Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition-based software platforms for immunopeptidomics: MaxQuant (https://maxquant.org/), FragPipe (https://fragpipe.nesvilab.org/), PEAKS (https://www.bioinfor.com/peaks-software/) and major histocompatibility complexquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-data-dependent acquisition-parallel accumulation and serial fragmentation method. We assessed each software's ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate control, achieving medium to high identification confidence correlations. The largest number of immunopeptides was identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.
Copyright © 2025 The Authors. Published by Elsevier Inc. All rights reserved.

3 The identification of MHC class I presented ligands by mass spectrometry (MS)-based immunopeptidomics is an essential tool to characterize antigen processing pathways and to define targets for tumor immunotherapies. However, existing sample preparation workflows typically require large sample inputs, limiting the applicability in high throughput drug screenings, kinetic immunopeptidome studies and for scare samples in clinical contexts. To address this challenge, we developed Mild Acid Elution in a Tip (MAETi), an antibody-free approach for low input MHC-1 immunopeptidome profiling. Using an optimized β-alanine MAE-buffer for MAETi reduces background interferences, enhances peptide coverage, and boosts reproducibility. Comparing bulk β-alanine-based MAE with bulk immunoprecipitation (IP), achieves similar or complementary immunopeptidome depth. Using two protocol layouts we further profiled initial inputs from 25,000 to 1 Million cells with HLA-tailored DDA- and DIA-PASEF schemes yielding on average over 1,000 and 3,000 predicted binders, respectively (DIA). This renders MAETi a facile, fast and scalable method enabling robust MS-based immunopeptidomics for minimal sample inputs. 2 Main messages / Highlights MAETi is a simplified and optimized MAE workflow enabling MHC1-ligandomics profiling from sub-million cell samples down to 25,000 cells We present a minimal, simplified sample preparation protocol termed Mild Acid Elution in a Tip (MAETi) enabling the MHC-1 ligandome analysis using 25,000 to 100,000,000 cells per sample as initial input Replacing citric acid in the elution buffer by β-alanine reduced unspecific contaminants, resulted in cleaner LC-MS chromatograms and boosted 8-13mer identifications by over 50% in MAEti samples. Bulk β-alanine-based MAE provides similar coverage of MHC class 1 ligands compared to bulk immunoprecipitation

Abstract Major histocompatibility complex (MHC, or Human leukocyte antigen, HLA) peptide ligands can be exploited to develop immunotherapies targeting immunogenic disease-specific immunopeptides, such as virus- or cancer mutation-derived peptides. Liquid chromatography-coupled with mass spectrometry (LC-MS)-based immunopeptidomics is the gold standard for identifying MHC ligands. We previously optimized a workflow enabling the identification of more than 10,000 MHC class I ligands per cell line. This process comprises three major steps: (I) a high-recovery immunopeptidome enrichment, (II) an optimized MS acquisition in the timsTOF Pro called Thunder-Data-Dependent Acquisition with Parallel Accumulation-SErial Fragmentation (Thunder-DDA-PASEF), (III) and peptide identification using PEAKS XPro boosted by MS2Rescore data-driven rescoring. Here, we describe our workflow for deep-coverage immunopeptidomics step-by-step, from sample preparation to data analysis and validation.

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.
© 2024. The Author(s).

Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies.
Copyright © 2023 Lim Kam Sian, Goncalves, Steele, Shamekhi, Bramberger, Jin, Shahbazy, Purcell, Ramarathinam, Stoychev and Faridi.