A Combined Gas-Phase Separation Strategy for ADP-ribosylated Peptides.

ADP-ribosylation (ADPr) is a post-translational modification that is best studied using mass spectrometry. Method developments that are permissive with low inputs or baseline levels of protein ribosylation represent the next frontier in the field. High-field asymmetric waveform ion mobility spectrometry (FAIMS) reduces peptide complexity in the gas phase, providing a means to achieve maximal ADPr peptide sequencing depth. We therefore investigated the extent to which FAIMS with or without traditional gas-phase fractionation-separation (GPS) can increase the number of ADPr peptides. We examined ADPr peptides enriched from mouse spleens. We gleaned additional insight by also reporting findings from the corresponding non-ADPr peptide contaminants and the peptide inputs for ADPr peptide enrichment. At increasingly higher negative compensation voltages, ADPr peptides were more stable, whereas the non-ADPr peptides were filtered out. A combination of 3 GPS survey scans, each with 8 compensation voltages, resulted in 790 high-confidence ADPr peptides, compared to 90 with GPS alone. A simplified acquisition strategy requiring only two injections corresponding to two MS1 scan ranges coupled to optimized compensation voltage settings provided 402 ADPr peptides corresponding to 234 ADPr proteins. We conclude that our combined GPS strategy is a valuable addition to any ADP-ribosylome workflow. The data are available via ProteomeXchange with identifier PXD040898.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

The RiboMaP Spectral Annotation Method Applied to Various ADP-Ribosylome Studies Including INF-γ-Stimulated Human Cells and Mouse Tissues.

ADP-ribosylation is a post-translational modification that is catalyzed by the ADP-ribosyltransferase enzyme family. Major emphasis to date has been ADP-ribosylation's role in cancer; however, there is growing interest in its role in inflammation and cardiovascular disease. Despite a recent boom in ADP-ribosylation mass spectrometry-based proteomics, there are limited computational resources to evaluate the quality of reported ADP-ribosylated (ADPr) proteins. We recently developed a novel mass spectral annotation strategy (RiboMaP) that facilitates identification and reporting of ADPr peptides and proteins. This strategy can monitor the fragmentation properties of ADPr peptide-unique fragment ions, termed m-ions and p-ions, that in turn provide spectral quality scores for candidate ADP-ribosyl peptides. In this study, we leveraged the availability of publicly available ADP-ribosylome data, acquired on various mass spectrometers, to evaluate the broader applicability of RiboMaP. We observed that fragmentation spectra of ADPr peptides vary considerably across datasets; nonetheless, RiboMaP improves ADPr peptide spectral annotation across all studies. We then reanalyzed our own previously published in vitro ADP-ribosylome data to determine common responses to the pro-inflammatory cytokine, IFN-γ. We conclude that despite these recent advances in the field of ADPr proteomics, studies in the context of inflammation and cardiovascular disease still require further bench-to-informatics workflow development in order to capture ADPr signaling events related to inflammatory pathways.

A Novel Spectral Annotation Strategy Streamlines Reporting of Mono-ADP-ribosylated Peptides Derived from Mouse Liver and Spleen in Response to IFN-γ.

