A blood transcriptomic signature anchored to central nervous system pathology enables noninvasive detection of multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) lacks noninvasive biomarkers anchored to central nervous system (CNS) pathology. This study aimed to identify a blood transcriptional signature anchored to CNS lesion biology for noninvasive MS detection. METHODS: We analyzed bulk RNA-seq data from MS white matter lesions across three independent cohorts and integrated paired CNS and blood transcriptomic data to identify genes with concordant directional dysregulation. A consensus multi-algorithm machine-learning framework was applied to derive a candidate gene signature. Validation included an independent external cohort, single-nucleus RNA-seq analysis, and an experimental autoimmune encephalomyelitis (EAE) mouse model. Immune cell composition in blood was estimated using deconvolution algorithms. RESULTS: We identified 277 lesion-associated differentially expressed genes, of which 66 exhibited concordant directional dysregulations between the CNS and peripheral blood. A 14-gene transcriptional signature effectively discriminated patients with MS from controls (training area under the curve (AUC) = 0.954; external validation AUC = 0.917). Models trained on genes with discordant expression patterns failed to generalize (validation AUC = 0.556), highlighting the importance of cross-tissue directional consistency for robust biomarker identification. Immune deconvolution revealed expansion of plasmacytoid dendritic cells and depletion of naive B cells in MS blood. Single-nucleus analysis confirmed the characteristic downregulation of these 14 biomarkers in lesion-associated microglia and T cells, mirroring their peripheral expression profiles. EAE validation demonstrated cross-tissue concordant dysregulation of representative biomarkers (Sort1, Pi4kb, and Csde1) in both CNS and peripheral blood. CONCLUSIONS: This study identifies a candidate blood-based transcriptional signature anchored to CNS pathology with potential for noninvasive MS detection. The cross-tissue concordance framework offers a generalizable strategy for biomarker discovery in neurological diseases. Prospective validation in independent cohorts is warranted to confirm its clinical utility.