Rapid lipidomic profiling based on ultra-high performance liquid chromatography–mass spectrometry and its application in diabetic retinopathy

Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases...

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Veröffentlicht in:Analytical and bioanalytical chemistry 2020-06, Vol.412 (15), p.3585-3594
Hauptverfasser: Xuan, Qiuhui, Zheng, Fujian, Yu, Di, Ouyang, Yang, Zhao, Xinjie, Hu, Chunxiu, Xu, Guowang
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Sprache:eng
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Zusammenfassung:Lipidomics aims to characterize lipid alteration in response to internal or external subtle perturbations in complex biological samples. Lipid abnormality is a major risk factor for many diseases. Large-scale lipidomic studies may offer new insights into the pathophysiological mechanisms of diseases, new opportunities in systems biology, functional biology, and personalized medicine. To this end, a highly efficient and stable lipidomic method is highly in demand. We herein present a rapid and relatively high coverage lipidomic profiling approach based on ultra-high performance liquid chromatography–mass spectrometry by comparing the performance of different chromatographic columns, optimizing the elution gradient and selecting an appropriate data acquisition mode of mass spectra. As a result, a total of 481 lipids were detected from 40 μL serum sample within 13 min, covering 20 common lipid (sub)classes. The developed method was well validated with satisfactory analytical characteristics in linearity, repeatability, stability, and lipid coverage. To show the usefulness, the method was employed to investigate serum lipid profiling of 43 subjects with mild diabetic retinopathy and 44 normal controls, and successfully defined the differential lipids related to diabetic retinopathy. We believe that this rapid method will be beneficial for lipidomic analysis of large-scale clinical samples.
ISSN:1618-2642
1618-2650
DOI:10.1007/s00216-020-02632-6