Promises and pitfalls of untargeted metabolomics

Evaluation of ion source parameters and liquid chromatography methods for plasma untargeted metabolomics using orbitrap mass spectrometer

Although untargeted metabolomics holds promise for study of metabolites in human health and disease, robust method development and optimization are needed to reduce potential analytical biases and to ensure comprehensive, high-throughput results. In this study, the effect of mass spectrometer (MS) ion source parameters on the signal reproducibility and number of metabolite annotations during untargeted metabolomics is shown. Furthermore, different mobile phase gradients and columns (five reversed phase (RP)-C18 and two hydrophilic interaction liquid chromatography (HILIC) columns) were evaluated for untargeted metabolomics of blood plasma extracts. Positioning the electrospray needle at the farthest on the Z-direction and the closest tested position on the Y-direction with respect to the mass spectrometry inlet produced the best signal reproducibility and the greatest number of metabolite annotations. Moreover, optimal ion source conditions included a positive spray voltage between 2.5 and 3.5 kV, a negative spray voltage between 2.5 and 3.0 kV, vaporization and ion transfer tube (ITT) temperature between 250 and 350 °C, 30 to 50 arbitrary units of sheath gas, and at least 10 auxiliary gas units. Despite the differences in chromatographic characteristics, the different RP columns assessed showed comparable performance in terms of number of metabolites annotated. For HILIC columns, a zwitterionic column demonstrated better performance than an amide column. Finally, as compared with use of a RP column alone, use of both the optimal RP and HILIC approaches expanded metabolome coverage: the number of metabolites annotated increased by 60 %. This study highlights the significance of fine-tuning the MS ion source parameters and optimizing chromatographic conditions on metabolome coverage during untargeted metabolomics of plasma samples.

Improved Accuracy and Reliability in Untargeted Analysis with LC-ESI-QTOF/MS1 by Ensemble Averaging

Untargeted liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is a powerful tool for comprehensive chemical analysis. Such techniques allow the detection and quantification of thousands of compounds in a sample. However, the complexity and variability in the data can introduce significant errors, impacting the reliability of the results. This study investigates ensemble averaging to mitigate these errors and improve signal-to-noise (S/N) ratios, feature detection, and data quality. In this work, 256 LC-qTOF/MS1 data sets from the analysis of Morning Glory seeds were averaged to generate merged data sets. The numbers of the pooled data sets in the merged files were varied, and the number of features, the S/N ratio, the accuracy and precision of the accurate masses, relative intensities, and migration time were examined. It was proved that ensemble averaging allows an increase in the S/N up to a factor of 10, and the relative standard deviation of the accurate masses and retention time decreased by a factor of 10. Moreover, the average number of features mined per data set increased from 1192 ± 129 with the original data set to 4408 when all data sets were averaged into one. Using known target compounds, ensemble averaging benefits on quantitative analysis were investigated. The measured and theoretical relative intensities between the [M+1]+H+, [M+2]+H+, and [M+3]+H+ and [M]+H+ isotopes of known alkaloids were used. The standard deviation decreased by up to a factor of 10, and the absolute error between theoretical and experimental relative intensities was below 3%, making the theoretical isotopic pattern a valid criterion for confirming a putative molecular formula. Using a targeted approach to recover quantitative data from the original data sets from information in the merged data sets provides an accurate quantitative means. Peak lists from the merged data sets and quantitative information from the original data sets were fused to obtain a robust clustering approach that allows recognizing features (adducts, isotopes, and fragments) generated by a common chemical in the ionization chamber. Two hundred and four clusters were obtained, characterized by two or more features with migration times that differ by less than 0.05 min and with similar response patterns.

DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts

BACKGROUND

Understanding the metabolic activities of the gut microbiome is vital for deciphering its impact on human health. While direct measurement of these metabolites through metabolomics is effective, it is often expensive and time-consuming. In contrast, microbial composition data obtained through sequencing is more accessible, making it a promising resource for predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, and interpretability.

METHOD

Here, we present the Deep Mixture of Variational Gaussian Process Experts (DMoVGPE) model, designed to overcome these issues. DMoVGPE utilizes a dynamic gating mechanism, implemented through a neural network with fully connected layers and dropout for regularization, to select the most relevant Gaussian Process experts. During training, the gating network refines expert selection, dynamically adjusting their contribution based on the input features. The model also incorporates an Automatic Relevance Determination (ARD) mechanism, which assigns relevance scores to microbial features by evaluating their predictive power. Features linked to metabolite profiles are given smaller length scales to increase their influence, while irrelevant features are down-weighted through larger length scales, improving both prediction accuracy and interpretability.

CONCLUSIONS

Through extensive evaluations on various datasets, DMoVGPE consistently achieves higher prediction performance than existing models. Furthermore, our model reveals significant associations between specific microbial taxa and metabolites, aligning well with findings from existing studies. These results highlight DMoVGPE's potential to provide accurate predictions and to uncover biologically meaningful relationships, paving the way for its application in disease research and personalized healthcare strategies.

Critical assessment of quenching and extraction/sample preparation methods for microorganisms in metabolomics

BACKGROUND

Advancements in the research of intracellular metabolome have the potential to affect our understanding of biological processes. The applications and findings of intracellular metabolome analysis are useful in understanding cellular pathways, microbial interactions, and the detection of secreted metabolites and their functions.

