The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics

Hinz, Christine; Liggi, Sonia; Griffin, Julian L.

doi:10.1016/j.cbpa.2017.10.018

Cited by 84 publications

(73 citation statements)

References 65 publications

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“…Hence, LC techniques for the separation of lipid mixtures have received widespread attention and many preeminent comprehensive reviews on MS‐based lipidomics techniques have been published (Cajka & Fiehn, 2014; Lv, Shi, Wang, & Xu, 2019). In addition, ion mobility spectrometry as a scalable analytical separation technique orthogonal to LC has gained attention and can enhance separations by separating isomeric lipids (Hinz, Liggi, & Griffin, 2018).…”

Section: The Analysis Workflow Of Ms‐based Lipidomicsmentioning

confidence: 99%

Mass spectrometry‐based lipidomics in food science and nutritional health: A comprehensive review

Sun

Wang

Cong

et al. 2020

Comp Rev Food Sci Food Safe

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With the advance in science and technology as well as the improvement of living standards, the function of food is no longer just to meet the needs of survival. Food science and its associated nutritional health issues have been increasingly debated. Lipids, as complex metabolites, play a key role both in food and human health. Taking advantages of mass spectrometry (MS) by combining its high sensitivity and accuracy with extensive selective determination of all lipid classes, MS‐based lipidomics has been employed to resolve the conundrum of addressing both qualitative and quantitative aspects of high‐abundance and low‐abundance lipids in complex food matrices. In this review, we systematically summarize current applications of MS‐based lipidomics in food field. First, common MS‐based lipidomics procedures are described. Second, the applications of MS‐based lipidomics in food science, including lipid composition characterization, adulteration, traceability, and other issues, are discussed. Third, the application of MS‐based lipidomics for nutritional health covering the influence of food on health and disease is introduced. Finally, future research trends and challenges are proposed. MS‐based lipidomics plays an important role in the field of food science, promoting continuous development of food science and integration of food knowledge with other disciplines. New methods of MS‐based lipidomics have been developed to improve accuracy and sensitivity of lipid analysis in food samples. These developments offer the possibility to fully characterize lipids in food samples, identify novel functional lipids, and better understand the role of food in promoting healt.

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Section: The Analysis Workflow Of Ms‐based Lipidomicsmentioning

confidence: 99%

Mass spectrometry‐based lipidomics in food science and nutritional health: A comprehensive review

Sun

Wang

Cong

et al. 2020

Comp Rev Food Sci Food Safe

View full text Add to dashboard Cite

show abstract

“…(15) where c is a constant depending on the voltages used in the transfer optics (obtained from the MassLynx software; note that the resulting corrections are typically of the order of 100 μs in measured drift times of several ms, and uncertainties in the determination of c therefore have a small effect on the final measurement). The derived CCS′ versus t′ data are fitted using an empirically determined power law, 202 CCS′ = A t' N (16) its linearized version, 198 ln CCS' = ln A + N  ln t' (17) or a polynomial 67 CCS′ = A t' 2 + B t' + C…”

Section: V533 Quality Control After Calibrationmentioning

confidence: 99%

“…In 2006, the availability of IM-MS instrumentation from a major MS manufacturer marked the onset of a growing use of IM for the separation, identification, and structural characterization of analytes across diverse fields of science. These include: ionic clusters, [4][5][6][7][8] catalysts, 9,10 supramolecular complexes, [11][12][13][14] small organic molecules including drugs, 15,16 lipids 17,18 and other metabolites, [19][20][21] glycans, [22][23][24] peptides, [25][26][27] proteins, [28][29][30] synthetic polymers, [31][32][33] biomolecules 34,35 and biomolecular complexes [36][37][38] (native IM-MS). However, as IM-MS instruments now commercially available from different manufacturers operate according to different principles of ion mobility separation, 39 this introduces complexity-and potentially confusion-amongst practitioners and readers.…”

Section: Introductionmentioning

confidence: 99%

Recommendations for Reporting Ion Mobility Mass Spectrometry Measurements

Gabelica

Afonso²,

Barran³

et al. 2018

Preprint

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Here we present a guide on ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties on mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N, (ii) ion mobility does not measure surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model, (iii) methods relying on calibration are empirical (and thus may provide method-dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort towards establishing primary standards and reference materials for ion mobility, and provides recommendations to do so. <br><br><br>

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“…An example of this is the popularization of LC‐IMS‐MS that combines different separation modes allowing the analysis of highly complex samples. This is a promising tool in fields such as lipidomics in which samples may contain multiple isobaric lipid species . These new options will broaden the possibility of using these multidimensional techniques in the different omics sciences.…”

Section: Guidelines and Prospectsmentioning

confidence: 99%

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

et al. 2018

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The increasing complexity of omics research has encouraged the development of new instrumental technologies able to deal with these challenging samples. In this way, the rise of multidimensional separations should be highlighted due to the massive amounts of information that provide with an enhanced analyte determination. Both proteomics and metabolomics benefit from this higher separation capacity achieved when different chromatographic dimensions are combined, either in LC or GC. However, this vast quantity of experimental information requires the application of chemometric data analysis strategies to retrieve this hidden knowledge, especially in the case of nontargeted studies. In this work, the most common chemometric tools and approaches for the analysis of this multidimensional chromatographic data are reviewed. First, different options for data preprocessing and enhancement of the instrumental signal are introduced. Next, the most used chemometric methods for the detection of chromatographic peaks and the resolution of chromatographic and spectral contributions (profiling) are presented. The description of these data analysis approaches is complemented with enlightening examples from omics fields that demonstrate the exceptional potential of the combination of multidimensional separation techniques and chemometric tools of data analysis.

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The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics

Cited by 84 publications

References 65 publications

Mass spectrometry‐based lipidomics in food science and nutritional health: A comprehensive review

Mass spectrometry‐based lipidomics in food science and nutritional health: A comprehensive review

Recommendations for Reporting Ion Mobility Mass Spectrometry Measurements

Chemometric Strategies for Peak Detection and Profiling from Multidimensional Chromatography

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