Abstract
Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies. Nevertheless, information on the biological variation of the majority of medium-to-high abundant plasma proteins is largely absent. In this study, we hypothesized that it is possible to generate data on inter-individual biological variation in combination with analytical variation of several hundred abundant plasma proteins, by applying LC-MS/MS in combination with relative quantification using isobaric tagging (10-plex TMT-labeling) to plasma samples. Using this analytical proteomic approach, we analyzed 42 plasma samples prepared in doublets, and estimated the technical, inter-individual biological, and total variation of 265 of the most abundant proteins present in human plasma thereby creating the prerequisites for power analysis and sample size determination in future clinical proteomics studies. Our results demonstrated that only five samples per group may provide sufficient statistical power for most of the analyzed proteins if relative changes in abundances >1.5-fold are expected. Seventeen of the measured proteins are present in the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database, and demonstrated remarkably similar biological CV’s to the corresponding CV’s listed in the EFLM database suggesting that the generated proteomic determined variation knowledge is useful for large-scale determination of plasma protein variations.
Highlights
IntroductionInformation on intra-, inter-individual, and analytical variation for measures of components in plasma is important for various reasons, for example for sample size calculations in clinical experiments, the evaluation of specific analytes as screening-, diagnostic-, or monitoring-marker for disease, and in studies attempting to define the influence of genetic 4.0/).and environmental variations on a specific biochemical component
Information on intra, inter-individual, and analytical variation for measures of components in plasma is important for various reasons, for example for sample size calculations in clinical experiments, the evaluation of specific analytes as screening, diagnostic, or monitoring-marker for disease, and in studies attempting to define the influence of genetic 4.0/).and environmental variations on a specific biochemical component
42 plasma samples from 42 individuals (41 men) made available to the present study from the DANCAVAS trial with low-density lipoprotein (LDL)-values ranging from normal to high levels were used for the determination of inter-individual biological variation of plasma proteins by nano-LCMSMS-based proteomics
Summary
Information on intra-, inter-individual, and analytical variation for measures of components in plasma is important for various reasons, for example for sample size calculations in clinical experiments, the evaluation of specific analytes as screening-, diagnostic-, or monitoring-marker for disease, and in studies attempting to define the influence of genetic 4.0/).and environmental variations on a specific biochemical component. The recent developments in mass spectrometers in terms of sensitivity, scan speed, and dynamic range have enabled the identification and quantification of hundreds to thousands of proteins in a plasma sample in a single proteomic experiment as recently demonstrated [2,3]. This number of proteins analyzed by mass spectrometry-based plasma proteomics is further extended by the introduction of immunoaffinity-based depletions methods [4] and affinityenrichment methods [5] prior to mass spectrometry analysis
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