Quantification of Lipids: Model, Reality, and Compromise

Khoury, Spiro; Canlet, Cécile; Lacroix, Marlène Z.; Berdeaux, Olivier; Jouhet, Juliette; Bertrand‐Michel, Justine

doi:10.3390/biom8040174

Cited by 51 publications

(36 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The lipidomics data were pre-processed with MZmine2 26 . Subsequent quality control, post-processing and data analysis were done in R. In brief: (1) lipids were semi-quantified by normalizing the peak areas to internal standards 27 ; (2) systematic day-to-day variation in the measurements was removed by median batch correction 28 , (3) Lipids with more than 20% missing/undetected values were omitted from subsequent analysis; (4) remaining missing values were imputed with the k-nearest neighbour algorithm 29 ; and (5) all values were log-2-transformed to achieve normal-distributed data.…”

Section: Methodsmentioning

confidence: 99%

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

Tofte

Suvitaival

Ahonen

et al. 2019

Sci Rep

View full text Add to dashboard Cite

There is an urgent need for a better molecular understanding of the pathophysiology underlying development and progression of diabetic nephropathy. The aim of the current study was to identify novel associations between serum lipidomics and diabetic nephropathy. Non-targeted serum lipidomic analyses were performed with mass spectrometry in 669 individuals with type 1 diabetes. Cross-sectional associations of lipid species with estimated glomerular filtration rate (eGFR) and urinary albumin excretion were assessed. Moreover, associations with register-based longitudinal follow-up for progression to a combined renal endpoint including ≥30% decline in eGFR, ESRD and all-cause mortality were evaluated. Median follow-up time was 5.0–6.4 years. Adjustments included traditional risk factors and multiple testing correction. In total, 106 lipid species were identified. Primarily, alkyl-acyl phosphatidylcholines, triglycerides and sphingomyelins demonstrated cross-sectional associations with eGFR and macroalbuminuria. In longitudinal analyses, thirteen lipid species were associated with the slope of eGFR or albuminuria. Of these lipids, phosphatidylcholine and sphingomyelin species, PC(O-34:2), PC(O-34:3), SM(d18:1/24:0), SM(d40:1) and SM(d41:1), were associated with lower risk of the combined renal endpoint. PC(O-34:3), SM(d40:1) and SM(d41:1) were associated with lower risk of all-cause mortality while an SM(d18:1/24:0) was associated with lower risk of albuminuria group progression. We report distinct associations between lipid species and risk of renal outcomes in type 1 diabetes, independent of traditional markers of kidney function.

show abstract

Section: Methodsmentioning

confidence: 99%

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

Tofte

Suvitaival

Ahonen

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Great care needs to be taken when quantifying lipids by ESI-MS because, compared to nuclear magnetic resonance spectroscopy (NMR), UV or flame-ionization (FID) ESI-MS detection is much less scalable for a variety of reasons, including the competitive nature of the ESI process and the isotopic distribution and mass dependence of the fragmentation patterns [59]. All of these obstacles can be circumvented by using stable isotope-labeled internal standards that differ from the target compounds in their physical properties (i.e., mass) but not their chemical properties (retention, ionization, fragmentation).…”

Section: Quantitative Aspectsmentioning

confidence: 99%

Lipidomics from sample preparation to data analysis: a primer

2019

View full text Add to dashboard Cite

Lipids are amongst the most important organic compounds in living organisms, where they serve as building blocks for cellular membranes as well as energy storage and signaling molecules. Lipidomics is the science of the large-scale determination of individual lipid species, and the underlying analytical technology that is used to identify and quantify the lipidome is generally mass spectrometry (MS). This review article provides an overview of the crucial steps in MS-based lipidomics workflows, including sample preparation, either liquid-liquid or solid-phase extraction, derivatization, chromatography, ion-mobility spectrometry, MS, and data processing by various software packages. The associated concepts are discussed from a technical perspective as well as in terms of their application. Furthermore, this article sheds light on recent advances in the technology used in this field and its current limitations. Particular emphasis is placed on data quality assurance and adequate data reporting; some of the most common pitfalls in lipidomics are discussed, along with how to circumvent them.

show abstract

Quantification of Lipids: Model, Reality, and Compromise

Cited by 51 publications

References 89 publications

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

Lipidomic analysis reveals sphingomyelin and phosphatidylcholine species associated with renal impairment and all-cause mortality in type 1 diabetes

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

Lipidomics from sample preparation to data analysis: a primer

Contact Info

Product

Resources

About