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Tanuwidjaya E, Lim Kam Sian TCC, Steele JR, Goncalves G, Woodhouse IB, Chang J, Ooi JD, Schittenhelm RB, Faridi P. SAPrIm 2.0: a semi-automated protocol for mid-throughput soluble HLA immunopeptidomics. Front Immunol 2025; 16:1546629. [PMID: 40342411 PMCID: PMC12058715 DOI: 10.3389/fimmu.2025.1546629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2024] [Accepted: 04/02/2025] [Indexed: 05/11/2025] Open
Abstract
Human leukocyte antigen (HLA) molecules are pivotal in guiding human adaptive immune responses through their presentation of peptide ligands, collectively known as the immunopeptidome. This process is central to the development of cancer immunotherapies, such as vaccines and T-cell therapies. Profiling the immunopeptidome from plasma and other biofluids has gained increasing traction, as it offers a minimally invasive approach for monitoring disease states and immune responses toward cancer therapy. Here we present the second iteration of SAPrIm, a refined immunopeptidomics tool optimized for soluble HLA analysis. It can process up to 12 samples per batch within a day. In this plasma-focused iteration, we identified approximately 1,200 to 4,000 immunopeptides from 100 µL to 1 mL of plasma, demonstrating high reproducibility across technical replicates, biological replicates, and inter-day analyses. This robust reproducibility highlights the method's strong potential for reliable relative quantification of immunopeptides in plasma-based studies. This workflow is positioned to advance the field of immunopeptidomics by enabling efficient plasma-based comparative analyses and mid-size cohort studies.
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Affiliation(s)
- Erwin Tanuwidjaya
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Terry C. C. Lim Kam Sian
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Joel R. Steele
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Gabriel Goncalves
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Isaac B. Woodhouse
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
| | - Janet Chang
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
| | - Joshua D. Ooi
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
| | - Ralf B. Schittenhelm
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Pouya Faridi
- Department of Medicine, School of Clinical Science, Faculty of Medicine, Nursing & Health Science, Monash University, Clayton, VIC, Australia
- Monash Proteomics & Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC, Australia
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2
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Leung K, Schaefer K, Lin Z, Yao Z, Wells JA. Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome. Chem Rev 2025; 125:4069-4110. [PMID: 40178992 PMCID: PMC12022999 DOI: 10.1021/acs.chemrev.4c00554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 02/05/2025] [Accepted: 03/07/2025] [Indexed: 04/05/2025]
Abstract
The cell surface proteome, or surfaceome, is the hub for cells to interact and communicate with the outside world. Many disease-associated changes are hard-wired within the surfaceome, yet approved drugs target less than 50 cell surface proteins. In the past decade, the proteomics community has made significant strides in developing new technologies tailored for studying the surfaceome in all its complexity. In this review, we first dive into the unique characteristics and functions of the surfaceome, emphasizing the necessity for specialized labeling, enrichment, and proteomic approaches. An overview of surfaceomics methods is provided, detailing techniques to measure changes in protein expression and how this leads to novel target discovery. Next, we highlight advances in proximity labeling proteomics (PLP), showcasing how various enzymatic and photoaffinity proximity labeling techniques can map protein-protein interactions and membrane protein complexes on the cell surface. We then review the role of extracellular post-translational modifications, focusing on cell surface glycosylation, proteolytic remodeling, and the secretome. Finally, we discuss methods for identifying tumor-specific peptide MHC complexes and how they have shaped therapeutic development. This emerging field of neo-protein epitopes is constantly evolving, where targets are identified at the proteome level and encompass defined disease-associated PTMs, complexes, and dysregulated cellular and tissue locations. Given the functional importance of the surfaceome for biology and therapy, we view surfaceomics as a critical piece of this quest for neo-epitope target discovery.
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Affiliation(s)
- Kevin
K. Leung
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Kaitlin Schaefer
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Zhi Lin
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Zi Yao
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - James A. Wells
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
- Department
of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
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3
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Kessler AL, Pieterman RFA, Doff WAS, Bezstarosti K, Bouzid R, Klarenaar K, Jansen DTSL, Luijten RJ, Demmers JAA, Buschow SI. HLA I immunopeptidome of synthetic long peptide pulsed human dendritic cells for therapeutic vaccine design. NPJ Vaccines 2025; 10:12. [PMID: 39827205 PMCID: PMC11742953 DOI: 10.1038/s41541-025-01069-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 01/03/2025] [Indexed: 01/22/2025] Open
Abstract
Synthetic long peptides (SLPs) are a promising vaccine modality that exploit dendritic cells (DC) to treat chronic infections or cancer. Currently, the design of SLPs relies on in silico prediction and multifactorial T cells assays to determine which SLPs are best cross-presented on DC human leukocyte antigen class I (HLA-I). Furthermore, it is unknown how TLR ligand-based adjuvants affect DC cross-presentation. Here, we generated a unique, high-quality immunopeptidome dataset of human DCs pulsed with 12 hepatitis B virus (HBV)-based SLPs combined with either a TLR1/2 (Amplivant®) or TLR3 (PolyI:C) ligand. The obtained immunopeptidome reflected adjuvant-induced differences, but no differences in cross-presentation of SLPs. We uncovered dominant (cross-)presentation on B-alleles, and identified 33 unique SLP-derived HLA-I peptides, several of which were not in silico predicted and some were consistently found across donors. Our work puts forward DC immunopeptidomics as a valuable tool for therapeutic vaccine design.