AIM OF REVIEW

This work focuses on the analysis of intracellular metabolomes in microorganisms. The techniques used for analyzing the intracellular metabolomes including metabolomics approaches such as mass spectrometry, nuclear magnetic resonance, liquid chromatography, and gas chromatography are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Challenges such as sample preparation, data analysis, metabolite extraction, sample storage and collection, and processing techniques were investigated, as they can highlight emerging technologies and advancements in metabolome analysis, future applications in drug discovery, personalized medicine, systems biology, and the limitations and challenges in studying the metabolome of microorganisms.

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning

Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests fail to diagnose early CKD. Therefore, this study aimed to identify early metabolic biomarkers. First, cats were retrospectively divided into two populations to conduct a case-control study, comparing the urinary and serum metabolome of healthy (n = 61) and CKD IRIS stage 2 cats (CKD2, n = 63). Subsequently, longitudinal validation was conducted in an independent population comprising healthy cats that remained healthy (n = 26) and cats that developed CKD2 (n = 22) within one year. Univariate, multivariate, and machine learning-based (ML) approaches were compared. The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. Furthermore, analysis of variable importance indicated consistent key serum metabolites, namely creatinine, SDMA, 2-hydroxyethanesulfonate, and aconitic acid. By enabling accurate diagnosis at least six months earlier, the highlighted metabolites may pave the way for improved diagnostics, ultimately contributing to timely disease management.

Integrative analysis of gut microbiota and fecal metabolites in cynomolgus monkeys with spontaneous type 2 diabetes mellitus

BACKGROUND

Accumulating evidence suggests that gut microbiota (GM) is clearly associated with the pathogenesis of type 2 diabetes mellitus (T2DM). However, the underlying mechanism of GM dysbiosis participates the onset of T2DM is not fully understood. The spontaneous T2DM cynomolgus monkeys are a powerful model for understanding the pathological mechanism of T2DM.

METHODS

Fecal samples were collected from 7 spontaneous T2DM cynomolgus monkeys and 7 healthy controls matched with similar age for multi-omics analysis, including shotgun metagenomic sequencing, untargeted metabolomics profiling, and targeted metabolomics focusing on short chain fatty acids (SCFAs). Lastly, the correlation network between differential gut microbial species and fecal metabolites was performed to explore the potential biomarkers of T2DM.

RESULTS

We found that 17 low-abundance species showed significant differences between the two groups. Analysis of gut microbial functions revealed that 16 KEGG pathways and 51 KEGG modules were significantly different in the two groups. Meanwhile, 276 fecal DEMs were identified, and these DEMs were enriched in the KEGG pathways, including Nucleotide metabolism, ABC transporters, Purine metabolism and so on. Lastly, Spearman correlation network analysis showed that the species of Anaerostipes_hadrus and Lachnoanaerobaculum_umeaense, and the metabolites including Glycerophospho-N-palmitoyl ethanolamine and 2-Hydroxycinnamic acid might serve as potential biomarkers of T2DM.

CONCLUSIONS

Our study provides novel insights into specific alterations in the GM composition, gene functions, and fecal metabolic profiles in spontaneous T2DM cynomolgus monkeys.

LC-MS-Based Global Metabolic Profiles of Alternative Blood Specimens Collected by Microsampling

Blood microsampling (BμS) has recently emerged as an interesting approach in the analysis of endogenous metabolites but also in metabolomics applications. Their non-invasive way of use and the simplified logistics that they offer renders these technologies highly attractive in large-scale studies, especially the novel quantitative microsampling approaches such as VAMs or qDBS. Objectives: Herein, we investigate the potential of BµS devices compared to the conventional plasma samples used in global untargeted mass spectrometry-based metabolomics of blood. Methods: Two novel quantitative devices, namely, Mitra, Capitainer, and the widely used Whatman cards, were selected for comparison with plasma. Venous blood was collected from 10 healthy, overnight-fasted individuals and loaded on the devices; plasma was also collected from the same venous blood. An extraction solvent optimization study was first performed on the three devices before the main study, which compared the global metabolic profiles of the four extracts (three BµS devices and plasma). Analysis was conducted using reverse phase LC-TOF MS in positive mode. Results: BµS devices, especially Mitra and Capitainer, provided equal or even superior information on the metabolic profiling of human blood based on the number and intensity of features and the precision and stability of some annotated metabolites compared to plasma. Despite their rich metabolic profiles, BµS did not capture metabolites associated with biological differentiation of sexes. Conclusions: Overall, our results suggest that a more in-depth investigation of the acquired information is needed for each specific application, as a metabolite-dependent trend was obvious.

