Metabolomics: A useful tool for ischemic stroke research

Li, Wentao; Shao, Chongyu; Li, Chang; Zhou, Huifen; Yu, Li; Yang, Jiehong; Wan, Haitong; He, Yu Guang

doi:10.1016/j.jpha.2023.05.015

Cited by 17 publications

(5 citation statements)

References 205 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different LC modalities, such as reverse-phase hydrophilic interaction liquid chromatography (HILIC) and ion-exchange chromatography, can be employed to separate metabolites based on their distinct properties, such as polarity, size, or ionic charge. Once separated, the metabolites are ionized (commonly using ESI) and then analyzed by MS [ 45 ]. This results in highly specific mass-to-charge ( m / z ) ratios that facilitate the accurate identification of metabolites.…”

Section: Ms Technology For Biomarker Identification In Metabolomics O...mentioning

confidence: 99%

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Zhang,

Shan,

et al. 2024

Molecules

View full text Add to dashboard Cite

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography–tandem mass spectrometry (GC-MS/MS), liquid chromatography–tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

show abstract

Section: Ms Technology For Biomarker Identification In Metabolomics O...mentioning

confidence: 99%

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Zhang,

Shan,

et al. 2024

Molecules

View full text Add to dashboard Cite

show abstract

“…By analyzing alterations in metabolic pathways in the brain tissues of ischemic stroke patients, it is possible to identify the most influential metabolic routes and offer new insights for the discovery of therapeutic targets. The metabolites identified can also serve as indicators used for early diagnosis and disease monitoring ( Sidorov et al, 2019 ; Chumachenko et al, 2022 ; Li et al, 2023 ).…”

Section: Application Of Omics Profiling and Network Pharmacology In D...mentioning

confidence: 99%

Systems-level computational modeling in ischemic stroke: from cells to patients

Li,

Zhao,

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development.

show abstract

“…Metabolomics refers to the science of determining the changing patterns of endogenous metabolites in biological systems subjected to external disturbances or under physiological and pathological conditions by means of modern analytical techniques. − Metabolomics is widely used in many fields related to biology and medicine, including the development of new drugs, determining the actions (pharmacodynamics) and toxicity mechanisms of drugs, disease diagnosis, , disease etiology, pathogenesis, and pathology. , In general, target compounds in metabolomics analysis are hydrophilic small-molecule compounds (<1000 Da), among which >90% contain hydroxyl, carbonyl, amine, and sulfhydryl functional groups. Because such compounds often suffer from poor chromatographic behavior and low mass spectral response, chemical derivatization for specific functional groups has been widely used in metabolomics studies …”

Section: Introductionmentioning

confidence: 99%

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Yuan,

Jin,

Zhang

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

show abstract

Metabolomics: A useful tool for ischemic stroke research

Cited by 17 publications

References 205 publications

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Systems-level computational modeling in ischemic stroke: from cells to patients

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Contact Info

Product

Resources

About