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.

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.

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Comparative analysis of DDA and SWATH-MS for the identification of HLA class I peptides.

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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).

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Figure 2—source data 1

Sources of HLA peptides used in this study.

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Figure 2—source data 2

Annotation of HLA peptides.

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Figure 2—source data 3

List of eluted HLA class I peptides that were identified at 1% and 5% peptide-level FDR.

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Figure 2—source data 4

HLA class I allele-specific peptide spectral libraries stored in PeptideAtlas.

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Figure 2—source data 5

HLA class I and II allele-specific peptide assay libraries stored in the SWATHAtlas database.

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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 JY^EBV+ 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 JY^EBV+ (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 peptide-binding repertoires of different affinity (Paul et al., 2013). Altogether, we demonstrated the feasibility of collecting DDA data from multiple international laboratories to generate standardized HLA allele-specific peptide assay libraries. We anticipate this global effort as a first step towards the development of a standardized Pan-human HLA peptide assay library, which could be used to rapidly and reproducibly quantify the entire repertoire of peptides presented by HLA molecules using SWATH-MS.

SWATH-MS is emerging as a robust next-generation proteomics 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 promote the worldwide development of SWATH-based MS platforms towards robust quantitative measurements of HLA peptidomes, we assessed whether the HLA allele-specific assay libraries described above could be used to extract quantitative information from digital SWATH maps acquired by different laboratories. Importantly, four independent laboratories generated their own digital SWATH maps using TripleTOF 5600 MS operated in DIA acquisition mode. Naturally presented HLA class I peptides were isolated from the cell types mentioned above (i.e., JY^EBV+, Jurkat, PBMC and C1R). Precursors in the range of 400–1200 Th were divided into 32 SWATH windows of 25 Da (Gillet et al., 2012). All ionized peptide precursors in this mass range were fragmented, generating comprehensive and quantitative digital fragment ion maps. The HLA peptidome of JY^EBV+ cells was analyzed using the OpenSWATH (Röst et al., 2014) software tool and a combined assay library containing 22,206 transitions for 1507 HLA-A02 and 2194 HLA-B07 peptides—the two dominant HLA alleles expressed on these cells. At an estimated peptide-level FDR of 1% (m-score < 0.01), a total of 3150 unique HLA class I peptides were identified from the digital SWATH map (Figure 3A,B,C, Figure 3—figure supplement 1A,B, Figure 3—figure supplement 7 and Figure 3—source data 1). Notably, assays generated from the synthetic EBV-related class I peptides enabled the identification of one EBV-derived HLA-A02 peptide (Figure 3C), thereby demonstrating that building high-quality assay libraries from synthetic class I peptides of pathogen origin could be useful for the identification of non-self HLA-bound peptides by SWATH-MS. To analyze self-HLA peptides isolated from PBMC (HLA-A03, -A26, -B51 and -B57), Jurkat (HLA-A03, B07 and -B35), C1R-B27 (HLA-B27) and C1R-B40 (HLA-B40) cells, the matching HLA class I allele-specific peptide assay libraries were combined accordingly using SpectraST and then processed in the OpenSWATH software. High-throughput targeted analysis from these four additional peptidomic datasets indicated that ∼81% of HLA class I peptides present in an assay library could be extracted from a quantitative digital SWATH map in a cell type-independent manner (peptide-level FDR 1%) (Figure 3—figure supplement 1C, Figure 3—figure supplements 2–6 and Figure 3—source data 1). We next optimized the SWATH acquisition conditions according to the size distribution of HLA class I peptides. Most class I peptide precursors (∼98%) fall within the range of 400–700 Th and were divided in 30 SWATH windows of 10 Da width each. Using SWATH data generated from JY^EBV+ cells, we found that narrowing the size of the windows by 2.5-fold resulted in a ∼13% fold-increase in the identification of class I peptides (Figure 3—figure supplement 1A). The R² value for SWATH-MS quantification was 0.979 from two technical replicates (Figure 3D). In accordance with previous studies, we also observed that the dynamic range of peptides quantified in different cell types using SWATH-MS, based on their signal intensity, was about 3-4 orders of magnitude (Figure 3E) (Hassan et al., 2013; Bassani-Sternberg et al., 2015). Altogether, we demonstrate 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 sites. We therefore provide a proof of concept that acquisition of SWATH-MS HLA peptidomic data may enable robust analysis of the human immunopeptidome on a global scale.

