Effects of processing and storage conditions on charged metabolomic profiles in blood

Hirayama, Akiyoshi; Sugimoto, Masahiro; Suzuki, Asako; Hatakeyama, Yoko; Enomoto, Ayame; Harada, Sei; Soga, Tomoyoshi; Tomita, Masaru; Takebayashi, Toru

doi:10.1002/elps.201400600

Cited by 72 publications

(67 citation statements)

References 20 publications

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“…This corresponds well to previous studies whereby ornithine, proline, and threonine (Davis et al 2009; Breier et al 2014), methionine (Breier et al 2014), pyroglutamic acid (Hirayama et al 2015) hexadecanoic acid (C16:0) stearic acid (C18:0) (Eijsden et al 2005; Breier et al 2014), urea (Heins et al 1995), uric acid (Holland et al 2005; Pinto et al 2014) as well as creatinine (Clark et al 2003) have been found in higher concentrations, while cysteine (Hirayama et al 2015) has been found in lower concentrations, in thawed blood samples. In the present study, metabolites involved in glycerol metabolism (glycerol, glycerol-3-phospahte and glyceric acid) were all observed in higher concentrations in the thawed plasma samples.…”

Section: Discussionsupporting

confidence: 92%

The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples

et al. 2017

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IntroductionPost-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome.ObjectivesThe aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. MethodsIn the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS).ResultsThe thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas.ConclusionsThe comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1196-9) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 92%

The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples

et al. 2017

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“…First, we used chloroform for metabolite extraction and organic solvents, such as lactate and glycerol, were educed from tube or filtration membrane [33]. Thus, these metabolites were not used for subsequent analyses.…”

Section: Discussionmentioning

confidence: 99%

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

Itoi

Sugimoto

Umeda

et al. 2017

IJMS

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This study evaluated the clinical use of serum metabolomics to discriminate malignant cancers including pancreatic cancer (PC) from malignant diseases, such as biliary tract cancer (BTC), intraductal papillary mucinous carcinoma (IPMC), and various benign pancreaticobiliary diseases. Capillary electrophoresis−mass spectrometry was used to analyze charged metabolites. We repeatedly analyzed serum samples (n = 41) of different storage durations to identify metabolites showing high quantitative reproducibility, and subsequently analyzed all samples (n = 140). Overall, 189 metabolites were quantified and 66 metabolites had a 20% coefficient of variation and, of these, 24 metabolites showed significant differences among control, benign, and malignant groups (p < 0.05; Steel–Dwass test). Four multiple logistic regression models (MLR) were developed and one MLR model clearly discriminated all disease patients from healthy controls with an area under receiver operating characteristic curve (AUC) of 0.970 (95% confidential interval (CI), 0.946–0.994, p < 0.0001). Another model to discriminate PC from BTC and IPMC yielded AUC = 0.831 (95% CI, 0.650–1.01, p = 0.0020) with higher accuracy compared with tumor markers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), pancreatic cancer-associated antigen (DUPAN2) and s-pancreas-1 antigen (SPAN1). Changes in metabolomic profiles might be used to screen for malignant cancers as well as to differentiate between PC and other malignant diseases.

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“…The usefulness of the proposed methodology is demonstrated in a comparative study of the metabolic profiles from baker's yeast ( S. cerevisiae ) samples. The development of high‐throughput CE‐MS metabolite measurement technologies has enabled the use of metabolomics by profiling metabolite concentrations in large cohorts of human blood samples . Effects of sampling procedures and storage conditions on the stability of charged metabolomic profiles in plasma and serum were studied.…”

Section: Analytical Applicationsmentioning

confidence: 99%

Recent advances in CE‐MS coupling: Instrumentation, methodology, and applications

2016

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This review focuses on the latest development of microseparation electromigration methods in capillaries and microfluidic devices coupled with MS for detection and identification of important analytes. It is a continuation of the review article on the same topic by Kleparnik (Electrophoresis 2015, 36, 159-178). A wide selection of 161 relevant articles covers the literature published from June 2014 till May 2016. New improvements in the instrumentation and methodology of MS interfaced with capillary or microfluidic versions of zone electrophoresis, isotachophoresis, and isoelectric focusing are described in detail. The most frequently implemented MS ionization methods include electrospray ionization, matrix-assisted desorption/ionization and inductively coupled plasma ionization. Although the main attention is paid to the development of instrumentation and methodology, representative examples illustrate also applications in the proteomics, glycomics, metabolomics, biomarker research, forensics, pharmacology, food analysis, and single-cell analysis. The combinations of MS with capillary versions of electrochromatography, and micellar electrokinetic chromatography are not included.

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Effects of processing and storage conditions on charged metabolomic profiles in blood

Cited by 72 publications

References 20 publications

The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples

The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples

Serum Metabolomic Profiles for Human Pancreatic Cancer Discrimination

Recent advances in CE‐MS coupling: Instrumentation, methodology, and applications

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