Author response: An open-source computational and data resource to analyze digital maps of immunopeptidomes (2015)

Full text Figures and data Side by side Abstract eLife digest Introduction Results and discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies. https://doi.org/10.7554/eLife.07661.001 eLife digest The cells of the immune system protect us by recognizing telltale molecules produced by damaged and diseased cells, or by infection-causing microorganisms (which are also called pathogens). To help with this process, the cells in our bodies display small fragments of proteins (called peptides) on their surface that are then checked by the immune cells. Collectively, these peptides are referred to as the ‘immunopeptidome’, and deciphering the complexity of the human immunopeptidome is important for both basic research and medical science. Such an achievement would help to guide the development of next-generation vaccines and therapies against autoimmune disorders, infectious diseases and cancers. In the past, immune peptides were mostly identified using a technique that is commonly called ‘shotgun’ mass spectrometry. However, this approach doesn't always provide reproducible results. In 2012, researchers reported the development of a new approach—which they called ‘SWATH’ mass spectrometry—that could yield more reproducible data. Now, Caron et al.—including many of the researchers involved in the 2012 study—have developed a large collection of standardized tests that use SWATH mass spectrometry to analyze the human immunopeptidome. The workflow and the computational and data resources developed as part of this international effort are the first steps toward highly reproducible and measurable analyses of the immunopeptidome across many samples. Moreover, the large repository of assays generated by the project has been made public and will serve a large community of researchers, which should enable better collaborations. In the future, SWATH mass spectrometry could be used as a robust technology for the reproducible detection and measurement of pathogen-specific or cancer-specific immune peptides. This could greatly help in the design of personalized immune-based therapies. https://doi.org/10.7554/eLife.07661.002 Introduction Next-generation immune-based therapies are expected to facilitate the eradication of intractable pathogens, cancer and autoimmune diseases (Koff et al., 2013). T cells play a critical role in such therapies by their ability to detect the presence of disease-specific antigens/peptides presented by major histocompatibility complex (MHC) molecules (human leukocyte antigen [HLA] molecules in humans). Under steady-state or pathological conditions, thousands of HLA class I-associated peptides of 8–12 amino acids in length are displayed on the surface of virtually all nucleated cells for scrutiny by CD8+ T cells. HLA class II-associated peptides are 10–25 amino acids in length and are normally found on the surface of specialized antigen-presenting cells including macrophages and dendritic cells for presentation to CD4+ T cells. Collectively, HLA class I and class II peptides are referred to as the immunopeptidome, also known as HLA ligandome/peptidome (Caron et al., 2011; Kowalewski et al., 2014). The composition of the immunopeptidome in the human population is complicated by the presence of more than 3000 HLA alleles, resulting in a high diversity of peptide repertoires characterized by the presence of HLA allele-specific binding motifs (Falk et al., 1991). To be successful in designing efficient immunotherapies against autoimmunity, cancer and infectious diseases, it is becoming increasingly important to comprehensively map the complexity of the human immunopeptidome and to gain a more quantitative understanding of its dynamics in various disease states. Mass spectrometry (MS) has evolved as the method of choice for the exploration of the human immunopeptidome (Hunt et al., 1992; Admon and Bassani-Sternberg, 2011; Granados et al., 2015). The largest HLA peptidomes reported to date using MS contain more than 10,000 class I or class II peptides (Hassan et al., 2013; Bergseng et al., 2014; Bassani-Sternberg et al., 2015). Estimates from various analytical and cell-based techniques also indicate that individual peptides are expressed on average at 50 copies per cell with extremes ranging from 1 to 10,000 copies per cell (Granados et al., 