Global Metabolomics Using LC-MS for Clinical Applications

Metabolomics can be used for a multitude of purposes, including monitoring of treatment effects and for increasing the knowledge of the pathophysiology of a wide range of diseases. Global (commonly referred to as "untargeted") metabolomics is hypothesis-generating and provides the opportunity to discover new biomarkers. Being versatile and having a high degree of selectivity and sensitivity, liquid chromatography-mass spectrometry (LC-MS) is the most common technique applied for metabolomics. We here present our global metabolomics LC-electrospray ionization-MS/MS method. The sample preparation procedures for plasma, serum, dried blood spots, urine, and cerebrospinal fluid are simple and nonspecific to reduce the risk of analyte loss. The method is based on reversed-phase chromatography using a diphenyl column. The high-resolution Q Exactive Orbitrap MS with data-dependent acquisition provides MS/MS spectra of a wide range of analytes. Our method covers a large part of the metabolome regarding hydrophobicity and compound class.

Understanding LC/MS-Based Metabolomics: A Detailed Reference for Natural Product Analysis

Liquid chromatography, when used in conjunction with mass spectrometry (LC/MS), is a powerful tool for conducting accurate and reproducible investigations of numerous metabolites in natural products (NPs). LC/MS has gained prominence in metabolomic research due to its high throughput, the availability of multiple ionization techniques and its ability to provide comprehensive metabolite coverage. This unique method can significantly influence various scientific domains. This review offers a comprehensive overview of the current state of LC/MS-based metabolomics in the investigation of NPs. This review provides a thorough overview of the state of the art in LC/MS-based metabolomics for the investigation of NPs. It covers the principles of LC/MS, various aspects of LC/MS-based metabolomics such as sample preparation, LC modes, method development, ionization techniques and data pre-processing. Moreover, it presents the applications of LC/MS-based metabolomics in numerous fields of NPs research such as including biomarker discovery, the agricultural research, food analysis, the study of marine NPs and microbiological research. Additionally, this review discusses the challenges and limitations of LC/MS-based metabolomics, as well as emerging trends and developments in this field.

HILIC-MS/MS Multi-targeted Method for Metabolomics Applications

Metabolomics aims at identification and quantitation of key end point metabolites, basically polar, in order to study changes in biochemical activities in response to pathophysiological stimuli or genetic modifications. Targeted profiling assays enjoying a growing popularity over the last years with LC-MS/MS as a powerful tool for development of such (semi-)quantitative methods for a large number of metabolites. Here we describe a method for absolute quantitation of ca. 100 metabolites belonging to key metabolite classes such as sugars, amino acids, nucleotides, organic acids, and amines with a hydrophilic interaction liquid chromatography (HILIC) system comprised with ultra (high) performance liquid chromatography (UHPLC) with detection on a triple quadrupole mass spectrometer operating in both positive and negative modes.

Metabolic Profiling Analysis of Congenital Adrenal Hyperplasia via an Untargeted Metabolomics Strategy

Congenital adrenal hyperplasia (CAH) manifests as an autosomal recessive disorder characterized by defects in the enzymes responsible for steroid synthesis. This work aims to perform metabolic profiling of patients with CAH, screen key differential metabolites compared to the control group, and discover the associated metabolic pathways implicated in CAH. Serum samples obtained from 32 pediatric male patients with CAH and 31 healthy control group candidates were subjected to analysis using non-targeted metabolomics strategy using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). A total of 278 differential metabolites were identified and annotated in KEGG. Operating characteristic curves (ROC) measurement exhibited 9 metabolites exhibiting high efficacy in differential diagnosis, as evidenced by an area under ROC curve (AUC) exceeding 0.85. Pathway analysis uncovered notable disruptions in steroid hormone biosynthesis (p <0.0001), purine metabolism and irregularities in lipid metabolism and amino acid metabolism, including tyrosine and alanine, in CAH patients. These findings demonstrate that metabolic pathways of purine, amino acid and lipid metabolism, apart from steroid hormone biosynthesis, may be disrupted and associated with CAH. This study helps provide insight into the metabolic profile of CAH patients and offers a new perspective for monitoring and administering follow-up care to CAH patients.

Sensitivity shift of the meta-metabolome and photosynthesis to the chemical stress in periphyton between months along one year and a half period: Case study of a terbuthylazine exposure

Despite the knowledge of the effects of contaminants on periphyton, information is limited about their natural fluctuations in sensitivity to chemical stress between various months. In particular, the molecular and biochemical mechanisms associated with sensitivity of photosynthesis and its fluctuations remain poorly described. To tackle this lack of knowledge, meta-metabolomics offers a comprehensive picture of the sensitive molecular response preceding the physiological impact. This study aimed to describe changes in the sensitivity of periphyton to chemical stress at different months over one year and a half period, at both the physiological and molecular levels by measuring photosynthetic yield and meta-metabolome responses (targeted and untargeted approaches). Periphyton was colonized for four weeks and then exposed to a range of terbuthylazine concentrations (0.3-30 μg L-1) under controlled conditions for 4 h. Sensitivity was assessed by determining the benchmark doses for the meta-metabolome and photosynthesis, along with the cumulative distribution of aggregated metabolomics signals. The results showed a strong sensitivity shift in the meta-metabolome compared to a smaller shift in photosynthetic yield at different months. This study also confirmed the high sensitivity of the meta-metabolome, as most signals responded before photosynthesis. The annotation highlighted the discrepancies in the molecular response to TBA between the months in terms of metabolite classes (e.g. amino acids, alkaloids, and lipids), their sensitivity, and trends in common classes across months, and correlation to photosynthesis inhibition, notably oxylipins. Overall, this study highlights that the molecular response of the periphyton to chemical stress, and thus toxicity pathways, may differ between the months but can still lead to similar physiological responses.

