Interindividual variation in the proteome of human peripheral blood mononuclear cells

Abstract

Peripheral blood mononuclear cells (PBMCs) are main actors in inflammatory processes and linked to many diseases, including rheumatoid arthritis, atherosclerosis, asthma, HIV and cancer. Moreover, they seem an interesting 'surrogate tissue' that can be used in biomarker discovery. In order to get a good experimental design for quantitative expression studies, the knowledge of the interindividual variation is an essential part. Therefore, PBMCs were isolated from 24 healthy volunteers (15 males, 9 females, ages 63-86) with no clinical signs of inflammation. The extracted proteins were separated using the two dimensional difference in gel electrophoresis technology (2D-DIGE), and the gel images were processed with the DeCyder 2D software. Protein spots present in at least 22 out of 24 healthy volunteers were selected for further statistical analysis. Determination of the coefficient of variation (CV) of the normalized spot volume values of these proteins, reveals that the total variation of the PBMC proteome varies between 12,99% to 148,45%, with a mean value of 28%. A supplemental look at the causes of technical variation showed that the isolation of PBMCs from whole blood is the factor which influences the experimental variance the most. This isolation should be handled with extra care and an additional washing step would be beneficial. Knowing the extent of variation, we show that at least 10 independent samples per group are needed to obtain statistical powerful data. This study demonstrates the importance of considering variance of a human population for a good experimental design for future protein profiling or biomarker studies.

Figure 1. Overview of high quality protein spots used for statistical analysis.

Deep purple stained gels with overview of high quality spots present in all gels (A) and 11 out of 12 gels (B). Panel C shows the standard Log abundance values of all high quality protein spots in at least 11 out of 12 gels (C). The Gaussian distribution of the SLA values confirms regular data.

Figure 2. Distribution of total variation data.

Distribution of CV values of high quality spots in at least 11 out of 12 gels (A). The CV values of 2 different subsets of high quality spots (B). The curves represents the spots matched in all 12 gels (111 spots) and 11 out of 12 gels (273 spots).

Figure 3. Overview of technical variation.

Panel A shows the technical variation due to Cydye labeling and the electrophoresis process. Panel B shows the technical variance due to sample preparation. Panel C shows the deviation of the Cydye ratio. Ideally, the ratio of Cy3 or Cy5 versus Cy2 should be equal to 1. Any deviation can be directed to technical variation (A). Panel D illustrates the contribution of technical issues of sample preparation to the total variation. The scatter plot indicates that this technical variation has a major role in the total variance.

Figure 4. Influence of coefficient of variation.

Panel A shows the influence of coefficient of variation on sample size, assuming a power of 0,8 and a fold change of 1,5. The higher the variation in a setup, the more replicates are needed to obtain the same power. Panel B illustrates power versus number of replicates when detecting various fold changes with following parameters: CV = 30% and a significance level of 0,05. The more subtile changes one wants to observe, the more replicates are required (B).