2015). Until recently, the most common strategy for the analysis of immunopeptidomes by MS has focused on the isolation of HLA-bound peptides by immunoaffinity chromatography and the collection of fragment ion spectra of selected peptides through automated MS operated in data-dependent acquisition (DDA) mode. Although DDA is a powerful strategy for exploring the peptidomic content of various cell and tissue types, it is not a reliable platform for solving problems that require the comparison of comprehensive, quantitative, and reproducible data sets across many samples or conditions. In fact, analyses of complex/unfractionated digests of cell lysate using DDA have shown that as many as 84% of peptides may remain unselected for fragmentation even though they are clearly detectable by the mass spectrometer (Michalski et al., 2011). Although the complexity of isolated HLA peptides is hardly comparable with that of cell lysate digests, as many as 20% of the selected HLA peptides can vary between replicate analyses of the same sample (Granados et al., 2014) (Figure 1—figure supplement 1A). A second strategy, referred to as selected/multiple reaction monitoring (S/MRM), is a targeting MS technique capable of generating highly reproducible, quantitatively accurate and sensitive datasets (Picotti and Aebersold, 2012). S/MRM is, however, limited by its capacity to detect only tens to hundreds of peptides per sample injection and thus is not ideally suited to comprehensively quantify HLA peptidomes. To overcome this limitation, we recently introduced SWATH-MS, a new mass spectrometric technique that combines data-independent acquisition (DIA) with a targeted data extraction strategy (Gillet et al., 2012; Röst et al., 2014). In DIA mode, all peptides in a sample are fragmented and the corresponding fragment ion spectra are acquired, resulting in a digital recording of the peptide sample. DIA is an unbiased MS technique and therefore represents a suitable strategy for efficiently generating consistent, reproducible and quantitatively accurate measurements of peptides across multiple samples (Gillet et al., 2012; Collins et al., 2013; Rosenberger et al., 2014; Röst et al., 2014; Guo et al., 2015; Liu et al., 2015; Schubert et al., 2015a). To extract quantitative information from digital SWATH-MS data, high-quality assay libraries are required. Such libraries contain retention-time and fragmentation information of the peptides to be targeted. Assay libraries are generated from native and/or synthetic peptides using a SWATH compatible mass spectrometer operated in DDA mode. To date, several generic SWATH assay libraries were generated for the analysis of proteomes in various species. These include Mycobacterium tuberculosis (Schubert et al., 2015a), Saccharomyces cerevisiae (Selevsek et al., 2015), and Homo sapiens (Rosenberger et al., 2014). Assay libraries were successfully employed to measure a limited number of MHC class I peptides by S/MRM in various contexts—that is, viral infection (Croft et al., 2013), autoimmunity (Schittenhelm et al., 2014a) and cancer (Gubin et al., 2014)—but have never been created for robust quantitative and high-throughput measurement of HLA-associated peptides by SWATH-MS. For the SWATH-MS technology to meet its potential to support rapid advances in the design of next-generation vaccines and immunotherapies, comprehensive HLA peptide assay libraries have to be created and made readily available to basic and translational scientists. Generating such assay libraries could ultimately enable the fast and reproducible quantification of the entire repertoire of HLA peptides across many samples. Towards this end, we developed a workflow to (1) generate a pilot repository of HLA allele-specific peptide spectral and assay libraries, and to (2) analyze SWATH-MS HLA peptidomic data acquired from multiple international laboratories (Figure 1). In this study, libraries were created from natural and/or synthetic HLA class I and II peptides whereas analysis of SWATH-MS HLA peptidomic data focused mainly on naturally presented class I peptides. Figure 1 with 3 supplements see all Download asset Open asset General workflow for building HLA allele-specific peptide assay libraries and for analyzing SWATH-MS HLA peptidomic data. (Left panel) A community-based repository of HLA class I allele-specific peptide spectral and assay libraries was created and stored