Peptide Spectral Libraries Research Articles

Reusing Peptide Spectral Reference Libraries to Discover Putative Plasma Biomarkers of Response to Cancer Chemotherapy.

Biofluids such as blood plasma are rich reservoirs of potential biomarkers for disease diagnosis, prognosis, and prediction of treatment response. However, mass spectrometry analysis of circulating plasma proteins remains challenging. The introduction of data-independent acquisition mass spectrometry (DIA-MS) is an important step toward addressing detection of less abundant plasma proteins. Numerous plasma peptide MS/MS spectral library datasets produced from extensive plasma fractionation are accessible from public archives, and these can be repurposed as spectral reference libraries to increase the depth of proteomic analysis when DIA-MS is used. Here we describe the workflow that relies on reusing the existing spectral reference libraries by populating them with locally obtained peptide MS/MS data acquired by DIA-MS. This approach was demonstrated effectively to identify putative plasma biomarkers of response to neoadjuvant chemotherapy in the setting of pancreatic ductal adenocarcinoma (PDAC) (O'Rourke et al., J Proteomics 231:103998, 2021).

Accurate de novo peptide sequencing using fully convolutional neural networks

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow.

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Interrogation of an ovine serum peptide spectral library to annotate ambiguous clinicopathological biomarkers using data-independent acquisition

Background: The use of data-independent data acquisition mass spectrometry (DIA-MS) on biological samples from domestic animals is still uncommon. Here, sequential window acquisition of all theoretical mass spectra (SWATH-MS) – a variant of DIA-MS was used to analyse serum peptides of healthy sheep as compared with serum of sick sheep by interrogating a novel peptide spectral library (PSL). This approach enabled the detection and annotation of a wide range of proteins, than conventional clinical pathology protein assays. Methods: Serum samples from healthy sheep were obtained from a commercial source and normalised to represent a healthy sheep proteome background and then compared with serum samples of sheep suffering from a range of naturally-acquired illnesses submitted to The University of Queensland, Australia. Purified tryptic peptides were subjected to liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a quadrupole time-of-flight instrument (TripleTOF 5600+, SCIEX) set in a cyclic data-independent acquisition (DIA) mode using a generic (SWATH™, SCIEX) acquisition method. Data were processed using PeakView® v2.2 software with SWATH™ Acquisition MicroApp 2.0 (SCIEX) and MarkerView™ v1.3 software (SCIEX) pipeline to generate protein lists for downstream gene ontology annotation and pathway analysis of identified proteins. Results: There were distinct differences in peptide chromatographic features of sick sheep samples compared to those from healthy sheep. Healthy and sick sheep serum samples yielded 335 and 236 protein identifications (IDs), respectively. There were 96 protein IDs unique to sick sheep serum. A total of 431 protein IDs were annotated by combining healthy control and sick sheep protein IDs. Conclusions: SWATH analysis successfully aided in the detection some established clinicopathological serum biochemical analytes. This approach enabled the distinction of protein profiles of sick sheep samples from a healthy control sample, thereby providing a promising future perspective for the application of SWATH analysis in veterinary clinical use.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing.

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma.

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS.

Developing a deep and comprehensive understanding of the collection of peptides presented by class I human leukocyte antigens (HLA ), collectively referred to as the immunopeptidome , is conducive to the success of a wide range of immunotherapies. The development of tools that enable the deconvolution of immunopeptidomes in the context of disease can help improve the specificity and effectiveness of therapeutic strategies targeting these peptides, such as adoptive T-cell therapy and vaccines. Here, we describe a computational workflow that facilitates the processing and interpretation of data-independent acquisition mass spectrometry (DIA-MS). We consider a specific variation of DIA-MS known as SWATH-MS. SWATH-MS is a promising technique that can be utilized to reproducibly characterize and quantify immunopeptidomes isolated from a wide range of biological sources. In this workflow, we use an assortment of database search engines and computational tools to build high-quality HLA allele-specific peptide spectral peptide libraries for the analysis of immunopeptidomic datasets acquired by SWATH-MS. Generating and sharing these spectral libraries are essential for the SWATH-MS technology to meet its full potential and to enable the rapid and reproducible quantification of HLA-specific peptides across multiple samples.

