Proteomics researchers today face an interesting challenge: how to choose among the dozens of data processing and analysis pipelines available for converting tandem mass spectrometry files to protein identifications. Due to the dominance of Orbitrap technology in proteomics in recent history, many researchers have defaulted to the vendor software Proteome Discoverer. Over the fourteen years since the initial release of the software, it has evolved in parallel with the increasingly complex demands faced by proteomics researchers. Today, Proteome Discoverer exists in two distinct forms with both powerful commercial versions and fully functional free versions in use in many labs today. Throughout the 11 main versions released to date, a central theme of the software has always been the ability to easily view and verify the spectra from which identifications are made. This ability is, even today, a key differentiator from other data analysis solutions. In this review I will attempt to summarize the history and evolution of Proteome Discoverer from its first launch to the versions in use today.

Significance: Reliable proteome-wide quantification in large biological cohorts is highly valuable for clinical and pharmaceutical research yet remains extremely challenging despite recent technical advancements. Specifically, elevated missing data levels and compromised quantitative quality are common issues for prevalent methods. Here, we describe an IonStar technique taking advantage of sensitive and selective MS1 ion current-base quantification via innovations in effective and reproducible quantitative feature generation. Compared with several label-free strategies, IonStar showed superior performance in large-cohort analysis, manifested by excellent accuracy/precision, extremely low missing data, and confident discovery of subtle protein changes. In a proof-of-concept study, we demonstrated that IonStar quantified >7,000 unique proteins in 100 brain samples with no missing data and excellent quantitative quality, which has not been achievable by existing methods.

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRAS^G12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRAS^G12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

Alzheimer’s disease (AD) is characterized by accumulation of misfolded proteins. Genetic studies implicate microglia, brain-resident phagocytic immune cells, in AD pathogenesis. As positive effectors, microglia clear toxic proteins, whereas as negative effectors, they release proinflammatory mediators. An imbalance of these functions contributes to AD progression. Polymorphisms of human CD33, an inhibitory microglial receptor, are linked to AD susceptibility; higher CD33 expression correlates with increased AD risk. CD33, also called Siglec-3, is a member of the sialic acid–binding immunoglobulin-type lectin (Siglec) family of immune regulatory receptors. Siglec-mediated inhibition is initiated by binding to complementary sialoglycan ligands in the tissue environment. Here, we identify a single sialoglycoprotein in human cerebral cortex that binds CD33 as well as Siglec-8, the most abundant Siglec on human microglia. The ligand, which we term receptor protein tyrosine phosphatase zeta (RPTPζ)^S3L, is composed of sialylated keratan sulfate chains carried on a minor isoform/glycoform of RPTPζ (phosphacan) and is found in the extracellular milieu of the human brain parenchyma. Brains from human AD donors had twofold higher levels of RPTPζ^S3L than age-matched control donors, raising the possibility that RPTPζ^S3L overexpression limits misfolded protein clearance contributing to AD pathology. Mice express the same structure, a sialylated keratan sulfate RPTPζ isoform, that binds mouse Siglec-F and crossreacts with human CD33 and Siglec-8. Brains from mice engineered to lack RPTPζ, the sialyltransferase St3gal4, or the keratan sulfate sulfotransferase Chst1 lacked Siglec binding, establishing the ligand structure. The unique CD33 and Siglec-8 ligand, RPTPζ^S3L, may contribute to AD progression.

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.