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Figure 3—source data 1

OpenSWATH analysis.

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To further establish the robustness of SWATH-MS for the measurement of HLA-associated peptides, we tested whether the JY^EBV+ HLA peptidome could be reproducibly detected across multiple MS injections. For this purpose, we prepared a sample of class I peptides by immunoaffinity purification from JY^EBV+ 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 above. At an estimated peptide-level FDR of 1%, a total of 2933 unique HLA class I peptides were identified by SWATH-MS and 2832 peptides (97%) were found in all the SWATH analyses (Figure 1—figure supplement 1B, Figure 1—source data 1). We then conducted a comparative analysis by acquiring three additional datasets in DDA mode from the same sample of class I peptides using the same chromatographic conditions. In total, 3153 HLA-A and -B peptides were identified at 1% peptide-level FDR and 1261 peptides (40%) were found in all the DDA analyses (Figure 1—figure supplement 1A, Figure 1—source data 1). Thus, the SWATH method clearly outperformed the DDA approach for the reproducible identification of JY^EBV+ HLA class I peptides across several technical replicates. Overall, our results indicate that SWATH-MS has the capability of detecting large numbers of HLA peptides across multiple injections at a high degree of reproducibility. By providing a community resource for the continuous expansion of the library contents and by improving the performance of the OpenSWATH software, it can be expected that additional HLA peptides—including cryptic and mutant peptides—will be reproducibly identified and quantified from the same digital SWATH maps in the future.

The life sciences community greatly benefits from robust technologies such as microarrays and RNA-seq. Similarly, robust generation and analysis of quantitative digital maps of HLA peptidomes is expected to have important implications in basic and translational research as these will allow research groups to accurately investigate the dynamics of immunopeptidomes in various immune-related diseases such as autoimmunity, infectious diseases and cancers. For instance, reproducible digital mapping of tumor-specific mutant HLA peptides during cancer progression will facilitate stratification of patients who might best benefit from innovative immunotherapeutic interventions (Gubin et al., 2014; Snyder et al., 2014; Schumacher et al., 2015). The workflow and the computational and data resources presented in this community-based study is a first step towards highly reproducible and quantitative MS-based measurements of HLA peptidomes across many samples and could therefore be greatly beneficial in the design of personalized immune-based therapies. Moreover, the storage of HLA peptide spectral and assay libraries by class and allele in the SWATHAtlas database provides an initial framework to collect, organize and share HLA peptidomic data, thereby supporting the recently proposed Human Immunopeptidome and Vaccines Projects (Admon and Bassani-Sternberg, 2011; Koff et al., 2014).

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Thank you for submitting your work entitled “An open-source computational and data resource to analyze digital maps of immunopeptidomes” for peer review at eLife. Your submission has been favorably evaluated by Tadatsugu Taniguchi (Senior editor), Arup K Chakraborty (Reviewing editor), and two reviewers.

The reviewers have discussed the reviews with one another and the Reviewing editor has drafted this decision to help you prepare a revised submission.

Summary:

This manuscript describes the use of the SWATH-MS methodology for identification and cataloging of HLA peptide repertoires – viz., 'the Immunopeptidome'. The manuscript is based on an international collaboration between a few laboratories specializing in HLA immunopeptidome analysis and the laboratory of Dr. Aebersold, who developed the SWATH_MS methodology. In the past, HLA peptides were mostly identified by shotgun proteomics, which is based on the selection of peptides from purified pools of peptides for fragmentation in the mass spectrometer during the LC-MS-MS analysis. The SWATH_MS approach does not select specific peptides for fragmentation, but instead scans repetitively incremental mass windows of about 25 mass units, every time fragmenting all the peptides that are present in each of these mass windows. The method allows fragmenting all the peptides which have the mass divided by charge of the mass region being scanned more than once. The advantage of this approach is the better reproducibility of the data while the disadvantage is the lower sensitivity and the need to establish beforehand a library of spectra of the individual peptides that will be used for identification by the SWATH-MS approach. The use of the peptide preparations and the LC-MS-MS data from the different collaborating labs resulted in successful establishment of a large repository of peptides and their spectra libraries in a format that will be made public and will serve a large community of researchers, enabling better collaborations.

