PubMed

Methods Mol Biol. 2025;2891:239-256. doi: 10.1007/978-1-0716-4334-1_13.ABSTRACTThe final aim of metabolomics is the comprehensive and holistic study of the metabolome in biological samples. Therefore, the use of instruments that enable the analysis of metabolites belonging to various chemical classes in a wide range of concentrations is essential, without compromising on robustness, resolution, sensitivity, specificity, and metabolite annotation. These characteristics are crucial for the analysis of very complex samples, such as wine, whose metabolome is the result of the sum of metabolites derived from grapes, yeast(s), bacteria(s), and chemical or physical modification during winemaking. In recent years, a big advantage, in this direction, was the hardware developments on hyphenated instruments that enable the integration of liquid chromatography (LC), ion mobility spectrometry (IMS), and mass spectrometry (MS). This chapter describes an LC-IMS-MS protocol for the analysis of wine and grape samples as well as the use of IMS data in metabolite annotation.PMID:39812986 | DOI:10.1007/978-1-0716-4334-1_13

Methods Mol Biol. 2025;2891:221-237. doi: 10.1007/978-1-0716-4334-1_12.ABSTRACTTrapped ion mobility spectrometry (TIMS) using parallel accumulation serial fragmentation (PASEF®) is an advanced analytical technique that offers several advantages in mass spectrometry (MS)-based lipidomics. TIMS provides an additional dimension of separation to mass spectrometry and accurate collision cross-section (CCS) measurements for ions, aiding in the structural characterization of molecules. This is especially valuable in lipidomics for identifying and distinguishing isomeric or structurally similar compounds. On the other hand, PASEF technology allows for fast and efficient data acquisition by accumulating ions in parallel and then serially fragmenting them. This accelerates the analysis process and improves throughput, making it suitable for high-throughput applications. Moreover, the combination of TIMS and PASEF reduces co-elution and ion coalescence issues, leading to cleaner and more interpretable mass spectra. This results in higher data quality and more confident identifications. In this chapter, a data-dependent TIMS-PASEF® workflow for lipidomics analysis is presented.PMID:39812985 | DOI:10.1007/978-1-0716-4334-1_12

Methods Mol Biol. 2025;2891:205-219. doi: 10.1007/978-1-0716-4334-1_11.ABSTRACTA simple analytical workflow is described for gas chromatographic-mass spectrometric (GC-MS)-based chiral profiling of secondary amino acids (AAs) in biological matrices. The sample preparation is carried out directly in aqueous biological sample extracts and involves in situ heptafluorobutyl chloroformate (HFBCF) derivatization-liquid-liquid microextraction of nonpolar products into hexane phase followed by subsequent formation of the corresponding methylamides from the HFB esters by direct treatment with methylamine reagent solution. The (O, N) HFB-butoxycarbonyl-methylamide AA products (HFBOC-MA) are separated on a Chirasil-L-Val capillary column and quantitatively measured by GC-MS operated in selected ion monitoring (SIM) mode. The protocol includes 12 simple pipetting steps and covers the quantitative analysis of 8 L, D pairs of secondary amino acids, including proline, isomeric 3-, 4-hydroxyprolines, pipecolic acid, nipecotic acid, azetidine-2-carboxylic acid, and cis- and trans-5-hydroxy-L-pipecolic acid using 13C5 -L-proline as an internal standard. The individual analytical steps are commented on and explained, with emphasis on the chiral GC-MS analysis of secondary amino acids in human urine, serum, and peptide hydrolysate samples.PMID:39812984 | DOI:10.1007/978-1-0716-4334-1_11

Methods Mol Biol. 2025;2891:181-204. doi: 10.1007/978-1-0716-4334-1_10.ABSTRACTMetabolomics 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.PMID:39812983 | DOI:10.1007/978-1-0716-4334-1_10