in the SWATHAtlas database. HLA typed-biological samples and synthetic HLA peptides were used to build the repository. Our workflow integrates (1) data-dependent acquisition (DDA) of HLA peptidomic data, (2) multiple open-source database search engines and statistical validation tools, (3) HLA allele annotation of the identified peptides, and (4) spectral and assay library generation tools. (Right panel) HLA peptidomic data from HLA-typed biological samples were acquired in data-independent acquisition (DIA) mode. The matching HLA class I allele-specific peptide assay libraries were combined and DIA data were analyzed using the OpenSWATH and the Skyline software. https://doi.org/10.7554/eLife.07661.003 Figure 1—source data 1 Comparative analysis of DDA and SWATH-MS for the identification of HLA class I peptides. https://doi.org/10.7554/eLife.07661.004 Download elife-07661-fig1-data1-v2.xlsx Results and discussion Large-scale DDA-based identification of immunoaffinity purified HLA class I peptides is supported by several software tools (e.g., MaxQuant, Perseus or X-PRESIDENT) and results in thousands of unclassified peptides of various lengths. Since large HLA peptidomic datasets are generated at an increasing pace, additional computational frameworks facilitating the HLA annotation and storage of such datasets need to be developed. Here, we first created a computational workflow to support the identification, classification/annotation, visualization and storage of HLA peptidomic data in an allele-dependent manner. The software tools described in the section below enable (1) systematic annotation of peptides to their respective HLA allele, (2) visualization of HLA peptidomic datasets, and (3) generation of HLA class I allele-specific peptide spectral libraries, which can be converted into high quality assay libraries for the processing of SWATH-data (Figure 2, Figure 2—figure supplement 1, Figure 2—source data 2 and Supplementary file 1). Figure 2 with 6 supplements see all Download asset Open asset Content and analysis of the pilot repository. (A) HLA peptides were isolated by immunoaffinity chromatography and were annotated to their respective HLA alleles following DDA mass spectrometry. (B) Heat map visualization of HLA class I peptides identified from 20 HLA-typed biological samples. HLA-A and -B alleles are indicated for each sample. (C) 35,812 distinct class I and class II HLA peptides were identified, annotated, and used to build 32 and 11 HLA allele-specific peptide spectral and SWATH assay libraries, respectively. (D) The distribution curve shows that 95% of the HLA-B07-annotated peptides were predicted to bind the HLA molecule with an IC50 below 531 nM. Inner pie chart: we assessed the predicted HLA binding affinity of all peptides contained in individual source proteins. The pie chart shows that 92% of naturally presented HLA-B07 peptides were ranked in the top 1% (blue) of predicted peptides (see also Figure 2—figure supplement 6). https://doi.org/10.7554/eLife.07661.008 Figure 2—source data 1 Sources of HLA peptides used in this study. https://doi.org/10.7554/eLife.07661.009 Download elife-07661-fig2-data1-v2.xlsx Figure 2—source data 2 Annotation of HLA peptides. https://doi.org/10.7554/eLife.07661.010 Download elife-07661-fig2-data2-v2.xlsx Figure 2—source data 3 List of eluted HLA class I peptides that were identified at 1% and 5% peptide-level FDR. https://doi.org/10.7554/eLife.07661.011 Download elife-07661-fig2-data3-v2.xlsx Figure 2—source data 4 HLA class I allele-specific peptide spectral libraries stored in PeptideAtlas. https://doi.org/10.7554/eLife.07661.012 Download elife-07661-fig2-data4-v2.xlsx Figure 2—source data 5 HLA class I and II allele-specific peptide assay libraries stored in the SWATHAtlas database. https://doi.org/10.7554/eLife.07661.013 Download elife-07661-fig2-data5-v2.xlsx To test our workflow, the generated data and computational resources, we first assessed the feasibility of generating HLA class I allele-specific peptide spectral libraries from a panel of fourteen PBMC samples (PBMC #1–14) expressing different combinations of HLA class I alleles. HLA class I-bound peptides were isolated from HLA-typed PBMC's by immunoaffinity chromatography and analyzed by DDA on an Orbitrap-XL mass spectrometer (Figure 2 and Figure 2—source data 1). Peptides were identified using multiple open-source database search engines. The