Overall, the work reported in this paper could be a significant resource for the community. But, the following issues need to be addressed to further establish the robustness of the method.

Major points:

1) In the Introduction, it is suggested that Data-Dependent Acquisition (DDA) used for such analyses of MHC peptidomes results in less reproducible results. This is a well-known fact, but it would be good to show the reproducibility of the results of the same three raw data files when analyzed by the SWATH-MS method and a figure with parallel Venn diagrams indicating how many peptides are identified in each analysis in total, and how many are shared between the runs.

2) Peptide presentation by HLA C is ignored because it is claimed that they are expressed in low amounts. In Schittenhelm et al. (Tissue Antigens, 2014, 83, 174-179, 2014), an HLA immunopeptidome of HLA C is reported using the C1R cell line. Since the same cell line is used in the present study, ignoring peptide presentation by HLA C seems to be inappropriate. This point needs clarification or inclusion of HLA C in the analysis.

3) Several search engines (Comet, MS-GF+, X!Tandem) were used to identify peptide sequences from the mass spectrometry data. As can be seen from Figure 1—figure supplement 2, different engines identify quite different numbers of potential peptides. How should these results be interpreted? Should the union or intersection of these peptide sets be used?

4) The HLA annotation score, based on the predicted IC₅₀ binding affinities from NetMHC server, is used to associate peptides with particular HLA alleles. In a similar server, Immune Epitope Database (IEDB), sometimes the values of experimentally measured and predicted values of IC₅₀ can differ by a factor of 10 or more. Using the NetMHC server are the results more robust, thus allowing use of cutoff of value of 3 for the HLA annotation score?

Minor points:

1) In the first paragraph of Results and Discussion, the comment “no reference computational framework is currently available to facilitate the analysis of such datasets” is not entirely correct, since software tools, such as MaxQuant, Perseus, or X-PRESIDENT can handle HLA-peptidome data without effort (for example: see the second reference cited, Bassani-Sternberg et al. 2015).

2) In the second paragraph of Results and Discussion: “of all identified peptides to their respective HLA allele” is an overstatement, since significant parts of the identified peptides are not annotated to their respective HLA allele.

3) In the third paragraph of Results and Discussion: “Three synthetic EBV-derived peptides were also used to build the HLA-A02 and -B07 library. How can three peptide by useful for building two libraries? Some clarification is required.

4) In the fourth paragraph of Results and Discussion: “Class I peptide precursors fall within the range of 400-700 Th” – this is not correct, since many peptides fall outside this range. We suggest clarifying the percentage of the peptides which fall within this range, and indicating if the loss of these peptides compensate for the additional 17% gained by use of this narrow range. Also, why was the 400-650 mass range selected?

5) In the subsection headed “Isolation of HLA peptides”: We suggest adding the reference by Hunt et al. 1992 to the references for the method of affinity purification and LC-MS-MS analysis of MHC peptidome.

6) In the subsection “Generation of HLA allele-specific peptide spectral and assay libraries”: Please clarify that the parameters are used for Spectrast, and explain what they mean for readers that do not use Spectrast.

1) In the Introduction, it is suggested that Data-Dependent Acquisition (DDA) used for such analyses of MHC peptidomes results in less reproducible results. This is a well-known fact, but it would be good to show the reproducibility of the results of the same three raw data files when analyzed by the SWATH-MS method and a figure with parallel Venn diagrams indicating how many peptides are identified in each analysis in total, and how many are shared between the runs.

This is an important point and we performed the analysis suggested by the reviewers. HLA class I peptides were freshly isolated from JY cells and six technical replicates were consecutively injected in a TripleTOF 5600 – three datasets were acquired in DDA mode and three datasets were acquired in SWATH/DIA mode. We inserted a new paragraph in the main text of the manuscript to describe the results of this analysis. We also added a new Venn diagram as suggested (Figure 1—figure supplement 1B) as well as a new supplementary table (Figure 1—source data 1). In the new version of the manuscript, we now mention (Results and Discussion): “To further establish the robustness of SWATH-MS […] reproducibly identified and quantified from the same digital SWATH maps in the future.”