Methods Mol Biol. 2025;2891:165-180. doi: 10.1007/978-1-0716-4334-1_9.ABSTRACTA protocol for the preparation of tissue extracts for the targeted analysis ca. 150 polar metabolites, including those involved in central carbon metabolism, is described, using a reversed phase ion pair U(H)PLC-MS method. Data collection enabled in high-resolution mass spectrometry detection provides highly specific and sensitive acquisition of metabolic intermediates with wide range physicochemical properties and pathway coverage. Technical aspects are discussed for method transfer along with the basic principles of sample sequence setup, data analysis, and validation. At last general comments are given to help the assessment of data quality and system performance.PMID:39812982 | DOI:10.1007/978-1-0716-4334-1_9

Methods Mol Biol. 2025;2891:153-163. doi: 10.1007/978-1-0716-4334-1_8.ABSTRACTThe gut's symbiome, a hidden metabolic organ, has gained scientific interest for its crucial role in human health. Acting as a biochemical factory, the gut microbiome produces numerous small molecules that significantly impact host metabolism. Metabolic profiling facilitates the exploration of its influence on human health and disease through the symbiotic relationship. Fecal metabolomics-based analysis is an indisputably valuable tool for elucidating the biochemistry of digestion and absorption in the gastrointestinal system, serving as the most suitable specimen to study the symbiotic relationship between the host and the intestinal microbiota. It is well-established that the balance of the intestinal microbiota changes in response to various stimuli, both physiological, such as gender, age, diet, and exercise, and pathological, such as gastrointestinal and hepatic diseases. Fecal samples have been analyzed using widely adopted analytical techniques, including NMR spectroscopy, GC-MS, and LC-MS/MS. Rat fecal samples are frequently used and particularly useful substrates for metabolomics-based studies in related fields.The complexity and diversity of fecal samples necessitate careful and skillful handling to extract metabolites, while avoiding their deterioration, effectively and quantitatively. Several determinative factors, such as the fecal sample weight to extraction solvent solution volume, the nature and pH value of the extraction solvent, and the homogenization process, play crucial roles in achieving optimal extraction for obtaining high-quality metabolic fingerprints, whether for untargeted or targeted metabolomics.PMID:39812981 | DOI:10.1007/978-1-0716-4334-1_8

Methods Mol Biol. 2025;2891:131-152. doi: 10.1007/978-1-0716-4334-1_7.ABSTRACTLipidomics has attracted attention in the discovery of unknown biomolecules and for capturing the changes in metabolism caused by genetic and environmental factors in an unbiased manner. However, obtaining reliable lipidomics data, including structural diversity and quantification data, is still challenging. Supercritical fluid chromatography (SFC) is a suitable technique for separating lipid molecules with high throughput and separation efficiency. Here, we describe a quantitative lipidomics method using SFC coupled with mass spectrometry. This technique is suitable for characterizing the structural diversity of lipids (e.g., phospholipids, sphingolipids, glycolipids, and glycerolipids) with high quantitative accuracy to understand their biological functions.PMID:39812980 | DOI:10.1007/978-1-0716-4334-1_7

Methods Mol Biol. 2025;2891:109-129. doi: 10.1007/978-1-0716-4334-1_6.ABSTRACTUntargeted analysis by LC-MS is a valuable tool for metabolic profiling (metabonomics/metabolomics), and applications of this technology have grown rapidly over the past decade. LC-MS offers advantages of speed, sensitivity, relative ease of sample preparation, and large dynamic range compared to other platforms in this role. However, like any analytical approach, there are still drawbacks and challenges that have to be overcome, some of which are being addressed by advances in both column chemistries and instrumentation. In particular, the combination of LC-MS with ion mobility offers many new possibilities for improved analyte separation, detection, and structural identification. There are many untargeted LC-MS approaches which can be applied to metabolic phenotyping, and these usually need to be optimized for the type of sample, the nature of the study, or the biological question. Some of the main LC-MS approaches for untargeted metabolic phenotyping are described in detail in the following protocol.PMID:39812979 | DOI:10.1007/978-1-0716-4334-1_6

Methods Mol Biol. 2025;2891:91-108. doi: 10.1007/978-1-0716-4334-1_5.ABSTRACTLiquid Chromatography-Mass Spectrometry (LC-MS) untargeted experiments require complex bioinformatic strategies to extract information from the experimental data. Here we discuss the "data preprocessing," the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigation.PMID:39812978 | DOI:10.1007/978-1-0716-4334-1_5