Untargeted Metabolomics for Inborn Errors of Metabolism: Development and Evaluation of a Sustainable Reference Material for Correcting Inter-Batch Variability

BACKGROUND

Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening.

METHODS

The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients.

RESULTS

The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%-25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%-108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls.

CONCLUSIONS

Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

Research progress on factors affecting the human milk metabolome

Human milk is the gold standard for infant nutrition and contains macronutrients, micronutrients, and various bioactive substances. The human milk composition and metabolite profiles are complex and dynamic, complicating its specific analysis. Metabolomics, a recently emerging technology, has been used to identify human milk metabolites classes. Applying metabolomics to study the factors affecting human milk metabolites can provide significant insights into the relationship between infant nutrition, health, and development and better meet the nutritional needs of infants during growth. Here, we systematically review the current status of human milk metabolomic research, and related methods, offering an in-depth analysis of the influencing factors and results of human milk metabolomics from a metabolic perspective to provide novel ideas to further advance human milk metabolomics.

Untargeted urine metabolomics suggests that ascorbic acid may serve as a promising biomarker for reduced feed intake in rabbits

Feed restriction is a common nutritional practice in rabbit farming; however, decreased feed intake can also signal potential digestive disorders at an early stage. This study endeavors to investigate the impact of feed restriction on selected productive traits and the urinary metabolome of juvenile rabbits across diverse genetic backgrounds. Our objective is to identify potential biomarkers capable of detecting periods of fasting. A total of 48 growing rabbits were used from two genetic types: Prat line (selected for litter size at weaning, n = 24) and Caldes line (selected for post-weaning growth rate, n = 24). At 60 days of age, a digestibility trial was carried out. Changes in productive traits (through bioelectrical impedance analysis, live weight control, average daily gain, energy, and protein retention) were evaluated when the animals were fed ad libitum from 60 to 64 days of age and when the same animals were subjected to feed restriction (50% of maintenance energy requirements) from 70 to 74 days of age, in a split-plot trial. In addition, untargeted urine metabolomics analysis was performed at both periods (ad libitum vs. restricted). Although some differences between genetic lines were observed in the animals' performance traits (average daily gain and retention of energy and protein), no differences in the urine metabolome were found between genetic types. However, feed restriction caused notable changes in the metabolome. When the animals were subjected to feed restriction, they had higher levels of ascorbic acid (P = 0.001) and p-cresol sulphate (P = 0.058) and lower levels of pyrocatechol sulphate/hydroquinone sulphate (P < 0.001), resorcinol sulphate (P = 0.002), enterolactone sulphate (P < 0.001), enterolactone (P < 0.001), kynurenic acid (P = 0.0002), proline betaine (P < 0.001), pipecolic acid betaine (P < 0.001), xanthurenic acid (P < 0.001) and quinaldic acid (P < 0.001) than the same animals when they were fed ad libitum. This study proposes urine ascorbic acid as potential biomarker for fasting events in rabbits. As urine ascorbic acid is the sole metabolite that significantly increases in the restricted group, it offers promising indicator for early detection and targeted management of digestive disorders in rabbits.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

The Application of Untargeted Metabolomic Approaches for the Search of Common Bioavailable Metabolites in Human Plasma Samples from Lippia citriodora and Olea europaea Extracts

Lippia citriodora and Olea europaea are known for their shared common bioactivities. Although both matrices are rich in similar families of bioactive compounds, their specific phytochemical compounds are mostly different. Since these compounds can be metabolized in the organism, this study hypothesized that common bioavailable metabolites may contribute to their similar bioactive effects. To test this, an acute double-blind intervention study in humans was conducted with blood samples collected at multiple time points. Using an untargeted metabolomic approach based on HPLC-ESI-QTOF-MS, 66 circulating metabolites were detected, including 9 common to both extracts, such as homovanillic acid sulfate and glucuronide derivates, hydroxytyrosol sulfate, etc. These common metabolites displayed significantly different Tmax values depending on the source, suggesting distinct metabolization pathways for each extract. The study highlights how shared bioavailable metabolites may underlie similar bioactivities observed between these two plant sources.

Dual column chromatography combined with high-resolution mass spectrometry improves coverage of non-targeted analysis of plant root exudates

BACKGROUND

Within the plant kingdom, there is an exceptional amount of chemical diversity that has yet to be annotated. It is for this reason that non-targeted analysis is of interest for those working in novel natural products. To increase the number and diversity of compounds observable in root exudate extracts, several workflows which differ at three key stages were compared: 1) sample extraction, 2) chromatography, and 3) data preprocessing.

RESULTS

Plants were grown in Hoagland's solution for two weeks, and exudates were initially extracted with water, followed by a 24-h regeneration period with subsequent extraction using methanol. Utilizing the second extraction showed improved results with less ion suppression and reduced retention time shifting compared to the first extraction. A single column method, utilizing a pentafluorophenyl column, paired with high-resolution mass spectrometry ionized and correctly identified 34 mock root exudate compounds, while the dual column method, incorporating a pentafluorophenyl column and a porous graphitic carbon column, retained and identified 43 compounds. In a pooled quality control sample of exudate extracts, the single column method detected 1,444 compounds. While the dual method detected fewer compounds overall (1,050), it revealed a larger number of small polar compounds. Three preprocessing methods (targeted, proprietary, and open source) successfully identified 43, 31, and 38 mock root exudate compounds to confidence level 1, respectively.

