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

IF 10.7 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
MedComm Pub Date : 2025-09-14 DOI:10.1002/mco2.70350
Rongli Xie, Guohui Xiao, Kaige Yang, Xiaofeng Wang, Cong Chen, Min Ding, Tong Zhou, Rajarshi Mukherjee, Robert Sutton, Erzhen Chen, Ying Chen, Wei Huang, Dan Xu, Jian Fei
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引用次数: 0

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.

Abstract Image

外周血单细胞转录组图谱揭示b细胞驱动特征预测急性胰腺炎严重程度
有效的早期预测急性胰腺炎(AP)严重程度仍然是一个未满足的临床需要,由于有限的分子表征的全身免疫反应。我们在入院后第1,3和7天对AP患者(n = 7)的外周血单个核细胞进行了综合单细胞RNA测序和T细胞和b细胞受体谱分析。免疫景观分析显示患者之间存在明显的异质性,表达mzb1的浆细胞明显扩增,这与复杂的AP和恢复密切相关。在一个独立队列(n = 14)中,功能验证证实了疾病相关的浆细胞标志物,以及血清免疫球蛋白和细胞因子谱的改变(n = 32)。根据这些发现,我们建立了9个基因b细胞来源的转录组特征(S100A8, DUSP1, JUN, HBA2, FOS, CYBA, JUNB, S100A9和WDR83OS)预测AP严重程度。该模型在内部验证中表现出较高的判别性能(n = 114; AUROC > 0.95,优于标准临床评分系统),在AP (n = 87)和AP合并非AP脓毒症(n = 174)的外部验证队列中,该模型在预测持续器官衰竭方面具有持续的准确性。我们的研究确定了MZB1 +浆细胞在AP发病机制中的机制和预测作用,提供了一种新的基于免疫的分层策略,具有精确临床管理的潜力。
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来源期刊
CiteScore
6.70
自引率
0.00%
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审稿时长
10 weeks
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