Immunopeptidomics for Dummies: Detailed Experimental Protocols and Rapid, User-Friendly Visualization of MHC I and II Ligand Datasets with MhcVizPipe

Immunopeptidomics refers to the science of investigating the composition and dynamics of peptides presented by major histocompatibility complex (MHC) class I and class II molecules using mass spectrometry (MS). Here, we aim to provide a technical report to any non-expert in the field wishing to establish and/or optimize an immunopeptidomic workflow with relatively limited computational knowledge and resources. To this end, we thoroughly describe step-by-step instructions to isolate MHC class I and II-associated peptides from various biological sources, including mouse and human biospecimens. Most notably, we created MhcVizPipe (MVP) (https://github.com/CaronLab/MhcVizPipe), a new and easy-to-use open-source software tool to rapidly assess the quality and the specific enrichment of immunopeptidomic datasets upon the establishment of new workflows. In fact, MVP enables intuitive visualization of multiple immunopeptidomic datasets upon testing sample preparation protocols and new antibodies for the isolation of MHC class I and II peptides. In addition, MVP enables the identification of unexpected binding motifs and facilitates the analysis of non-canonical MHC peptides. We anticipate that the experimental and bioinformatic resources provided herein will represent a great starting point for any non-expert and will therefore foster the accessibility and expansion of the field to ultimately boost its maturity and impact.

[1]  Tanveer S. Batth,et al.  Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation* , 2019, Molecular & Cellular Proteomics.

[2]  T. Veres,et al.  Polymer-based microfluidic chip for rapid and efficient immunomagnetic capture and release of Listeria monocytogenes. , 2015, Lab on a chip.

[3]  X. D. Hoa,et al.  Development of a multiplexed microfluidic proteomic reactor and its application for studying protein-protein interactions. , 2011, Analytical chemistry.

[4]  Dario Neri,et al.  High‐sensitivity HLA class I peptidome analysis enables a precise definition of peptide motifs and the identification of peptides from cell lines and patients’ sera , 2016, Proteomics.

[5]  Mark Cobbold,et al.  Complementary IMAC enrichment methods for HLA-associated phosphopeptide identification by mass spectrometry , 2015, Nature Protocols.

[6]  Robert P. Luoma,et al.  Digital microfluidic magnetic separation for particle-based immunoassays. , 2012, Analytical chemistry.

[7]  Morten Nielsen,et al.  IEDB-AR: immune epitope database—analysis resource in 2019 , 2019, Nucleic Acids Res..

[8]  Kevin A. Kovalchik,et al.  The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases* , 2019, Molecular & Cellular Proteomics.

[9]  Sri H. Ramarathinam,et al.  Response to Comment on “A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands” , 2019, Science Immunology.

[10]  H. Röst Deep learning adds an extra dimension to peptide fragmentation , 2019, Nature Methods.

[11]  A. Goldberg,et al.  Degradation of cell proteins and the generation of MHC class I-presented peptides. , 1999, Annual review of immunology.

[12]  Sébastien Lemieux,et al.  MAPDP: a cloud-based computational platform for immunopeptidomics analyses. , 2020, Journal of proteome research.

[13]  C. Perreault,et al.  Exploiting non-canonical translation to identify new targets for T cell-based cancer immunotherapy , 2018, Cellular and Molecular Life Sciences.

[14]  Ruedi Aebersold,et al.  A Case for a Human Immuno‐Peptidome Project Consortium , 2017, Immunity.

[15]  Jürgen Cox,et al.  High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis , 2019, Nature Methods.

[16]  Ngoc Hieu Tran,et al.  Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry , 2018, Nature Methods.

[17]  Mathias Wilhelm,et al.  Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning , 2019, Nature Methods.

[18]  Morten Nielsen,et al.  GibbsCluster: unsupervised clustering and alignment of peptide sequences , 2017, Nucleic Acids Res..

[19]  M. Nielsen,et al.  The Length Distribution of Class I–Restricted T Cell Epitopes Is Determined by Both Peptide Supply and MHC Allele–Specific Binding Preference , 2016, The Journal of Immunology.

[20]  W. van Eden,et al.  An Unexpected Major Role for Proteasome-Catalyzed Peptide Splicing in Generation of T Cell Epitopes: Is There Relevance for Vaccine Development? , 2017, Front. Immunol..

[21]  S. Stevanović,et al.  Purification and Identification of Naturally Presented MHC Class I and II Ligands. , 2019, Methods in molecular biology.

[22]  M. Nielsen,et al.  NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data , 2017, The Journal of Immunology.

[23]  Alex Rubinsteyn,et al.  MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing. , 2020, Cell systems.

[24]  Morten Nielsen,et al.  Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data. , 2020, Journal of proteome research.

[25]  Teodor Veres,et al.  Active pneumatic control of centrifugal microfluidic flows for lab-on-a-chip applications. , 2015, Lab on a chip.

[26]  Sri H. Ramarathinam,et al.  Spliced peptides and cytokine driven changes in the immunopeptidome of melanoma , 2019, bioRxiv.