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Affiliation(s)
- Amy L Kessler
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical sciences, University of Utrecht, Utrecht, The Netherlands
| | - Roel F A Pieterman
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Wouter A S Doff
- Proteomics Center, Department of Biochemistry, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Karel Bezstarosti
- Proteomics Center, Department of Biochemistry, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rachid Bouzid
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Merus N.V., Utrecht, The Netherlands
| | - Kim Klarenaar
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Division of Laboratories, Pharmacy and Biomedical Genetics, UMC Utrecht, Utrecht, The Netherlands
| | - Diahann T S L Jansen
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Robbie J Luijten
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeroen A A Demmers
- Proteomics Center, Department of Biochemistry, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sonja I Buschow
- Department of Gastroenterology & Hepatology, Erasmus MC University Medical Center Rotterdam, Rotterdam, The Netherlands.
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4
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Wei F, Kouro T, Nakamura Y, Ueda H, Iiizumi S, Hasegawa K, Asahina Y, Kishida T, Morinaga S, Himuro H, Horaguchi S, Tsuji K, Mano Y, Nakamura N, Kawamura T, Sasada T. Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation. Comput Struct Biotechnol J 2024; 23:859-869. [PMID: 38356658 PMCID: PMC10864759 DOI: 10.1016/j.csbj.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.
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Affiliation(s)
- Feifei Wei
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Taku Kouro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yuko Nakamura
- Isotope Science Center, The University of Tokyo, Tokyo, Japan
| | - Hiroki Ueda
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Susumu Iiizumi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Kyoko Hasegawa
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | - Yuki Asahina
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
| | - Takeshi Kishida
- Department of Urology, Kanagawa Cancer Center, Yokohama, Japan
| | - Soichiro Morinaga
- Department of Hepato-Biliary and Pancreatic Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Hidetomo Himuro
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Shun Horaguchi
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
- Department of Pediatric Surgery, Nihon University School of Medicine, Tokyo, Japan
| | - Kayoko Tsuji
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Yasunobu Mano
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
| | - Norihiro Nakamura
- Research & Early Development Division, BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan
| | | | - Tetsuro Sasada
- Division of Cancer Immunotherapy, Kanagawa Cancer Center Research Institute, Yokohama, Japan
- Cancer Vaccine and Immunotherapy Center, Kanagawa Cancer Center, Yokohama, Japan
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5
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Leddy O, Yuki Y, Carrington M, Bryson BD, White FM. PathMHC: a workflow to selectively target pathogen-derived MHC peptides in discovery immunopeptidomics experiments for vaccine target identification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.11.612454. [PMID: 39314426 PMCID: PMC11419027 DOI: 10.1101/2024.09.11.612454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Vaccine-elicited T cell responses can contribute to immune protection against emerging infectious disease risks such as antimicrobials-resistant (AMR) microbial pathogens and viruses with pandemic potential, but rapidly identifying appropriate targets for T cell priming vaccines remains challenging. Mass spectrometry (MS) analysis of peptides presented on major histocompatibility complexes (MHCs) can identify potential targets for protective T cell responses in a proteome-wide manner. However, pathogen-derived peptides are outnumbered by self peptides in the MHC repertoire and may be missed in untargeted MS analyses. Here we present a novel approach, termed PathMHC, that uses computational analysis of untargeted MS data followed by targeted MS to discover novel pathogen-derived MHC peptides more efficiently than untargeted methods alone. We applied this workflow to identify MHC peptides derived from multiple microbes, including potential vaccine targets presented on MHC-I by human dendritic cells infected with Mycobacterium tuberculosis . PathMHC will facilitate antigen discovery campaigns for vaccine development.
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6
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Flender D, Vilenne F, Adams C, Boonen K, Valkenborg D, Baggerman G. Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39152539 DOI: 10.1002/mas.21905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024]
Abstract
Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.
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Affiliation(s)
- Daniel Flender
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- Health Unit, VITO, Mol, Belgium
| | - Frédérique Vilenne
- Health Unit, VITO, Mol, Belgium
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- ImmuneSpec, Niel, Belgium
| | - Dirk Valkenborg
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Geert Baggerman
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
- ImmuneSpec, Niel, Belgium
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7
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Fröhlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics. Mol Cell Proteomics 2024; 23:100800. [PMID: 38880244 PMCID: PMC11380018 DOI: 10.1016/j.mcpro.2024.100800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024] Open
Abstract
Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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Affiliation(s)
- Klemens Fröhlich
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Adrianna Seredynska
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Maximilian Maldacker
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany.
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8
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Lautenbacher L, Yang KL, Kockmann T, Panse C, Chambers M, Kahl E, Yu F, Gabriel W, Bold D, Schmidt T, Li K, MacLean B, Nesvizhskii AI, Wilhelm M. Koina: Democratizing machine learning for proteomics research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596953. [PMID: 38895358 PMCID: PMC11185529 DOI: 10.1101/2024.06.01.596953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.
