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Validation of metabolomic and lipidomic analyses of human tears using ultra-high-performance liquid chromatography tandem mass spectrometry

脂质体 脂类学 代谢组学 化学 色谱法 串联质谱法 眼泪 代谢组 质谱法 液相色谱-质谱法 生物化学 医学 外科
作者
Sophie Catanese,R.K. Khanna,Antoine Lefèvre,Hugo Alarcan,Pierre‐Jean Pisella,Patrick Emond,Hélène Blasco
出处
期刊:Talanta [Elsevier BV]
卷期号:253: 123932-123932 被引量:10
标识
DOI:10.1016/j.talanta.2022.123932
摘要

To facilitate application in ophthalmological and systemic diseases, there is a need to standardize preanalytical and analytical steps for metabo-lipidomics in human tears. We assessed different methods for each step of the workflow, from sampling to omics profiles acquisition, to provide the largest metabo-lipidomic coverage with the most robust analytical criteria in human tears. We compared reproducibility according to different extraction methods, two sampling techniques, three volumes (2 μL, 5 μL, 10 μL) and eye laterality using ultra-high-performance liquid chromatography coupled with tandem high-resolution mass spectrometry for metabolomic and lipidomic application. The effect of age on the tear metabo-lipidome was also investigated in healthy subjects. The extraction method using methanol/water provided the best results for Schirmer strip metabolomics, while Folch extraction was superior for lipidomics, whatever the sampling method used. When comparing both sampling methods, microcapillary glass tube was superior to Schirmer strip for metabolomics but comparable for lipidomics. The 5 μL volume provided a satisfying metabo-lipidomic coverage. There was no significant difference in tear metabo-lipidome between both eyes in healthy subjects. While most metabolites and lipids where not influenced by age, the phenylalanine-tyrosine-tryptophan pathway, aminoacyl t-RNA biosynthesis, and alanine-aspartate-glutamate metabolism were the 3 principal pathways associated with the 15 most variable metabolites according to age. The current findings will contribute to improve metabo-lipidomic workflow in human tears for the identification of new biomarkers. Preanalytical and analytical standardization is mandatory in order to perform better between-study comparisons and increase the chances of transferring laboratory findings into clinical practice.
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