P–172 Data-independent acquisition-proteomics of human embryo-spent medium and identification of potential embryo biomarkers

Abstract Study question Can human embryo-derived protein(s) serve as viability biomarkers to predict pregnancy outcome, post embryo transfer? Summary answer The human embryo-spent medium proteome, using data-independent acquisition (DIA) approach, could identify novel biomarkers for use in elective embryo transfer. What is known already Morphological assessment is used for elective embryo transfer. To improve IVF outcomes and to avoid multiple gestations, embryo-viability assessment is required toward single embryo transfer. Embryo proteomics could provide a non-invasive approach to assess embryo viability. With the advent of DIA mode proteomics, a robust proteome of E-SM could be determined. Study design, size, duration This was a retrospective study performed between May and December, 2020 using ten E-SMs obtained from ten individual transferable-quality embryos. Frozen E-SMs, following post-thaw, were subjected to LC-MS-MS analysis. Identified proteome profiles were being potentially correlated to embryo quality scores and pregnancy outcomes in terms of live births. Participants/materials, setting, methods The E-SMs were processed for proteomic analysis and subjected to reduction, alkylation and trypsin digestion. Trypsin digested samples were desalted followed by LC-MS/MS using DIA method. Obtained results were searched against human peptide spectral library using Skyline. Differentially expressed proteins were identified by MSStat. Individual peptide peak area under the curve was normalized and analyzed using Student t-test. Fold change was calculated to identify differentially regulated proteins in blank and E-SM samples. Main results and the role of chance Using a high-resolution mass spectrometer and high throughput DIA method, we identified 5,502 peptides corresponding to 3,396 proteins from blank and E-SM samples, derived from five non-transferred embryos. We observed that 516 proteins were specific to E-SMs vies-a-vies those of embryo-free blank medium. Statistical analysis showed that 25 proteins were significantly present E-SMs vs. blank. Interestingly, we observed that 16 proteins were down regulated and 9 were up regulated in E-SMs vs. blank medium. Furthermore, E-SMs, from transferred embryos, contained 2,467 peptides corresponding to 1,741 proteins; of these, 1, 689 proteins were specific to E-SMs with 60 (58 down regulated and 2 up regulated) of them being significantly expressed in E-SMs vis-à-vis embryo-free blank medium. Considering the available met analysis published data, our study is the first to use DIA acquisition for high-throughput analysis of human embryo proteome and identification of biomarkers of embryo viability and for possible prediction of pregnancy outcome. Limitations, reasons for caution Proteins, other than HAS, detected in the blank medium could be because of non-purified HAS or undeclared proteins and DIA approach used. A large cohort study and meta-analysis using DIA mode are required to establish the embryo-proteome having predictive potential for embryo biological viability. Wider implications of the findings: For the first time, using DIA mode, a global embryo proteome assessment could be made, establishing a novel embryo viability biomarkers. This, along with the morphological analysis, could be practiced for selection of transferable quality embryo(s) Trial registration number Not applicable

Data independent acquisition of plasma biomarkers of response to neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma

