Abstract

High-quality biological samples are required for the favorable outcome of research studies, and valid data sets are crucial for successful biomarker identification. Prolonged storage of biospecimens may have an artificial effect on compound levels. In order to investigate the potential effects of long-term storage on the metabolome, human ethylenediaminetetraacetic acid (EDTA) plasma samples stored for up to 16 years were analyzed by gas and liquid chromatography-tandem mass spectrometry-based metabolomics. Only 2% of 231 tested plasma metabolites were altered in the first seven years of storage. However, upon longer storage periods of up to 16 years and more time differences of few years significantly affected up to 26% of the investigated metabolites when analyzed within subject age groups. Ontology classes that were most affected included complex lipids, fatty acids, energy metabolism molecules, and amino acids. In conclusion, the human plasma metabolome is adequately stable to long-term storage at −80 °C for up to seven years but significant changes occur upon longer storage. However, other biospecimens may display different sensitivities to long-term storage. Therefore, in retrospective studies on EDTA plasma samples, analysis is best performed within the first seven years of storage.

Highlights

  • High-throughput metabolomics is a powerful tool for systematic metabolite profiling of complex biological systems to understand disease mechanisms and to identify novel clinical biomarkers for diagnosis, prognosis and treatment response

  • We investigated the impact of storage time on the human ethylenediaminetetraacetic acid (EDTA) plasma metabolome in samples that were stored at −80 ◦ C for up to 16 years prior to analysis

  • Due to the study design storage time was correlated with subject’s age and our analysis focused on storage differences within each of the three subject age groups (70, 75 and 80 years of age)

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Summary

Introduction

High-throughput metabolomics is a powerful tool for systematic metabolite profiling of complex biological systems to understand disease mechanisms and to identify novel clinical biomarkers for diagnosis, prognosis and treatment response. In recent years, this technique has been successfully applied for developing biomarkers and gaining deeper knowledge of common and devastating diseases like cancer [1,2], type 2 diabetes [3,4,5] or cardiovascular diseases [6,7,8]. One of the most common types of sample matrix used in these research areas is blood-based material, such as serum or plasma, because of its minimally invasive accessibility and the extensive coverage of the human metabolome.

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