2) Peptide presentation by HLA C is ignored because it is claimed that they are expressed in low amounts. In Schittenhelm et al. (Tissue Antigens, 2014, 83, 174-179, 2014), an HLA immunopeptidome of HLA C is reported using the C1R cell line. Since the same cell line is used in the present study, ignoring peptide presentation by HLA C seems to be inappropriate. This point needs clarification or inclusion of HLA C in the analysis.

We thank the reviewers for pointing this out. In this study, we focused on HLA-A and -B alleles because of the high reliability of the NetMHC 3.4 predictor for a wide 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). We now mention (Results and Discussion): “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).”

Please note that a large number of HLA-C alleles will be fully integrated in the next version of the workflow. For instance, the next upgrade will integrate additional epitope prediction algorithms such as NetMHCpan and others from IEDB and will annotate supertype peptides. We also plan to integrate a predictor-independent strategy [e.g. alignment- and clustering-based approach similar to GibbsCluster and NNalign (Andreatta et al. 2013; Nielson et al. 2009)] since the HLA annotation score used in this manuscript can only be calculated for well-characterized HLA alleles. Nevertheless, it is correct that C1R cells express a significant number of HLA-C04 peptide ligands. Using C1R cells, Schittenhelm et al. have indeed identified 734 HLA-C04 specific peptides from 10 independent immunoaffinity purifications (Schittenhelm et al., 2014). In our study, 205 HLA-C04 peptide ligands expressed on the surface of C1R cells were identified and are now included in the new version of the manuscript. We now state: “Notably, endogenous HLA-C04 peptides were recently shown to be expressed on the surface of C1R cells (Schittenhelm et al. 2014b) and were therefore considered in this study. In total, 3,528 HLA-A peptides, 4,208 HLA-B peptides and 205 HLA-C04 peptides were recorded in the spectral libraries”. We also state: “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.”

We updated the total number of unique peptides, alleles and transitions in the main text (Results and Discussion) and an HLA-C04-specific peptide assay library is now available in the SWATH Atlas repository: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetDIALibs?SBEAMSentrycode=HLASWATH2015. We also updated Figure 2—figure supplement 2 and Figure 2—source data 2. However, we did not modify the Figure 2 as the focus of this study is on HLA-A and -B alleles and because HLA-C04 peptide ligands would be poorly represented on the heat maps (see Author response image 1).

Author response image 1

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3) Several search engines (Comet, MS-GF+, X!Tandem) were used to identify peptide sequences from the mass spectrometry data. As can be seen from Figure 1—figure supplement 2, different engines identify quite different numbers of potential peptides. How should these results be interpreted? Should the union or intersection of these peptide sets be used?

Indeed, clarification is needed to explain what are the peptide sets that were used to build the spectral libraries. Combining results of multiple search engines in proteomics has been shown to be beneficial using particular softwares such as MSblender, PepArML and iProphet (Shteynberg et al. 2013). In Figure 1—figure supplement 2, the venn diagrams were essentially used to roughly compare and visualize the numbers of HLA class I peptides identified by different search engines. However, neither the unions nor the intersections of the venn diagrams were used to build the spectral libraries. Here, the software iProphet was used to combine the search results generated by Comet, MS-GF+ and X!Tandem. In fact, iProphet allows accurate and effective integration of the results from multiple database search engines applied to the same data (Shteynberg et al. 2011). In the revised version of the manuscript, we added a new table in Figure 1—figure supplement 2. The new table shows the numbers of HLA class I peptides obtained from the iProphet combined search results that were used to build the spectral libraries. The new table also shows the sum of peptides identified by the three search engines (Union) as well as the number of overlapping peptides (Intersection) for each Venn diagram/sample. Notably, the numbers of peptides obtained from ‘iProphet’ and ‘Union’ are very similar although not identical. In the revised version of the manuscript, we now state (Results and Discussion): “Peptides were identified using multiple open-source database search engines. The search identifications were combined and statistically scored using PeptideProphet and iProphet within the TPP as shown previously (Figure 1) (Shteynberg et al. 2011; Shteynberg et al. 2013).” In addition, the Figure 1—figure supplement 2 is now entitled: “Combining results of three open-source database search engines in immunopeptidomics using iProphet.” Following the addition of the new table, we have decided to remove the Venn diagrams from the search identifications at 1% peptide-level FDR since this information was not essential and a bit redundant.

