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Manifestations and mechanisms of central nervous system injury brought on by SARS-CoV-2.
r and proved very useful to the surgeon in both the preoperative surgical planning, patient and family education and operative phases. Future studies will be planned to evaluate surgery procedure duration and other outcomes.Mass spectrometry is a powerful analytical technique used to identify unknown compounds, to quantify known compounds, and to elucidate the structure and chemical properties of molecules. Nevertheless, the transfer of data from one instrument to another is one of the main problems, and obtaining the same or similar information from an analogous instrument but from a different manufacturer or even with the same instrument after carrying out the analyses in different times spacing is not possible. Hence, a general methodology to provide a chromatographic signal (or chromatogram) independent of the instrument is needed. In this sense, this book chapter describes the standardization procedure of chromatographic signals obtained from mass spectrometry platforms to obtain instrument-agnostic chromatographic signals for the determination of standard retention scores. This parameter may be used for the quantification of compounds when different mass spectrometry platforms coupled to ultrahigh-performance liquid chromatography are employed.Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. ALK5 Inhibitor II Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds less then 1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.Metabolites represent the most downstream level of the cellular organization. Hence, an in vitro untargeted metabolomics approach is extremely valuable to deepen the understanding of how endogenous metabolites in cells are altered under a given biological condition. This chapter describes a robust liquid chromatography-high-resolution mass spectrometry-based metabolomics and lipidomics platform applied to cell culture extracts. The analytical workflow includes an optimized sample preparation procedure to cover a wide range of metabolites using liquid-liquid extraction and validated instrumental operation procedures with the implementation of comprehensive quality assurance and quality control measures to ensure high reproducibility. The lipidomics platform is based on reversed-phase liquid chromatography for the separation of slightly polar to apolar metabolites and covers a broad range of lipid classes, while the metabolomics platform makes use of two hydrophilic interaction liquid chromatography methods for the separation of polar metabolites, such as organic acids, amino acids, and sugars. The chapter focuses on the analysis of cultured HepaRG cells that are derived from a human hepatocellular carcinoma; however, the sample preparation and analytical platforms can easily be adapted for other types of cells.Extracellular vesicles (EVs) are secreted by cells and can be found in biological fluids (e.g., blood, saliva, urine, cerebrospinal fluid, and milk). EV isolation needs to be optimized carefully depending on the type of biofluid and tissue. Human milk (HM) is known to be a rich source of EVs, and they are thought to be partially responsible for the benefits associated with breastfeeding. Here, a workflow for the isolation and lipidomic analysis of HM-EVs is described. The procedure encompasses initial steps such as sample collection and storage, a detailed description for HM-EV isolation by multistage ultracentrifugation, metabolite extraction, and analysis by liquid chromatography coupled to mass spectrometry, as well as data analysis and curation.Multiple sclerosis is a demyelinating disease of the central nervous system characterized by the loss of the myelin sheath-the nonconductive membrane surrounding neuronal axons. Demyelination interrupts neuronal transmission, which can impair neurological pathways and present a variety of neurological deficits. Prolonged demyelination can damage neuronal axons resulting in irreversible neuronal damage. Efforts have been made to identify agents that can promote remyelination. However, the assessment of remyelination that new therapies promote can be challenging. The method described in this chapter addresses this challenge by using isobaric C13-histidine as a tag for monitoring its incorporation into myelin proteins and thus monitoring the remyelination process.Imaging mass spectrometry (IMS) allows for visualization of the spatial distribution of proteins, lipids, and other metabolites in a targeted or untargeted approach. The identification of compounds through mass spectrometry combined with the mapping of compound distribution in the sample establishes IMS as a powerful tool for metabolomics. IMS analysis for serotonin will allow researchers to pinpoint areas of deficiencies or accumulations associated with ocular disorders such as serotonin selective reuptake inhibitor optic neuropathy. Furthermore, L-DOPA has shown great promise as a therapeutic approach for disorders such as age-related macular degeneration, and IMS allows for localization, and relative magnitudes, of L-DOPA in the eye. We describe here an end-to-end approach of IMS from sample preparation to data analysis for