Giamarellos-Bourboulis, E. J. et al. The pathophysiology of sepsis and precision-medicine-based immunotherapy. Nat. Immunol. 25, 19–28 (2024).
Cavaillon, J.-M., Singer, M. & Skirecki, T. Sepsis therapies: learning from 30 years of failure of translational research to propose new leads. EMBO Mol. Med. 12, e10128 (2020).
Biebelberg, B., Rhee, C., Chen, T., McKenna, C. & Klompas, M. Heterogeneity of sepsis presentations and mortality rates. Ann. Intern. Med. 177, 985–987 (2024).
Stortz, J. A. et al. Phenotypic heterogeneity by site of infection in surgical sepsis: a prospective longitudinal study. Crit. Care 24, 203 (2020).
Peters-Sengers, H. et al. Source-specific host response and outcomes in critically ill patients with sepsis: a prospective cohort study. Intensive Care Med. 48, 92–102 (2022).
Burnham, K. L. et al. Shared and distinct aspects of the sepsis transcriptomic response to fecal peritonitis and pneumonia. Am. J. Respir. Crit. Care Med. 196, 328–339 (2017).
Gogos, C. et al. Early alterations of the innate and adaptive immune statuses in sepsis according to the type of underlying infection. Crit. Care 14, R96 (2010).
Wang, T. et al. Single-cell RNA sequencing reveals the sustained immune cell dysfunction in the pathogenesis of sepsis secondary to bacterial pneumonia. Genomics 113, 1219–1233 (2021).
Hong, Y. et al. Single-cell transcriptome profiling reveals heterogeneous neutrophils with prognostic values in sepsis. iScience 25, 105301 (2022).
Reyes, M. et al. An immune-cell signature of bacterial sepsis. Nat. Med. 26, 333–340 (2020).
Kwok, A. J. et al. Neutrophils and emergency granulopoiesis drive immune suppression and an extreme response endotype during sepsis. Nat. Immunol. 24, 767–779 (2023).
Yao, R.-Q. et al. Single-cell transcriptome profiling of sepsis identifies HLA-DRlowS100Ahigh monocytes with immunosuppressive function. Mil. Med. Res. 10, 27 (2023).
Wen, M. et al. Single-cell transcriptomics reveals the alteration of peripheral blood mononuclear cells driven by sepsis. Ann. Transl. Med. 8, 125–125 (2020).
Darden, D. B. et al. Single-cell RNA-seq of human myeloid-derived suppressor cells in late sepsis reveals multiple subsets with unique transcriptional responses: a pilot study. Shock 55, 587–595 (2021).
Darden, D. B. et al. A novel single cell RNA-seq analysis of non-myeloid circulating cells in late sepsis. Front. Immunol. 12, 696536 (2021).
Das, A. et al. Identifying immune signatures of sepsis to increase diagnostic accuracy in very preterm babies. Nat. Commun. 15, 388 (2024).
Alhamdan, F., Koutsogiannaki, S. & Yuki, K. The landscape of immune dysregulation in pediatric sepsis at a single-cell resolution. Clin. Immunol. 262, 110175 (2024).
Olin, A. et al. Stereotypic immune system development in newborn children. Cell 174, 1277–1292 (2018).
Simon, A. K., Hollander, G. A. & McMichael, A. Evolution of the immune system in humans from infancy to old age. Proc. Biol. Sci. 282, 20143085 (2015).
Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).
Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Huang, S. et al. Tim-3 regulates sepsis-induced immunosuppression by inhibiting the NF-κB signaling pathway in CD4 T cells. Mol. Ther. 30, 1227–1238 (2022).
Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Zhou, P. et al. Single-cell CRISPR screens in vivo map T cell fate regulomes in cancer. Nature 624, 154–163 (2023).
Oyadomari, S. & Mori, M. Roles of CHOP/GADD153 in endoplasmic reticulum stress. Cell Death Differ. 11, 381–389 (2004).
Liu, X. et al. Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction. Nature 567, 525–529 (2019).
Chen, J. et al. NR4A transcription factors limit CAR T cell function in solid tumours. Nature 567, 530–534 (2019).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Li, F., Tian, J., Zhang, L., He, H. & Song, D. A multi-omics approach to reveal critical mechanisms of activator protein 1 (AP-1). Biomed. Pharmacother. 178, 117225 (2024).
Schnoegl, D., Hiesinger, A., Huntington, N. D. & Gotthardt, D. AP-1 transcription factors in cytotoxic lymphocyte development and antitumor immunity. Curr. Opin. Immunol. 85, 102397 (2023).
Shaulian, E. & Karin, M. AP-1 as a regulator of cell life and death. Nat. Cell Biol. 4, E131–E136 (2002).
Wagner, E. F. & Eferl, R. Fos/AP-1 proteins in bone and the immune system. Immunol. Rev. 208, 126–140 (2005).