The detection of disease-related plasma biomarkers has challenged the proteomic community for years. Attractive features for plasma proteomics includes the ease of collection and small volume needed for analysis, but on the other hand, the presence of highly abundant proteins complicates sample preparation procedures and reduces dynamic range. Data independent acquisition label free quantitation (DIA-LFQ) by mass spectrometry partly overcomes the dynamic range issue; however, generating the peptide spectral reference libraries that allow extensive analysis of the plasma proteome can be a slow and expensive task which is unattainable for many laboratories. We investigated the re-purposing of publically available plasma proteome datasets and the impact on peptide/protein detection for DIA-LFQ. We carried out these studies in the context of identifying putative biomarkers of response to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma, as no useful plasma biomarkers have been clinically adopted. We demonstrated the benefit in searching DIA data against multiple spectral libraries to show that complement proteins were linked to NAC response in PDAC patients, confirming previous observations of the prognostic utility of complement following adjuvant chemotherapy. Our workflow demonstrates that DIA-LFQ can be readily applied in the oncology setting for the putative assignment of clinically relevant plasma biomarkers. Statement of significanceThe proteomic mass spectrometry analysis of undepleted, unfractionated human plasma has benefits for sample throughput but remains challenging to obtain deep coverage. This work evaluated the re-purposing of open source peptide mass spectrometry data from human plasma to create spectral reference libraries for use in Data independent acquisition (DIA). We showed how seeding in locally acquired data to integrate iRT peptides into spectral libraries increased identification confidence by facilitating querying of multiple libraries. This workflow was applied to the discovery of putative plasma biomarkers for response to neoadjuvant chemotherapy (NAC) in pancreatic ductal adenocarcinoma patients. There is a paucity of prior information in the literature on this topic and we show that good responder patients have reduced levels of complement proteins.

Sensitive Method for the Confident Identification of Genetically Variant Peptides in Human Hair Keratin.

Recent reports have demonstrated that genetically variant peptides derived from human hair shaft proteins can be used to differentiate individuals of different biogeographic origins. We report a method involving direct extraction of hair shaft proteins more sensitive than previously published methods regarding GVP detection. It involves one step for protein extraction and was found to provide reproducible results. A detailed proteomic analysis of this data is presented that led to the following four results: (i) A peptide spectral library was created and made available for download. It contains all identified peptides from this work, including GVPs that, when appropriately expanded with diverse hair‐derived peptides, can provide a routine, reliable, and sensitive means of analyzing hair digests; (ii) an analysis of artifact peptides arising from side reactions is also made using a new method for finding unexpected modifications; (iii) detailed analysis of the gel‐based method employed clearly shows the high degree of cross‐linking or protein association involved in hair digestion, with major GVPs eluting over a wide range of high molecular weights while others apparently arise from distinct non‐cross‐linked proteins; and (v) finally, we show that some of the specific GVP identifications depend on the sample preparation method.

Constructing a Tandem Mass Spectral Library for Forensic Ricin Identification.

Ricin, a protein found in castor seeds, is a lethal toxin that is designated as a category 2 select agent, and cases of attempted ricin poisoning are relatively common. Many methods to detect protein toxins such as ricin use targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify toxin peptides, usually tryptic peptides. The successful use of untargeted methods has also been reported. However, the use of untargeted proteomics methods, including database search, for peptide and protein identification is less common in forensic practice and may be unfamiliar to forensic science practitioners. Here, we propose a method to create spectral libraries of tryptic ricin peptides and use these libraries for ricin identification by spectral library search, which may be more familiar to forensic scientists because of the use of spectral libraries in small molecule identification. Peptide spectral libraries offer a direct comparison to an authentic standard, a key element of forensic analysis, but have not previously been used in a forensic context. To construct these spectral libraries, two pure ricin samples (one from a proposed standard reference material) were digested with trypsin and analyzed using a standard shotgun LC-MS/MS protocol. Spectral libraries were created from resulting tryptic peptides identified from filtered search results from four database search tools. The library was then used in a search using SpectraST on forensically realistic castor seed extracts. These castor seed samples were made using the crude methods commonly encountered in real-world ricin cases. Analysis showed that the spectral library search resulted in more peptides identified from crude castor seed samples compared to MS-GF+ and Sequest plus Percolator database searches. These results, the first published use of spectral library search to detect protein toxins in forensically relevant samples, suggest that computational comparison of putative ricin peptide spectra to library spectra can be an effective method to detect ricin in an unknown sample. Data are available via ProteomeXchange with identifier PXD013711.