search identifications were combined and statistically scored using PeptideProphet and iProphet within the Trans-Proteomic Pipeline (TPP) as shown previously (Figure 1) (Shteynberg et al., 2011, 2013). We next annotated the identified peptides to their respective HLA allele. Previously, HLA binding prediction algorithms such as SYFPETHI, NetMHC and SMM were used for manual or semi-automated annotation of HLA peptides (Fortier et al., 2008; Berlin et al., 2014; Granados et al., 2014). Here, we designed a fully automated annotation strategy integrating the stand-alone software package of the HLA binding prediction algorithm NetMHC 3.4 with a set of in-house software tools (Figure 2—figure supplement 1). The in-house software tools enable an automated, consistent and effective annotation of the majority of the identified peptides to their respective HLA allele (Supplementary file 1). Briefly, each identified peptide was given a predicted HLA binding affinity (IC50) for each of the HLA alleles expressed in the corresponding healthy donor. An HLA annotation score was then computed for each individual peptide by dividing its second best IC50 value (i.e., the second best predicted allele) by its best IC50 value (i.e., the best predicted allele). The higher this annotation score was, the higher the probability was for the peptide to be correctly annotated to a specific HLA allele. As an example, in PMBC#2, an annotation score of 77 was computed for the KLEEQARAK peptide by dividing 21,400 nM (second best IC50 value predicted for HLA-B39) by 278 nM (best IC50 value predicted for HLA-A03) (Figure 2—figure supplement 1A). Peptides with an HLA annotation score ≥3 (selected cutoff value; see ‘Materials and methods’ and Supplementary file 1) were systematically annotated to the allele predicted to bind best (e.g., HLA-A03 for the KLEEQARAK peptide). Using this scoring strategy, ∼80% of all identified 8–12-mers were annotated to a specific HLA-A or -B allele (Figure 2—source data 2). HLA-A and -B alleles were prioritized due to the high reliability of the NetMHC 3.4 predictor for a broad diversity of HLA-A and -B alleles as well as for their high expression levels (Kim et al., 2014; Bassani-Sternberg et al., 2015; Trolle et al., 2015). Peptides with an annotation score below 3 were considered as non-annotated in this study and were discarded for the process of building the HLA allele-specific peptide spectral libraries. Tables including scored peptides were then used to generate heat maps and visualize HLA-A and -B peptidomes of PBMC's as described (Figure 2B and Supplementary file 1). Of note, allele-supertype peptides (i.e., peptides predicted to strongly bind more than one allele with an IC50 below 500 nM) were curated in the output files but were not visualized on the heat maps in this study. A corrected false discovery rate (cFDR) was estimated for each PBMC sample following removal of all non-annotated contaminant peptides (Figure 1—figure supplement 2 and Figure 1—figure supplement 3), resulting in a total of 4153 (peptide-level FDR 1%; average cFDR 0.5%) or 7921 (peptide-level FDR 5%; average cFDR 2.5%) distinct annotated peptides distributed across eighteen HLA class I alleles (Figure 2—figure supplement 2A and Figure 2—source data 3). All annotated peptides identified from the 14 PBMC samples were then used in SpectraST (Lam et al., 2008) to build the HLA class I allele-specific peptide spectral libraries (‘Materials and methods’). The same procedure was applied to peptides identified from JYEBV+ and C1R cells. Notably, endogenous HLA-C04 peptides were recently shown to be significantly expressed on the surface of C1R cells (Schittenhelm et al., 2014b) and were therefore considered in this study. In total, 3528 HLA-A peptides, 4208 HLA-B peptides and 205 HLA-C04 peptides were recorded in the spectral libraries, which were then stored in the public PeptideAtlas database (Figure 2—source data 4). In summary, we generated a computational workflow to effectively annotate and visualize HLA peptidomic data, which were finally converted and stored into HLA allele-specific peptide spectral libraries consisting of consensus fragment ion spectra. This strategy could be further refined to collect, store and share HLA peptidomic information obtained from various cell lines and from larger cohorts of donors. Importantly, this computational approach can be broadly applied to generate