4) The HLA annotation score, based on the predicted IC₅₀ binding affinities from NetMHC server, is used to associate peptides with particular HLA alleles. In a similar server, Immune Epitope Database (IEDB), sometimes the values of experimentally measured and predicted values of IC₅₀ can differ by a factor of 10 or more. Using the NetMHC server are the results more robust, thus allowing use of cutoff of value of 3 for the HLA annotation score?

The results obtained using the NetMHC server are not more robust. In fact, the IEDB uses NetMHC predictors, as well as other methods. Performance, reliability and comparability of IEDB and NetMHC predictors were evaluated in details (Trolle et al. 2015; Kim et al. 2014; Peters et al. 2006). Here, a reasonable cutoff value of 3 was selected mainly because >90% of the identified peptides with an annotation score above 3 have a predicted IC₅₀ below 1000nM – we added this statement in the new version of the manuscript (see Materials and methods, subsection “HLA allele annotation”; and Results and Discussion). Increasing the cutoff value to 5, 50, or 500 would increase the stringency of the annotation process but would reduce significantly the number of peptides in each HLA allele-specific spectral library. In our opinion, this annotation strategy is still in a very early stage and we plan to improve it in the future. In this regard, we now clearly mention in Supplementary file 1 (2. Annotation and Visualization): “The next version of the software tools will integrate statistical bootstrapping analysis to determine the optimal annotation cutoff value for individual datasets. The next upgrade will also integrate additional epitope prediction algorithms from IEDB and will annotate supertype peptides. We also plan to integrate a predictor-independent strategy [e.g. alignment- and clustering-based approach similar to GibbsCluster and NNalign (Andreatta et al. 2013; Nielson et al. 2009)] since the HLA annotation score used in this manuscript can only be calculated for well-characterized HLA alleles.”

Minor points:

1) In the first paragraph of Results and Discussion, the comment “no reference computational framework is currently available to facilitate the analysis of such datasets" is not entirely correct, since software tools, such as MaxQuant, Perseus, or X-PRESIDENT can handle HLA-peptidome data without effort (for example: see the second reference cited, Bassani-Sternberg et al. 2015).

We agree with the reviewers. We now state (Results and Discussion, first paragraph): “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.”

2) In the second paragraph of Results and Discussion: “of all identified peptides to their respective HLA allele" is an overstatement, since significant parts of the identified peptides are not annotated to their respective HLA allele.

We agree with the reviewers that ‘all identified peptides’ is an overstatement. We now state: ‘the majority of the identified peptides’.

3) In the third paragraph of Results and Discussion: “Three synthetic EBV-derived peptides were also used to build the HLA-A02 and -B07 library. How can three peptide by useful for building two libraries? Some clarification is required.

We agree with the reviewers that some clarification is needed. The idea here was to provide a proof-of-principle that building high-quality assay libraries from synthetic peptides could be useful for the identification of non-self HLA-bound peptides. In the revised version of the manuscript, we now mention “Synthetic EBV-derived peptides known to bind HLA-A02 or -B07 were also used to build the libraries”. We also state: “Notably, assays generated from the synthetic EBV-related class I peptides enabled the identification of one EBV-derived HLA-A02 peptide (Figure 3C), thereby demonstrating that building high-quality assay libraries from synthetic class I peptides of pathogen origin could be useful for the identification of non-self HLA-bound peptides by SWATH-MS.”

4) In the fourth paragraph of Results and Discussion: “Class I peptide precursors fall within the range of 400-700 Th" – this is not correct, since many peptides fall outside this range. We suggest clarifying the percentage of the peptides which fall within this range, and indicating if the loss of these peptides compensate for the additional 17% gained by use of this narrow range. Also, why was the 400-650 mass range selected?

We thank the reviewers for this suggestion. To select the 400-650 mass range, we initially used 3,079 manually validated HLA peptide ligands isolated from 15 renal cell carcinomas (RCCs) and we define the range containing 99% of the ligands (see Author response image 2). Following the publication by Mommen et al. 2014, we re-evaluated the window by opening it up to 350-900m/z and we observed a decrease in unique peptides by ∼25%. The 400-650 mass range is therefore optimal in our hands using an Orbitrap-XL but might be influenced by the dynamic range and scan speeds of individual instruments and sample concentration. Using the Triple-TOF 5600, we observed that 98% of all class I peptide precursors fall within the 400-700 mass range. Thus, we now mention in the revised version of the manuscript: “Most class I peptide precursors (∼98%) fall within the range of 400-700 Th and were divided in 30 SWATH windows of 10 Da width each.”

