Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood

Yin, Peiyuan; Peter, Andreas; Franken, Holger; Zhao, Xinjie; Neukamm, Sabine S.; Rosenbaum, Lars; Lucio, Marianna; Zell, Andreas; Häring, Hans-Ulrich; Xu, Guowang; Lehmann, Rainer

doi:10.1373/clinchem.2012.199257

Cited by 237 publications

(239 citation statements)

References 38 publications

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“…But heat, direct sunlight, humidity, and moisture are detrimental to the stability of DBS biospecimens and to analyte recovery (34 ) ( Table 2). Preanalytical collection factors affecting the metabolome and proteome are biospecimen type (whole blood, serum, plasma, DBS) (19 ), tube additive (35)(36)(37)(38)(39)(40), protease inhibitors (41 ), hemolysis (19 ), volume (19 ), polymers contained in different tubes (39,42 ), short-term storage temperature until analysis (38,40,41,43 ), and delay from collection to analysis (38,43,44 ). These factors can cause matrix effects or affect sensitivity, recovery, or resolution.…”

Section: Document If Blood Taken From IV Linementioning

confidence: 99%

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Ellervik

Vaught²

2015

Clinical Chemistry

139

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BACKGROUND Most errors in a clinical chemistry laboratory are due to preanalytical errors. Preanalytical variability of biospecimens can have significant effects on downstream analyses, and controlling such variables is therefore fundamental for the future use of biospecimens in personalized medicine for diagnostic or prognostic purposes. CONTENT The focus of this review is to examine the preanalytical variables that affect human biospecimen integrity in biobanking, with a special focus on blood, saliva, and urine. Cost efficiency is discussed in relation to these issues. SUMMARY The quality of a study will depend on the integrity of the biospecimens. Preanalytical preparations should be planned with consideration of the effect on downstream analyses. Currently such preanalytical variables are not routinely documented in the biospecimen research literature. Future studies using biobanked biospecimens should describe in detail the preanalytical handling of biospecimens and analyze and interpret the results with regard to the effects of these variables.

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Section: Document If Blood Taken From IV Linementioning

confidence: 99%

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Ellervik

Vaught²

2015

Clinical Chemistry

139

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“…Two or 3 samples were collected from each animal. Immediately after collection, the blood samples were placed on ice to inhibit tracer metabolism (18). Centrifugation and collection of the plasma sample were performed as described above.…”

Section: Metabolite Analysismentioning

confidence: 99%

“…Other second-generation high-affinity TSPO ligands, such as 18 F-FEPPA (9), 18 F-PBR06, 18 F-PBR111, 18 F-DPA-714, 11 C-DPA-113, and 11 C-DAA1106 (10)(11)(12), are also to some extent sensitive to this polymorphism, which is a major limitation for their use in clinical studies.…”

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confidence: 99%

Pharmacokinetic Analysis of ¹¹C-PBR28 in the Rat Model of Herpes Encephalitis: Comparison with (R)-¹¹C-PK11195

et al. 2016

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11 C-PBR28 is a second-generation translocator protein (TSPO) tracer with characteristics supposedly superior to the most commonly used tracer for neuroinflammation, (R)-11 C-PK11195. Despite its use in clinical research, no studies on the imaging properties and pharmacokinetic analysis of 11 C-PBR28 in rodent models of neuroinflammation have been published yet. Therefore, this study aimed to evaluate 11 C-PBR28 as a tool for detection and quantification of neuroinflammation in preclinical research and to compare its imaging properties with (R)-11 C-PK11195. The herpes simplex encephalitis (HSE) model was used for induction of neuroinflammation in male Wistar rats. Six or 7 d after virus inoculation, a dynamic 11 C-PBR28 or (R)-11 C-PK11195 PET scan with arterial blood sampling was obtained. Pharmacokinetic modeling was performed on the PET data and analyzed using volumes of interest and a voxel-based approach. Volume-of-interest-and voxel-based analysis of 11 C-PBR28 images showed overexpression of TSPO in brain regions known to be affected in the HSE rat model. 11 C-PBR28 was metabolized faster than (R)-11 C-PK11195, with a metabolic half-life in plasma of 5 and 21 min, respectively. Overall, 11 C-PBR28 was more sensitive than (R)-11 C-PK11195 in detecting neuroinflammation. The binding potential (BP ND ) of 11 C-PBR28 was significantly higher (P , 0.05) in the medulla (176%), pons (146%), midbrain (101%), hippocampus (85%), thalamus (73%), cerebellum (54%), and hypothalamus (49%) in HSE rats than in control rats, whereas (R)-11 C-PK11195 showed a higher BP ND only in the medulla (32%). The BP ND in control animals was not significantly different between tracers, suggesting that the nonspecific binding of both tracers is similar. 11 C-PBR28 was more sensitive than (R)-11 C-PK11195 in the detection of TSPO overexpression in the HSE rat model, because more brain regions with significantly increased tracer uptake could be found, irrespective of the data analysis method used. These results suggest that 11 C-PBR28 should be able to detect more subtle changes in microglial activation in preclinical models of neuroinflammation.

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“…To date, no single existing technique alone can be expected to provide the full picture in any diabetes study with metabolomics. In expectation of future technical improvements, it is thus advisable to store valuable specimens in adequate bio-banks for future re-analysis, ideally aliquoted into small volumes so that no additional thawing cycles are required (Fliniaux et al 2011, Yin et al 2013.…”

Section: Introductionmentioning

confidence: 99%

Metabolic profiling in diabetes

Suhre¹

2014

Journal of Endocrinology

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Metabolic profiling, or metabolomics, has developed into a mature science in recent years. It has major applications in the study of metabolic disorders. This review addresses issues relevant to the choice of the metabolomics platform, study design and data analysis in diabetes research, and presents recent advances using metabolomics in the identification of markers for altered metabolic pathways, biomarker discovery, challenge studies, metabolic markers of drug efficacy and off-target effects. The role of genetic variance and intermediate metabolic phenotypes and its relevance to diabetes research is also addressed.

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Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood

Cited by 237 publications

References 38 publications

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Pharmacokinetic Analysis of ¹¹C-PBR28 in the Rat Model of Herpes Encephalitis: Comparison with (R)-¹¹C-PK11195

Metabolic profiling in diabetes

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Preanalytical Aspects and Sample Quality Assessment in Metabolomics Studies of Human Blood

Cited by 237 publications

References 38 publications

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Preanalytical Variables Affecting the Integrity of Human Biospecimens in Biobanking

Pharmacokinetic Analysis of 11C-PBR28 in the Rat Model of Herpes Encephalitis: Comparison with (R)-11C-PK11195

Metabolic profiling in diabetes

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Product

Resources

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Pharmacokinetic Analysis of ¹¹C-PBR28 in the Rat Model of Herpes Encephalitis: Comparison with (R)-¹¹C-PK11195