Mass-spectrometry-enabled ADP-ribosylation workflows are developing rapidly, providing researchers a variety of ADP-ribosylome enrichment strategies and mass spectrometric acquisition options. Despite the growth spurt in upstream technologies, systematic ADP-ribosyl (ADPr) peptide mass spectral annotation methods are lacking. HCD-dependent ADP-ribosylome studies are common, but the resulting MS2 spectra are complex, owing to a mixture of b/y-ions and the m/p-ion peaks representing one or more dissociation events of the ADPr moiety (m-ion) and peptide (p-ion). In particular, p-ions that dissociate further into one or more fragment ions can dominate HCD spectra but are not recognized by standard spectral annotation workflows. As a result, annotation strategies that are solely reliant upon the b/y-ions result in lower spectral scores that in turn reduce the number of reportable ADPr peptides. To improve the confidence of spectral assignments, we implemented an ADPr peptide annotation and scoring strategy. All MS2 spectra are scored for the ADPr m-ions, but once spectra are assigned as an ADPr peptide, they are further annotated and scored for the p-ions. We implemented this novel workflow to ADPr peptides enriched from the liver and spleen isolated from mice post 4 h exposure to systemic IFN-γ. HCD collision energy experiments were first performed on the Orbitrap Fusion Lumos and the Q Exactive, with notable ADPr peptide dissociation properties verified with CID (Lumos). The m-ion and p-ion series score distributions revealed that ADPr peptide dissociation properties vary markedly between instruments and within instrument collision energy settings, with consequences on ADPr peptide reporting and amino acid localization. Consequentially, we increased the number of reportable ADPr peptides by 25% (liver) and 17% (spleen) by validation and the inclusion of lower confidence ADPr peptide spectra. This systematic annotation strategy will streamline future reporting of ADPr peptides that have been sequenced using any HCD/CID-based method.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics.

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results.

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.

In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.

Expanding the Use of Spectral Libraries in Proteomics.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

A Novel Lipidomics Workflow for Improved Human Plasma Identification and Quantification Using RPLC-MSn Methods and Isotope Dilution Strategies.

Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)-a fast and efficient in vivo labeling strategy in Pichia pastoris-to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the 13C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4-5 orders of magnitude were observed applying compound-specific quantification with 13C-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.

Building ProteomeTools based on a complete synthetic human proteome.

We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.

The Escherichia coli phosphotyrosine proteome relates to core pathways and virulence.

While phosphotyrosine modification is an established regulatory mechanism in eukaryotes, it is less well characterized in bacteria due to low prevalence. To gain insight into the extent and biological importance of tyrosine phosphorylation in Escherichia coli, we used immunoaffinity-based phosphotyrosine peptide enrichment combined with high resolution mass spectrometry analysis to comprehensively identify tyrosine phosphorylated proteins and accurately map phosphotyrosine sites. We identified a total of 512 unique phosphotyrosine sites on 342 proteins in E. coli K12 and the human pathogen enterohemorrhagic E. coli (EHEC) O157:H7, representing the largest phosphotyrosine proteome reported to date in bacteria. This large number of tyrosine phosphorylation sites allowed us to define five phosphotyrosine site motifs. Tyrosine phosphorylated proteins belong to various functional classes such as metabolism, gene expression and virulence. We demonstrate for the first time that proteins of a type III secretion system (T3SS), required for the attaching and effacing (A/E) lesion phenotype characteristic for intestinal colonization by certain EHEC strains, are tyrosine phosphorylated by bacterial kinases. Yet, A/E lesion and metabolic phenotypes were unaffected by the mutation of the two currently known tyrosine kinases, Etk and Wzc. Substantial residual tyrosine phosphorylation present in an etk wzc double mutant strongly indicated the presence of hitherto unknown tyrosine kinases in E. coli. We assess the functional importance of tyrosine phosphorylation and demonstrate that the phosphorylated tyrosine residue of the regulator SspA positively affects expression and secretion of T3SS proteins and formation of A/E lesions. Altogether, our study reveals that tyrosine phosphorylation in bacteria is more prevalent than previously recognized, and suggests the involvement of phosphotyrosine-mediated signaling in a broad range of cellular functions and virulence.

Comprehensive proteomic analysis of human bile.

Bile serves diverse functions from metabolism to transport. In addition to acids and salts, bile is composed of proteins secreted or shed by the hepatobiliary system. Although there have been previous efforts to catalog biliary proteins, an in-depth analysis of the bile proteome has not yet been reported. We carried out fractionation of non-cancerous bile samples using a multipronged approach (SDS-PAGE, SCX and OFFGEL) followed by MS analysis on an LTQ-Orbitrap Velos mass spectrometer using high resolution at both MS and MS/MS levels. We identified 2552 proteins - the largest number of proteins reported in human bile till date. To our knowledge, there are no previous studies employing high-resolution MS reporting a more detailed catalog of any body fluid proteome in a single study. We propose that extensive fractionation coupled to high-resolution MS can be used as a standard methodology for in-depth characterization of any body fluid. This catalog should serve as a baseline for the future studies aimed at discovering biomarkers from bile in gallbladder, hepatic, and biliary cancers.