SIGNIFICANCE

Enhancing signal strength and analytical method stability involves removing the high ionic strength nutrient solution before sampling root exudate extracts. Despite signal intensity loss, a dual column method enhances compound coverage, particularly for small polar metabolites. Open-source software proves a viable alternative for non-targeted analysis, even surpassing proprietary software in peak picking.

Metabolomic Alterations Associated with Phthalate Exposures among Pregnant Women in Puerto Rico

Although phthalate exposure has been linked with multiple adverse pregnancy outcomes, their underlying biological mechanisms are not fully understood. We examined associations between biomarkers of phthalate exposures and metabolic alterations using untargeted metabolomics in 99 pregnant women and 86 newborns [mean (SD) gestational age = 39.5 (1.5) weeks] in the PROTECT cohort. Maternal urinary phthalate metabolites were quantified using isotope dilution high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS), while metabolic profiles in maternal and cord blood plasma were characterized via reversed-phase LC-MS. Multivariable linear regression was used in metabolome-wide association studies (MWAS) to identify individual metabolic features associated with elevated phthalate levels, while clustering and correlation network analyses were used to discern the interconnectedness of biologically relevant features. In the MWAS adjusted for maternal age and prepregnancy BMI, we observed significant associations between specific phthalates, namely, di(2-ethylhexyl) phthalate (DEHP) and mono(3-carboxypropyl) phthalate (MCPP), and 34 maternal plasma metabolic features. These associations predominantly included upregulation of fatty acids, amino acids, purines, or their derivatives and downregulation of ceramides and sphingomyelins. In contrast, fewer significant associations were observed with metabolic features in cord blood. Correlation network analysis highlighted the overlap of features associated with phthalates and those identified as differentiating markers for preterm birth in a previous study. Overall, our findings underscore the complex impact of phthalate exposures on maternal and fetal metabolism, highlighting metabolomics as a tool for understanding associated biological processes. Future research should focus on expanding the sample size, exploring the effects of phthalate mixtures, and validating identified metabolic features in larger, more diverse populations.

Integrated metabolic-transcriptomic network identifies immunometabolic modulations in human macrophages

Macrophages exhibit diverse phenotypes and respond flexibly to environmental cues through metabolic remodeling. In this study, we present a comprehensive multi-omics dataset integrating intra- and extracellular metabolomes with transcriptomic data to investigate the metabolic impact on human macrophage function. Our analysis establishes a metabolite-gene correlation network that characterizes macrophage activation. We find that the concurrent inhibition of tryptophan catabolism by IDO1 and IL4I1 inhibitors suppresses the macrophage pro-inflammatory response, whereas single inhibition leads to pro-inflammatory activation. We find that a subset of anti-inflammatory macrophages activated by Fc receptor signaling promotes glycolysis, challenging the conventional concept of reduced glycolysis preference in anti-inflammatory macrophages. We demonstrate that cholesterol accumulation suppresses macrophage IFN-γ responses. Our integrated network enables the discovery of immunometabolic features, provides insights into macrophage functional metabolic reprogramming, and offers valuable resources for researchers exploring macrophage immunometabolic characteristics and potential therapeutic targets for immune-related disorders.

An untargeted metabolomics approach applied to the study of the bioavailability and metabolism of three different bioactive plant extracts in human blood samples

Advances in the understanding of bioavailability and metabolism of bioactive compounds have been achieved primarily through targeted or semi-targeted metabolomics approaches using the hypothesis of potential metabolized compounds. The recent development of untargeted metabolomics approaches can present great advantages in this field, such as in the discovery of new metabolized compounds or to study the metabolism of compounds from multiple matrices simultaneously. Thus, this study proposes the use of an untargeted metabolomics strategy based on HPLC-ESI-QTOF-MS for the study of bioavailability and metabolism of bioactive compounds from different vegetal sources. Specifically, this study has been applied to plasma samples collected in an acute human intervention study using three matrices (Hibiscus sabdariffa, Silybum marianum and Theobroma cacao). This approach allowed the selection of those significant variables associated with exogenous metabolites derived from the consumption of bioactive compounds for their subsequent identification. As a result, 14, 25 and 3 potential metabolites associated with supplement intake were significantly detected in the plasma samples from volunteers who ingested the H. sabdariffa (HS), S. marianum (SM) and T. cacao (TC) extracts. Furthermore, Tmax values have been computed for each detected compound. The results highlight the potential of untargeted metabolomics for rapid and comprehensive analysis when working with a wide range of exogenous metabolites from different plant sources in biological samples.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Exploring the chemical subspace of RPLC: A data driven approach

BACKGROUND

The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC.

RESULTS

We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace.

