Unbiased Lipidomics and Metabolomics of Human Brain Samples

Advances of Ion Mobility Platform for Plant Metabolomics

Metabolomics aims to profile the extensive array of metabolites that exists in different types of matrices using modern analytical techniques. These techniques help to separate, identify, and quantify the plethora of chemical compounds at various analytical platforms. Hence, ion mobility spectrometry (IMS) has emerged as an advanced analytical approach, exclusively owing to the 3D separation of metabolites and their isomers. Furthermore, separated metabolites are identified based on their mass fragmentation pattern and CCS (collision cross-section) values. The IMS provides an advanced alternative dimension to separate the isomeric metabolites with enhanced throughput with lesser chemical noise. Thus, the present review highlights the types, factors affecting the resolution, and applications of IMMS (Ion mobility mass spectrometry) for isomeric separations, and ionic contaminants in the plant samples. Furthermore, an overview of IMS-based applications for the identification of plant metabolites (volatile and non-volatile) over the last few decades has been discussed, followed by future assumptions for creating IM-based databases. Such approaches could be significant to accelerate and improve our knowledge of the vast chemical diversity found in plants.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Brain lipidomics: From functional landscape to clinical significance

Lipids are crucial components of cellular function owing to their role in membrane formation, intercellular signaling, energy storage, and homeostasis maintenance. In the brain, lipid dysregulations have been associated with the etiology and progression of neurodegeneration and other neurological pathologies. Hence, brain lipids are emerging as important potential targets for the early diagnosis and prognosis of neurological diseases. This review aims to highlight the significance and usefulness of lipidomics in diagnosing and treating brain diseases. We explored lipid alterations associated with brain diseases, paying attention to organ-specific characteristics and the functions of brain lipids. As the recent advances in brain lipidomics would have been impossible without advances in analytical techniques, we provide up-to-date information on mass spectrometric approaches and integrative analysis with other omic approaches. Last, we present the potential applications of lipidomics combined with artificial intelligence techniques and interdisciplinary collaborative research for treating brain diseases with clinical heterogeneities.

Integrated Metabolomics and Lipidomics Reveal High Accumulation of Glycerophospholipids in Human Astrocytes under the Lipotoxic Effect of Palmitic Acid and Tibolone Protection

Lipotoxicity is a metabolic condition resulting from the accumulation of free fatty acids in non-adipose tissues which involves a series of pathological responses triggered after chronic exposure to high levels of fatty acids, severely detrimental to cellular homeostasis and viability. In brain, lipotoxicity affects both neurons and other cell types, notably astrocytes, leading to neurodegenerative processes, such as Alzheimer (AD) and Parkinson diseases (PD). In this study, we performed for the first time, a whole lipidomic characterization of Normal Human Astrocytes cultures exposed to toxic concentrations of palmitic acid and the protective compound tibolone, to establish and identify the set of potential metabolites that are modulated under these experimental treatments. The study covered 3843 features involved in the exo- and endo-metabolome extracts obtained from astrocytes with the mentioned treatments. Through multivariate statistical analysis such as PCA (principal component analysis), partial least squares (PLS-DA), clustering analysis, and machine learning enrichment analysis, it was possible to determine the specific metabolites that were affected by palmitic acid insult, such as phosphoethanolamines, phosphoserines phosphocholines and glycerophosphocholines, with their respective metabolic pathways impact. Moreover, our results suggest the importance of tibolone in the generation of neuroprotective metabolites by astrocytes and may be relevant to the development of neurodegenerative processes.

Identification of Lipid Heterogeneity and Diversity in the Developing Human Brain

The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.

Challenges in LC-MS-based metabolomics for Alzheimer's disease early detection: targeted approaches versus untargeted approaches

BACKGROUND

Alzheimer's disease (AD) is one of the most common causes of dementia in old people. Neuronal deficits such as loss of memory, language and problem-solving are severely compromised in affected patients. The molecular features of AD are Aβ deposits in plaques or in oligomeric structures and neurofibrillary tau tangles in brain. However, the challenge is that Aβ is only one piece of the puzzle, and recent findings continue to support the hypothesis that their presence is not sufficient to predict decline along the AD outcome. In this regard, metabolomic-based techniques are acquiring a growing interest for either the early diagnosis of diseases or the therapy monitoring. Mass spectrometry is one the most common analytical platforms used for detection, quantification, and characterization of metabolic biomarkers. In the past years, both targeted and untargeted strategies have been applied to identify possible interesting compounds.

