Immunopeptidomics: Unlocking the antigen landscape for precision immunotherapy

Introduction to immunopeptidomics

Immunopeptidomics is the study of a diverse set of peptides presented by Major Histocompatibility Complex (MHC) molecules. These peptides enable immune cells to see infection, tumor growth, or alterations to the body’s own cells. Immunopeptidomics combines proteomics and immunology to define the repertoire of peptides, or immunopeptidome, that defines what a cell appears to the immune system. High-resolution mass spectrometry enables identification of vital peptides, including neoantigens, pathogen-derived peptides, and self-peptides, that drive immune responses and guide therapeutic development. Advances in peptide separation, LC-MS/MS sensitivity, and computational analysis have facilitated detailed mapping of MHC-bound peptides in cancer, infections, and autoimmunity. Such insights are vital for creating diagnostics, vaccines, and targeted immunotherapies, establishing immunopeptidomics as a cornerstone of translational medicine^1-4. OHMX.bio employs advanced proteomics and bioinformatics platforms to characterize MHC peptides. The resulting data turn complex antigen landscapes into knowledge that advances both clinical immunology and therapeutic innovation.

What is immunopeptidomics?

Immunopeptidomics is the systematic, large-scale analysis of peptides bound to MHC molecules, providing a direct window into what T cells “see” on cell surfaces. These peptides, together known as the immunopeptidome, are derived from proteins (both endogenous and exogenous) that are digested, by the proteasome, transported into the endoplasmic reticulum, and assembled onto MHC complexes for display on the cell surface. The immune system can identify and respond to self, altered-self, or non-self because of the unique array of peptide–MHC complexes expressed on a cell’s surface, which show whether the cell is normal or aberrant.

The identification of canonical, post-translationally modified (PTM), and noncanonical peptides has been greatly aided by contemporary technological advancements, enhanced MHC isolation techniques, higher LC–MS/MS sensitivity, and proteogenomic pipelines. Conventional proteomics would miss some noncanonical peptides because they come from different reading frames, untranslated regions, or cryptic translation events. These advancements increase the relevance of translational research by enabling the profiling of even limited or scarce clinical samples.

Integration with genomic, transcriptomic, and ribosome profiling (Ribo-seq) information further enriches this discovery. Ribo-seq, conversely, shows new sites of translation that create novel peptide fragments, some of which are visible on MHC molecules but not represented in reference proteomes. Integrating two complimentary approaches, immunopeptidomics delivers a comprehensive map of antigen presentation, showing both predicted and unrecognised epitopes. This integrated framework combines molecular expression with immunological recognition to place immunopeptidomics at the convergence of proteomics and immunology, and a platform for biomarker discovery, immunotherapy design, and precision vaccine manufacture.^1,2

Applications in disease and therapy

Immunopeptidomics has become a central modality in oncology due to its capability to precisely detect tumor-specific neoantigens, peptides generated by somatic mutations, RNA splicing, gene fusions, or non-coding regions, that are presented on the surface of tumor cells by MHC molecules. The tumor neoantigens, absent in nonmalignant tissue, elicit strong CD8⁺ T-cell responses and are the basis for individualized cancer immunotherapies, including peptide-based vaccines, immune checkpoint inhibitors, and adoptive T-cell therapies. Mass spectrometry immuno-peptidomics coupled with next-generation sequencing and bioinformatics facilitates biomarker discovery and neoantigen profiling to inform precision immunotherapy and personalized cancer vaccine design for the patient.⁴

Understanding host-pathogen interactions, designing rational vaccines, and discovering pathogen-derived peptides that are displayed on MHC molecules during infection all depend on immunopeptidomics. Strong CD4⁺ and CD8⁺ T-cell responses are induced by bacterial antigens from Mycobacterium TB and Chlamydia trachomatis, as well as viral epitopes from SARS-CoV-2 spike proteins that have been identified using mass spectrometry techniques. These pathogen-specific peptides serve as the basis for new vaccines, such as those based on viral and mRNA vectors. Furthermore, immunopeptidomic profiling offers insights into immune recognition, antigen presentation, and protective immunity against the majority of viral disorders, as well as recommendations for biomarker identification.^5,6

