Combinatorial peptide ligand library treatment followed by a dual-enzyme, dual-activation approach on a nanoflow liquid chromatography/orbitrap/electron transfer dissociation system for comprehensive analysis of swine plasma proteome

Abstract

The plasma proteome holds enormous clinical potential, yet an in-depth analysis of the plasma proteome remains a daunting challenge due to its high complexity and the extremely wide dynamic range in protein concentrations. Furthermore, existing antibody-based approaches for depleting high-abundance proteins are not adaptable to the analysis of the animal plasma proteome, which is often essential for experimental pathology/pharmacology. Here we describe a highly comprehensive method for the investigation of the animal plasma proteome which employs an optimized combinatorial peptide ligand library (CPLL) treatment to reduce the protein concentration dynamic range and a dual-enzyme, dual-activation strategy to achieve high proteomic coverage. The CPLL treatment enriched the lower abundance proteins by >100-fold when the samples were loaded in moderately denaturing conditions with multiple loading-washing cycles. The native and the CPLL-treated plasma were digested in parallel by two enzymes (trypsin and GluC) carrying orthogonal specificities. By performing this differential proteolysis, the proteome coverage is improved where peptides produced by only one enzyme are poorly detectable. Digests were fractionated with high-resolution strong cation exchange chromatography and then resolved on a long, heated nano liquid chromatography column. MS analysis was performed on a linear triple quadrupole/orbitrap with two complementary activation methods (collisionally induced dissociation (CID) and electron transfer dissociation). We applied this optimized strategy to investigate the plasma proteome from swine, a prominent animal model for cardiovascular diseases (CVDs). This large-scale analysis results in identification of a total of 3421 unique proteins, spanning a concentration range of 9-10 orders of magnitude. The proteins were identified under a set of commonly accepted criteria, including a precursor mass error of <15 ppm, Xcorr cutoffs, and ≥2 unique peptides at a peptide probability of ≥95% and a protein probability of ≥99%, and the peptide false-positive rate of the data set was 1.8% as estimated by searching the reversed database. CPLL treatment resulted in 55% more identified proteins over those from native plasma; moreover, compared with using only trypsin and CID, the dual-enzyme/activation approach enabled the identification of 2.6-fold more proteins and substantially higher sequence coverage for most individual proteins. Further analysis revealed 657 proteins as significantly associated with CVDs (p < 0.05), which constitute five CVD-related pathways. This study represents the first in-depth investigation of a nonhuman plasma proteome, and the strategy developed here is adaptable to the comprehensive analysis of other highly complex proteomes.

Fig. 1

The scheme of the overall strategy for the comprehensive analysis of swine plasma proteome.

Fig. 2

Comparison of the enrichment of low-abundance proteins by single loading-washing cycle vs. triple loading-washing cycles. Each cycle loaded 60 mg of total proteins (the mass ratio of plasma vs. E. Coli. proteins was 200:1, n=4). Quantitative values were obtained by 2D-DIGE scanning of the labeled E. Coli. The ratios for the most intensive 500 spots matched on both gels are shown.

Fig. 3

The high-resolution SCX chromatograms of (A) tryptic digest mixture of the native swine plasma, (B) tryptic digest mixture of the CPLL-treated swine plasma, (C) GluC digest mixture of the native swine plasma and (D) GluC digest mixture of the CPLL-treated swine plasma. Peptides derived from 1.2 mg of total proteins were injected for each run.

Fig. 4

The charge state distributions of distinct precursor ions detected respectively in (A) tryptic digest and (B) GluC digest of a CPLL-treated swine plasma sample (n=3). The nano-LC/Oribtrap was employed to measure the charge states of MS precursors (resolution: 100,000, m/z range: 310–2000) detected in the digests, and only peaks with charge states determined using isotope clusters were included. A 6-hr gradient on a heated nano-column (75 μmID × 50 cm, 3-μm particle size) was employed to resolve the mixtures efficiently. The statistics of charge states of unique ions were summarized using Rawmeat (Thermofisher Scientific) software package. Ions with differed in m/z by <0.02 and in retention times by < 2 min are grouped as one ion.

Fig. 5

The contributions of treatment approaches, enzyme and activation methods in the identification of swine plasma proteins. (A) The distribution of proteins identified in CPLL-treated plasma vs. native plasma; (B) contributions of enzymes for CPLL-treated plasma; (C) contributions of activation methods for CPLL-treated plasma; (D) contributions of enzymes for native plasma; (E) contributions of activation methods for native plasma. Numbers in the venn diagrams denote the numbers of unique proteins identified.

Fig. 6

Representative CVD-associated proteins identified in this study, which span a wide protein concentration dynamic range. A total of 53 proteins were plotted. As swine data is not available, the protein concentrations data were obtained from previously reported levels in the plasma/serum of healthy humans^–.