[27]  Juliane Liepe,et al.  Post-translational peptide splicing and T cell response , 2017 .

[28]  R Higuchi,et al.  High-resolution, high-throughput HLA genotyping by next-generation sequencing. , 2009, Tissue antigens.

[29]  Morten Nielsen,et al.  NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data , 2020, Nucleic Acids Res..

[30]  H. Rammensee,et al.  The HLA Ligand Atlas. A resource of natural HLA ligands presented on benign tissues , 2019, bioRxiv.

[31]  Etienne Caron,et al.  Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry* , 2015, Molecular & Cellular Proteomics.

[32]  Hanspeter Pfister,et al.  UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.

[33]  Sri H. Ramarathinam,et al.  A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands , 2018, Science Immunology.

[34]  M. Mishto What We See, What We Do Not See, and What We Do Not Want to See in HLA Class I Immunopeptidomes , 2020, Proteomics.

[35]  Roman A. Zubarev,et al.  The SysteMHC Atlas project , 2017, Nucleic Acids Res..

[36]  Ruedi Aebersold,et al.  The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses. , 2020, Methods in molecular biology.

[37]  Morten Nielsen,et al.  Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data , 2019, Immunogenetics.

[38]  Martin Eisenacher,et al.  The PRIDE database and related tools and resources in 2019: improving support for quantification data , 2018, Nucleic Acids Res..

[39]  Juan Antonio Vizcaíno,et al.  Minimal Information About an Immuno‐Peptidomics Experiment (MIAIPE) , 2018, Proteomics.

[40]  A. Tholey,et al.  Miniaturized sample preparation on a digital microfluidics device for sensitive bottom-up microproteomics of mammalian cells using magnetic beads and mass spectrometry-compatible surfactants. , 2019, Lab on a chip.

[41]  Ilan Beer,et al.  Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome* , 2018, Molecular & Cellular Proteomics.

[42]  D. Harlan,et al.  Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion , 2016, Science.

[43]  G. Learn,et al.  Contribution of proteasome-catalyzed peptide cis-splicing to viral targeting by CD8+ T cells in HIV-1 infection , 2019, Proceedings of the National Academy of Sciences.

[44]  Sri H. Ramarathinam,et al.  Mass spectrometry–based identification of MHC-bound peptides for immunopeptidomics , 2019, Nature Protocols.

[45]  Alessandro Sette,et al.  An open-source computational and data resource to analyze digital maps of immunopeptidomes , 2015, eLife.

[46]  Xiaohui Liu,et al.  In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics , 2020, Nature Communications.

[47]  M. Bassani-Sternberg,et al.  High-Throughput, Fast, and Sensitive Immunopeptidomics Sample Processing for Mass Spectrometry. , 2019, Methods in molecular biology.

[48]  P. Kloetzel,et al.  Multi-level Strategy for Identifying Proteasome-Catalyzed Spliced Epitopes Targeted by CD8+ T Cells during Bacterial Infection. , 2017, Cell reports.

[49]  S. Lemieux,et al.  Global proteogenomic analysis of human MHC class I-associated peptides derived from non-canonical reading frames , 2016, Nature Communications.

[50]  Alex Rubinsteyn,et al.  MHCflurry: Open-Source Class I MHC Binding Affinity Prediction. , 2018, Cell systems.

[51]  G. Coukos,et al.  Mass spectrometry–driven exploration reveals nuances of neoepitope-driven tumor rejection , 2019, JCI insight.

[52]  Michal Bassani-Sternberg,et al.  Mass Spectrometry Based Immunopeptidomics for the Discovery of Cancer Neoantigens. , 2018, Methods in molecular biology.

[53]  George Coukos,et al.  Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes , 2019, Nature Biotechnology.

[54]  Timo Sachsenberg,et al.  MHCquant: Automated and reproducible data analysis for immunopeptidomics. , 2019, Journal of proteome research.

[55]  Patrick G. A. Pedrioli,et al.  A tissue-based draft map of the murine MHC class I immunopeptidome , 2018, Scientific Data.

[56]  S. Lemieux,et al.  MHC class I-associated peptides derive from selective regions of the human genome. , 2016, The Journal of clinical investigation.

[57]  M. D. Chamberlain,et al.  Digital Microfluidics for Immunoprecipitation. , 2016, Analytical chemistry.

[58]  Mathieu Courcelles,et al.  Comparison of the MHC I Immunopeptidome Repertoire of B‐Cell Lymphoblasts Using Two Isolation Methods , 2018, Proteomics.

[59]  P. Kloetzel,et al.  A large fraction of HLA class I ligands are proteasome-generated spliced peptides , 2016, Science.

[60]  S. Stevanović,et al.  Biochemical large-scale identification of MHC class I ligands. , 2013, Methods in molecular biology.

[61]  H. Ovaa,et al.  Why do proteases mess up with antigen presentation by re-shuffling antigen sequences? , 2018, Current opinion in immunology.

[62]  P. Gendron,et al.  Noncoding regions are the main source of targetable tumor-specific antigens , 2018, Science Translational Medicine.