A sensitive and simple targeted proteomics approach to quantify transcription factor and membrane proteins of the unfolded protein response pathway in glioblastoma cells.

Many cellular events are driven by changes in protein expression, measurable by mass spectrometry or antibody-based assays. However, using conventional technology, the analysis of transcription factor or membrane receptor expression is often limited by an insufficient sensitivity and specificity. To overcome this limitation, we have developed a high-resolution targeted proteomics strategy, which allows quantification down to the lower attomol range in a straightforward way without any prior enrichment or fractionation approaches. The method applies isotope-labeled peptide standards for quantification of the protein of interest. As proof of principle, we applied the improved workflow to proteins of the unfolded protein response (UPR), a signaling pathway of great clinical importance, and could for the first time detect and quantify all major UPR receptors, transducers and effectors that are not readily detectable via antibody-based-, SRM- or conventional PRM assays. As transcription and translation is central to the regulation of UPR, quantification and determination of protein copy numbers in the cell is important for our understanding of the signaling process as well as how pharmacologic modulation of these pathways impacts on the signaling. These questions can be answered using our newly established workflow as exemplified in an experiment using UPR perturbation in a glioblastoma cell lines.

Combining De Novo Peptide Sequencing Algorithms, A Synergistic Approach to Boost Both Identifications and Confidence in Bottom-up Proteomics.

Complex mass spectrometry based proteomics data sets are mostly analyzed by protein database searches. While this approach performs considerably well for sequenced organisms, direct inference of peptide sequences from tandem mass spectra, i.e., de novo peptide sequencing, oftentimes is the only way to obtain information when protein databases are absent. However, available algorithms suffer from drawbacks such as lack of validation and often high rates of false positive hits (FP). Here we present a simple method of combining results from commonly available de novo peptide sequencing algorithms, which in conjunction with minor tweaks in data acquisition ensues lower empirical FDR compared to the analysis using single algorithms. Results were validated using state-of-the art database search algorithms as well specifically synthesized reference peptides. Thus, we could increase the number of PSMs meeting a stringent FDR of 5% more than 3-fold compared to the single best de novo sequencing algorithm alone, accounting for an average of 11 120 PSMs (combined) instead of 3476 PSMs (alone) in triplicate 2 h LC-MS runs of tryptic HeLa digestion.