Integrating virtual cell modeling with patient-derived transcriptomics to uncover target signals in rheumatoid arthritis

Abstract

Rheumatoid arthritis affects millions of people worldwide, yet a substantial fraction of patients fail to achieve lasting benefit from current therapies. AI-based virtual cell models offer a promising route to simulate the effects of thousands of potential drug targets computationally, but whether their predictions genuinely reflect what happens in the disease-relevant cells of patients undergoing treatment has been unclear. Here, we introduce ImmunoSTATE, a clinically anchored framework that benchmarks empirical and virtual perturbations directly against patient-derived treatment trajectories in pathogenic CXCL13+ synovial T cells from nine patients with rheumatoid arthritis sampled before and after treatment. Empirical genome-wide CRISPR interference screening prioritizes T cell receptor (TCR)-proximal signaling over the measurable components of the JAK-STAT pathway for transcriptomic reversal of the disease signature, a result that remained robust across leave-one-patient-out reanalyses. Comparison of virtual perturbation signals across healthy donor CD4+ T cells and rheumatoid-arthritis patient cells reveals broad preservation of target priorities, together with selected disease-context-enriched shifts, including stronger prioritization of SOX4 in patient cells. ImmunoSTATE provides a clinically anchored framework for evaluating virtual cell models in disease-relevant immune contexts and demonstrates that distinguishing training-set status and cellular context are essential steps in the reliable interpretation of virtual perturbation outputs.