Methods Mol Biol. 2025;2891:67-89. doi: 10.1007/978-1-0716-4334-1_4.ABSTRACTMetabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative. These tasks include, among others, examples to work with chemical formulae, handle and process mass spectrometry data, or calculate similarities between fragment spectra.PMID:39812977 | DOI:10.1007/978-1-0716-4334-1_4

Methods Mol Biol. 2025;2891:53-66. doi: 10.1007/978-1-0716-4334-1_3.ABSTRACTMetabolic profiling performed using untargeted metabolomics of different, complex biological samples aims to apply agnostic/holistic, hypothesis-free, analysis of the small molecules that are present in the analyzed sample. This approach has been the center of major investments and dedicated efforts from the research community for many years. However, limitations and challenges remain, particularly with regard to the validation and the quality of the obtained results. This has led to increasing community engagement, with the formation of think tanks, the establishment of working groups, and the many seminars on quality control (QC) in metabolomics. Here we describe a quality control (QC) protocol used to monitor LC-MS-based metabolomics analysis. A key target is the monitoring of analytical precision. This methodology is described for the analysis of urine but can be applied to different biological matrices, such as various biofluids, cell, and tissue extracts.PMID:39812976 | DOI:10.1007/978-1-0716-4334-1_3

Methods Mol Biol. 2025;2891:15-51. doi: 10.1007/978-1-0716-4334-1_2.ABSTRACTMetabolic profiling (untargeted metabolomics) aims for a global unbiased analysis of metabolites in a cell or biological system. It remains a highly useful research tool used across various analytical platforms. Incremental improvements across multiple steps in the analytical process may have large consequences for the end quality of the data. Thus, this chapter concentrates on which aspects of quality assurance can be implemented by a lab in the (pre-)analytical stages of the analysis to improve the overall end quality of their data. The scope of this chapter is limited to liquid-chromatography-mass spectrometry (LC-MS)-based profiling, which is one of the most widely utilized platforms, although the general principles are applicable to all metabolomics experiments.PMID:39812975 | DOI:10.1007/978-1-0716-4334-1_2

Methods Mol Biol. 2025;2891:1-14. doi: 10.1007/978-1-0716-4334-1_1.ABSTRACTMetabolic profiling continues to develop, and research is now conducted on this topic globally in hundreds of laboratories, from small groups up to national centers and core facilities. Here we briefly provide a perspective on the current status and challenges facing metabolic phenotyping (metabonomics/metabolomics) and consider future directions for this important area of biomarker and systems biology research.PMID:39812974 | DOI:10.1007/978-1-0716-4334-1_1

Appl Biochem Biotechnol. 2025 Jan 15. doi: 10.1007/s12010-024-05138-4. Online ahead of print.ABSTRACTCyanobacteria are advantageous hosts for industrial applications toward achieving sustainable society due to their unique and superior properties such as atmospheric CO2 fixation via photosynthesis. However, cyanobacterial productivities tend to be weak compared to heterotrophic microbes. To enhance them, it is necessary to understand the fundamental metabolic mechanisms unique to cyanobacteria. In cyanobacteria, NADPH and ATP regenerated by linear and cyclic electron transfers using light energy are consumed by CO2 fixation in a central metabolic pathway. The previous study demonstrated that the strain deleted a part of respiratory chain complex (ΔndhF1) perturbed NADPH levels and photosynthetic activity in Synechocystis sp. PCC 6803. It is expected that disruption of ndhF1 would result in a decrease in the function of cyclic electron transfer, which controls the ATP/NAD(P)H production ratio properly. In this study, we evaluated the effects of ndhF1 deletion on central metabolism and photosynthesis by 13C-metabolic flux analysis. As results of culturing the control and ΔndhF1 strains in a medium containing [1,2-13C] glucose and estimating the flux distribution, CO2 fixation rate by RuBisCO was decreased to be less than half in the ΔndhF1 strain. In addition, the regeneration rate of NAD(P)H and ATP by the photosystem, which can be estimated from the flux distribution, also decreased to be less than half in the ΔndhF1 strain, whereas no significant difference was observed in ATP/NAD(P)H production ratio between the control and the ΔndhF1 strains. Our result suggests that the ratio of utilization of cyclic electron transfer is not reduced in the ΔndhF1 strain unexpectedly.PMID:39812922 | DOI:10.1007/s12010-024-05138-4

