The accurate quantification of changes in the abundance of proteins is one of the main applications of proteomics. The maintenance of accuracy can be affected by bias and error that can occur at many points in the experimental process, and normalization strategies are crucial to attempt to overcome this bias and return the sample to its regular biological condition, or normal state. Much work has been published on performing normalization on data post-acquisition with many algorithms and statistical processes available. However, there are many other sources of bias that can occur during experimental design and sample handling that are currently unaddressed. This article aims to cast light on the potential sources of bias and where normalization could be applied to return the sample to its normal state. Throughout we suggest solutions where possible but, in some cases, solutions are not available. Thus, we see this article as a starting point for discussion of the definition of and the issues surrounding the concept of normalization as it applies to the proteomic analysis of biological samples. Specifically, we discuss a wide range of different normalization techniques that can occur at each stage of the sample preparation and analysis process.
BackgroundPreeclampsia is still the leading cause of morbidity and mortality in pregnancy, however there are no current effective therapeutic strategies. This has been impeded by poorly understood pathogeneses of preeclampsia and its multifactorial and heterogeneous nature. Two phenotypes of preeclampsia have been characterised based on the time of diagnosis, early-onset (EOPE, before 34 weeks’ of gestation) and late-onset (LOPE, from 34 weeks’ of gestation). However, the molecular differences between these two phenotypes are not fully elucidated. ObjectivesThis study aimed to facilitate better understanding of the mechanisms of pathogenesis of EOPE and LOPE, and identify specific biomarkers of preeclampsia.MethodsIn this study, we conducted an untargeted proteomic analyses of plasma samples from pregnant women with EOPE (n=17) and LOPE (n=11), and age, BMI-matched normotensive controls (n=18).ResultsIn total, there were 26 and 20 differentially abundant proteins between EOPE or LOPE, and normotensive controls, respectively. Only inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3) was differentially abundant and 11 proteins were commonly shared between EOPE and LOPE. A series of angiogenic proteins, including insulin-like growth factor-binding protein 4 (IGFBP4; EOPE: FDR =0.30 x 10-3 and LOPE: FDR =3.96 x 10-3) and histidine-rich glycoprotein (HRG), were significantly perturbed in both phenotypes (EOPE: FDR=7.8 x 10-3; LOPE: FDR =4.1 x 10-3). ConclusionsOverall, proteins associated with lipid metabolism were the key proteins perturbed in EOPE, however, ECM proteins had a more pronounced role in LOPE. The homeostasis-related pathway including platelet activation, signalling and aggregation were more perturbed in EOPE than LOPE.
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