SWATH-compatible assay libraries as described below. Libraries of consensus fragment ion spectra can be converted into high quality assays for high-throughput targeted analysis of SWATH-MS data, an emerging approach for reproducible, consistent and accurate quantitative measurements of peptides (Gillet et al., 2012; Collins et al., 2013; Rosenberger et al., 2014; Röst et al., 2014; Guo et al., 2015; Liu et al., 2015; Selevsek et al., 2015; Schubert et al., 2015a). Here, we aimed at initiating a worldwide community-based effort to generate pilot HLA allele-specific peptide assay libraries that could be further used for the analysis of SWATH-MS HLA peptidomic data. Naturally presented and/or synthetic HLA class I and class II peptides were provided from six independent laboratories and were analyzed using four distinct TripleTOF 5600 MS instruments operated in DDA acquisition mode in four different international institutions. Naturally presented HLA class I peptides from JYEBV+ (HLA-A02 and -B07), PBMC (HLA-A03, -A26, -B51 and -B57), and Jurkat (HLA-A03, -B07 and -B35) cells were isolated by immunoaffinity chromatography (Figure 2—source data 1). Natural class I peptides from three C1R cell lines—stably expressing HLA-C04 as well as HLA-B27, -B39 or -B40 molecules—were also isolated using the same procedure. Synthetic EBV-derived peptides known to bind HLA-A02 or -B07 were also used to build the libraries (Figure 2—source data 2). All laboratories used the spiked-in landmark iRT peptides for retention time normalization (Escher et al., 2012). The DDA data generated by the different groups were shared and pipelined through the computational workflow described above, resulting in the identification of 7668 (peptide-level FDR 1%; average cFDR 0.5%) or 11,275 (peptide-level FDR 5%; average cFDR 2.5%) distinct HLA class I peptides distributed across eleven different HLA class I alleles (Figure 2—figure supplement 2B and Figure 2—source data 3). To properly assess the efficiency of generating HLA peptide assay libraries from synthetic peptides, a large collection of 20,176 synthetic HLA class II peptides was analyzed by DDA using different mass spectrometers and fragmentation methods (Figure 2—figure supplement 3 and Figure 2—source data 2). Our results show that a total of 15,875 peptides (∼79%) were identified (Figure 2—source data 2). A large collection of synthetic HLA class I peptides was not available but could be used in the future to extend the contents of the present class I libraries derived from native peptides. All identified peptides were used to build the HLA allele-specific peptide assay libraries (‘Materials and methods’). To date, the pilot libraries contain a total of 223,735 transitions for 26,857 unique peptides and were stored by class and allele in the SWATHAtlas database (Figure 2—source data 5 and http://www.swathatlas.org). By using the automated HLA peptide annotation method described above, we observed that similar binding affinities were predicted for HLA class I peptides identified at peptide-level FDR 1% and peptide-level FDR 5% (Figure 2—figure supplement 4 and Figure 2—figure supplement 5), suggesting that a large fraction of true positives were excluded at peptide-level FDR 1%. Our data also show that 95% of the annotated class I peptides in this study were predicted to bind their respective HLA molecules with an IC50 ranging from 72 nM (for HLA-A01) to 5682 nM (for HLA-B51) at peptide-level FDR 1% (Figure 2—figure supplement 4). Similar results were obtained at peptide-level FDR 5% (Figure 2—figure supplement 5). This result supports a recent study indicating that HLA class I alleles are associated with repertoires of different affinity et al., 2013). we the feasibility of DDA data from multiple international laboratories to generate standardized HLA allele-specific peptide assay libraries. We this effort as a first step towards the development of a standardized HLA peptide assay which could be used to and reproducibly quantify the entire repertoire of peptides presented by HLA molecules using SWATH-MS. SWATH-MS is emerging as a robust next-generation technique for efficiently generating reproducible, consistent and quantitatively accurate measurements of peptides across multiple samples (Gillet et al., 2012; Collins et al., 2013; Rosenberger et al., 2014; Röst et al., 2014; Guo et al., 2015; Liu et al., 2015; Selevsek et al., 