Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis

Abstract

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.

Figure 1

Flowchart describing the experimental design for analysis of variability for technical components. The variances denoted on the left of the schematic represent the experimentally measured quantities. The right demonstrates utilization of these quantities to isolate variance for each component. This variance is then divided by the total experimental variance, σ₁², to determine the contribution to the total variability.

Figure 2

Comparison of spike-in normalization with global scaling normalization. The first boxplot shows distribution of peptide CV's using raw data, the second boxplot shows peptide CV's normalized using spike-in peptide intensities, the third shows peptide CV's after global scaling normalization. A) digestion replicates B) instrumental stability replicates.

Figure 3

Boxplots showing the distribution of Pearson correlation coefficients among the different levels of replicates.

Figure 4

Boxplots showing the distributions of peptide-level CV's for the 4 technical components. Notches were added to the boxplot to demonstrate that the difference in median between components is statistically significant.

Figure 5

Contributions to variability. A.) Boxplot showing the distribution of contributions to variability calculated for individual peptides. B.) Piechart demonstrating the median contribution to variability for each of the four technical components: extraction 72%, digestion 3.1 %, instrumental stability 8.4%, instrumental variance 16%.

Figure 6

Plot of estimated study size necessary to make a statistically significant measurement vs. % effect size to be detected. A p-value cutoff of 0.05 was used after adjustment using the Bonferroni correction to conservatively account for multiple testing. Calculations are based on comparison between two different study groups using a two-sided t-test. Sample sizes were estimated using Cohen's d and a study power of 0.80.