids including leucine, isoleucine, glycine, methionine, tyrosine and glutamine were co-downregulated in the obese or OP mice and exhibited inverse association with body weight. 16S rDNA analysis revealed that the genera Lactobacillus and Dubosiella were also inversely associated with body weight and positively correlated with fecal amino acids. Collectively, our work provides an effective and simplified method for simultaneous quantifying the gut microbiota-derived core nutrient metabolome in mouse feces, which could assist various future studies on host-microbiota metabolic interaction.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Integrating the potential of ion mobility spectrometry-mass spectrometry in the separation and structural characterisation of lipid isomers

It is increasingly evident that a more detailed molecular structure analysis of isomeric lipids is critical to better understand their roles in biological processes. The occurrence of isomeric interference complicates conventional tandem mass spectrometry (MS/MS)-based determination, necessitating the development of more specialised methodologies to separate lipid isomers. The present review examines and discusses recent lipidomic studies based on ion mobility spectrometry combined with mass spectrometry (IMS-MS). Selected examples of the separation and elucidation of structural and stereoisomers of lipids are described based on their ion mobility behaviour. These include fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids. Recent approaches for specific applications to improve isomeric lipid structural information using direct infusion, coupling imaging, or liquid chromatographic separation workflows prior to IMS-MS are also discussed, including: 1) strategies to improve ion mobility shifts; 2) advanced tandem MS methods based on activation of lipid ions with electrons or photons, or gas-phase ion-molecule reactions; and 3) the use of chemical derivatisation techniques for lipid characterisation.

Lipidomics analysis in drug discovery and development

Despite being a relatively new addition to the Omics' landscape, lipidomics is increasingly being recognized as an important tool for the identification of druggable targets and biochemical markers. In this review we present recent advances of lipid analysis in drug discovery and development. We cover current state of the art technologies which are constantly evolving to meet demands in terms of sensitivity and selectivity. A careful selection of important examples is then provided, illustrating the versatility of lipidomics analysis in the drug discovery and development process. Integration of lipidomics with other omics', stem-cell technologies, and metabolic flux analysis will open new avenues for deciphering pathophysiological mechanisms and the discovery of novel targets and biomarkers.

Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study

Steroids play key roles in various biological processes and are characterized by many isomeric variants, which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is seen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS), and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5%) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for nontargeted analysis are critically discussed. To the best of our knowledge, this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.

iMS2Net: A multiscale networking methodology to decipher metabolic synergy of organism

The metabolic responses of organism to external stimuli are characterized by the multicellular- and multiorgan-based synergistic regulation. Network analysis is a powerful tool to investigate this multiscale interaction. The imaging mass spectrometry (iMS)-based spatial omics provides multidimensional and multiscale information, thus offering the possibility of network analysis to investigate metabolic response of organism to environmental stimuli. We present iMS dataset-sourced multiscale network (iMS2Net) strategy to uncover prenatal environmental pollutant (PM2.5)-induced metabolic responses in the scales of cell and organ from metabolite abundances and metabolite-metabolite interaction using mouse fetal model, including metabotypic similarity, metabolic vulnerability, metabolic co-variability and metabolic diversity within and between organs. Furthermore, network-based analysis results confirm close associations between lipid metabolites and inflammatory cytokine release. This networking methodology elicits particular advantages for modeling the dynamic and adaptive processes of organism under environmental stresses or pathophysiology and provides molecular mechanism to guide the occurrence and development of systemic diseases.

Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics

Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.

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.

A proteome-wide map of 20(S)-hydroxycholesterol interactors in cell membranes

Oxysterols (OHCs) are hydroxylated cholesterol metabolites that play ubiquitous roles in health and disease. Due to the non-covalent nature of their interactions and their unique partitioning in membranes, the analysis of live-cell, proteome-wide interactions of OHCs remains an unmet challenge. Here, we present a structurally precise chemoproteomics probe for the biologically active molecule 20(S)-hydroxycholesterol (20(S)-OHC) and provide a map of its proteome-wide targets in the membranes of living cells. Our target catalog consolidates diverse OHC ontologies and demonstrates that OHC-interacting proteins cluster with specific processes in immune response and cancer. Competition experiments reveal that 20(S)-OHC is a chemo-, regio- and stereoselective ligand for the protein transmembrane protein 97 (Tmem97/the σ2 receptor), enabling us to reconstruct the 20(S)-OHC-Tmem97 binding site. Our results demonstrate that multiplexed, quantitative analysis of cellular target engagement can expose new dimensions of metabolite activity and identify actionable targets for molecular therapy.