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

•Mass spectrometry proteomics has become an important part of modern immunology.•Interpretation of high-throughput proteomics data requires specialized computational tools and expertise.•New developments in experimental and computational techniques of proteomics offer increasing opportunities to explore the human immune system.•Proteome profiling studies have revealed proteomic landscapes of human CD4+ T cell subsets in health and disease. 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.