timsTOF mass spectrometry-based immunopeptidomics

Immunopeptidomics, at the intersection of immunology and proteomics, plays a pivotal role in understanding the immune system's response to disease, particularly in cancer immunotherapy and vaccine development. This cutting-edge field focuses on identifying and characterizing the peptides presented by major histocompatibility complex (MHC) molecules, also known as human leukocyte antigens (HLAs), on the cell surface. These peptides, called immunopeptides, are essential for T cell recognition and subsequent immune responses.

Advancements in mass spectrometry (MS) have revolutionized immunopeptidomics by enabling the direct identification and characterization of HLA-presented peptides. Among these technologies, the timsTOF platform has emerged as a frontrunner due to its high sensitivity, speed, and ability to handle complex samples. Leveraging timsTOF technology, researchers can delve deep into the immunopeptidome, uncovering novel antigens, understanding immune responses, and paving the way for personalized immunotherapies.

We are delighted to present on-demand webinar talks from two leading experts in the field of immunopeptidomics, both leveraging timsTOF technology to unravel the complexities of the immunopeptidome. These talks offer valuable insights into the latest methodologies, challenges, and discoveries in immunopeptidomics, providing a comprehensive overview for researchers and practitioners in the life sciences field.

Key Learnings from Naomi Hoenisch Gravel's Talk (Ph.D. Student at University of Tübingen, Group of Prof. Juliane Walz):

• Significance of T Cell Recognition in Cancer Immunotherapy: T cell recognition of HLA-presented tumor-associated peptides is crucial for cancer immune surveillance and the development of T cell-based immunotherapies.
• Role of MS-Based Immunopeptidomics: Mass spectrometry (MS)-based immunopeptidomics is essential for the direct identification and characterization of naturally presented tumor-associated peptides, providing valuable insights for immunotherapy development.
• Advantages of timsTOF MS in Immunopeptidomics: The implementation of ion mobility separation-based timsTOF MS enables high-speed and sensitive detection of HLA-presented peptides, surpassing traditional technologies like orbitrap.
• Application in Tumor Antigen Discovery: timsTOF-based immunopeptidomics facilitates tumor antigen discovery by expanding benign reference immunopeptidome databases, refining known tumor antigens, and identifying novel tumor antigens, including low abundant neoepitopes. These findings offer potential targets for future cancer immunotherapy development.

Article: TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification | Nature Communications

Key Learnings from Prof. Mathias Wilhelm's Talk (Prof. at the Technical University of Munich):

• Challenges in Immunopeptidomics: Immunopeptidomics presents challenges due to the complexity of peptide generation from parent proteins, requiring consideration of all possible protein subsequences within HLA class-specific length restrictions. This complexity leads to a vast search space and lower spectrum annotation rates.
• Rescoring for Enhanced Spectrum Annotation: Rescoring proves to be a powerful enhancement for standard sequence database searching in immunopeptidomics. By applying rescoring techniques, spectrum annotation performance can be significantly boosted, improving the identification of immunopeptides.
• Role of Highly Sensitive Instruments: Low abundant peptides are common in immunopeptidomics, emphasizing the importance of highly sensitive instruments. The timsTOF instruments, known for their sensitivity, are increasingly preferred in this field due to their ability to detect immunopeptides even from low input samples.
• Deep Learning-Based Fragment Ion Intensity Prediction: To further improve rescoring for immunopeptides measured using timsTOF instruments, a deep learning-based fragment ion intensity prediction model was developed. This model, trained on a large dataset, demonstrates up to a 3-fold improvement in the identification of immunopeptides, enhancing detection even from low input samples.

Article: Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF | bioRxiv