Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis

The immune repertoire (IR) reflects the adaptive immune system of an organism by encompassing the diversity and composition of receptor sequences expressed by T and/or B lymphocytes. Despite significant advances in the efficiency and accuracy of antigen discrimination facilitated by high-throughput sequencing technologies, IR analysis remains challenging due to its extraordinary heterogeneity and the high dimensional nature of sequencing data. This review provides an overview of the key steps involved in IR profiling, highlighting factors that impact data accuracy, including sample sources, sequencing data quality control, repertoire characterization derived from the vast diversity of millions of receptors, and the specificity in lymphocyte-peptide interactions. Several bioinformatics pipelines have emerged to facilitate IR analysis, offering various computational tools to process and interpret complex datasets. Furthermore, we explore the integration of machine learning and multi-omics approaches, which are driving deeper insights into IR analysis. By examining different perspectives, we summarize recent methodological and computational advancements in the field and provide unique insights into the evolving landscape of IR data analysis.