Zhang, X. et al. Depletion of BATF in CAR-T cells enhances antitumor activity by inducing resistance against exhaustion and formation of central memory cells. Cancer Cell 40, 1407–1422 (2022).
Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).
Verzella, D. et al. GADD45β loss ablates innate immunosuppression in cancer. Cancer Res. 78, 1275–1292 (2018).
Nascimento, D. C. et al. Sepsis expands a CD39+ plasmablast population that promotes immunosuppression via adenosine-mediated inhibition of macrophage antimicrobial activity. Immunity 54, 2024–2041 (2021).
Ellebedy, A. H. et al. Defining antigen-specific plasmablast and memory B cell subsets in human blood after viral infection or vaccination. Nat. Immunol. 17, 1226–1234 (2016).
Li, S. et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat. Immunol. 15, 195–204 (2014).
Cecconi, M., Evans, L., Levy, M. & Rhodes, A. Sepsis and septic shock. Lancet 392, 75–87 (2018).
Garten, A. et al. Physiological and pathophysiological roles of NAMPT and NAD metabolism. Nat. Rev. Endocrinol. 11, 535–546 (2015).
Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49, 971–986 (2018).
Tang, F. et al. A pan-cancer single-cell panorama of human natural killer cells. Cell 186, 4235–4251 (2023).
Rebuffet, L. et al. High-dimensional single-cell analysis of human natural killer cell heterogeneity. Nat. Immunol. 25, 1474–1488 (2024).
Jiang, Y. et al. Single cell RNA sequencing identifies an early monocyte gene signature in acute respiratory distress syndrome. JCI Insight 5, e135678 (2020).
Qiu, X. et al. Dynamic changes in human single-cell transcriptional signatures during fatal sepsis. J. Leukoc. Biol. 110, 1253–1268 (2021).
Arunachalam, P. S. et al. Systems vaccinology of the BNT162b2 mRNA vaccine in humans. Nature 596, 410–416 (2021).
Waickman, A. T. et al. Temporally integrated single cell RNA sequencing analysis of PBMC from experimental and natural primary human DENV-1 infections. PLoS Pathog. 17, e1009240 (2021).
Lee, J. S. et al. Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19. Sci. Immunol. 5, eabd1554 (2020).
Su, Y. et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell 183, 1479–1495 (2020).
Kazer, S. W. et al. Integrated single-cell analysis of multicellular immune dynamics during hyperacute HIV-1 infection. Nat. Med. 26, 511–518 (2020).
Scicluna, B. P. et al. Classification of patients with sepsis according to blood genomic endotype: a prospective cohort study. Lancet Respir. Med. 5, 816–826 (2017).
Wong, H. R. et al. Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum. Crit. Care Med. 37, 1558–1566 (2009).
Argelaguet, R. et al. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol. Syst. Biol. 14, e8124 (2018).
Mahajan, S. et al. Nuclear receptor Nr4a2 promotes alternative polarization of macrophages and confers protection in sepsis. J. Biol. Chem. 290, 18304–18314 (2015).
Ren, X. et al. COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas. Cell 184, 1895–1913 (2021).
Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).
Liu, N. et al. Single-cell analysis of COVID-19, sepsis, and HIV infection reveals hyperinflammatory and immunosuppressive signatures in monocytes. Cell Rep. 37, 109793 (2021).
Dufva, O. et al. Single-cell functional genomics reveals determinants of sensitivity and resistance to natural killer cells in blood cancers. Immunity 56, 2816–2835 (2023).
De Cevins, C. et al. Single-cell RNA-sequencing of PBMCs from SAVI patients reveals disease-associated monocytes with elevated integrated stress response. Cell Rep. Med. 4, 101333 (2023).
Shi, C. & Pamer, E. G. Monocyte recruitment during infection and inflammation. Nat. Rev. Immunol. 11, 762–774 (2011).
Liu, D. et al. Sepsis-induced immunosuppression: mechanisms, diagnosis and current treatment options. Mil. Med. Res. 9, 56 (2022).
Venet, F. & Monneret, G. Advances in the understanding and treatment of sepsis-induced immunosuppression. Nat. Rev. Nephrol. 14, 121–137 (2018).
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).
Sturm, G. et al. Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data. Bioinformatics 36, 4817–4818 (2020).
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Massoni-Badosa, R. et al. An atlas of cells in the human tonsil. Immunity 57, 379–399 (2024).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Ashburner, M. et al. Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
Draghici, S. et al. A systems biology approach for pathway level analysis. Genome Res. 17, 1537–1545 (2007).
Yaari, G., Bolen, C. R., Thakar, J. & Kleinstein, S. H. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Res. 41, e170 (2013).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).
Davenport, E. E. et al. Genomic landscape of the individual host response and outcomes in sepsis: a prospective cohort study. Lancet Respir. Med. 4, 259–271 (2016).
Yao, R. et al. Single-cell transcriptome profiling of the immune space-time landscape reveals dendritic cell regulatory program in polymicrobial sepsis. Theranostics 12, 4606–4628 (2022).