bioRxivImmunologyPreprint
Molecular and spatial profiling identifies immune endotypes for the stratification of OA patients
Nicolas GaigeardiD, Anaïs CardoniD, Romain GuihoiD, Cankut ÇubukiD, Astrid Ouattara, Celia Lamothe, Jordan BrouardiD, Julien De LimaiD, Manzoor AhmediD, Mathilde Le Mercier, A. DefoisiD, Lucie DanetiD, Jimmy PerrotiD, Nazim BenzerdjebiD, Claire S. VinatieriD, Richard DangeriD, Laurence Bouchet‐DelbosiD, Sophie BrouardiD, Nicolas DegauqueiD, Simona PagliucaiD, Myles LewisiD, Liliane Fossati‐JimackiD, Alessandra NervianiiD, Denis WaastiD, Benoît Le GoffiD, , Marie‐Astrid BoutetiD
Abstract
Osteoarthritis (OA) is a prevalent and heterogeneous joint disease in which synovial inflammation drives structural progression and pain. Despite the recognized heterogeneity of OA, the cellular and molecular organization of synovial tissue remains poorly characterized and defining distinct histological and immune endotypes could guide precision medicine and therapeutic targeting. We show that histologically defined synovial pathotypes are conserved across independent cohorts and correspond to distinct molecular immune endotypes. Integration of bulk and spatial transcriptomics with proteomics revealed niche-specific gene and protein signatures, reflecting the anatomical and functional diversity of OA synovium. The lympho-myeloid pathotype was characterized by mature ectopic lymphoid structures containing CD21+CD23+ follicular dendritic cells, spatially organized T and B cell zones, and clonally expanded T and B cells with shared immune cell receptor motifs, consistent with local adaptive immune activity correlating with radiological joint damage. These findings highlight how immune organization and cellular composition shape OA pathogenesis and provide a framework for endotype-guided stratification and therapeutic targeting.
- organism: human
- assay: bulk RNA-seq
- assay: spatial transcriptomics
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- research focus: spatial domain detection
- approach: experimental assay
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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