Optimizing the Use of Quality Control Samples for Signal Drift Correction in Large-Scale Urine Metabolic Profiling Studies

The evident importance of metabolic profiling for biomarker discovery and hypothesis generation has led to interest in incorporating this technique into large-scale studies, e.g., clinical and molecular phenotyping studies. Nevertheless, these lengthy studies mandate the use of analytical methods with proven reproducibility. An integrated experimental plan for LC–MS profiling of urine, involving sample sequence design and postacquisition correction routines, has been developed. This plan is based on the optimization of the frequency of analyzing identical quality control (QC) specimen injections and using the QC intensities of each metabolite feature to construct a correction trace for all the samples. The QC-based methods were tested against other current correction practices, such as total intensity normalization. The evaluation was based on the reproducibility obtained from technical replicates of 46 samples and showed the feature-based signal correction (FBSC) methods to be superior to other methods, resulting in ∼1000 and 600 metabolite features with coefficient of variation (CV) < 15% within and between two blocks, respectively. Additionally, the required frequency of QC sample injection was investigated and the best signal correction results were achieved with at least one QC injection every 2 h of urine sample injections (<i>n</i> = 10). Higher rates of QC injections (1 QC/h) resulted in slightly better correction but at the expense of longer total analysis time

The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age

<div><p>Metabolomic approaches are increasingly used to identify new disease biomarkers, yet normal values of many plasma metabolites remain poorly defined. The aim of this study was to define the “normal” metabolome in healthy volunteers. We included 800 French volunteers aged between 18 and 86, equally distributed according to sex, free of any medication and considered healthy on the basis of their medical history, clinical examination and standard laboratory tests. We quantified 185 plasma metabolites, including amino acids, biogenic amines, acylcarnitines, phosphatidylcholines, sphingomyelins and hexose, using tandem mass spectrometry with the Biocrates AbsoluteIDQ p180 kit. Principal components analysis was applied to identify the main factors responsible for metabolome variability and orthogonal projection to latent structures analysis was employed to confirm the observed patterns and identify pattern-related metabolites. We established a plasma metabolite reference dataset for 144/185 metabolites. Total blood cholesterol, gender and age were identified as the principal factors explaining metabolome variability. High total blood cholesterol levels were associated with higher plasma sphingomyelins and phosphatidylcholines concentrations. Compared to women, men had higher concentrations of creatinine, branched-chain amino acids and lysophosphatidylcholines, and lower concentrations of sphingomyelins and phosphatidylcholines. Elderly healthy subjects had higher sphingomyelins and phosphatidylcholines plasma levels than young subjects. We established reference human metabolome values in a large and well-defined population of French healthy volunteers. This study provides an essential baseline for defining the “normal” metabolome and its main sources of variation.</p></div

Main characteristics of the healthy volunteers according to sex.

<p>Main characteristics of the healthy volunteers according to sex.</p

Age effect on the metabolite profile of healthy volunteers.

<p><b>(A) Scores plot from OPLS multivariate analysis, cross-validated score plot resulting from OPLS modeling of age.</b> Each age group is represented in different colors, from blue to red. <b>(B) Age S-plot.</b> Metabolites in the upper-right corner correlate positively with age, while those in the bottom-left corner correlate negatively with age. The p axis describes the contribution of each variable to the model. (<b>C) Total sphingomyelins concentration according to the age group. (D) Total phosphatidylcholines concentration according to the age group. (E) Phospholipase activity according to the age group.</b> *p<0.05; ** p<0.01; ***p<0.001.</p

Gender effect on the metabolic profile of healthy volunteers.

<p><b>(A) Scores plot from OPLS multivariate analysis, cross-validated score plot resulting from OPLS modeling of gender.</b> Females are represented with red dots, males with blue dots. <b>(B) Gender S-Plot.</b> Metabolites in the upper-right corner are higher in women; those in the lower-left corner are higher in males. The p axis describes the contribution of each variable to the model. <b>(C) Total sphingomyelins concentration according to sex. (D) Total lysophosphatidylcholines concentration according to sex. (E) Phospholipase activity according to sex.</b> ***p<0.001.</p

Correlation of TBC with metabolome values in healthy volunteers.

<p><b>(A) Scores plot from OPLS multivariate analysis, cross-validated score plot resulting from OPLS modeling of Total Blood Cholesterol.</b> Clinically relevant limits have been set on the TBC concentrations; the red dots represent HVs with TBC above 6.2 mmol/L, the green dots, HVs with TBC below 5.1 mmol/L, and the yellow dots are in-between. <b>(B) TBC S-plot.</b> Metabolites in the upper right corner correlate positively with total blood cholesterol. The p axis describes the contribution of each variable to the model. <b>(C) Total phosphatidylcholines concentration according to total blood cholesterol.</b> TBC below 5.1 mmol/L (green), between 5.11 and 6.2 mmol/L (orange) and above 6.21 mmol/L (red). <b>(D) Total sphingomyelins concentration according to total blood cholesterol. (E) Phospholipase activity according to total blood cholesterol.</b> ** p<0.01;***p<0.001.</p