Literature DB >> 32387766

In-depth mining of the immunopeptidome of an acute myeloid leukemia cell line using complementary ligand enrichment and data acquisition strategies.

Kirti Pandey1, Nicole A Mifsud1, Terry C C Lim Kam Sian1, Rochelle Ayala1, Nicola Ternette2, Sri H Ramarathinam3, Anthony W Purcell4.   

Abstract

The identification of T cell epitopes derived from tumour specific antigens remains a significant challenge for the development of peptide-based vaccines and immunotherapies. The use of mass spectrometry-based approaches (immunopeptidomics) can provide powerful new avenues for the identification of such epitopes. In this study we report the use of complementary peptide antigen enrichment methods and a comprehensive mass spectrometric acquisition strategy to provide in-depth immunopeptidome data for the THP-1 cell line, a cell line used widely as a model of human leukaemia. To accomplish this, we combined robust experimental workflows that incorporated ultrafiltration or off-line reversed phase chromatography to enrich peptide ligand as well as a multifaceted data acquisition strategy using an Orbitrap Fusion LC-MS instrument. Using the combined datasets from the two ligand enrichment methods we gained significant depth in immunopeptidome coverage by identifying a total of 41,816 HLA class I peptides from THP-1 cells, including a significant number of peptides derived from different oncogenes or over expressed proteins associated with cancer. The physicochemical properties of the HLA-bound peptides dictated their recovery using the two ligand enrichment approaches and their distribution across the different precursor charge states considered in the data acquisition strategy. The data highlight the complementarity of the two enrichment procedures, and in cases where sample is not limiting, suggest that the combination of both approaches will yield the most comprehensive immunopeptidome information.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Acute myeloid leukemia; Cancer; HLA; Immunopeptidomics

Year:  2020        PMID: 32387766     DOI: 10.1016/j.molimm.2020.04.008

Source DB:  PubMed          Journal:  Mol Immunol        ISSN: 0161-5890            Impact factor:   4.407


  6 in total

Review 1.  T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles.

Authors:  Luca Hensen; Patricia T Illing; Louise C Rowntree; Jane Davies; Adrian Miller; Steven Y C Tong; Jennifer R Habel; Carolien E van de Sandt; Katie L Flanagan; Anthony W Purcell; Katherine Kedzierska; E Bridie Clemens
Journal:  Front Immunol       Date:  2022-05-06       Impact factor: 8.786

2.  IFNγ Modulates the Immunopeptidome of Triple Negative Breast Cancer Cells by Enhancing and Diversifying Antigen Processing and Presentation.

Authors:  Gabriel Goncalves; Kerry A Mullan; Divya Duscharla; Rochelle Ayala; Nathan P Croft; Pouya Faridi; Anthony W Purcell
Journal:  Front Immunol       Date:  2021-04-22       Impact factor: 7.561

Review 3.  Critical Review of Existing MHC I Immunopeptidome Isolation Methods.

Authors:  Alexandr Kuznetsov; Alice Voronina; Vadim Govorun; Georgij Arapidi
Journal:  Molecules       Date:  2020-11-19       Impact factor: 4.411

4.  Mild Acid Elution and MHC Immunoaffinity Chromatography Reveal Similar Albeit Not Identical Profiles of the HLA Class I Immunopeptidome.

Authors:  Theo Sturm; Benedikt Sautter; Tobias P Wörner; Stefan Stevanović; Hans-Georg Rammensee; Oliver Planz; Albert J R Heck; Ruedi Aebersold
Journal:  J Proteome Res       Date:  2020-11-03       Impact factor: 4.466

Review 5.  Know thy immune self and non-self: Proteomics informs on the expanse of self and non-self, and how and where they arise.

Authors:  Sebastian Joyce; Nicola Ternette
Journal:  Proteomics       Date:  2021-08-09       Impact factor: 3.984

6.  Immunopeptidogenomics: Harnessing RNA-Seq to Illuminate the Dark Immunopeptidome.

Authors:  Katherine E Scull; Kirti Pandey; Sri H Ramarathinam; Anthony W Purcell
Journal:  Mol Cell Proteomics       Date:  2021-09-10       Impact factor: 5.911

  6 in total

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