Related Experiment Video
Updated: Jun 8, 2026
High-throughput Flow Cytometry Cell-based Assay to Detect Antibodies to N-Methyl-D-aspartate Receptor or Dopamine-2 Receptor in Human Serum
Published on: November 23, 2013
Low-input deep learning platform for citrullinated peptide identification, autoantigen discovery and rheumatoid
Meng Hu1, Chenxi Zhu1,2,3, Rui Sun1
1Department of Rheumatology and Immunology and Department of Laboratory Medicine, National Clinical Research Center for Geriatrics, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
Abstract:
Post-translationally modified proteins are crucial autoantigens in autoimmune diseases, with citrullinated proteins being key targets of autoantibodies in rheumatoid arthritis (RA). However, accurate citrullinome profiling and autoantigen identification remain limited by insufficient detection methods and computational tools. Here we develop Iseq-Cit (internal standard-assisted enrichment-free approach for high-throughput quantitative analysis of citrullinome), for global citrullinome profiling in individuals at RA risk and in patients with RA across a longitudinal cohort, requiring less than 1% of the sample input needed for conventional methods. We find that plasma citrullinome profiles closely correlate with RA development and severity. Moreover, we develop models integrating clinical indicators and citrullination data, achieving high accuracy in predicting treatment response. To evaluate the RA-sera reactivity of identified citrullinated peptides, we train a bidirectional gated recurrent unit model using 67,399 RA-sera negative and 8,816 RA-sera positive peptides. External validation through enzyme-linked immunosorbent assays confirms 84.2% accuracy in predicting RA-sera reactivity of citrullinated peptides, yielding 19 promising candidates for RA diagnosis. This work provides strategies for citrullinated peptide identification, autoantigen discovery and RA treatment stratification.