A first step towards building spectral libraries as complementary tools for snake venom proteome/peptidome studies

Snake venoms are complex mixtures of a large number of distinct proteins and peptides with biological activity. Peptide spectral libraries are compilations of previously identified MS/MS spectra obtained from proteomics experiments. Here we present the generation and use of a Venom Peptidome and a Venom Proteome spectral library for the analysis of venom proteomes and peptidomes from distinct snake species.

Sample Fractionation Techniques for CSF Peptide Spectral Library Generation.

Data-independent acquisition (DIA) is becoming more prominent as a method for comprehensive proteomic analysis of clinical samples due to its ability to acquire essentially all fragment ion spectra in a single LC-ESI-MS/MS experiment. Since the direct correlation between a precursor and its fragment ions is lost when acquiring all ions in a defined m/z range, one data analysis strategy is using so-called peptide spectral libraries. These are usually generated by measuring similar biological samples in data-dependent (DDA) mode. The peptide spectral library content is a major limitation for the successful identification from DIA data. This is because a fragment ion spectrum from the sample can only be matched, and thus identified, when it is present in the peptide spectral library. In order to enhance peptide spectral library size, the sample for generating the peptide spectral library can be subjected to extended separation strategies prior to DDA. These strategies are of special relevance for biological samples containing a few very high-abundant proteins, such as CSF, as they enlarge the identification of low-abundant proteins. In instances of CSF separation, suitable methods include the 1D SDS-PAGE of proteins and high-pH reversed-phase peptide fractionation. Both methods are based on different protein/peptide characteristics, are complementary with one another, and are inexpensive and easy to establish. Ideally, DDA spectra from samples generated with both methods combine to achieve a comprehensive spectral library.

Fractionation Techniques to Increase Plant Proteome Coverage: Combining Separation in Parallel at the Protein and the Peptide Level.

Peptide spectral libraries enable targeted identification and quantitation of low-abundance proteins in a complex plant proteome. Here we describe parallel protein and peptide fractionation techniques to improve plant proteome coverage and facilitate construction of spectral libraries.

Development of a Trypanosoma cruzi strain typing assay using MS2 peptide spectral libraries (Tc-STAMS2).

BackgroundChagas disease also known as American trypanosomiasis is caused by the protozoan Trypanosoma cruzi. Over the last 30 years, Chagas disease has expanded from a neglected parasitic infection of the rural population to an urbanized chronic disease, becoming a potentially emergent global health problem. T. cruzi strains were assigned to seven genetic groups (TcI-TcVI and TcBat), named discrete typing units (DTUs), which represent a set of isolates that differ in virulence, pathogenicity and immunological features. Indeed, diverse clinical manifestations (from asymptomatic to highly severe disease) have been attempted to be related to T.cruzi genetic variability. Due to that, several DTU typing methods have been introduced. Each method has its own advantages and drawbacks such as high complexity and analysis time and all of them are based on genetic signatures. Recently, a novel method discriminated bacterial strains using a peptide identification-free, genome sequence-independent shotgun proteomics workflow. Here, we aimed to develop a Trypanosoma cruzi Strain Typing Assay using MS/MS peptide spectral libraries, named Tc-STAMS2.Methods/Principal findingsThe Tc-STAMS2 method uses shotgun proteomics combined with spectral library search to assign and discriminate T. cruzi strains independently on the genome knowledge. The method is based on the construction of a library of MS/MS peptide spectra built using genotyped T. cruzi reference strains. For identification, the MS/MS peptide spectra of unknown T. cruzi cells are identified using the spectral matching algorithm SpectraST. The Tc-STAMS2 method allowed correct identification of all DTUs with high confidence. The method was robust towards different sample preparations, length of chromatographic gradients and fragmentation techniques. Moreover, a pilot inter-laboratory study showed the applicability to different MS platforms.Conclusions and significanceThis is the first study that develops a MS-based platform for T. cruzi strain typing. Indeed, the Tc-STAMS2 method allows T. cruzi strain typing using MS/MS spectra as discriminatory features and allows the differentiation of TcI-TcVI DTUs. Similar to genomic-based strategies, the Tc-STAMS2 method allows identification of strains within DTUs. Its robustness towards different experimental and biological variables makes it a valuable complementary strategy to the current T. cruzi genotyping assays. Moreover, this method can be used to identify DTU-specific features correlated with the strain phenotype.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017.

