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Metabolites. 2018 Dec 4;8(4). pii: E88. doi: 10.3390/metabo8040088.

Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome.

Author information

1
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Charmion.Cruickshank-Quinn@ucdenver.edu.
2
Department of Medicine, St. Joseph Hospital, Denver, CO 80218, USA. Laura.Zheng@sclhealth.org.
3
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Kevin.Quinn@ucdenver.edu.
4
Department of Medicine, National Jewish Health, Denver, CO 80206, USA. BowlerR@njhealth.org.
5
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Richard.Reisdorph@ucdenver.edu.
6
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA. Nichole.Reisdorph@ucdenver.edu.

Abstract

Background: Metabolomics is emerging as a valuable tool in clinical science. However, one major challenge in clinical metabolomics is the limited use of standardized guidelines for sample collection and handling. In this study, we conducted a pilot analysis of serum and plasma to determine the effects of processing time and collection tube on the metabolome. Methods: Blood was collected in 3 tubes: Vacutainer serum separator tube (SST, serum), EDTA (plasma) and P100 (plasma) and stored at 4 degrees for 0, 0.5, 1, 2, 4 and 24 h prior to centrifugation. Compounds were extracted using liquid-liquid extraction to obtain a hydrophilic and a hydrophobic fraction and analyzed using liquid chromatography mass spectrometry. Differences among the blood collection tubes and sample processing time were evaluated (ANOVA, Bonferroni FWER ≤ 0.05 and ANOVA, Benjamini Hochberg FDR ≤ 0.1, respectively). Results: Among the serum and plasma tubes 93.5% of compounds overlapped, 382 compounds were unique to serum and one compound was unique to plasma. There were 46, 50 and 86 compounds affected by processing time in SST, EDTA and P100 tubes, respectively, including many lipids. In contrast, 496 hydrophilic and 242 hydrophobic compounds differed by collection tube. Forty-five different chemical classes including alcohols, sugars, amino acids and prenol lipids were affected by the choice of blood collection tube. Conclusion: Our results suggest that the choice of blood collection tube has a significant effect on detected metabolites and their overall abundances. Perhaps surprisingly, variation in sample processing time has less of an effect compared to collection tube; however, a larger sample size is needed to confirm this.

KEYWORDS:

blood; clinical; collection tubes; lipidomics; metabolomics; plasma; serum

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