SIGNIFICANCE AND NOVELTY

The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

Sansevieria trifasciata's specific metabolite improves tolerance and efficiency for particulate matter and volatile organic compound removal

Phytoremediation has become famous for removing particulate matter (PM) and volatile organic compounds (VOCs), but the ability is affected by plant health. Lately, the priming technique was a simple approach to studying improving plant tolerance against abiotic stress by specific metabolites that accumulated, known as "memory", but the mechanism underlying this mechanism and how long this "memory" was retained in the plant was a lack of study. Sansevieria trifasciata was primed for one week for PM and VOC stress to improve plant efficiency on PM and VOC. After that, the plant was recovered for two- or five-weeks, then re-exposed to the same stress with similar PM and VOC concentrations from cigarette smoke. Primed S. trifasciata showed improved removal of PMs entirely within 2 h and VOC within 24 h. The primed plant can maintain a malondialdehyde (MDA) level and retain the "memory" for two weeks. Metabolomics analysis showed that an ornithine-related compound was accumulated as a responsive metabolite under exposure to PM and VOC stress. Exogenous ornithine can maintain plant efficiency and prevent stress by increasing proline and antioxidant enzymes. This study is the first to demonstrate plant "memory" mechanisms under PM and VOC stress.

Exploring metabolomic dynamics in acute stress disorder: amino acids, lipids, and carbohydrates

Acute Stress Disorder (ASD) is a psychiatric condition that can develop shortly after trauma exposure. Although molecular studies of ASD are only beginning, groups of metabolites have been found to be significantly altered with acute stress phenotypes in various pre-clinical and clinical studies. ASD implicated metabolites include amino acids (β-hydroxybutyrate, glutamate, 5-aminovalerate, kynurenine and aspartate), ketone bodies (β-hydroxybutyrate), lipids (cortisol, palmitoylethanomide, and N-palmitoyl taurine) and carbohydrates (glucose and mannose). Network and pathway analysis with the most prominent metabolites shows that Extracellular signal-regulated kinases and c-AMP response element binding (CREB) protein can be crucial players. After highlighting main recent findings on the role of metabolites in ASD, we will discuss potential future directions and challenges that need to be tackled. Overall, we aim to showcase that metabolomics present a promising opportunity to advance our understanding of ASD pathophysiology as well as the development of novel biomarkers and therapeutic targets.

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Bile acids and the gut microbiome are involved in the hyperthermia mediated by 3,4-methylenedioxymethamphetamine (MDMA)

Hyperthermia induced by phenethylamines, such as 3,4-methylenedioxymethamphetamine (MDMA), can lead to life-threatening complications and death. Activation of the sympathetic nervous system and subsequent release of norepinephrine and activation of uncoupling proteins have been demonstrated to be the key mediators of phenethylamine-induced hyperthermia (PIH). Recently, the gut microbiome was shown to also play a contributing role in PIH. Here, the hypothesis that bile acids (BAs) produced by the gut microbiome are essential to PIH was tested. Changes in the serum concentrations of unconjugated primary BAs cholic acid (CA) and chenodeoxycholic acid (CDCA) and secondary BA deoxycholic acid (DCA) were measured following MDMA (20 mg/kg, sc) treatment in antibiotic treated and control rats. MDMA-induced a significant hyperthermic response and reduced the serum concentrations of three BAs 60 min post-treatment. Pretreatment with antibiotics (vancomycin, bacitracin and neomycin) in the drinking water for five days resulted in the depletion of BAs and a hypothermic response to MDMA. Gut bacterial communities in the antibiotic-treated group were distinct from the MDMA or saline treatment groups, with decreased microbiome diversity and alteration in taxa. Metagenomic functions inferred using the bioinformatic tool PICRUSt2 on 16S rRNA gene sequences indicated that bacterial genes associated to BA metabolism are less abundant in the antibiotic-MDMA treated group. Overall, these findings suggest that gut bacterial produced BAs might play an important role in MDMA-induced hyperthermia.

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Web-based multi-omics integration using the Analyst software suite

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Neighbour-induced changes in root exudation patterns of buckwheat results in altered root architecture of redroot pigweed

Roots are crucial in plant adaptation through the exudation of various compounds which are influenced and modified by environmental factors. Buckwheat root exudate and root system response to neighbouring plants (buckwheat or redroot pigweed) and how these exudates affect redroot pigweed was investigated. Characterising root exudates in plant-plant interactions presents challenges, therefore a split-root system which enabled the application of differential treatments to parts of a single root system and non-destructive sampling was developed. Non-targeted metabolome profiling revealed that neighbour presence and identity induces systemic changes. Buckwheat and redroot pigweed neighbour presence upregulated 64 and 46 metabolites, respectively, with an overlap of only 7 metabolites. Root morphology analysis showed that, while the presence of redroot pigweed decreased the number of root tips in buckwheat, buckwheat decreased total root length and volume, surface area, number of root tips, and forks of redroot pigweed. Treatment with exudates (from the roots of buckwheat and redroot pigweed closely interacting) on redroot pigweed decreased the total root length and number of forks of redroot pigweed seedlings when compared to controls. These findings provide understanding of how plants modify their root exudate composition in the presence of neighbours and how this impacts each other's root systems.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Methylprednisolone therapy induces differential metabolic trajectories in severe COVID-19 patients