Quantitative temporal proteomic analysis of human embryonic stem cell differentiation into oligodendrocyte progenitor cells.

Oligodendrocytes (OLs) are glial cells of the central nervous system, which produce myelin. Cultured OLs provide immense therapeutic opportunities for treating a variety of neurological conditions. One of the most promising sources for such therapies is human embryonic stem cells (ESCs) as well as providing a model to study human OL development. For these purposes, an investigation of proteome level changes is critical for understanding the process of OL differentiation. In this report, an iTRAQ-based quantitative proteomic approach was used to study multiple steps during OL differentiation including neural progenitor cells, glial progenitor cells and oligodendrocyte progenitor cells (OPCs) compared to undifferentiated ESCs. Using a 1% false discovery rate cutoff, ∼3145 proteins were quantitated and several demonstrated progressive stage-specific expression. Proteins such as transferrin, neural cell adhesion molecule 1, apolipoprotein E and wingless-related MMTV integration site 5A showed increased expression from the neural progenitor cell to the OPC stage. Several proteins that have demonstrated evidence or been suspected in OL maturation were also found upregulated in OPCs including fatty acid-binding protein 4, THBS1, bone morphogenetic protein 1, CRYAB, transferrin, tenascin C, COL3A1, TGFBI and EPB41L3. Thus, by providing the first extensive proteomic profiling of human ESC differentiation into OPCs, this study provides many novel proteins that are potentially involved in OL development.

A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry.

Anopheles gambiae is a major mosquito vector responsible for malaria transmission, whose genome sequence was reported in 2002. Genome annotation is a continuing effort, and many of the approximately 13,000 genes listed in VectorBase for Anopheles gambiae are predictions that have still not been validated by any other method. To identify protein-coding genes of An. gambiae based on its genomic sequence, we carried out a deep proteomic analysis using high-resolution Fourier transform mass spectrometry for both precursor and fragment ions. Based on peptide evidence, we were able to support or correct more than 6000 gene annotations including 80 novel gene structures and about 500 translational start sites. An additional validation by RT-PCR and cDNA sequencing was successfully performed for 105 selected genes. Our proteogenomic analysis led to the identification of 2682 genome search-specific peptides. Numerous cases of encoded proteins were documented in regions annotated as intergenic, introns, or untranslated regions. Using a database created to contain potential splice sites, we also identified 35 novel splice junctions. This is a first report to annotate the An. gambiae genome using high-accuracy mass spectrometry data as a complementary technology for genome annotation.

In-depth quantitative cardiac proteomics combining electron transfer dissociation and the metalloendopeptidase Lys-N with the SILAC mouse.

In quantitative proteomics stable isotope labeling has progressed from cultured cells toward the total incorporation of labeled atoms or amino acids into whole multicellular organisms. For instance, the recently introduced (13)C(6)-lysine labeled SILAC mouse allows accurate comparison of protein expression directly in tissue. In this model, only lysine, but not arginine, residues are isotope labeled, as the latter may cause complications to the quantification by in vivo conversion of arginine to proline. The sole labeling of lysines discourages the use of trypsin, as not all peptides will be quantifiable. Therefore, in the initial work Lys-C was used for digestion. Here, we demonstrate that the lysine-directed protease metalloendopeptidase Lys-N is an excellent alternative. As lysine directed peptides generally yield longer and higher charged peptides, alongside the more traditional collision induced dissociation we also implemented electron transfer dissociation in a quantitative stable isotope labeling with amino acid in cell culture workflow for the first time. The utility of these two complementary approaches is highlighted by investigating the differences in protein expression between the left and right ventricle of a mouse heart. Using Lys-N and electron transfer dissociation yielded coverage to a depth of 3749 proteins, which is similar as earlier investigations into the murine heart proteome. In addition, this strategy yields quantitative information on ∼ 2000 proteins with a median coverage of four peptides per protein in a single strong cation exchange-liquid chromatography-MS experiment, revealing that the left and right ventricle proteomes are very similar qualitatively as well as quantitatively.

