Affinity depletion of abundant proteins from human plasma has become a routine sample preparation strategy in proteomics used prior to protein identification and quantitation. To date, there have been limited published studies comparing the performance of commercially available depletion products. We conducted a thorough evaluation of six depletion columns using 2-DE combined with sophisticated image analysis software, examining the following criteria: (i) efficiency of high-abundance protein depletion, (ii) non-specific removal of other than the targeted proteins and (iii) total number of protein spots detected on the gels following depletion. From all the products investigated, the Seppro IgY system provided the best results. It displayed the greatest number of protein spots on the depleted plasma gels, minimal non-specific binding and high efficiency of abundant protein removal. Nevertheless, the increase in the number of detected spots compared with the second best performing and cheaper multiple affinity removal column (MARC) was not shown to be statistically significant. The ProteoPrep spin column, considered to be the ''deepest'' depletion technique available at the time of conducting the study, surprisingly displayed significantly fewer spots on the flow-through fraction gels compared with both the Seppro and the MARC. The spin column format and low plasma capacity were also found to be impractical for 2-DE. To conclude, we succeeded in providing an overview of the depletion columns performances with regard to the three examined areas. Our study will serve as a reference to other scientists when deciding on the optimal product for their particular projects.
Tissue samples (plasma, saliva, serum or urine) from 169 patients classified as either normal or having one of seven possible diseases are analysed across three 96-well plates for the presences of 37 analytes using cytokine inflammation multiplexed immunoassay panels. Censoring for concentration data caused problems for analysis of the low abundant analytes. Using fluorescence analysis over concentration based analysis allowed analysis of these low abundant analytes. Mixed-effects analysis on the resulting fluorescence and concentration responses reveals a combination of censoring and mapping the fluorescence responses to concentration values, through a 5PL curve, changed observed analyte concentrations. Simulation verifies this, by showing a dependence on the mean florescence response and its distribution on the observed analyte concentration levels. Differences from normality, in the fluorescence responses, can lead to differences in concentration estimates and unreliable probabilities for treatment effects. It is seen that when fluorescence responses are normally distributed, probabilities of treatment effects for fluorescence based t-tests has greater statistical power than the same probabilities from concentration based t-tests. We add evidence that the fluorescence response, unlike concentration values, doesn’t require censoring and we show with respect to differential analysis on the fluorescence responses that background correction is not required.
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