SNIC
SUPR
SNIC SUPR
Prediction and modelling of pre-analytical sampling errors as a strategy to improve plasma LC-MS metabolomics data
Dnr:

SNIC 2018/8-164

Type:

SNAC Small

Principal Investigator:

Rui Zheng

Affiliation:

Uppsala universitet

Start Date:

2018-05-03

End Date:

2019-05-01

Primary Classification:

10610: Bioinformatics and Systems Biology (methods development to be 10203)

Webpage:

Allocation

Abstract

Biobanks are important infrastructures for human samples storage and biomedical researches. Sample storage, handing and processing before sample analysis are closely associated with sample integrity and these meta data should be carefully considered during statistical analysis, especially when involving multiple study centers and legacy samples without complete documentation. This project aims at investigating whether pre-analytical sample variability could be predicted and reduced by modelling alterations in the plasma metabolome, measured by LC/MS, as a function of pre-centrifugation conditions (fasting/non-fasting status, 4-150 min prior to centrifuge, pre-centrifugation delay time at 4 °C, 25 °C, and 37 °C) in 28 individuals. This study may offer the possibility to predict pre-centrifugation treatments in biobanked samples before used in costly down-stream applications.