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

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 effec...

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Autores principales: Cruickshank-Quinn, Charmion, Zheng, Laura K., Quinn, Kevin, Bowler, Russell, Reisdorph, Richard, Reisdorph, Nichole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316012/
https://www.ncbi.nlm.nih.gov/pubmed/30518126
http://dx.doi.org/10.3390/metabo8040088
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author Cruickshank-Quinn, Charmion
Zheng, Laura K.
Quinn, Kevin
Bowler, Russell
Reisdorph, Richard
Reisdorph, Nichole
author_facet Cruickshank-Quinn, Charmion
Zheng, Laura K.
Quinn, Kevin
Bowler, Russell
Reisdorph, Richard
Reisdorph, Nichole
author_sort Cruickshank-Quinn, Charmion
collection PubMed
description 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.
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spelling pubmed-63160122019-01-10 Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome Cruickshank-Quinn, Charmion Zheng, Laura K. Quinn, Kevin Bowler, Russell Reisdorph, Richard Reisdorph, Nichole Metabolites Article 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. MDPI 2018-12-04 /pmc/articles/PMC6316012/ /pubmed/30518126 http://dx.doi.org/10.3390/metabo8040088 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cruickshank-Quinn, Charmion
Zheng, Laura K.
Quinn, Kevin
Bowler, Russell
Reisdorph, Richard
Reisdorph, Nichole
Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title_full Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title_fullStr Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title_full_unstemmed Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title_short Impact of Blood Collection Tubes and Sample Handling Time on Serum and Plasma Metabolome and Lipidome
title_sort impact of blood collection tubes and sample handling time on serum and plasma metabolome and lipidome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316012/
https://www.ncbi.nlm.nih.gov/pubmed/30518126
http://dx.doi.org/10.3390/metabo8040088
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