Acta Diabetol. 2025 Jan 15. doi: 10.1007/s00592-024-02428-5. Online ahead of print.ABSTRACTOBJECTIVE: The objective is to investigate the differences in urinary organic acid (OA) profiles and metabolism between healthy control (HC) pregnant women and those with gestational diabetes mellitus (GDM) during the second trimester and third trimester of pregnancy.METHODS: A total of 66 HC pregnant women and 32 pregnant women with GDM were assessed for 107 hydrophilic metabolites in urine samples collected during the second and third trimester of pregnancy using tandem mass spectrometry. The urine OA profiles for each group were obtained, and metabolomic analysis and discussion were conducted.RESULTS: This study identified a total of 50 metabolic biomarkers. In the third trimester of pregnancy, short-chain dicarboxylic acids (DCAs) and tryptophan (Trp)-related metabolites were significantly upregulated in the urine of both the HC group and the GDM group. Comparatively, the glycine (Gly) levels and related synthetic precursor metabolites were lower in the GDM2 group. The overall dietary polyphenol metabolic intermediates level in the GDM group was lower than in the HC group. Among the pathways enriched for differentially expressed metabolites, the predominant metabolic pathway in the GDM group was the citric acid cycle. In contrast, in the HC group, it was the metabolism of alanine, aspartate, and glutamate.CONCLUSIONS: The study reveals the differences in metabolomics between pregnant women with HC and those with GDM, identifying several metabolites associated with the occurrence and development of GDM. Demonstrating the presence of abnormal mitochondrial and peroxisomal functions at the metabolite level in GDM will contribute to future exploration of the condition.PMID:39812790 | DOI:10.1007/s00592-024-02428-5

Funct Integr Genomics. 2025 Jan 15;25(1):14. doi: 10.1007/s10142-024-01524-7.ABSTRACTLarge-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.PMID:39812750 | DOI:10.1007/s10142-024-01524-7

Microbiol Resour Announc. 2025 Jan 15:e0099724. doi: 10.1128/mra.00997-24. Online ahead of print.ABSTRACTmpactR automates pre-processing of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data from microbiological samples to correct mispicked peaks, resolve inter-sample variation in abundance across technical replicates, account for in-source ion fragmentation, and remove background noise to yield high-quality mass spectrometry features. The package is available through CRAN and GitHub.PMID:39812609 | DOI:10.1128/mra.00997-24

Acta Otolaryngol. 2025 Jan 15:1-14. doi: 10.1080/00016489.2024.2445738. Online ahead of print.ABSTRACTBACKGROUND: Noise-induced hearing loss (NIHL) is a kind of acquired sensorineural hearing loss and has shown an increasing incidence in recent years. Hence, elucidating the exact pathophysiological mechanisms and proposing effective treatment and prevention methods become the top priority. Though a great number of researches have been carried out on NIHL, few of them were focused on metabolites.AIMS/OBJECTIVES: To reveal the metabolomic changes in cochlear fluid after noise injury and search for underlying inner ear biomarkers of NIHL.MATERIAL AND METHODS: In this study, cochlea fluid extracted from guinea pigs after impulse noise exposure were subjected to GC-MS and LC-MS untargeted metabolomics analysis.RESULTS: After impulse noise exposure, 62 significantly changed metabolites in guinea pig cochlea fluid were screened out and deoxyribose 1-phosphate was selected as the key metabolite and underlying biomarker for NIHL. KEGG pathway analysis showed that oxidative phosphorylation, glycerophospholipid metabolism, pyrimidine metabolism and pentose phosphate pathway were significantly changed at all observed time points after noise.CONCLUSIONS AND SIGNIFICANCE: This study effectively promoted the application of metabolomics in hearing research. The pathophysiology process of NIHL in the inner ear was closely connected with oxidative phosphorylation, glycerophospholipid metabolism, pyrimidine metabolism and pentose phosphate pathway and deoxyribose 1-phosphate could be the biomarker for NIHL.PMID:39812472 | DOI:10.1080/00016489.2024.2445738