Although the majority of autoantigens are still unknown, T cells use their capacity to identify self-peptides displayed on MHC/HLA molecules to trigger autoimmune diseases. Immunopeptidomics continues to uncover this hidden antigenic landscape following the identification of PTM epitopes, hybrid insulin peptides (HIPs), splice variants, and cryptic peptides as putative autoantigens for Type 1 diabetes, SLE, and rheumatoid arthritis. By combining immunopeptidome data sets with antigen identification and T cell reactivity assays, researchers can identify therapeutic targets and diagnose biomarkers directly associated with pathogenic autoreactivity.⁷

Subsequent improvements further reveal that the combination of immunopeptidomics with multi-omics platforms including as transcriptomics, phosphoproteomics, and single-cell sequencing enables it to end-to-end map antigen presentation networks during health and illness. The integration enhances epitope prediction, improves patient classification, and captures dynamic biomarkers that are able to predict the success of immunotherapy or autoimmune relapse. Further, modern high-throughput platforms like the Immune Epitope Database (IEDB) and SysteMHC Atlas have enabled the translation of immunopeptidomic data into therapeutically valuable information on antigen presentation maps to cancer, infection, and autoimmune treatment responses.^6,7

Technologies driving immunopeptidomics

Immunopeptidomics works by first purifying MHC-peptide complexes, subsequent chromatographic separation of the peptides, sophisticated mass spectrometry-based analysis, and ultimate identification by use of computers. Because MHC-bound peptides are rare and contain no areas where enzymes are likely to cleave them, they are hard to find. The most common method to achieve this is immunoaffinity purification. Monoclonal antibodies targeting HLA class I or II molecules are used to isolate peptide–MHC complexes from tissue or cell lysates. Recent advances, including automated and microfluidic technologies, have streamlined immunoaffinity capture and peptide extraction, minimizing sample loss and enabling the analysis of trace amounts of clinical material.

Post-purification peptide identification and separation are achieved through liquid chromatography coupled with LC-MS/MS. The most common techniques used are Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA). In DDA, the instrument scans the most intense precursor ions in each MS¹, a “shotgun” method that can miss low-abundance peptides. DIA fragments all precursors within defined m/z windows, more reproducible with deeper coverage, and thus is more preferred in immunopeptidomics.

Recent advancements in MS have integrated ion mobility to improve separation. Time-of-flight MS with ion mobility (TOF-IMS) enhances sensitivity and throughput in resolving HLA peptides, detecting low-abundance neoepitopes missed by standard platforms. Thunder-DDA-PASEF (LC-IMS-MS/MS) combines ion mobility with DDA to semi-selectively fragment HLA ligands, doubling class I peptide coverage and increasing identifications by about 40%. Trapped ion mobility spectrometry (timsTOF) reduces interference and improves resolution, identifying over 15,000 distinct HLA peptides from ~4 × 10⁷ cells with sensitivity down to 1 × 10⁶ cells. Targeted MS modes like parallel reaction monitoring and optimized “optiPRM” enable highly sensitive analysis of specific peptides, especially when sample input is limited.^3,4,8,9

Immunopeptidomics can be optimally utilized when integrated in conjunction with other layers of omics. Integration with transcriptomics (RNA-seq) allows for the discovery of the levels of gene expression and regulatory mechanisms beyond the simple raw gene sequence. These layers enable for detection of levels of gene expression, regulatory processes, and proteins connected to specific peptides. Whole-exome sequencing in particular improves the prediction and validation of mutation-derived neoepitopes, which is crucial for cancer immunotherapy. While metabolomics offers information on the metabolic state of cells, phosphoproteomics determines the post-translational changes that impact peptide binding or processing. Protein levels alone cannot detect the change in neoantigen presentation caused by IFN-γ; this can be done by combining genomic, proteomic, and immunopeptidomic studies. In order to obtain deeper insights, low-abundance peptide identification capabilities are being improved by single-cell proteomics and the sensitivity of MS, which can be integrated with transcriptomics.¹⁰

