Phage ImmunoPrecipitation sequencing (PhIP-Seq) in autoimmunity research: From high-resolution epitope mapping to...

Autoimmune diseases (AIDs) affect 5-10% of the global population, yet effective diagnosis and treatment remain challenging due to their complexity and heterogeneity. Autoantibodies serve as crucial biomarkers for disease classification and prognosis, making comprehensive autoantigen profiling essential for advancing our understanding of autoimmune pathogenesis. Phage ImmunoPrecipitation sequencing (PhIP-Seq) has emerged as a transformative high-throughput technology that combines phage display with next-generation sequencing to comprehensively profile antibody-antigen interactions. This review systematically examines PhIP-Seq's methodology, comparing it with conventional approaches including protein microarrays, immunoprecipitation-mass spectrometry, and traditional serological assays. We detail the experimental workflow encompassing library design, immunoprecipitation protocols, and computational analysis pipelines, while highlighting recent algorithmic advances including generalized Poisson models, Z-score methods, and machine learning approaches for hit determination. PhIP-Seq's applications in autoimmune diseases span autoantibody discovery; clinical model development for disease stratification; high-resolution epitope mapping enabling resolution at tens of amino acids; and longitudinal studies tracking disease progression. The technology's integration with other omics platforms and investigation of viral-autoimmune disease associations further demonstrates its versatility. While PhIP-Seq excels in linear epitope identification and high-throughput screening, limitations include inability to detect conformational epitopes, challenges with low-abundance antigens, and lack of post-translational modifications. This review discusses ongoing advancements in library design, methodological improvements, and data integration to address these issues and unlock PhIP-Seq's full potential in autoimmune research.