Optimization of reversed-phase solid-phase extraction for shotgun proteomics analysis.

Reversed-phase solid-phase extraction (SPE) is the method of choice for the purification of proteomics samples. Even though the efficacy of SPE methods is sample type-dependent, the manufacturers' protocols are used in most studies. Using an optimized SPE method can lead to a substantial gain in identification and recovery. In this tutorial, we give a brief introduction to the most important parameters influencing SPE performance, and we present a short workflow (16 measurements) for optimizing the SPE procedure. This is complemented by method performance assessment instructions and a short troubleshooting guide to help users further understand and investigate their SPE methods.

Comparison of solid-phase extraction methods for efficient purification of phosphopeptides with low sample amounts.

Efficient phosphoproteomic analysis of small amounts of biological samples (e.g. tissue biopsies) requires carefully selected enrichment and purification steps prior to the nanoflow HPLC-MS/MS analysis. Solid-phase extraction (SPE) is one of the most commonly used approaches for sample preparation. Several stationary phases are available for peptide SPE purification, however, most of the published methods are not optimized to provide good recoveries of phosphorylated peptides. Our goal was to investigate the performance of 13 self-packed and 3 commercial centrifugal SPE cartridges/spin tips, thus enhancing the efficiency of the phosphoproteomic analysis of small amounts of complex protein mixtures. Eight reversed-phase (RP), five graphite, two ion-exchange, and one hydrophilic-lipophilic balance (HLB) stationary phase were evaluated. Two RP, one graphite, and the HLB self-packed centrifugal SPE tips provided excellent results for the purification of 1 µg tissue and cell line digests. Using these methods, the sample loss was significantly reduced compared to one of the commercial SPE methods, 22-58% more unique phosphopeptides were identified, and the recovery was higher by 132-155%.

Optimized Sample Preparation and Microscale Separation Methods for High-Sensitivity Analysis of Hydrophilic Peptides.

The optimization of solid-phase extraction (SPE) purification and chromatographic separation is usually neglected during proteomics studies. However, the effects on detection performance are not negligible, especially when working with highly glycosylated samples. We performed a comparative study of different SPE setups, including an in-house optimized method and reversed-phase chromatographic gradients for the analysis of highly glycosylated plasma fractions as a model sample for glycopeptide analysis. The in-house-developed SPE method outperformed the graphite-based and hydrophilic interaction liquid chromatography (HILIC) purification methods in detection performance, recovery, and repeatability. During optimization of the chromatography, peak distribution was maximized to increase the peptide detection rate. As a result, we present sample purification and chromatographic separation methods optimized for the analysis of hydrophilic samples, the most important of which is heavily N-glycosylated protein mixtures.

Proteomic Analysis of Lung Cancer Types-A Pilot Study.

Lung cancer is the leading cause of tumor-related mortality, therefore significant effort is directed towards understanding molecular alterations occurring at the origin of the disease to improve current treatment options. The aim of our pilot-scale study was to carry out a detailed proteomic analysis of formalin-fixed paraffin-embedded tissue sections from patients with small cell or non-small cell lung cancer (adenocarcinoma, squamous cell carcinoma, and large cell carcinoma). Tissue surface digestion was performed on relatively small cancerous and tumor-adjacent normal regions and differentially expressed proteins were identified using label-free quantitative mass spectrometry and subsequent statistical analysis. Principal component analysis clearly distinguished cancerous and cancer adjacent normal samples, while the four lung cancer types investigated had distinct molecular profiles and gene set enrichment analysis revealed specific dysregulated biological processes as well. Furthermore, proteins with altered expression unique to a specific lung cancer type were identified and could be the targets of future studies.

Alterations in protein expression and site-specific N-glycosylation of prostate cancer tissues.

Identifying molecular alterations occurring during cancer progression is essential for a deeper understanding of the underlying biological processes. Here we have analyzed cancerous and healthy prostate biopsies using nanoLC-MS(MS) to detect proteins with altered expression and N-glycosylation. We have identified 75 proteins with significantly changing expression during disease progression. The biological processes involved were assigned based on protein-protein interaction networks. These include cellular component organization, metabolic and localization processes. Multiple glycoproteins were identified with aberrant glycosylation in prostate cancer, where differences in glycosite-specific sialylation, fucosylation, and galactosylation were the most substantial. Many of the glycoproteins with altered N-glycosylation were extracellular matrix constituents, and are heavily involved in the establishment of the tumor microenvironment.

Influence of Post-Translational Modifications on Protein Identification in Database Searches.

Comprehensive analysis of post-translation modifications (PTMs) is an important mission of proteomics. However, the consideration of PTMs increases the search space and may therefore impair the efficiency of protein identification. Using thousands of proteomic searches, we investigated the practical aspects of considering multiple PTMs in Byonic searches for the maximization of protein and peptide hits. The inclusion of all PTMs, which occur with at least 2% frequency in the sample, has an advantageous effect on protein and peptide identification. A linear relationship was established between the number of considered PTMs and the number of reliably identified peptides and proteins. Even though they handle multiple modifications less efficiently, the results of MASCOT (using the Percolator function) and Andromeda (the search engine included in MaxQuant) became comparable to those of Byonic, in the case of a few PTMs.