{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T17:43:37Z","timestamp":1793727817951,"version":"4.6.1"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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 \u2018uncontrolled inflammation\u2019 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Forty-six studies (2014\u20132024) were retrieved under PRISMA-2020 across 12 databases. Raw single-cell RNA-seq, ATAC-seq and CITE-seq matrices (\u22481 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Multi-omics fusion increased immune-cell classification accuracy from 72.3% to 89.4% (adjusted Rand index, <jats:italic>p<\/jats:italic>&amp;lt; 0.001). Three phase-defining checkpoints emerged: monocyte-to-macrophage fate bifurcation at 16\u201324 h, initiation of TOX-driven CD8<jats:sup>+<\/jats:sup> T-cell exhaustion at 36\u201348 h, and irreversible immunosuppression beyond 72 h. Dynamical simulations identified two intervention windows\u20140\u201318 h (selective MyD88\u2013NF-\u03baB blockade) and 36\u201348 h (PD-1\/TIM-3 dual inhibition)\u2014forecasting 2.1-fold and 1.6-fold survival gains, respectively, in pre-clinical models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In this study, an \u201cimmune clock\u201d 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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1616794","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T04:10:18Z","timestamp":1754885418000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Single-cell multi-omics-based immune temporal network resolution in sepsis: unravelling molecular mechanisms and precise therapeutic targets"],"prefix":"10.3389","volume":"16","author":[{"given":"Han","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)32989-7","article-title":"Global, regional, and national sepsis incidence and mortality, 1990\u20132017: analysis for the Global Burden of Disease Study","volume":"395","author":"Rudd","year":"2020","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","first-page":"100945","DOI":"10.1016\/j.xcrm.2025.100945","article-title":"Single-cell transcriptomic analysis reveals immune cell dysfunction in post-intensive care syndrome","volume":"6","author":"Sun","year":"2025","journal-title":"Cell Rep Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1172\/JCI82224","article-title":"Sepsis-induced immune dysfunction: can immune therapies reduce mortality","volume":"126","author":"Delano","year":"","journal-title":"J Clin Invest"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01329-2","article-title":"Single-cell multi-omics analysis of the immune response in COVID-19","volume":"27","author":"Stephenson","year":"2021","journal-title":"Nat Med"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2022.02.013","article-title":"Immune phenotypic linkage between colorectal cancer and liver metastasis","volume":"40","author":"Liu","year":"2022","journal-title":"Cancer Cell"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.immuni.2020.07.009","article-title":"Single-cell sequencing of peripheral mononuclear cells reveals distinct immune response landscapes of COVID-19 and influenza patients","volume":"53","author":"Zhu","year":"2020","journal-title":"Immunity"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/S2213-2600(16)00046-1","article-title":"Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study","volume":"4","author":"Davenport","year":"2016","journal-title":"Lancet Respir Med"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2021.110132","article-title":"Temporal dynamics of the human immune system response to sepsis","volume":"37","author":"Zhao","year":"2021","journal-title":"Cell Rep"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2004.04.044","article-title":"The dynamics of acute inflammation","volume":"230","author":"Kumar","year":"2004","journal-title":". 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