AIM OF REVIEW

The overall goal of this review is to guide the reader through the most recent studies in which LC-MS-based metabolomics has been proposed as a powerful tool for the identification of new diagnostic biomarkers in AD. To this aim, herein studies spanning the period 2009-2020 have been reported. Advantages and disadvantages of targeted vs untargeted metabolomic approaches have been outlined and critically discussed.

N-Glycomics of Cerebrospinal Fluid: Method Comparison

Cerebrospinal fluid (CSF) contains valuable biological and neurological information. However, its glycomics analysis is hampered due to the low amount of protein in the biofluid, as has been demonstrated by other glycomics studies using a substantial amount of CSF. In this work, we investigated different N-glycan sample preparation approaches to develop a more sensitive method. These methods, one with an increased amount of buffer solution during the N-glycan release step with a lower amount of sample volume and the other with Filter-Aided N-Glycan Separation (FANGS), were compared with recent work to demonstrate their effectiveness. It was demonstrated that an increased amount of buffer solution showed higher intensity in comparison to the previously published method and FANGS. This suggested that digestion efficiency during the N-glycan release step was not in an optimal condition from the previously published method, and that there is a substantial loss of sample with FANGS when preparing N-glycans from CSF.

Lipidomic UPLC-MS/MS Profiles of Normal-Appearing White Matter Differentiate Primary and Secondary Progressive Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative inflammatory disease where an autoimmune response to components of the central nervous system leads to a loss of myelin and subsequent neurological deterioration. People with MS can develop primary or secondary progressive disease (PPMS, SPMS) and differentiation of the specific differences in the pathogenesis of these two courses, at the molecular level, is currently unclear. Recently, lipidomics studies using human biofluids, mainly plasma and cerebrospinal fluid, have highlighted a possible role for lipids in the initiation and progression of MS. However, there is a lack of lipidomics studies in MS on CNS tissues, such as normal-appearing white matter (NAWM), where local inflammation initially occurs. Herein, we developed an untargeted reverse phase ultra-performance liquid chromatography time of flight tandem mass spectrometry (RP-UPLC-TOF MS^E)-based workflow, in combination with multivariate and univariate statistical analysis, to assess significant differences in lipid profiles in brain NAWM from post-mortem cases of PPMS, SPMS and controls. Groups of eight control, nine PPMS and seven SPMS NAWM samples were used. Correlation analysis of the identified lipids by RP-UPLC-TOF MS^E was undertaken to remove those lipids that correlated with age, gender and post-mortem interval as confounding factors. We demonstrate that there is a significantly altered lipid profile of control cases compared with MS cases and that progressive disease, PPMS and SPMS, can be differentiated on the basis of the lipidome of NAWM with good sensitivity, specificity and prediction accuracy based on receiver operating characteristic (ROC) curve analysis. Metabolic pathway analysis revealed that the most altered lipid pathways between PPMS and SPMS were glycerophospholipid metabolism, glycerophosphatidyl inositol (GPI) anchor synthesis and linoleic acid metabolism. Further understanding of the impact of these lipid alterations described herein associated with progression will provide an increased understanding of the mechanisms underpinning progression and highlight possible new therapeutic targets.

Distinctive lipid signatures of bronchial epithelial cells associated with cystic fibrosis drugs, including Trikafta

In recent years, a number of drugs have been approved for the treatment of cystic fibrosis (CF). Among them, newly released Trikafta, a combination of 3 drugs (VX-661/VX-445/VX-770), holds great promise to radically improve the quality of life for a large portion of patients with CF carrying 1 copy of F508del, the most frequent CF transmembrane conductance regulator (CFTR) mutation. Currently available disease-modifying CF drugs work by rescuing the function of the mutated CFTR anion channel. Recent research has shown that membrane lipids, and the cell lipidome in general, play a significant role in the mechanism of CFTR-defective trafficking and, on the other hand, its rescue. In this paper, by using untargeted lipidomics on CFBE41o- cells, we identified distinctive changes in the bronchial epithelial cell lipidome associated with treatment with Trikafta and other CF drugs. Particularly interesting was the reduction of levels of ceramide, a known molecular player in the induction of apoptosis, which appeared to be associated with a decrease in the susceptibility of cells to undergo apoptosis. This evidence could account for additional beneficial roles of the triple combination of drugs on CF phenotypes.

