Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes

Gómez-Zepeda, David; Arnold-Schild, Danielle; Beyrle, Julian; Kumm, Elena; Distler, Ute; Schild, Hansjörg

doi:10.21203/rs.3.rs-2625909/v1

Cited by 5 publications

(9 citation statements)

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“…cThe HIPP has organized several high-profile webinars. The second HUPO-HIPP Summer School in Oxford, UK, in September 2022 addressed HLA-antibody specificities and their impact on sample quality, optimized workflows for improved identification of PTMs, advantages in automating sample preparation now emerging in specialized laboratories, − improved HLA-peptide acquisition technology, , and the importance of accurate and improved spectral annotation and validation strategies, , specifically in the context of tumor-specific, noncanonical peptide discovery. , The third HUPO-HIPP Summer School will take place in Montreal in August 2024. The HIPP organized a pre-HUPO Congress workshop in Korea on the noncanonical proteomea novel class of clinically targetable T cell antigens in conjunction with genome annotation and Ribo-Seq experts.…”

Section: Biology and Disease-driven Hppmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1–4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

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Section: Biology and Disease-driven Hppmentioning

confidence: 99%

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Omenn,

Lane,

Overall

et al. 2024

J. Proteome Res.

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“…From this aspect, the rescoring system for PSM using artificial intelligence (AI) prediction nodes has become a more standard technique. In addition to the previously used metrics, mass to charge ration (m/z), retention time (RT) and IM, the additional metrics, such as predicted spectral angle (SA) and the predicted RT, have been introduced to enhance the (immune)peptide identification 57‐59 . These synergic progresses both in MS hardware and the supportive search systems make robust identification possible from the growing large‐scale MS raw data.…”

Section: Transformative Technologies That Shed Light Upon the Dark Ma...mentioning

confidence: 99%

“…In addition to the previously used metrics, mass to charge ration (m/z), retention time (RT) and IM, the additional metrics, such as predicted spectral angle (SA) and the predicted RT, have been introduced to enhance the (immune)peptide identification. 57 , 58 , 59 These synergic progresses both in MS hardware and the supportive search systems make robust identification possible from the growing large‐scale MS raw data. As handling of gigantic MS data has become more generalized, machine‐learned search‐assisting algorithms play a greater role in this field.…”

Section: Transformative Technologies That Shed Light Upon the Dark Ma...mentioning

confidence: 99%

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

Minegishi,

Haga,

Ueda

2024

Cancer Science

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With significant advances in analytical technologies, research in the field of cancer immunotherapy, such as adoptive T cell therapy, cancer vaccine, and immune checkpoint blockade (ICB), is currently gaining tremendous momentum. Since the efficacy of cancer immunotherapy is recognized only by a minority of patients, more potent tumor‐specific antigens (TSAs, also known as neoantigens) and predictive markers for treatment response are of great interest. In cancer immunity, immunopeptides, presented by human leukocyte antigen (HLA) class I, play a role as initiating mediators of immunogenicity. The latest advancement in the interdisciplinary multiomics approach has rapidly enlightened us about the identity of the “dark matter” of cancer and the associated immunopeptides. In this field, mass spectrometry (MS) is a viable option to select because of the naturally processed and actually presented TSA candidates in order to grasp the whole picture of the immunopeptidome. In the past few years the search space has been enlarged by the multiomics approach, the sensitivity of mass spectrometers has been improved, and deep/machine‐learning‐supported peptide search algorithms have taken immunopeptidomics to the next level. In this review, along with the introduction of key technical advancements in immunopeptidomics, the potential and further directions of immunopeptidomics will be reviewed from the perspective of cancer immunotherapy.

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“…In this regard, Mass Spectrometry (MS)based immunopeptidomics stands out as a powerful scalable method for the unbiased identification of HLA-associated peptides [17][18][19] . In fact, MS has been increasingly applied in recent years to characterize the SARS-CoV-2 immunopeptidome [20][21][22][23][24][25][26][27][28][29][30] . These efforts have not only revealed conventional SARS-CoV-2 T-cell epitopes but also uncovered unconventional epitopes, including those arising from noncanonical translation, often escaping detection by conventional epitope mapping approaches 22,23 .…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines

Caron,

Kovalchik,

Hamelin

et al. 2024

Preprint

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Next-generation T-cell-directed vaccines for COVID-19 aim to induce durable T-cell immunity against circulating and future hypermutated SARS-CoV-2 variants. Mass Spectrometry (MS)-based immunopeptidomics holds promise for guiding vaccine design, but computational challenges impede the precise and unbiased identification of conserved T-cell epitopes crucial for vaccines against rapidly mutating viruses. We introduce a computational framework and analysis platform integrating a novel machine learning algorithm, immunopeptidomics, intra-host data, epitope immunogenicity, and geo-temporal CD8+ T-cell epitope conservation analyses. Central to our approach is MHCvalidator, a novel artificial neural network algorithm enhancing MS-based immunopeptidomics sensitivity by modeling antigen presentation and sequence features. MHCvalidator identified a novel nonconventional SARS-CoV-2 T-cell epitope presented by B7 supertype molecules, originating from a +1-frameshift in a truncated Spike (S) antigen, supported by ribo-seq data. Intra-host analysis of SARS-CoV-2 proteomes from ~100,000 COVID-19 patients revealed a prevalent S antigen truncation in ~51% of cases, exposing a rich source of frameshifted viral antigens. Our framework includes EpiTrack, a new computational pipeline tracking global mutational dynamics of MHCvalidator-identified SARS-CoV-2 CD8+ epitopes from vaccine BNT162b4. While most vaccine-encoded CD8+ epitopes exhibit global conservation from January 2020 to October 2023, a highly immunodominant A*01-associated epitope, especially in hospitalized patients, undergoes substantial mutations in Delta and Omicron variants. Our approach unveils unprecedented SARS-CoV-2 T-cell epitopes, elucidates novel antigenic features, and underscores mutational dynamics of vaccine-relevant epitopes. The analysis platform is applicable to any viruses, and underscores the need for continual vigilance in T-cell vaccine development against the evolving landscape of hypermutating SARS-CoV-2 variants.

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Thunder-DDA-PASEF enables high-coverage immunopeptidomics and identifies HLA class-I presented SarsCov-2 spike protein epitopes

Cited by 5 publications

References 0 publications

The 2023 Report on the Proteome from the HUPO Human Proteome Project

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Emerging potential of immunopeptidomics by mass spectrometry in cancer immunotherapy

Integrating Machine Learning-Enhanced Immunopeptidomics and SARS-CoV-2 Population-Scale Analyses Unveils Novel Antigenic Features for Next-Generation COVID-19 Vaccines

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