Liu Han, Liang Qun
Department of Epidemiology and Public Health, University College London, London, United Kingdom.
The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
Front Immunol. 2025 Aug 11;16:1616794. doi: 10.3389/fimmu.2025.1616794. eCollection 2025.
Sepsis is the leading cause of death globally (49 million cases per year with a 25-30% morbidity and mortality rate), but its immunopathology remains incompletely elucidated. Conventional models of 'uncontrolled inflammation' fail to explain the diversity of immune status in patients at different stages of the disease, and there is an urgent need for a dynamic, time-series perspective to reveal key regulatory nodes.
Forty-six studies (2014-2024) were retrieved under PRISMA-2020 across 12 databases. Raw single-cell RNA-seq, ATAC-seq and CITE-seq matrices (≈1 million immune cells) were uniformly reprocessed, harmonised with scMGNN, and mapped onto pseudotime and RNA-velocity trajectories. Ordinary and stochastic differential-equation models quantified pro-/anti-inflammatory flux.
Multi-omics fusion increased immune-cell classification accuracy from 72.3% to 89.4% (adjusted Rand index, < 0.001). Three phase-defining checkpoints emerged: monocyte-to-macrophage fate bifurcation at 16-24 h, initiation of TOX-driven CD8 T-cell exhaustion at 36-48 h, and irreversible immunosuppression beyond 72 h. Dynamical simulations identified two intervention windows-0-18 h (selective MyD88-NF-κB blockade) and 36-48 h (PD-1/TIM-3 dual inhibition)-forecasting 2.1-fold and 1.6-fold survival gains, respectively, in pre-clinical models.
In this study, an "immune clock" model of sepsis was constructed based on single-cell multi-omics data, which accurately depicted three key decision nodes, namely, monocyte-macrophage differentiation, initiation of T-cell depletion and irreversible immune suppression, and identified the corresponding molecular targets (e.g., IRF8, TOX). This model provides a clear time window and targeting strategy for individualised immune intervention in sepsis.
脓毒症是全球主要的死亡原因（每年有4900万例病例，发病率和死亡率为25%-30%），但其免疫病理学仍未完全阐明。传统的“不受控制的炎症”模型无法解释疾病不同阶段患者免疫状态的多样性，迫切需要从动态、时间序列的角度来揭示关键调控节点。
在PRISMA-2020标准下，从12个数据库中检索了46项研究（2014年至2024年）。对原始的单细胞RNA测序、ATAC测序和CITE测序矩阵（约100万个免疫细胞）进行统一再处理，与scMGNN进行整合，并映射到伪时间和RNA速度轨迹上。使用普通和随机微分方程模型量化促炎/抗炎通量。
多组学融合将免疫细胞分类准确率从72.3%提高到89.4%（调整兰德指数，<0.001）。出现了三个定义阶段的检查点：16-24小时时单核细胞向巨噬细胞命运的分叉，36-48小时时TOX驱动的CD8 T细胞耗竭的起始，以及72小时后不可逆的免疫抑制。动态模拟确定了两个干预窗口——0-18小时（选择性MyD88-NF-κB阻断）和36-48小时（PD-1/TIM-3双重抑制）——预测在临床前模型中生存率分别提高2.1倍和1.6倍。
在本研究中，基于单细胞多组学数据构建了脓毒症的“免疫时钟”模型，该模型准确描绘了三个关键决策节点，即单核细胞-巨噬细胞分化、T细胞耗竭的起始和不可逆的免疫抑制，并确定了相应的分子靶点（如IRF8、TOX）。该模型为脓毒症的个体化免疫干预提供了明确的时间窗口和靶向策略。
