Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample

Abstract

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope (18)O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the (18)O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on (16)O/(18)O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free (16)O-MS intensity data extracted from the same data sets. The results showed that the (18)O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the (18)O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

Figures

Figure 1

The quantitation strategy. A reference sample was generated by pooling aliquots from all patient samples. All samples were individually processed into peptides. The reference sample was 18O-labeled and an identical aliquot of reference sample was spiked into each of the patient samples prior to LC-MS analysis.

Figure 2

Histogram of mass error distribution (A), NET error distribution (B), and the reproducibility of 18O-reference peptide abundances (C). The pairwise correlation of reference peptide abundances in C were shown in scatter plot style and as Pearson correlation coefficients for 10 selected samples.

Figure 3

Data processing scheme for the dual-quantitation data. All patient samples to be analyzed by LC-MS contained equal amounts of 16O-peptides and 18O-reference peptides. For labeling quantitation, the relative peptide abundances for labeled peptides were automatically reported as 16O/18O ratios. Following normalization, protein abundance ratios were generated by averaging peptide abundance ratios within a given protein. For label-free quantitation, a 16O-reference data set was computed using the median abundance for each peptide across the patients. All data sets were normalized against the reference prior to the conversion to a relative ratio format similar to the labeled data. Similar transformation of protein abundance ratios for label-free and labeled data was applied for direct comparison of the two approaches.

Figure 4

Peptide abundance profiles for C-reactive protein. Seven peptides quantitated commonly by both the label-free and labeling approaches were plotted. The relative abundance ratios are displayed in Log2 transformed format.

Figure 5

Precision of quantitation for the labeling and label-free data. In panels A and B, relative protein abundance ratios comparing two selected patients for all proteins with two or more peptides were plotted with standard deviations of peptide abundance ratios shown for each protein. (C) The distribution of proteins in CV ranges. CV values were calculated based on the standard deviations of multiple peptide abundance ratios belonging to the same protein relative to the protein abundance ratio for a given data set. The CV values for all proteins in individual samples were calculated to generate this plot.

Figure 6

Correlation of relative protein abundance differences between label-free data and the labeled data. (A) Relative protein abundance differences comparing two selected patients. (B) Relative abundance differences comparing two groups of patients (9 females vs 9 males). Log2(Protein abundance ratio) values derived from label-free data are plotted on the x-axis, while values from labeled data are on the y-axis.