Refereed journal article or data article (A1)

Statistical and machine learning methods to study human CD4+ T cell proteome profiles




List of Authors: Suomi Tomi, Elo Laura L

Publisher: Elsevier

Publication year: 2022

Journal: Immunology Letters

Journal name in source: Immunology letters

Journal acronym: Immunol Lett

Volume number: 245

ISSN: 0165-2478

eISSN: 1879-0542

DOI: http://dx.doi.org/10.1016/j.imlet.2022.03.006

URL: https://doi.org/10.1016/j.imlet.2022.03.006

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175189283


Abstract
Mass spectrometry proteomics has become an important part of modern immunology, making major contributions to understanding protein expression levels, subcellular localizations, posttranslational modifications, and interactions in various immune cell populations. New developments in both experimental and computational techniques offer increasing opportunities for exploring the immune system and the molecular mechanisms involved in immune responses. Here, we focus on current computational approaches to infer relevant information from large mass spectrometry based protein profiling datasets, covering the different steps of the analysis from protein identification and quantification to further mining and modelling of the protein abundance data. Additionally, we provide a summary of the key proteome profiling studies on human CD4+ T cells and their different subtypes in health and disease.

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Last updated on 2022-16-05 at 11:33