Low-pH Solid-Phase Amino Labeling of Complex Peptide Digests with TMTs Improves Peptide Identification Rates for Multiplexed Global Phosphopeptide Analysis

We present a novel tandem mass tag solid-phase amino labeling (TMT-SPAL) protocol using reversible immobilization of peptides onto octadecyl-derivatized (C18) solid supports. This method can reduce the number of steps required in complex protocols, saving time and potentially reducing sample loss. In our global phosphopeptide profiling workflow (SysQuant), we can cut 24 h from the protocol while increasing peptide identifications (20%) and reducing side reactions. Solid-phase labeling with TMTs does require some modification to typical labeling conditions, particularly pH. It has been found that complete labeling equivalent to standard basic pH solution-phase labeling for small and large samples can be achieved on C18 resins under slightly acidic buffer conditions. Improved labeling behavior on C18 compared to that with standard basic pH solution-phase labeling is demonstrated. We analyzed our samples for histidine, serine, threonine, and tyrosine labeling to determine the degree of overlabeling and observed higher than expected levels (25% of all peptide spectral matches (PSMs)) of overlabeling at all of these amino acids (predominantly at tyrosine and serine) in our standard solution-phase labeling protocol. Overlabeling at all of these sites is greatly reduced (4-fold, to 7% of all PSMs) by the low-pH conditions used in the TMT-SPAL protocol. Overlabeling seems to represent a so-far overlooked mechanism causing reductions in peptide identification rates with NHS-activated TMT labeling compared to that with label-free methods. Our results also highlight the importance of searching data for overlabeling when labeling methods are used

Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze–thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2_IV^7‑3). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786−7794, ref ) produced a data-matrix containing quantitative information on 2 559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low <i>t</i>-test significance (<i>p</i>-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient’s blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model

Signaling pathways highly modulated in pancreatic cancer.

<p>This schema summarizes all proteins identified as phosphorylated from the following KEGG signaling pathways; Tight Junction, Adherens Junction and Focal Adhesion. Red stars indicate those proteins identified as phosphorylated in any of 12 cases. Proteins highlighted by coloured circles are known drug targets.</p

Signaling pathways highly modulated in pancreatic cancer.

<p>This schema summarizes all proteins identified as phosphorylated from the following KEGG signaling pathways; Tight Junction, Adherens Junction and Focal Adhesion. Red stars indicate those proteins identified as phosphorylated in any of 12 cases. Proteins highlighted by coloured circles are known drug targets.</p

Significantly modulated phosphopeptides from DNA damage or repair proteins.

<p>All phosphopeptides here were significantly modulated in tumor compared to non-tumor tissue in at least one arm of the SysQuant workflow, and from proteins associated to the GO terms ‘DNA damage’ or ‘DNA repair’. Here we display the Uniprot accession number, the protein name, the global position of the phosphorylation site on the full length protein, the sequence of the quantified phosphopeptides where lower case s/t/y signifies the phosphorylated residues, the median log₂ T/NT ratio over all three arms (non-enriched, TiO₂ & IMAC) in each case, the t-test p-values calculated from all 12 cases for each arm of the workflow, and the median log₂ T/NT ratio from all cases in either the non-enriched arm or TiO₂ arm, or IMAC arm of the workflow.</p

Significantly modulated proliferation proteins.

<p>All proteins in this figure were associated with the GO term ‘proliferation’ and also shown to be significantly (p≤0.05) up- or down- regulated in tumor compared to non-tumor tissue and quantifiable in each case (e.g. all proteins containing NA for any case were excluded from the table). Log₂ T/NT ratios of the non-phosphorylated peptides from each protein were used as surrogates to calculate the relative abundance of the respective proteins. Log₂ T/NT ratios of the non-phosphorylated peptides were averaged over three arms of the workflow (IMAC, TiO₂, Non-enrich).</p

Number of Identified Peptides.

<p>Venn diagrams demonstrate the number of; A: unique phosphopeptide sequences, B: unique non-phosphopeptide sequences, and C: total number of unique peptide sequences identified in the TiO₂, IMAC, and/or non-enrich arm of the SysQuant workflow, across all three TMT 8-plex samples in total (TMT 8-plex-ALL) and individually per TMT 8-plex (TMT 8-plex 1, TMT 8-plex 2, TMT 8-plex 3). D: demonstrates the level of overlap we observe for peptide identifications from analytical run 1, analytical run 2, and analytical run 3 (including time dependent rejection list compiled from identifications from run 1 and 2).</p

Phosphorylation indicates activity of drug targets.

<p>Here are examples of phosphopeptides that contain activator and inhibitor phosphorylation sites on proteins known to be anti-cancer drug targets. Here we display the inhibitory drug, the protein name, the global position of the phosphorylation site on the phosphoprotein, and the sequence of the phosphopeptide. The phosphorylated s/t/y residue in each peptide sequence is in lower case. The log₂ T/NT ratios displayed in each case were median values calculated from all three arms of the workflow. Phosphopeptides in red contain activator phosphorylation sites, while phosphopeptides in blue contain inhibitor phosphorylation sites. Phosphopeptides in black contain phosphorylation sites with no known function.</p

Mesenchymal (Vimentin), general cellularity and blood protein markers.

<p>Log₂ T/NT ratios of the non-phosphorylated peptides from each protein were used as surrogates to calculate the relative abundance of the respective proteins. Log₂ T/NT ratios of the non-phosphorylated peptides were averaged over three arms of the workflow (IMAC, TiO₂, Non-enrich).</p

Significantly modulated phosphopeptides from key signaling proteins.

<p>All phosphopeptides here were significantly modulated in tumor compared to non-tumor tissue in at least one arm of the SysQuant workflow, quantifiable in all 12 cases, and from proteins shown to be associated with the Focal Adhesion (FA), Adherens Junction (AJ), and Tight Junction (TJ) KEGG signaling pathways. Here we display the KEGG pathway associated to the protein, the protein name, the global position of the phosphorylation site on the full length protein, the sequence of the quantified phosphopeptides where lower case s/t/y signifies the phosphorylated residues, the median log₂ T/NT ratio over all three arms (non-enriched, TiO₂ & IMAC) in each case, the t-test p-values calculated from all 12 cases for each arm of the workflow, and the median log₂ T/NT ratio from all cases in either the non-enriched arm or TiO₂ arm, or IMAC arm of the workflow.</p