Influence of collection tubes during quantitative targeted metabolomics studies in human blood samples

Paglia, Giuseppe; Greco, Fabiola M. Del; Sigurdsson, Baldur B.; Rainer, Johannes Michael; Volani, Chiara; Hicks, Andrew A.; Pramstaller, Peter P.; Smarason, Sigurdur

doi:10.1016/j.cca.2018.08.014

Cited by 48 publications

(50 citation statements)

References 32 publications

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“…These methods may provide additional constrains and reasons for choosing one sample type and collection protocol over the other. As described for metabolomics [140][141][142] , a common suggestion is to process samples as quickly as possible (e.g. < 30 min) and store these soon as possible at −80°C.…”

Section: Resultsmentioning

confidence: 99%

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

et al. 2019

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The proteomic analyses of human blood and blood-derived products (e.g. plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g. inter-individual variability), analysis of biospecimen (e.g. sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest whilst enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

et al. 2019

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“…Serum Versus Plasma: Whether serum or plasma is better for blood metabolomic studies has been addressed by direct comparison of metabolomic profiles from the two blood fractions as well as by the sensitivity of each to various pre-analytical variables. Seven studies directly investigated differences in metabolomic findings from serum and plasma samples [9,10,11,12,13,14,15]. Comparing nuclear magnetic resonance (NMR)-derived metabolomic profiles from four independent samples, Teahan et al [9] found that differences between serum and plasma were minimal.…”

Section: Resultsmentioning

confidence: 99%

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

et al. 2019

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Metabolomics provides a comprehensive assessment of numerous small molecules in biological samples. As it integrates the effects of exogenous exposures, endogenous metabolism, and genetic variation, metabolomics is well-suited for studies examining metabolic profiles associated with a variety of chronic diseases. In this review, we summarize the studies that have characterized the effects of various pre-analytical factors on both targeted and untargeted metabolomic studies involving human plasma, serum, and urine and were published through 14 January 2019. A standardized protocol was used for extracting data from full-text articles identified by searching PubMed and EMBASE. For plasma and serum samples, metabolomic profiles were affected by fasting status, hemolysis, collection time, processing delays, particularly at room temperature, and repeated freeze/thaw cycles. For urine samples, collection time and fasting, centrifugation conditions, filtration and the use of additives, normalization procedures and multiple freeze/thaw cycles were found to alter metabolomic findings. Consideration of the effects of pre-analytical factors is a particularly important issue for epidemiological studies where samples are often collected in nonclinical settings and various locations and are subjected to time and temperature delays prior being to processed and frozen for storage.

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“…Serum samples were prepared according to the manufacturer's instructions adding several stable isotope-labelled standards to the samples prior to the derivatization and extraction steps. Using LC/MS, up to 184 metabolites from 5 different compound classes, namely, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, and sphingolipids can be quantified [20]. Sample order was randomized, and pooled quality control (QC) samples (minimum 3) were plated at different positions on the 96-well plate and injected multiple times for covariance variation (CV) calculation for data quality control.…”

Section: Oral Glucose Tolerance Test (Ogtt)mentioning

confidence: 99%

Diet Alters Serum Metabolomic Profiling in the Mouse Model of Chronic Chagas Cardiomyopathy

Lizardo¹,

Ayyappan²,

Ganapathi³

et al. 2019

Disease Markers

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Chagas disease is caused by Trypanosoma cruzi which is endemic in Latin America. T. cruzi infection results in a latent infection with approximately a third of latently infected patients developing chronic Chagas cardiomyopathy (CCM). CCM is a common cause of cardiomyopathy in endemic regions and has a poor prognosis compared to other cardiomyopathies. The factors responsible for the transition from the asymptomatic indeterminate latent stage of infection to CCM are poorly understood. Our previous studies demonstrated that lipid metabolism and diet are important determinants of disease progression. In the present study, we analyzed various serum metabolomic biomarkers such as acylcarnitines, amino acids, biogenic amines, glycerophospholipids, and sphingolipids in murine models of CCM, where the mice specifically develop either left or right ventricular cardiomyopathy based on the diets fed during the indeterminate stage in a murine model of Chagas disease. Our data provide new insights into the metabolic changes that may predispose patients to CCM and biomarkers that may help predict the risk of developing cardiomyopathy from T. cruzi infection. Author Summary. Chronic Chagas cardiomyopathy (CCM) is a parasitic disease prevalent in Latin America. Currently, no effective drugs or vaccines are available to prevent or cure CCM. The factors involved in the disease severity and progression are poorly understood to design new therapeutic interventions. In order to rapidly identify Chagas patients with a higher risk to develop CCM, a new set of biomarkers specific to Chagas disease is needed. We performed serum metabolomic analyses in chronic T. cruzi-infected mice fed on different diets and identified cardiac ventricular-specific metabolite biomarkers that could define CCM severity. In this paper, we present the results of serum metabolomic analyses and discuss its correlations to the diet-induced metabolic regulations in the pathogenesis of CCM in a murine model of Chagas disease.

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Influence of collection tubes during quantitative targeted metabolomics studies in human blood samples

Abstract: Serum samples provide higher sensitivity for biomarker discovery studies. Due to the presence of spurious amount of sarcosine in vacutainer EDTA tubes, plasma EDTA is not suitable for studies requiring accurate quantification of sarcosine.

Cited by 48 publications

References 32 publications

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review

Diet Alters Serum Metabolomic Profiling in the Mouse Model of Chronic Chagas Cardiomyopathy

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