Integrative mass spectrometry-driven multi-omics and single cell technologies in ankylosing spondylitis: insights into pathogenesis, biomarker discovery, and precision medicine

Ankylosing spondylitis (AS), a chronic inflammatory arthritis primarily affecting the axial skeleton, presents significant clinical challenges due to its complex pathogenesis, delayed diagnosis, and heterogeneous therapeutic responses. This review highlights the pivotal role of mass spectrometry (MS)-based multi-omics technologies in elucidating AS pathogenesis, identifying disease-specific biomarkers, and advancing precision medicine for AS. The fundamental principles of MS are outlined, encompassing ionization methods like electrospray and matrix-assisted laser desorption/ionization, mass analyzers such as orbitrap and time-of-flight, and separation systems including liquid and gas chromatography. These technologies enable highly sensitive and comprehensive profiling of proteomes, metabolomes, and lipidomes. Proteomics analyses have revealed dysregulated pathways and identified key biomarkers, including complement components, matrix metalloproteinases and the panel “C-reactive protein + serum amyloid A1”, for distinguishing active AS from healthy controls and stable AS. Metabolomics studies emphasize disturbances in tryptophan-kynurenine metabolism and gut microbiome-derived metabolites, including short-chain fatty acids, thereby linking microbial imbalance to inflammatory responses. A combination of three metabolites (3-amino-2-piperidone, hypoxanthine, and octadecylamine) has shown promise as serum biomarkers for AS diagnosis. Lipidomics profiling reveals significant changes in phospholipid composition. Furthermore, emerging single cell technologies (e.g., mass cytometry) have dissected immune heterogeneity in AS, revealing chemokine signaling dysregulation in monocyte and T-cell subclusters. Persistent challenges and future advancements, such as data heterogeneity, cohort limitations, and the interpretability of artificial intelligence models for multi-omics integration were discussed. By integrating technological innovation with clinical insights, this review systematically summarizes multiple potential biomarker panels for AS, in which multi-omics-driven strategies facilitate early diagnosis, mechanistic subtyping, and personalized therapies, ultimately improving patient outcomes in AS.