Lipidomics from sample preparation to data analysis: a primer

Züllig, Thomas; Trötzmüller, Martin; Köfeler, Harald

doi:10.1007/s00216-019-02241-y

Cited by 246 publications

(178 citation statements)

References 140 publications

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“…cer specimens (data not shown). This could be the consequence of an alteration, during cancer progression, to a larger number of molecular species that dominate the mass spectrum in positive ion mode 30 . Studies are ongoing to address this point, with metabolomics approaches, in order to identify molecular species, mainly involved in this process.…”

Section: Discussionmentioning

confidence: 99%

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

Giordano

Takeda

Donadon

et al. 2020

Liver International

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Background and aims Complete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra‐operative assessment of tumour resection margins are time‐consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS‐based system for rapid and objective liver cancer identification and classification. Methods A large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non‐tumours) and 96 patients with mass‐forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non‐tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated. Results The overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non‐tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non‐tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity. Conclusions The herein reported MS‐based system, combined with AI, permits liver cancer identification with high accuracy. Its bench‐top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision‐making process in real‐time with the ultimate aim of improving cancer patient cure.

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Section: Discussionmentioning

confidence: 99%

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

Giordano

Takeda

Donadon

et al. 2020

Liver International

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“…Because of the lack of commercial standards for many lipid species [41] and practical limitations in generating hundreds of calibration curves for each sample batch [42] untargeted LC-MS based lipidomic studies largely rely on comparative studies, i.e. the comparison of lipid profiles from control and treatment, to arrive at conclusions [5,[43][44][45].…”

Section: Discussionmentioning

confidence: 99%

A comprehensive comparison of four methods for extracting lipids from Arabidopsis tissues

et al. 2020

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Background The plant lipidome is highly complex, and the composition of lipids in different tissues as well as their specific functions in plant development, growth and stress responses have yet to be fully elucidated. To do this, efficient lipid extraction protocols which deliver target compounds in solution at concentrations adequate for subsequent detection, quantitation and analysis through spectroscopic methods are required. To date, numerous methods are used to extract lipids from plant tissues. However, a comprehensive analysis of the efficiency and reproducibility of these methods to extract multiple lipid classes from diverse tissues of a plant has not been undertaken. Results In this study, we report the comparison of four different lipid extraction procedures in order to determine the most effective lipid extraction protocol to extract lipids from different tissues of the model plant Arabidopsis thaliana. Conclusion While particular methods were best suited to extract different lipid classes from diverse Arabidopsis tissues, overall a single-step extraction method with a 24 h extraction period, which uses a mixture of chloroform, isopropanol, methanol and water, was the most efficient, reproducible and the least labor-intensive to extract a broad range of lipids for untargeted lipidomic analysis of Arabidopsis tissues. This method extracted a broad range of lipids from leaves, stems, siliques, roots, seeds, seedlings and flowers of Arabidopsis. In addition, appropriate methods for targeted lipid analysis of specific lipids from particular Arabidopsis tissues were also identified.

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“…health and diseases, such as cancer, cardiovascular disorders, neurodegenerative diseases, obesity, and diabetes [1][2][3][4][5][6][7]. A comprehensive list of vast lipids classes and subclasses combined with their common abbreviations can be found in the classification system provided by Lipid MAPS consortium [8].…”

Section: Introductionmentioning

confidence: 99%

Recent progresses of derivatization approaches in the targeted lipidomics analysis by mass spectrometry

Zhao

Zhu

Liu

2020

J of Separation Science

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Lipidomics plays an essential role in the development of an improved understanding of lipids metabolism and the identification of new biomarkers or therapeutic targets of related diseases. The strong analytical power of mass spectrometry and its rapid developments in the respect of instruments and techniques have significantly accelerated the emerging lipidomics and related application fields in biology, medicine, and pharmacy. The strategy of chemical derivatization can remarkably improve the shortcomings of mass spectrometric analytical technologies of shotgun lipidomics and liquid chromatography mass spectrometry, and in the past decade many related studies have been reported for fatty acids, glycerophospholipids, sphingomyelins, monoglycerides, diacylglycerols, long-chain bases, steroids, and so on. Therefore, this review will focus on new chemical derivatization approaches about the research progresses of shotgunbased and liquid chromatography mass spectrometry-based targeted lipidomics (from 2005 to July 2019, most of reports emerged in the past 5 years), and put forward the problems and prospects in this field. It is expected to be helpful for the design and synthesis of new derivatization reagents, especially the outstanding stable isotope labeling derivatization reagents, and the development and application of new chemical derivatization strategies and matched mass spectrometric analysis methods. K E Y W O R D Schemical derivatization, lipidomics, liquid chromatography mass spectrometry, shotgun lipidomics, stable isotope labeling 1838

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Lipidomics from sample preparation to data analysis: a primer

Cited by 246 publications

References 140 publications

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

A comprehensive comparison of four methods for extracting lipids from Arabidopsis tissues

Recent progresses of derivatization approaches in the targeted lipidomics analysis by mass spectrometry

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