serotonin and L-DOPA analysis.Aqueous humor (AH) is a transparent fluid that fills the anterior segment of the eye. The composition and level of metabolites in AH are important for understanding its physiology and changes caused by the occurrence of eye disease. A simple method for the preparation and analysis of AH samples was developed using the liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique. The analyses were performed using two types of chromatography reversed-phase liquid chromatography-mass spectrometry (LC-RP-MS) and hydrophilic interaction liquid chromatography-mass spectrometry (LC-HILIC-MS), in the sample prepared by one protocol.Cholesterol is an essential lipid molecule for several biological functions including the proper functioning of cell membranes, lipoproteins, and lipid rafts, as well as the synthesis of bile acids, vitamin D, and steroid hormones. Cholesterol can be extracted from liver tissue by multiple methods of lipid extraction. Subsequently, gas chromatography-mass spectrometry (GC-MS) can be used to obtain the highest level of sensitivity and selectivity in the analysis of cholesterol. This chapter describes two methods of lipid extraction for liver tissue, Bligh and Dyer and methyl tertiary butyl ether (MTBE), followed by an analysis with GC-MS.Metabolomics continues to progress, but obstacles remain. The preservation of metabolites in the target tissue and gathering information on the current metabolic state of the organism of interest proves challenging. Robustness, reproducibility, and reliable quantification are necessary for confident metabolite identification and should always be considered for effective biomarker discovery. Recent advancements in analytical platforms, techniques, and data analysis make metabolomics a promising omics for significant research. However, there is no single approach to effectively capturing the metabolome. Coupling separation techniques may improve the power of the analysis and facilitate confident metabolite identification, especially when performing untargeted metabolomics. In this chapter, we will present an untargeted metabolomic analysis of brain tissue from C57BL/6 mice using two UHPLC-MS methods based on reversed-phase and HILIC chromatography.In this chapter, we describe a metallomics method based on protein precipitation under non-denaturing conditions and further analysis by inductively coupled plasma mass spectrometry for high-throughput metal speciation in plasma and erythrocyte samples. This methodology enables to study the total multielemental profile of these biological matrices, as well as to quantify the metal fractions conforming the metallometabolome and the metalloproteome. Furthermore, the analytical coverage comprises several essential and toxic metal elements, namely aluminum, arsenic, cadmium, cobalt, chromium, copper, iron, lithium, manganese, molybdenum, nickel, lead, selenium, vanadium, and zinc. Altogether, the metallomics method here proposed represents an excellent approach to comprehensively characterize the metal biodistribution in human peripheral blood, which would enable to decipher the role of metal homeostasis in health and disease, and particularly in childhood obesity.The circulating metabolome of human peripheral blood provides valuable information to investigate the molecular mechanisms underlying the development of diseases and to discover candidate biomarkers. In particular, erythrocytes have been proposed as potential systemic indicators of the metabolic and redox status of the organism. To accomplish wide-coverage metabolomics analysis, the combination of complementary analytical techniques is necessary to manage the physicochemical complexity of the human metabolome. Herein, we describe an untargeted metabolomics method to capture the plasmatic and erythroid metabolomes based on ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry, combining reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography. The method provides comprehensive metabolomics fingerprinting of plasma and erythrocyte samples, thereby enabling the elucidation of the distinctive metabolic disturbances behind childhood obesity and associated comorbidities, such as insulin resistance.The simultaneous analysis of cationic and anionic metabolites using capillary electrophoresis-mass spectrometry (CE-MS) has been considered challenging, as often two different analytical methods are required. Although CE-MS methods for cationic metabolite profiling have already shown good performance metrics, the profiling of anionic metabolites often results in relatively low sensitivity and poor repeatability caused by problems related to unstable electrospray and corona discharge when using reversed CE polarity and detection by MS in negative ionization mode. In this protocol, we describe a chemical derivatization procedure that provides a permanent positive charge to acidic metabolites, thereby allowing us to profile anionic metabolites by CE-MS using exactly the same separation conditions as employed for the analysis of basic metabolites. The utility of the overall approach is demonstrated for the analysis of energy metabolism-related metabolites in low numbers of HepG2 cells.
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