IMPORTANCE

The SARS-CoV-2 virus infection in humans induces significant inflammatory and systemic reactions and complications of which corticosteroids like methylprednisolone have been recommended as treatment. Our understanding of the metabolic and metabolomic pathway dysregulations while using intravenous corticosteroids in COVID-19 is limited. This study will help enlighten the metabolic and metabolomic pathway dysregulations underlying high daily doses of intravenous methylprednisolone in COVID-19 patients compared to those receiving placebo. The information on key metabolites and pathways identified in this study together with the crosstalk with the inflammation and biochemistry components may be used, in the future, to leverage the use of methylprednisolone in any future pandemics from the coronavirus family.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Lipidomics profiles of human spermatozoa: insights into capacitation and acrosome reaction using UPLC-MS-based approach

Introduction

Lipidomics elucidates the roles of lipids in both physiological and pathological processes, intersecting with many diseases and cellular functions. The maintenance of lipid homeostasis, essential for cell health, significantly influences the survival, maturation, and functionality of sperm during fertilization. While capacitation and the acrosome reaction, key processes before fertilization, involve substantial lipidomic alterations, a comprehensive understanding of the changes in human spermatozoa's lipidomic profiles during these processes remains unknown. This study aims to explicate global lipidomic changes during capacitation and the acrosome reaction in human sperm, employing an untargeted lipidomic strategy using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

Methods

Twelve semen specimens, exceeding the WHO reference values for semen parameters, were collected. After discontinuous density gradient separation, sperm concentration was adjusted to 2 x 106 cells/ml and divided into three groups: uncapacitated, capacitated, and acrosome-reacted. UPLC-MS analysis was performed after lipid extraction from these groups. Spectral peak alignment and statistical analysis, using unsupervised principal component analysis (PCA), bidirectional orthogonal partial least squares discriminant analysis (O2PLS-DA) analysis, and supervised partial least-squares-latent structure discriminate analysis (PLS-DA), were employed to identify the most discriminative lipids.

Results

The 1176 lipid peaks overlapped across the twelve individuals in the uncapacitated, capacitated, and acrosome-reacted groups: 1180 peaks between the uncapacitated and capacitated groups, 1184 peaks between the uncapacitated and acrosome-reacted groups, and 1178 peaks between the capacitated and acrosome-reacted groups. The count of overlapping peaks varied among individuals, ranging from 739 to 963 across sperm samples. Moreover, 137 lipids had VIP values > 1.0 and twenty-two lipids had VIP > 1.5, based on the O2PLS-DA model. Furthermore, the identified twelve lipids encompassed increases in PI 44:10, LPS 20:4, LPA 20:5, and LPE 20:4, and decreases in 16-phenyl-tetranor-PGE2, PC 40:6, PS 35:4, PA 29:1, 20-carboxy-LTB4, and 2-oxo-4-methylthio-butanoic acid.

Discussion

This study has been the first time to investigate the lipidomics profiles associated with acrosome reaction and capacitation in human sperm, utilizing UPLC-MS in conjunction with multivariate data analysis. These findings corroborate earlier discoveries on lipids during the acrosome reaction and unveil new metabolites. Furthermore, this research highlights the effective utility of UPLC-MS-based lipidomics for exploring diverse physiological states in sperm. This study offers novel insights into lipidomic changes associated with capacitation and the acrosome reaction in human sperm, which are closely related to male reproduction.

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Spoilage markers for freshwater fish: A comprehensive workflow for non-targeted analysis of VOCs using DHS-GC-HRMS

Changes of volatile organic compounds (VOCs) patterns during 6 days of storage at +4 °C were investigated in different freshwater fish species, namely carp and trout, using dynamic headspace gas chromatography time-of-flight mass spectrometry (DHS-GC-TOFMS). DHS parameters were systematically optimized to establish optimum extraction and pre-concentration of VOCs. Moreover, different sample preparation methods were tested: mincing with a manual meat grinder, as well as mincing plus homogenization with a handheld homogenizer both without and with water addition. The addition of water during sample preparation led to pronounced changes of the volatile profiles, depending on the molecular structure and lipophilicity of the analytes, resulting in losses of up to 98 % of more lipophilic compounds (logP > 3). The optimized method was applied to trout and carp. Trout samples of different storage days were compared using univariate (Mann-Whitney U test, fold change calculation) and multivariate (OPLS-DA) statistics. 37 potential spoilage markers were selected; for 11 compounds identity could be confirmed via measurement of authentic standards and 10 compounds were identified by library spectrum match. 22 compounds were also found to be statistically significant spoilage markers in carp. Merging results of the different statistical approaches, the list of 37 compounds could be narrowed down to the 14 most suitable for trout spoilage assessment. This study comprises a systematic evaluation of the capabilities of DHS-GC coupled to high-resolution (HR) MS for studying spoilage in different freshwater fish species, including a comprehensive data evaluation workflow.

