Leukocyte Phenotyping in Sepsis Using Omics, Functional Analysis, and in Silico Modeling

Abstract

Sepsis is a major health issue and a leading cause of death in hospitals globally. The treatment of sepsis is largely supportive, and there are no therapeutics available that target the underlying pathophysiology of the disease. The development of therapeutics for the treatment of sepsis is hindered by the heterogeneous nature of the disease. The presence of multiple, distinct immune phenotypes ranging from hyperimmune to immunosuppressed can significantly impact the host response to infection. Recently, omics, biomarkers, cell surface protein expression, and immune cell profiles have been used to classify immune status of sepsis patients. However, there has been limited studies of immune cell function during sepsis and even fewer correlating omics and biomarker alterations to functional consequences. In this review, we will discuss how the heterogeneity of sepsis and associated immune cell phenotypes result from changes in the omic makeup of cells and its correlation with leukocyte dysfunction. We will also discuss how emerging techniques such as in silico modeling and machine learning can help in phenotyping sepsis patients leading to precision medicine.

Man Versus Machine: Provider Directed Versus Precision Automated Critical Care Management in a Porcine Model of Distributive Shock

“…At present, multiple machine learning techniques are being employed in critical care studies. Specific applications include early detection of acute kidney injury, pulmonary embolism, gene expression in sepsis, and leukocyte phenotyping when trying to understand the pathophysiology of sepsis (45)(46)(47)(48)(49). Reinforcement learning has also been used to formulate ICU electrolyte replacement protocols and determine treatment decisions in sepsis (50,51).…”

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

“…and the varied composition of systemic leukocytes in both species (humans have higher numbers of circulating neutrophils compared to mice) ( Drake, 2013 ; Efron et al., 2015 ). These factors could significantly alter the trajectory of the disease ( Langston et al., 2023 ). A recent review discussed the repurposing of oncology drugs to treat sepsis and identified several potential compounds (e.g., Topotecan, Olaparib, Trametinib) that are currently being evaluated in sepsis models ( Rumienczyk et al., 2022 ).…”

“…Machine learning (ML), a branch of Artificial intelligence (AI), can be used to improve sepsis diagnosis, prognosis and clinical/drug monitoring of the disease ( Langston et al., 2023 ). For example, Goto et al.…”

“…Unfortunately, none of these treatments demonstrate targeted efficacy against sepsis. Numerous reports have highlighted the heterogeneous nature of sepsis (Scicluna et al, 2017;Fenner et al, 2021;Yang et al, 2021b;Langston et al, 2023), confirming the unlikelihood that a one-sizefits-all treatment will be appropriate for every septic patient. Thus, the lack of therapeutic options for the treatment of sepsis underscores the urgent need for further research and innovation to develop novel therapeutic interventions that can address the complexities of this life-threatening condition (Langston et al, 2023).…”

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

“…Omics methodologies, including lipomics, proteomics, and transcriptomics, are broad-scale data-intensive techniques that offer a holistic view of biological systems. Langston et al (3) reviewed leukocyte phenotyping in sepsis using omics, functional analysis, and silicon modeling. Omics provides a system-level view through simultaneous analysis of multiple biological pathways.…”