首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Thawing as a critical pre-analytical step in the lipidomic profiling of plasma samples: New standardized protocol
Authors:Consuelo Pizarro  Irene Arenzana-Rámila  Nuria Pérez-del-Notario  Patricia Pérez-Matute  José María González-Sáiz
Institution:1. Department of Chemistry, University of La Rioja, C/ Madre de Dios 51, 26006, Logroño, La Rioja, Spain;2. HIV and Associated Metabolic Alterations Unit, Infectious Diseases Area, Center for Biomedical Research of La Rioja (CIBIR), C/ Piqueras, 98, 26006, Logroño, La Rioja, Spain
Abstract:Lipid profiling is a promising tool for the discovery and subsequent identification of biomarkers associated with various diseases. However, data quality is quite dependent on the pre-analytical methods employed. To date, potential confounding factors that may affect lipid metabolite levels after the thawing of plasma for biomarker exploration studies have not been thoroughly evaluated. In this study, by means of experimental design methodology, we performed the first in-depth examination of the ways in which thawing conditions affect lipid metabolite levels. After the optimization stage, we concluded that temperature, sample volume and the thawing method were the determining factors that had to be exhaustively controlled in the thawing process to ensure the quality of biomarker discovery. Best thawing conditions were found to be: 4 °C, with 0.25 mL of human plasma and ultrasound (US) thawing. The new US proposed thawing method was quicker than the other methods we studied, allowed more features to be identified and increased the signal of the lipids. In view of its speed, efficiency and detectability, the US thawing method appears to be a simple, economical method for the thawing of plasma samples, which could easily be applied in clinical laboratories before lipid profiling studies.
Keywords:Biomarker discovery  Experimental design methodology  Lipidomics  Plasma  Thawing  Ultrasound
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号