The rapidly expanding field of immunopeptidomics is focused on the identification and characterization of peptides, presented by major histocompatibility complex (MHC) molecules on the surfaces of cells. Research into this antigenic landscape has garnered considerable attention for its potential to deepen knowledge of the immune system, and is instrumental in advancing our comprehension of many different diseases, notably in the context of personalized cancer immunotherapies and disease pathogens.
Heightened sensitivity and enhanced peak capacity make trapped ion mobility spectrometry (TIMS) a game changer for immunopeptidomics research. Recent developments in TIMS and 4D proteomics have opened up new possibilities for researchers to explore the immunopeptidome with minimal cell amounts, demonstrating the potential for significant contribution to the advancement of personalized medicine.TIMS-enhanced mass spectrometry (MS) is a vital tool to identify and quantify MHC-bound peptides to aid vaccine development, autoimmune disease understanding, and cancer immunotherapy development, facilitating the precise analysis required to unlock breakthrough discoveries about the immune system and its role in disease.
Navigating immunopeptidomics challenges
While numerous breakthroughs have been achieved, it is imperative to address specific challenges to fully harness the full potential of immunopeptidomics research and facilitate the identification of a diverse range of MHC-bound peptides.
A key hurdle is the inherently low abundance of immunopeptides within complex biological samples, making the detection and identification of rare and clinically relevant immunopeptides a daunting task with conventional liquid chromatography MS (LC-MS) techniques. Highly sensitive and complex workflows are required, with steps including sample preparation, cell isolation, MHC peptide enrichment, data acquisition, and data analysis.
The diversity of MHC alleles and their binding ability to a diverse array of peptides of similar length further complicates the identification of peptides that specifically bind to MHC molecules, necessitating the development of comprehensive MHC-peptide binding databases. Since immunopeptide data is intricate and the source of peptides often unknown, specialized software and computational tools are indispensable in extracting meaningful insights from the data. Overcoming these obstacles holds the potential to unlock valuable insights into immune responses, disease mechanisms, and personalized immunotherapies.
TIMS is a separation technique in gas phase where ions are propelled through the TIMS tunnel by gas flow. An electrical field controls each ion from moving beyond a position defined by the ion’s mobility, where the push it experiences from the gas flow matches the force of the electrical field. Ramping down the electrical field allows ions to be selectively released from the TIMS tunnel according to their mobility. This allows researchers to reproducibly measure the collisional cross section (CCS) values for all detected ions, and those can be used to further increase the system’s selectivity.
Adding this extra dimension of separation to LC enhances peak capacity and overcomes traditional challenges. Furthermore, TIMS accumulates and concentrates ions of a specific mass-to-charge (m/z) and mobility, significantly boosting sensitivity and speed. By using a polygon filter in the m/z and mobility space (Figure 1), TIMS allows precise targeting of low-abundance MHC class I and II peptides. Moreover, it enables the differentiation of ions that are co-eluting and have similar masses by utilizing the cutting-edge mobility offset mass aligned (MOMA) technology, thereby enhancing the precision and certainty of ion identification.
Parallel accumulation serial fragmentation (PASEF) is a novel time of flight (TOF) MS-based acquisition method enabled by TIMS and employed for fast peptide separation and unambiguous identification. The specificity and high speed of MOMA and PASEF enable the confident identification of MHC-bound class I and class II peptides using TIMS. Particularly in the field of immunopeptidomics, MHC class I peptides often ionize as single charged species, thereby hampering their identification with traditional workflows. The space charge separation resulting from TIMS allows researchers to readily include single and multiply charged ion species in their analysis. MOMA reduces the interference from co-isolation while the inbuilt polygon filter allows the specific inclusion of precursor ions of interest without compromise.
General proteomics database searching is the preferred approach for peptide identification, though this becomes impractical in the field of immunopeptidomics as enzyme specificity is lacking, and reference databases are unavailable due to the investigation of non-canonical proteins. To address this challenge, an optimized de novo sequencing engine (BPS Novor) can be integrated into a specialist GPU-driven software to allow for fast, accurate, and precise peptide de novo sequencing of immunopeptidomics data, at a rate of more than 1,000 spectra/second. This means that an hour-long acquisition can be fully de novo sequenced in around two minutes. These rapid results are obtained without sacrificing accuracy or the availability of database searching of data-dependent acquisition (DDA) and data-independent acquisition (DIA) data on the same platform in real time.
By precisely detecting peptides at low abundance or with highly similar sequences across different charge states, TIMS addresses key challenges in the rapidly expanding field of immunopeptidomics. Along with new developments in PASEF and 4D-proteomics, researchers can now efficiently isolate and detect ions of interest with enhanced peptide separation, discrimination of highly similar peptides, and comprehensive peptidome profiling. Together, these capabilities enhance our comprehension of intricate biological systems and have the potential to drive progress in immunotherapy.
Meet the Author
Torsten Müller (Business Development Manager, Bruker Daltonics) is an LC-MS enthusiast with a strong interest in sample preparation, method optimization, and workflow enhancement within the field of proteomics. He gained valuable experience working in the labs of Hanno Steen in Boston and Ruedi Aebersold in Zurich before completing his PhD at the German Cancer Research Center in Heidelberg, Germany, under the supervision of Prof. Jeroen Krijgsveld, focused on improving the robustness, reproducibility, and throughput of proteome profiling in clinical specimens.
During his time as a postdoctoral researcher, he successfully implemented an automated workflow based on the SP3 methodology. This streamlined approach allowed for the efficient processing of diverse clinical sample types, such as tissue, FFPE material, serum, plasma, CSF, and cells. His work contributed to the MSCoreSys/SMART-CARE consortium, where he utilized the timsTOF platform. As Business Development Manager for Proteomics at Bruker, Torsten’s motivation is to make proteomics more accessible to the scientific community at large by fostering collaborations, driving innovation, and empowering researchers to tackle the complexities of the proteome and advance proteomics research.
 Gomez-Zepeda D, Arnold-Schild D, Beyrle J, Kumm E, Distler U, Schild H, et al. Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes. www.researchsquare.com. Published March 9, 2023. Accessed November 24, 2023. https://www.researchsquare.com/article/rs-2625909/v1