{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:55:49Z","timestamp":1781070949397,"version":"3.54.1"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"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>By employing a high-dimensionality approach, this study aims to identify mechanistically relevant cellular immune signatures that predict poor outcomes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This prospective study recruited 39 children with sepsis admitted to the intensive care unit and 19 healthy age-matched children. Peripheral blood mononuclear cells were studied with mass cytometry. Unique cell subsets were identified in the paediatric sepsis immunome and depicted with t-distributed stochastic neighbour embedding (tSNE) plots. Network analysis was performed to quantify interactions between immune subsets. Enriched immune subsets were included in a model for distinguishing sepsis and validated by flow cytometry in an independent cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The median (interquartile range) age and paediatric sequential organ failure assessment (pSOFA) score in this cohort was 5.6(2.0, 11.3) years and 6.6 (IQR: 2.5, 10.1), respectively. High-dimensionality analyses of the immunome in sepsis revealed a loss of coordinated communication between immune subsets, particularly a loss of regulatory\/inhibitory interaction between cell types, fewer interactions between cell subsets, and fewer negatively correlated edges than controls. Four independent immune subsets (CD45RA<jats:sup>\u2212<\/jats:sup>CX3CR1<jats:sup>+<\/jats:sup>CTLA4<jats:sup>+<\/jats:sup>CD4<jats:sup>+<\/jats:sup> T cells, CD45RA<jats:sup>\u2212<\/jats:sup>17A<jats:sup>+<\/jats:sup>CD4<jats:sup>+<\/jats:sup> T cells CD15<jats:sup>+<\/jats:sup>CD14<jats:sup>+<\/jats:sup> monocytes, and Ki67<jats:sup>+<\/jats:sup> B cells) were increased in sepsis and provide a predictive model for diagnosis with area under the receiver operating characteristic curve, AUC 0.90 (95% confidence interval, CI 0.82\u20130.98) in the discovery cohort and AUC 0.94 (95% CI 0.83\u20131.00) in the validation cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The sepsis immunome is deranged with loss of regulatory\/inhibitory interactions. Four immune subsets increased in sepsis could be used in a model for diagnosis and prediction of poor outcomes.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1569096","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T05:30:59Z","timestamp":1747114259000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Clinical and mechanistic relevance of high-dimensionality analysis of the paediatric sepsis immunome"],"prefix":"10.3389","volume":"16","author":[{"given":"Dandan","family":"Pi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith Ju Ming","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Nay Yaung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas Kim 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