OHMX.bio’s expertise in immunopeptidomics

OHMX.bio offers comprehensive solutions to enhance all aspects of immunopeptidomics, from rigorous sample preparation to combined data analysis. Our solutions enable accurate peptide identification and measurement, serving to resolve patterns of antigen presentation. Immunoaffinity capture in sample preparation followed by rigorous washing steps is used for the conservation of low-abundance MHC-bound peptides. Improved detection sensitivity using high-resolution mass spectrometry enables recovery of valuable peptide information even from smaller or more difficult samples. Layering the data with transcriptomic and sequencing data, we map gene expression to peptide presentation and rank antigens of potential biological importance. Peptide-level findings at the analytical stage are combined with transcriptomic and sequencing datasets to provide information on gene expression, peptide display, and antigen prioritisation. This integrated strategy converts sophisticated antigen maps into usable information.

Conclusion

Immunopeptidomics is playing an increasingly important role in personalised medicine, offering insights into the diverse range of MHC-bound peptides that shape immune recognition in cancer, infectious diseases, and autoimmune conditions. By combining mass spectrometry with transcriptomic and genomic data, this approach helps uncover neoantigens and biomarkers that are key to developing personalised vaccines and targeted immunotherapies. Interpreting this complex data, however, demands specialised tools and expertise. At OHMX.bio, we support the entire workflow, from sample preparation to bioinformatic analysis, delivering high-quality immunopeptidome data that helps researchers make confident decisions and advance therapeutic discovery in precision immunology.

References

[1] Thibault, P., & Perreault, C. (2022). Immunopeptidomics: reading the immune signal that defines self from nonself. Molecular & Cellular Proteomics, 21(6).

[2] Balakrishnan, A., Winiarek, G., Hołówka, O., Godlewski, J., & Bronisz, A. (2025). Unlocking the secrets of the immunopeptidome: MHC molecules, ncRNA peptides, and vesicles in immune response. Frontiers in Immunology, 16, 1540431.

[3] Hoenisch Gravel, N., Nelde, A., Bauer, J., Mühlenbruch, L., Schroeder, S. M., Neidert, M. C., … & Walz, J. S. (2023). TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification. Nature communications, 14(1), 7472.

[4] Pongcharoen, S., Kaewsringam, N., Somaparn, P., Roytrakul, S., Maneerat, Y., Pintha, K., & Topanurak, S. (2024). Immunopeptidomics in the cancer immunotherapy era. Exploration of Targeted Anti-tumor Therapy, 5(4), 801.

[5] Chavda, V. P., & Redwan, E. M. (2022). SARS-CoV-2: immunopeptidomics and other immunological studies. Vaccines, 10(11), 1975.

[6] Mayer, R. L., & Impens, F. (2021). Immunopeptidomics for next-generation bacterial vaccine development. Trends in microbiology, 29(11), 1034-1045.

[7] Arshad, S., Cameron, B., & Joglekar, A. V. (2025). Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets. npj Systems Biology and Applications, 11(1), 10.

[8] Kuznetsov, A., Voronina, A., Govorun, V., & Arapidi, G. (2020). Critical review of existing MHC I immunopeptidome isolation methods. Molecules, 25(22), 5409.

[9] Salek, M., Förster, J. D., Becker, J. P., Meyer, M., Charoentong, P., Lyu, Y., … & Riemer, A. B. (2024). optiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material. Molecular & Cellular Proteomics, 23(9).

[10] Huber, F., Arnaud, M., Stevenson, B. J., Michaux, J., Benedetti, F., Thevenet, J., … & Bassani-Sternberg, M. (2024). A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy. Nature biotechnology, 1-13.

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