Single-Cell Transcriptomic Atlas of Peripheral Blood Reveals B-Cell-Driven Signature Predictive of Acute Pancreatitis Severity

Abstract

Effective early prediction of acute pancreatitis (AP) severity remains an unmet clinical need due to limited molecular characterization of systemic immune responses. We performed integrated single-cell RNA sequencing with T- and B-cell receptor profiling on peripheral blood mononuclear cells from AP patients (n = 7) at days 1, 3, and 7 after admission. Immune landscape analysis revealed marked inter-patient heterogeneity, with a distinct expansion of MZB1-expressing plasma cells that were strongly associated with complicated AP and recovery. Functional validation in an independent cohort (n = 14) confirmed disease-associated plasma cell markers, alongside altered serum immunoglobulin and cytokine profiles (n = 32). From these findings, we established a nine-gene B-cell-derived transcriptomic signature (S100A8, DUSP1, JUN, HBA2, FOS, CYBA, JUNB, S100A9, and WDR83OS) predictive of AP severity. This model demonstrated high discriminative performance in internal validation (n = 114; AUROC > 0.95, superior to standard clinical scoring systems), and sustained accuracy in external validation cohorts of AP (n = 87) and AP combined with non-AP sepsis (n = 174) for predicting persistent organ failure. Our study identifies a mechanistic and predictive role for MZB1⁺ plasma cells in AP pathogenesis, offering a novel immune-based stratification strategy with potential for precision clinical management.

Keywords

acute pancreatitis / B cells / machine learning / molecular biomarker / severity prediction

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