Metabolomic profiling between vitiligo patients and healthy subjects in plateau exhibited significant differences with those in plain

Vitiligo is the most common disorder of depigmentation, which is caused by multiple factors like metabolic abnormality, oxidative stress and the disorders of immune. In recent years, several studies have used untargeted metabolomics to analyze differential metabolites in patients with vitiligo, however, the subjects in these studies were all in plain area. In our study, multivariate analysis indicated a distinct separation between the healthy subjects from plateau and plain areas in electrospray positive and negative ions modes, respectively. Similarly, a distinct separation between vitiligo patients and healthy controls from plateau and plain areas was detected in the two ions modes. Among the identified metabolites, the serum levels of sphingosine 1-phosphate (S1P) were markedly higher in vitiligo patients compare to healthy subjects in plain and markedly higher in healthy subjects in plateau compare to those in plain. There are significant differences in serum metabolome between vitiligo patients and healthy subjects in both plateau and plain areas, as well as in healthy subjects from plateau and plain areas. S1P metabolism alteration may be involved in the pathogenesis of vitiligo.

Seminal plasma untargeted metabolomic and lipidomic profiling for the identification of a novel panel of biomarkers and therapeutic targets related to male infertility

Male infertility occurs approximately in about 50% of all infertility cases and represents a serious concern worldwide. Traditional semen analysis alone is insufficient to diagnose male infertility. Over the past two decades, advances in omics technologies have led to the widespread application of metabolomics profiling as a valuable diagnostic tool for various diseases and disorders. Seminal plasma represents a rich and easily accessible source of metabolites surrounding spermatozoa, a milieu that provides several indispensable nutrients to sustain sperm motility and fertilization. Changes of metabolic profiles in seminal plasma reflect male reproductive tract disorders. Here, we performed seminal plasma metabolomics and lipidomics profiling to identify a new pattern of biomarkers of male infertility. Seminal plasma samples from unfertile subjects (n = 31) and fertile controls (n = 19) were analyzed using an untargeted metabolomics/lipidomics integrated approach, based on Ultra-High-Pressure Liquid Chromatography-tandem Mass Spectrometry. Partial Least Squares-Discriminant Analysis showed a distinct separation between healthy fertile men and infertile subjects. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, phosphatidylethanolamine (PE) (18:1; 18:1) resulted with the highest score. In total, 40 molecular species showed statistically significant variations between fertile and infertile men. Heat-map and volcano plot analysis indicated that acylcarnitines, phosphatidylserine (PS) (40:2) and lactate were decreased, while PE (18:1; 18:1), Phosphatidic acid (PA) (O-19:2; 18:1), Lysophosphatidylethanolamine (LPE) (O-16:1) and Phosphatidylcholine (PC) (O-16:2; 18:1)-CH3 were increased in the infertile group. The present study is the first one to analyze the metabolomics/lipidomics dysregulation in seminal plasma between fertile and infertile individuals regardless of sub-infertility condition. Association of several metabolites/lipids dysregulation with male infertility reinforced data of previous studies performed with different approaches. In particular, we confirmed significantly decreased levels of PS and carnitines in infertile patients as well as the positive correlation with sperm motility and morphology. If validated on a larger prospective cohort, the metabolite biomarkers of infertility in seminal plasma we identified in the present study might inform novel strategies for diagnosis and interventions to overcome male infertility.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Metabolomic profiling of a neurodegenerative retina following optic nerve transection

The degeneration of retinal ganglion cells (RGCs) often causes irreversible vision impairment. Prevention of RGC degeneration can prevent or delay the deterioration of visual function. The present study aimed to investigate retinal metabolic profiles following optic nerve transection (ONT) injury and identify the potential metabolic targets for the prevention of RGC degeneration. Retinal samples were dissected from ONT group and non‑ONT group. The untargeted metabolomics were carried out using liquid chromatography‑tandem mass spectrometry. The involved pathways and biomarkers were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and MetaboAnalyst 5.0. In the ONT group, 689 disparate metabolites were detected, including lipids and lipid‑like molecules. A total of 122 metabolites were successfully annotated and enriched in 50 KEGG pathways. Among them, 'sphingolipid metabolism' and 'primary bile acid biosynthesis' were identified involved in RGC degeneration. A total of five metabolites were selected as the candidate biomarkers for detecting RGC degeneration with an AUC value of 1. The present study revealed that lipid‑related metabolism was involved in the pathogenesis of retinal neurodegeneration. Taurine, taurochenodesoxycholic acid, taurocholic acid (TCA), sphingosine, and galabiosylceramide are shown as the promising biomarkers for the diagnosis of RGC degeneration.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Exploring the mechanisms of clozapine-induced blood-brain barrier dysfunction using untargeted metabolomics and cellular metabolism analysis

Brain microvascular endothelial cells (BMVECs) from the blood- brain barrier form a highly selective membrane that protects the brain from circulating blood and maintains a stable microenvironment for the central nervous system. BMVEC dysfunction has been implicated in a variety of neurological and psychiatric disorders. Clozapine, a widely used antipsychotics, has been demonstrated to alter the permeability of BMVECs, but the underlying mechanisms of this effect are not fully understood. In this study, we investigated the effects of clozapine in BMVECs using untargeted metabolomics analysis. Our results illustrated that treatment with clozapine led to significant changes in the metabolic profile of BMVECs, including alterations in amino acid and energy metabolism. These findings suggest that clozapine affects BMVEC permeability through its effects on cellular metabolism. Our study could inform the development of more targeted and effective treatments for understanding the relationships among clozapine, cellular metabolism, and BMVECs in more detail.