Mass Spectrometry-Based Cellular Metabolomics: Current Approaches, Applications, and Future Directions

Domenick, Taylor M; Gill, Emily L.; Vedam‐Mai, Vinata; Yost, Richard A.

doi:10.1021/acs.analchem.0c04363

Cited by 32 publications

(22 citation statements)

References 225 publications

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“…Among these small molecules, bile acids are of particular interest in NASH given their potent roles in mediating metabolic functions [48], as illustrated by the fact that several agonists of the bile acid receptor-Farnesoid X receptor (FXR) and its downstream target FGF19 are in phase I and II trials in treating NASH [49][50][51]. The structural diversity of the human metabolome poses a major challenge for analytical methods [52] resulting in various analytical approaches suited for detecting different classes of small molecules based on MS: LC-MS, gas chromatography mass spectrometry (GC-MS), imaging mass spectrometry, capillary electrophoresis-mass spectrometry, nuclear magnetic resonance, and Fourier transform infrared spectroscopy [53,54]. MS is the most commonly applied technology in metabolomics for the possibility of structural elucidation based on MS/MS spectra and metabolite annotation with higher confidence [55].…”

Section: Ms-based Metabolomics and Lipidomicsmentioning

confidence: 99%

Defining NASH from a Multi-Omics Systems Biology Perspective

Niu

Sulek

Vasilopoulou

et al. 2021

JCM

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Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

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Section: Ms-based Metabolomics and Lipidomicsmentioning

confidence: 99%

Defining NASH from a Multi-Omics Systems Biology Perspective

Niu

Sulek

Vasilopoulou

et al. 2021

JCM

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“…Wide practical application of liquid chromatography in combination with mass spectrometry has been observed recently in proteomics [ 1 , 2 ] and metabolomics [ 3 , 4 ] as a routine method for the qualitative and quantitative analysis of biological samples. For example, when optimizing expression, performing quality control, or studying pharmacokinetics of recombinant proteins, it is crucial that the best conditions for production or analysis of the drug products are found [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Skyline for Analysis of Protein–Protein Interactions In Vivo

Kulyyassov

2021

Molecules

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Quantitative and qualitative analyses of cell protein composition using liquid chromatography/tandem mass spectrometry are now standard techniques in biological and clinical research. However, the quantitative analysis of protein–protein interactions (PPIs) in cells is also important since these interactions are the bases of many processes, such as the cell cycle and signaling pathways. This paper describes the application of Skyline software for the identification and quantification of the biotinylated form of the biotin acceptor peptide (BAP) tag, which is a marker of in vivo PPIs. The tag was used in the Proximity Utilizing Biotinylation (PUB) method, which is based on the co-expression of BAP-X and BirA-Y in mammalian cells, where X or Y are interacting proteins of interest. A high level of biotinylation was detected in the model experiments where X and Y were pluripotency transcription factors Sox2 and Oct4, or heterochromatin protein HP1γ. MRM data processed by Skyline were normalized and recalculated. Ratios of biotinylation levels in experiment versus controls were 86 ± 6 (3 h biotinylation time) and 71 ± 5 (9 h biotinylation time) for BAP-Sox2 + BirA-Oct4 and 32 ± 3 (4 h biotinylation time) for BAP-HP1γ + BirA-HP1γ experiments. Skyline can also be applied for the analysis and identification of PPIs from shotgun proteomics data downloaded from publicly available datasets and repositories.

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“…In the past decade, metabolomics has emerged as a powerful tool to understand the metabolic changes, particularly in small molecules (<1,000 Da), that occur in response to pathophysiological conditions (15). To detect changes in cellular metabolites, liquid chromatography in conjunction with mass spectrometry techniques can accurately and effectively detect a large number of isolated cellular metabolites (16). Analysis of changes in these metabolites can allow us to quickly determine the changes in the external environment that have affected the interior of the cell, and thus, more intuitively study and explain many physiological phenomena.…”

Section: Introductionmentioning

confidence: 99%

Stress-related hormone reduces autophagy through the regulation of phosphatidylethanolamine in breast cancer cells

Zhu

Yang

et al. 2021

Ann Transl Med

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Background: An increasing number of studies indicate that adrenergic signaling plays a fundamental role in tumor progression and metastasis induced by chronic stress. However, despite the growing attention, an understanding of the mechanisms linking chronic stress and cancer is still insufficient. Methods: Western blot analysis and transmission electron microscopy (TEM) were used to observe the changes in autophagy level in a breast cancer cell line (MCF-7) after epinephrine treatment. Non-targeted metabolomics was also used to detect MCF-7 metabolites after epinephrine treatment. The xenograft model was used to detect the level of autophagy after epinephrine intervention. Results: The results showed that epinephrine treatment reduced the autophagy level of breast cancer cells. Epinephrine changed the level of phosphatidylethanolamine (PE) in breast cancer cells as detected by nontargeted metabolomics. Epinephrine also changed autophagy in breast cancer cells by decreasing the level of PE in cells. When autophagy decreased, the invasion and migration of breast cancer cells increased in vitro, and the progression of breast cancer accelerated in vivo.Conclusions: These findings suggest that stress-related hormones affect the tumor progression of breast cancer. Therefore, strengthening the emotional management strategies of patients during the process of antitumor treatment as a supplement to the existing treatments may be beneficial.

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Mass Spectrometry-Based Cellular Metabolomics: Current Approaches, Applications, and Future Directions

Cited by 32 publications

References 225 publications

Defining NASH from a Multi-Omics Systems Biology Perspective

Defining NASH from a Multi-Omics Systems Biology Perspective

Application of Skyline for Analysis of Protein–Protein Interactions In Vivo

Stress-related hormone reduces autophagy through the regulation of phosphatidylethanolamine in breast cancer cells

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