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

Reverse and Random Decoy Methods for False Discovery Rate Estimation in High Mass Accuracy Peptide Spectral Library Searches.

Spectral library searching (SLS) is an attractive alternative to sequence database searching (SDS) for peptide identification due to its speed, sensitivity, and ability to include any selected mass spectra. While decoy methods for SLS have been developed for low mass accuracy peptide spectral libraries, it is not clear that they are optimal or directly applicable to high mass accuracy spectra. Therefore, we report the development and validation of methods for high mass accuracy decoy libraries. Two types of decoy libraries were found to be suitable for this purpose. The first, referred to as Reverse, constructs spectra by reversing a library's peptide sequences except for the C-terminal residue. The second, termed Random, randomly replaces all non-C-terminal residues and either retains the original C-terminal residue or replaces it based on the amino-acid frequency of the library's C-terminus. In both cases the m/z values of fragment ions are shifted accordingly. Determination of FDR is performed in a manner equivalent to SDS, concatenating a library with its decoy prior to a search. The utility of Reverse and Random libraries for target-decoy SLS in estimating false-positives and FDRs was demonstrated using spectra derived from a recently published synthetic human proteome project (Zolg, D. P.; et al. Nat. Methods 2017, 14, 259-262). For data sets from two large-scale label-free and iTRAQ experiments, these decoy building methods yielded highly similar score thresholds and spectral identifications at 1% FDR. The results were also found to be equivalent to those of using the decoy-free PeptideProphet algorithm. Using these new methods for FDR estimation, MSPepSearch, which is freely available search software, led to 18% more identifications at 1% FDR and 23% more at 0.1% FDR when compared with other widely used SDS engines coupled to postprocessing approaches such as Percolator. An application of these methods for FDR estimation for the recently reported "hybrid" library search (Burke, M. C.; et al. J. Proteome Res. 2017, 16, 1924-1935) method is also made. The application of decoy methods for high mass accuracy SLS permits the merging of these results with those of SDS, thereby increasing the assignment of more peptides, leading to deeper proteome coverage.

New targeted approaches for the quantification of data-independent acquisition mass spectrometry.

The use of data-independent acquisition (DIA) approaches for the reproducible and precise quantification of complex protein samples has increased in the last years. The protein information arising from DIA analysis is stored in digital protein maps (DIA maps) that can be interrogated in a targeted way by using ad hoc or publically available peptide spectral libraries generated on the same sample species as for the generation of the DIA maps. The restricted availability of certain difficult-to-obtain human tissues (i.e., brain) together with the caveats of using spectral libraries generated under variable experimental conditions limits the potential of DIA. Therefore, DIA workflows would benefit from high-quality and extended spectral libraries that could be generated without the need of using valuable samples for library production. We describe here two new targeted approaches, using either classical data-dependent acquisition repositories (not specifically built for DIA) or ad hoc mouse spectral libraries, which enable the profiling of human brain DIA data set. The comparison of our results to both the most extended publically available human spectral library and to a state-of-the-art untargeted method supports the use of these new strategies to improve future DIA profiling efforts.

A first dataset toward a standardized community-driven global mapping of the human immunopeptidome.

We present the first standardized HLA peptidomics dataset generated by the immunopeptidomics community. The dataset is composed of native HLA class I peptides as well as synthetic HLA class II peptides that were acquired in data-dependent acquisition mode using multiple types of mass spectrometers. All laboratories used the spiked-in landmark iRT peptides for retention time normalization and data analysis. The mass spectrometric data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD001872. The generated data were used to build HLA allele-specific peptide spectral and assay libraries, which were stored in the SWATHAtlas database. Data presented here are described in more detail in the original eLife article entitled ‘An open-source computational and data resource to analyze digital maps of immunopeptidomes’.