Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Abstract

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Keywords: biomarkers; flow cytometry; immune response; lymphopenia; personalized medicine; phenotyping; sepsis.

Figure 1

Sepsis Immune Response Timeline. 1: Early phase: intense immune activation causing high initial mortality due to “cytokine storm”. 2: Late phase: prolonged immunosuppression leading to secondary infections and organ failure. 3: Advances in ICU care have improved outcomes, yet late-phase mortality remains a challenge.

Figure 2

Immune Cell Modulations in Sepsis. The image provides a comparative overview of immune responses by different cell types during sepsis. The initial hyperactivation with reduced apoptosis in neutrophils and elevated activation in NK cells is depicted, along with increased apoptosis in γδ T cells and MAIT cells. Subsequently, it shows the transition to immunosuppression, with Tregs enhancing suppressive functions and B cells becoming exhausted. Both CD4⁺ and CD8⁺ T cells are characterized by increased anergy and apoptosis, signaling a state of immune exhaustion, which collectively portrays the biphasic immune landscape in sepsis.

Figure 3

Evaluating Sepsis Phenotyping. This infographic details key considerations in sepsis phenotyping, divided into three aspects: UTILITY, questioning the immediate benefits for patient care; FEASIBILITY, addressing the practicality of incorporating phenotyping into clinical routines; and STABILITY, discussing the consistency of phenotypic classifications. A final note on RUDIMENTARY aspects suggests a need for deeper biological understanding to support phenotype-based treatments in intensive care settings.

Figure 4

Clinical Phenotyping in Sepsis. The figure illustrates the categorization of sepsis into distinct phenotypes based on clinical traits like temperature, hemodynamics, and multi-organ dysfunction. It underscores the shift towards personalized medicine, highlighting the potential of combining clinical and immunological data to inform targeted therapies and improve outcomes in sepsis care.

Figure 5

Quantification and phenotypic characterization of MAIT cells in septic patients. (A) The UMAP plot illustrates the 2D spatial distribution of CD3⁺ T cells across 15 samples, integrated with FlowSOM-based clusters. The color of the different clusters is reported on the left side of the heatmap. (B) Heatmap of the median marker intensities of the 4 lineage markers across the 12 cell populations obtained with FlowSOM algorithm (meta-20). The colors of cluster_id column correspond to the colors used to label the UMAP plot clusters. The color in the heatmap is referred to the median of the arcsinh marker expression (0–1 scaled) calculated over cells from all the samples. Blue represents lower expression, while red represents higher expression. The light gray line along the rows (clusters) indicates the relative sizes of the clusters. DP = double positive; DN = double negative. (C) Representative dot plot showing the percentages of CD8⁺ MAIT cells (within CD3⁺ T cells) in healthy donors (HD) and septic patients (SEPSIS). (D) Violin plot representing the percentage of CD8⁺MAIT cells in healthy donors (HD) and septic patients (SEPSIS) at time of admission in Intensive Care. Non-parametric student t-test; exact p-value is shown in the figure. [data produced by the Laboratory of Immunology, directed by Prof. Andrea Cossarizza, University of Modena and Reggio Emilia].