Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma

Abstract

A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.

Blood‐Based Biomarkers of Quinpirole Pharmacology: Cluster‐Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

“…Second, although the analytical platforms that have been used in the current study are well‐developed with proven robustness, glycine measured by platform A was described by cluster 3 dynamics, whereas the glycine response as analyzed by platform B was closer to the cluster 2 pattern ( Figure ). Interlaboratory reproducibility is currently a topic of investigation in the field of metabolomics, although early research suggests good robustness of metabolomics platforms toward this type of variation . An explanation could be nonlinearity of the apparatus response given the fact that platform B provided response ratios (analyte peak area/internal standard peak area), whereas platform A presented concentrations.…”

“…Interlaboratory reproducibility is currently a topic of investigation in the field of metabolomics, although early research suggests good robustness of metabolomics platforms toward this type of variation. 38 An explanation could be nonlinearity of the apparatus response given the fact that platform B provided response ratios (analyte peak area/ internal standard peak area), whereas platform A presented concentrations.…”

Validation and automation of a high-throughput multi-targeted method for semi-quantification of endogenous metabolites from different biological matrices using tandem mass spectrometry

“…We sent our internal QC samples to the BIOCRATES Life Sciences AG (Innsbruck, Austria) (N=3 replicates) and to the NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland (Kuopio, Finland) (N=3 replicates). Our QC samples were extracted and analyzed as described previously for the AbsoluteIDQ p180 kit [40] and for the NMR analysis of small molecules [41]. We compared these results with the results obtained from our method (N=4-5 replicates).…”

Review of emerging metabolomic tools and resources: 2015–2016

“…With realization of that fact that, metabolomics is going to play a greater role in personalized and precision medicine , as evident with recent rise in service providers for personalized metabolomic data at an affordable cost, the challenges for integrated workflows for data analysis, interpretation, and integration with other ‐omics and biomedical datasets remains to be addressed. More multicenter and multilaboratory collaborative efforts would help the community toward reproducibility of the protocols and toward large‐scale integration of data generated from different instruments in epidemiological studies . Recently, study considerations for metabolomics in cardiovascular health and disease research were proposed by an academic society and such efforts are necessary in setting standards for the academic, research, and industry community.…”