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Affiliation(s)
- Ludwig Lautenbacher
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Kevin L. Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Tobias Kockmann
- Functional Genomics Center Zurich (FGCZ) - University of Zurich | ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich (FGCZ) - University of Zurich | ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Quartier Sorge - Batiment Amphipole, CH-1015 Lausanne, Switzerland
| | - Matthew Chambers
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Elias Kahl
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Fengchao Yu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Dulguun Bold
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | | | - Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
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9
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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10
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Paromov V, Uversky VN, Cooley A, Liburd LE, Mukherjee S, Na I, Dayhoff GW, Pratap S. The Proteomic Analysis of Cancer-Related Alterations in the Human Unfoldome. Int J Mol Sci 2024; 25:1552. [PMID: 38338831 PMCID: PMC10855131 DOI: 10.3390/ijms25031552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/12/2024] Open
Abstract
Many proteins lack stable 3D structures. These intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains with intrinsically disordered protein regions (IDPRs) often carry out regulatory functions related to molecular recognition and signal transduction. IDPs/IDPRs constitute a substantial portion of the human proteome and are termed "the unfoldome". Herein, we probe the human breast cancer unfoldome and investigate relations between IDPs and key disease genes and pathways. We utilized bottom-up proteomics, MudPIT (Multidimensional Protein Identification Technology), to profile differentially expressed IDPs in human normal (MCF-10A) and breast cancer (BT-549) cell lines. Overall, we identified 2271 protein groups in the unfoldome of normal and cancer proteomes, with 148 IDPs found to be significantly differentially expressed in cancer cells. Further analysis produced annotations of 140 IDPs, which were then classified to GO (Gene Ontology) categories and pathways. In total, 65% (91 of 140) IDPs were related to various diseases, and 20% (28 of 140) mapped to cancer terms. A substantial portion of the differentially expressed IDPs contained disordered regions, confirmed by in silico characterization. Overall, our analyses suggest high levels of interactivity in the human cancer unfoldome and a prevalence of moderately and highly disordered proteins in the network.
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Affiliation(s)
- Victor Paromov
- Meharry Proteomics Core, RCMI Research Capacity Core, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
| | - Vladimir N. Uversky
- Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA; (V.N.U.); (I.N.)
| | - Ayorinde Cooley
- Meharry Bioinformatics Core, Department of Microbiology, Immunology and Physiology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
| | - Lincoln E. Liburd
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA (S.M.)
| | - Shyamali Mukherjee
- Department of Biochemistry, Cancer Biology, Neuroscience & Pharmacology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA (S.M.)
| | - Insung Na
- Department of Molecular Medicine, USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL 33613, USA; (V.N.U.); (I.N.)
| | - Guy W. Dayhoff
- Department of Chemistry, College of Art and Sciences, University of South Florida, Tampa, FL 33613, USA;
| | - Siddharth Pratap
- Meharry Proteomics Core, RCMI Research Capacity Core, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
- Meharry Bioinformatics Core, Department of Microbiology, Immunology and Physiology, School of Medicine, Meharry Medical College, Nashville, TN 37208, USA;
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11
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Shahbazy M, Ramarathinam SH, Li C, Illing PT, Faridi P, Croft NP, Purcell AW. MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes. Brief Bioinform 2024; 25:bbae087. [PMID: 38487848 PMCID: PMC10940831 DOI: 10.1093/bib/bbae087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.
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Affiliation(s)
- Mohammad Shahbazy
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Chen Li
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Pouya Faridi
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC 3168, Australia
- Monash Proteomics and Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
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12
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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13
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Lim Kam Sian TCC, Goncalves G, Steele JR, Shamekhi T, Bramberger L, Jin D, Shahbazy M, Purcell AW, Ramarathinam S, Stoychev S, Faridi P. SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics. Front Immunol 2023; 14:1107576. [PMID: 37334365 PMCID: PMC10272402 DOI: 10.3389/fimmu.2023.1107576] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/10/2023] [Indexed: 06/20/2023] Open
Abstract
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.
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Affiliation(s)
- Terry C. C. Lim Kam Sian
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Gabriel Goncalves
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Joel R. Steele
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Tima Shamekhi
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Liesl Bramberger
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Dongbin Jin
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
| | - Mohammad Shahbazy
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Anthony W. Purcell
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Sri Ramarathinam
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | | | - Pouya Faridi
- Department of Medicine, School of Clinical Sciences, Faculty of Medicine, Nursing & Health Sciences, Monash University, Clayton, VIC, Australia
- Monash Proteomics and Metabolomics Platform, Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
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14
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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15
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Phulphagar KM, Ctortecka C, Jacome ASV, Klaeger S, Verzani EK, Hernandez GM, Udeshi ND, Clauser KR, Abelin JG, Carr SA. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry. Mol Cell Proteomics 2023:100563. [PMID: 37142057 DOI: 10.1016/j.mcpro.2023.100563] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.
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