Prostate and breast cancer cells death induced by xanthohumol investigated with Fourier transform infrared spectroscopy

Fourier Transform Infrared spectroscopy was applied to detect in vitro cell death induced in prostate (PC-3) and breast (T47D) cancer cell lines treated with xanthohumol (XN). After incubation of the cancer cells with XN, specific spectral shifts in the infrared spectra arising from selected cellular components were identified that reflected biochemical changes characteristic for apoptosis and necrosis. Detailed analysis of specific absorbance intensity ratios revealed the compositional changes in the secondary structure of proteins and membrane lipids. In this study, for the first time we examined the changes in these molecular components and linked them to deduce the involvement of molecular mechanisms in the XN-induced death of the selected cancer cells. We showed that XN concentration-dependent changes were attributed to phospholipid ester carbonyl groups, especially in the case of T47D cells, suggesting that XN acts as an inhibitor of cell proliferation. Additionally, we observed distinct changes in the region assigned to the absorption of DNA, which were correlated with a specific marker of cell death and dependent on the XN dose and the type of cancer cells. The microscopic observation and flow cytometry analysis revealed that the decrease in cancer cell viability was mainly related to the induction of necrotic cell death. Moreover, the T47D cells were slightly more sensitive to XN than the PC-3 cells. Considering the results obtained, it can be assumed that apoptosis and necrosis induced by XN may contribute to the anti-proliferative and cytotoxic properties of this flavonoid against cancer cell lines PC-3 and T47D.

Integrated metabolomics and lipidomics profiling of hippocampus reveal metabolite biomarkers in a rat model of chronic unpredictable mild stress-induced depression

Background

Prolonged exposure to stress triggers depression, threatening human health. Thus, to thoroughly understand the underlying pathophysiologic mechanism of chronic unpredictable mild stress (CUMS)-induced depression is urgently needed. Ultra-high-performance liquid chromatography-mass spectroscopy (UPLC-MS)-based lipidomic and metabolomic approaches has been used for discovering metabolite biomarkers to develop new diagnostic and therapeutic means. Thus, our study aimed to conduct integrated metabolomics and lipidomics to identify metabolites and lipids biomarkers in the hippocampus in rat models of CUMS-induced depression.

Methods

Twelve eight-week-old male Sprague-Dawley rats (weight 210±30 g) were randomly distributed to one of the following two groups (n=6): control or CUMS. Established UPLC-MS-based lipidomic and metabolomic approaches were used to determine the metabolites and lipids in the hippocampus of rats. SICMA-P and GraphPad software were performed to discover potential metabolites and lipids biomarkers in the hippocampus of rats between the two groups.

Results

A total of 35 potential metabolites and 171 lipids were identified and found to be mainly related to amino acid and lipid metabolism. These metabolites were involved in different metabolic pathways and connected to each other, which might participate in the occurrence and development of depression.

Conclusions

Our findings underlined the metabolites, lipids and metabolic pathways that were changed in the hippocampus in CUMS compared to the controls, providing novel insights in the metabolism in the hippocampus of rats and revealing the new lipid-related targets.

Metabolomic approaches to study the tumor microenvironment

Tumors are characterized by metabolic dysregulation, reprogramming, and the presence of metabolites, which can act both as energy mediators and signaling messengers. Measuring the concentration and composition of metabolites in the tumor microenvironment can help to better understand the tumor pathology and might improve therapeutic treatments. Metabolomics can provide a description of the physiological and pathological status, as well as help to identify biomarkers of the disease. Additionally, mass spectrometry-based tissue imaging techniques can show the spatial distribution of metabolites. In this chapter we present protocols for the extraction and analysis of metabolites and lipids, with emphasis on liquid chromatography-mass spectrometry and mass spectrometry imaging.