Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice.

Traditionally considered as homogeneous innate immune cells, neutrophils are now found to exhibit phenotypic and functional heterogeneity. How to determine whether the functional changes of neutrophils are caused by activation or the result of gene reprogramming? Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, have comprehensively explained the mechanism of neutrophil heterogeneity. At the same time, artificial intelligence, especially machine learning, has promoted the in-depth analysis of multi-omics. Here, we introduce the latest progress in the discovery of neutrophil subsets by omics research. We will further discuss the application of machine learning in analyzing the heterogeneity of neutrophils through omics methods. Our goal is to provide a comprehensive overview of how machine learning and multi-omics are reshaping our understanding of neutrophil biology and pathophysiology.

Sepsis represents a dynamic, dysregulated host immune response to infection in which unconventional T cells—γδ T cells, mucosal-associated invariant T (MAIT) cells, natural killer T (NKT) cells, and double-negative T cells—actively shape the balance between early hyperinflammation and subsequent immune paralysis across time and tissues. These cells employ unique antigen recognition mechanisms to trigger rapid immune responses. γδ T cells facilitate early pathogen elimination and immune regulation, whereas MAIT cells detect microbial metabolites and modulate the systemic inflammation. NKT cells balance immune homeostasis through dual pro- and anti-inflammatory cytokine production. This review classifies these subsets and examines their sepsis-related functions alongside immunotherapies targeting them, such as cytokine manipulation, immunomodulators, and checkpoint inhibitors. Elucidating the precise mechanisms underlying sepsis could advance therapies that restore immune equilibrium and potentially improve clinical outcomes. Future studies should unravel the interactions between unconventional T cells and broader immune networks while translating the findings into practical treatments. Understanding the dynamic roles of these cells provides pathways for tailored interventions in sepsis management.

Sepsis is a dysregulated host response in which hyperinflammation, immunoparalysis, lymphoid depletion, myeloid dysfunction, tissue injury and defective pathogen control frequently coexist and evolve over time. Immune checkpoint pathways have therefore attracted interest as candidates for immune restoration, but receptor upregulation alone is an unreliable therapeutic signal. This review develops a cell type- and phase-dependent framework for immune checkpoint targeting in sepsis, using TIM-3 as a model checkpoint node. We first discuss the conceptual pitfalls in translating checkpoint blockade from cancer and chronic infection to acute heterogeneous sepsis. We then examine why the immune phase should be defined by dynamic host response states rather than by the clock time and why checkpoint signals must be interpreted according to their cellular source, including effector lymphocytes, NKT cells, CD4+ T cells, monocytes/macrophages, dendritic cells and suppressive myeloid populations. TIM-3 illustrates this complexity. In sepsis-relevant studies, TIM-3 has been linked to NKT cell and hepatic CD8+ T-cell apoptosis, CD4/HMGB1/TIM-3-associated hyporesponsiveness, Galectin-9-related ligand effects and potential myeloid or tissue-resident functions involving phosphatidylserine-positive debris, extracellular vesicles and tissue homeostasis. These observations argue against treating TIM-3 as a single-purpose inhibitory receptor or assuming that antibody blockade, ligand administration, receptor mutation and Galectin–glycan interference perturb the same biology. We propose that future checkpoint-directed studies should enroll immune states rather than syndromic sepsis alone; integrate the inflammatory burden, innate immune competence, lymphoid function and ligand context; and prioritize mechanistic endpoints such as lymphocyte apoptosis, IFN-γ recovery, monocyte HLA-DR restoration, LPS-induced TNF responses, bacterial clearance and secondary infection. Future precision immunomodulation studies in sepsis should evaluate defined checkpoint-associated circuits rather than infer therapeutic relevance from receptor expression alone.

Sepsis-associated lung injury remains a major cause of respiratory failure and death, yet treatment is still largely supportive once inflammatory tissue damage is established. Macrophage inflammasomes, especially the NLRP3–ASC–caspase-1 axis, occupy a central but paradoxical position in this process. They contribute to pathogen sensing and antimicrobial defense, but excessive or poorly timed activation promotes IL-1β and IL-18 release, gasdermin D-mediated pyroptosis, extracellular ASC speck propagation, neutrophil amplification, barrier disruption, alveolar edema and acute respiratory distress syndrome. Therefore, a clinically meaningful strategy may not be to abolish inflammasome activity, but to tame it. Bats provide a natural model of this principle. Despite hosting many viruses and maintaining high metabolic activity, bats limit inflammatory execution through dampened NLRP3 responsiveness, altered DNA inflammasome sensing, restrained caspase-1/IL-1β processing and ASC2-mediated inhibition of ASC-dependent inflammasomes, while preserving antiviral readiness. These findings suggest a host-defense-preserving framework for septic lung injury. This review proposes bat-inspired design rules: raising inflammasome activation thresholds, braking ASC-dependent propagation, limiting lytic effector output, targeting inflammasome-active macrophage states instead of macrophages as a bulk population, and applying biomarker-guided timing to avoid intervention during immune paralysis. We also discuss the need to define an “inflammasome-high septic lung injury” phenotype using lung injury progression, inflammasome output markers, macrophage-state signatures and immune competence indicators. Bats do not provide a ready-made therapy, but they offer a useful biological lesson: damaging inflammatory amplification can be restrained without erasing host defense.