Proteome profiling of wild type and lumican-deficient mouse corneas.

To elucidate how the deficiency of a major corneal proteoglycan, lumican, affects corneal homeostasis, we used mass spectrometry to derive the proteome profile of the lumican-deficient and the heterozygous mouse corneas and compared these to the wild type corneal proteome. 2108 proteins were quantified in the mouse cornea. Selected proteins and transcripts were investigated by Western blot and quantitative RT-PCR, respectively. We observed major changes in the composition of the stromal extracellular matrix (ECM) proteins in the lumican-deficient mice. Lumican deficiency altered cellular proteins in the stroma and the corneal epithelium. The ECM changes included increases in fibril forming collagen type I, Collagen type VI, fibromodulin, perlecan, laminin ß2, collagen type IV, nidogen/entactin and anchoring collagen type VII in the Lum⁺/⁻ and the Lum⁻/⁻ mouse corneas, while the stromal proteoglycans decorin, biglycan and keratocan were decreased in the Lum⁻/⁻( corneas. Cellular protein changes included increases in alcohol dehydrogenase, superoxide dismutase and decreases in epithelial cytokeratins 8 and 14. We also detected proteins that are novel to the cornea. The proteomes will provide an insight into the lumican-deficient corneal phenotype of stromal thinning and loss of transparency and a better understanding of pathogenic changes in corneal and ocular dystrophies.

Systematic evaluation of alternating CID and ETD fragmentation for phosphorylated peptides.

CID has become a routine method for fragmentation of peptides in shotgun proteomics, whereas electron transfer dissociation (ETD) has been described as a preferred method for peptides carrying labile PTMs. Though both of these fragmentation techniques have their obvious advantages, they also have their own drawbacks. By combining data from CID and ETD fragmentation, some of these disadvantages can potentially be overcome because of the complementarity of fragment ions produced. To evaluate alternating CID and ETD fragmentation, we analyzed a complex mixture of phosphopeptides on an LTQ-Orbitrap mass spectrometer. When the CID and ETD-derived spectra were searched separately, we observed 2504, 491, 2584, and 3249 phosphopeptide-spectrum matches from CID alone, ETD alone, decision tree-based CID/ETD, and alternating CID and ETD, respectively. Combining CID and ETD spectra prior to database searching should, intuitively, be superior to either method alone. However, when spectra from the alternating CID and ETD method were merged prior to database searching, we observed a reduction in the number of phosphopeptide-spectrum matches. The poorer identification rates observed after merging CID and ETD spectra are a reflection of a lack of optimized search algorithms for carrying out such searches and perhaps inherent weaknesses of this approach. Thus, although alternating CID and ETD experiments for phosphopeptide identification are desirable for increasing the confidence of identifications, merging spectra prior to database search has to be carefully evaluated further in the context of the various algorithms before adopting it as a routine strategy.

A comprehensive map of the human urinary proteome.

The study of the human urinary proteome has the potential to offer significant insights into normal physiology as well as disease pathology. The information obtained from such studies could be applied to the diagnosis of various diseases. The high sensitivity, resolution, and mass accuracy of the latest generation of mass spectrometers provides an opportunity to accurately catalog the proteins present in human urine, including those present at low levels. To this end, we carried out a comprehensive analysis of human urinary proteome from healthy individuals using high-resolution Fourier transform mass spectrometry. Importantly, we used the Orbitrap for detecting ions in both MS (resolution 60 000) and MS/MS (resolution 15 000) modes. To increase the depth of our analysis, we characterized both unfractionated as well as lectin-enriched proteins in our experiments. In all, we identified 1,823 proteins with less than 1% false discovery rate, of which 671 proteins have not previously been reported as constituents of human urine. This data set should serve as a comprehensive reference list for future studies aimed at identification and characterization of urinary biomarkers for various diseases.