2015; Schubert et al., 2015a). To the worldwide development of MS towards robust quantitative measurements of HLA we assessed the HLA allele-specific assay libraries described could be used to extract quantitative information from digital SWATH maps acquired by different Importantly, four independent laboratories generated their digital SWATH maps using TripleTOF 5600 MS operated in DIA acquisition mode. Naturally presented HLA class I peptides were isolated from the cell (i.e., PBMC and in the range of were into 32 SWATH of (Gillet et al., 2012). All peptide in this mass range were generating comprehensive and quantitative digital fragment ion The HLA of JYEBV+ cells was analyzed using the OpenSWATH et al., 2014) software and a combined assay library transitions for HLA-A02 and HLA-B07 HLA alleles expressed on these cells. an estimated peptide-level FDR of 1% a total of unique HLA class I peptides were identified from the digital SWATH map (Figure Figure supplement Figure supplement and Figure data 1). Notably, assays generated from the synthetic class I peptides the identification of one EBV-derived HLA-A02 peptide (Figure that building high-quality assay libraries from synthetic class I peptides of could be for the identification of HLA-bound peptides by SWATH-MS. To analyze peptides isolated from PBMC (HLA-A03, -A26, -B51 and -B57), Jurkat (HLA-A03, and and cells, the matching HLA class I allele-specific peptide assay libraries were combined using SpectraST and then in the OpenSWATH software. targeted analysis from these four additional peptidomic datasets indicated that of HLA class I peptides present in an assay library could be from a quantitative digital SWATH map in a cell (peptide-level FDR (Figure supplement Figure supplements and Figure data 1). We next the SWATH acquisition to the distribution of HLA class I peptides. class I peptide within the range of and were in SWATH of Using SWATH data generated from JYEBV+ cells, we found that the of the by in a in the identification of class I peptides (Figure supplement 1A). The value for SWATH-MS quantification was from (Figure In with we also observed that the range of peptides in different cell using SWATH-MS, on their was of (Figure (Hassan et al., 2013; Bassani-Sternberg et al., 2015). we the feasibility of an international effort to build standardized HLA allele-specific peptide assay libraries, which were used to extract quantitative information from digital SWATH maps acquired in different We therefore provide a of that acquisition of SWATH-MS HLA peptidomic data may enable robust analysis of the human immunopeptidome on a Figure 3 with supplements see all Download asset Open asset targeted analysis of HLA peptidomic data by SWATH-MS. (A) SWATH-MS of HLA class I allele-specific assay libraries (HLA-A02 and were combined to extract SWATH data generated from the HLA of JYEBV+ cells. groups are shown for of SWATH groups corresponding to the and the HLA-A02 (D) of measurements for range of following targeted analysis of SWATH-MS HLA peptidomic data generated from various cell expressing different combinations of HLA alleles. data were acquired in four independent international Figure data 1 OpenSWATH Download To further the of SWATH-MS for the measurement of HLA-associated peptides, we the JYEBV+ HLA could be reproducibly across multiple MS For this we a sample of class I peptides by immunoaffinity from JYEBV+ cells and we acquired three datasets in SWATH mode. The datasets were analyzed using OpenSWATH and a combined HLA-A02 and -B07 peptide assay library as described an estimated peptide-level FDR of a total of unique HLA class I peptides were identified by SWATH-MS and peptides were found in all the SWATH analyses (Figure 1—figure supplement Figure 1—source data 1). We then a analysis by three additional datasets in DDA mode from the same sample of class I peptides using the same conditions. In total, HLA-A and -B peptides were identified at 1% peptide-level FDR and peptides were found in all the DDA analyses (Figure 1—figure supplement Figure 1—source data 1). the SWATH method clearly the DDA approach for the reproducible identification of JYEBV+ HLA class I peptides across several our results indicate that SWATH-MS has the of large of HLA peptides across multiple at a high of By a community resource for the of the library contents and by the of the OpenSWATH it can be expected that additional HLA and