Physiol Genomics. 2025 Jan 15. doi: 10.1152/physiolgenomics.00040.2024. Online ahead of print.ABSTRACTPurpose: The study aimed to verify the physiological and metabolic parameters associated with the time to task failure (TTF) during cycling exercise performed within the severe-intensity domain. Methods: Forty-five healthy and physically active males participated in two independent experiments. In experiment 1, after a graded exercise test, participants underwent constant work rate cycling efforts (CWR) at 115% of peak power output to assess neuromuscular function (Potentiated twitch) pre- and post-exercise. Experiment 2 was similarl to experiment 1, but with physiological (respiratory parameters, energetic pathway contribution) and metabolic parameters in the blood (gasometry and blood lactate responses) and vastus lateralis muscle tissue (target metabolomic analysis, glycogen content, muscle pH and buffering capacity in vitro) measured instead of neuromuscular function. Results: Experiment 1 evidenced a significant decrease in muscle force with instauration of peripheral fatigability indices and no change in central fatigue indices. Severe-intensity domain exercise in Experiment 2 was accompanied by changes in physiological and metabolic parameters and in blood and muscle parameters. However, the TTF was associated with oxidative contribution (r=0.811, p<0.001), as well as anaerobic capacity (r=0.554, p=0.027), muscle buffering capacity (r=0.792, p=0.035), phosphagen energy contribution (r=0.583, p=0.017), and carnitine changes (r=0.855, p=0.016), but no correlated with electromyographic response, blood acid-base balance, and muscular glycogen content and pH. Conclusion: TTF during CWR exercise within the severe-intensity domain is likely explained by a combination of interacting mechanisms, with oxidative and phosphagen contributions, and muscle buffering capacity suggested as the main peripherals limiting factors to exercise within this exercise intensity domain.PMID:39812441 | DOI:10.1152/physiolgenomics.00040.2024

ACS Appl Mater Interfaces. 2025 Jan 15. doi: 10.1021/acsami.4c17563. Online ahead of print.ABSTRACTHepatocellular carcinoma (HCC) is a common malignancy and generally develops from liver cirrhosis (LC), which is primarily caused by the chronic hepatitis B (CHB) virus. Reliable liquid biopsy methods for HCC screening in high-risk populations are urgently needed. Here, we establish a porous silicon-assisted laser desorption ionization mass spectrometry (PSALDI-MS) technology to profile metabolite information hidden in human serum in a high throughput manner. Serum metabolites can be captured in the pore channel of APTES-modified porous silicon (pSi) particles and well-preserved during storage or transportation. Furthermore, serum metabolites captured in the APTES-pSi particles can be directly detected on the LDI-MS without the addition of an organic matrix, thus greatly accelerating the acquisition of metabolic fingerprints of serum samples. The PSALDI-MS displays the capability of high throughput (5 min per 96 samples), high reproducibility (coefficient of variation <15%), high sensitivity (LOD ∼ 1 pmol), and high tolerance to background salt and proteins. In a multicenter cohort study, 1433 subjects including healthy controls (HC), CHB, LC, and HCC volunteers were enrolled and nontargeted serum metabolomic analysis was performed on the PSALDI-MS platform. After the selection of feature metabolites, a stepwise diagnostic model for the classification of different liver disease stages was constructed by the machine learning algorithm. In external testing, the accuracy of 91.2% for HC, 71.4% for CHB, 70.0% for LC, and 95.3% for HCC was achieved by chemometrics. Preliminary studies indicated that the diagnostic model constructed from serum metabolic fingerprint also displays good predictive performance in a prospective observation. We believe that the combination of PSALDI-MS technology and machine learning may serve as an efficient tool in clinical practice.PMID:39812132 | DOI:10.1021/acsami.4c17563