Author response image 2

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5) In the subsection headed “Isolation of HLA peptides”: We suggest adding the reference by Hunt et al. 1992 to the references for the method of affinity purification and LC-MS-MS analysis of MHC peptidome.

We added the reference (to the subsection “Isolation of HLA peptides”).

6) In the subsection “Generation of HLA allele-specific peptide spectral and assay libraries”: Please clarify that the parameters are used for Spectrast, and explain what they mean for readers that do not use Spectrast.

We clarified that the parameters are used for Spectrast and we now guide the reader to a link where individual parameters are accurately defined. We now state in the revised version of the manuscript: “This section was adapted from Schubert et al. (2015b). The parameters below were used for Spectrast (Lam et al., 2008). Exact meaning of each parameter can be found in the following link: http://tools.proteomecenter.org/wiki/index.php?title=Software:SpectraST.”

Author details

1. Etienne Caron

   Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

   Contribution

   EC, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   caron@imsb.biol.ethz.ch

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-2770-6970

2. Lucia Espona

   Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

   Contribution

   LE, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Daniel J Kowalewski

   1. Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
   2. DKFZ partner site Tübingen, German Cancer Consortium, Tübingen, Germany

   Contribution

   DJK, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Heiko Schuster

   1. Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
   2. DKFZ partner site Tübingen, German Cancer Consortium, Tübingen, Germany

   Contribution

   HS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Nicola Ternette

   Target Discovery Institute Mass Spectrometry Laboratory, University of Oxford, Oxford, United Kingdom

   Contribution

   NT, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Adán Alpízar

   Proteomics Unit, Spanish National Biotechnology Centre, Madrid, Spain

   Contribution

   AA, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Ralf B Schittenhelm

   Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia

   Contribution

   RBS, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Sri H Ramarathinam

   Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia

   Contribution

   SHR, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Cecilia S Lindestam Arlehamn

   La Jolla Institute for Allergy and Immunology, La Jolla, United States

   Contribution

   CSLA, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

10. Ching Chiek Koh

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

    Contribution

    CCK, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Ludovic C Gillet

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

    Contribution

    LCG, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Armin Rabsteyn

    1. Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    2. DKFZ partner site Tübingen, German Cancer Consortium, Tübingen, Germany

    Contribution

    AR, Acquisition of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

13. Pedro Navarro

    Institute for Immunology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Contribution

    PN, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

14. Sangtae Kim

    Pacific Northwest National Laboratory, Richland, United States

    Contribution

    SK, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

15. Henry Lam

    Division of Biomedical Engineering and Department of Chemical and Biomolecular Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Contribution

    HL, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

16. Theo Sturm

    Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland

    Contribution

    TS, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

17. Miguel Marcilla

    Proteomics Unit, Spanish National Biotechnology Centre, Madrid, Spain

    Contribution

    MM, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Alessandro Sette

    La Jolla Institute for Allergy and Immunology, La Jolla, United States

    Contribution

    AS, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

19. David S Campbell

    Institute for Systems Biology, Seattle, United States

    Contribution

    DSC, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

20. Eric W Deutsch

    Institute for Systems Biology, Seattle, United States

    Contribution

    EWD, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

21. Robert L Moritz

    Institute for Systems Biology, Seattle, United States

    Contribution

    RLM, Conception and design, Drafting or revising the article, Contributed unpublished essential data or reagents

    Competing interests

    The authors declare that no competing interests exist.

22. Anthony W Purcell

    Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia

    Contribution

    AWP, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

23. Hans-Georg Rammensee

    1. Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    2. DKFZ partner site Tübingen, German Cancer Consortium, Tübingen, Germany

    Contribution

    H-GR, Conception and design, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

24. Stefan Stevanovic

    1. Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
    2. DKFZ partner site Tübingen, German Cancer Consortium, Tübingen, Germany

    Contribution

    SS, Conception and design, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

25. Ruedi Aebersold

    1. Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
    2. Faculty of Science, University of Zurich, Zurich, Switzerland

    Contribution

    RA, Conception and design, Drafting or revising the article

    For correspondence

    aebersold@imsb.biol.ethz.ch

    Competing interests

    The authors declare that no competing interests exist.

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