Sepsis has been called the graveyard of pharmaceutical companies due to the numerous failed clinical trials. The lack of tools to monitor the immunological status in sepsis constrains the development of therapies. Here, we evaluated a test based on whole plasma peptidome acquired by MALDI-TOF-mass spectrometer and machine-learning algorithms to discriminate two lipopolysaccharide-(LPS) induced murine models emulating the pro- and anti-inflammatory/immunosuppression environments that can be found during sepsis. The LPS group was inoculated with a single high dose of LPS and the IS group was subjected to increasing doses of LPS, to induce proinflammatory and anti-inflammatory/immunosuppression profiles respectively. The LPS group showed leukopenia and higher levels of cytokines and tissue damage markers, and the IS group showed neutrophilia, lymphopenia and decreased humoral response. Principal component analysis of the plasma peptidomes formed discrete clusters that mostly coincided with the experimental groups. In addition, machine-learning algorithms discriminated the different experimental groups with a sensitivity of 95.7% and specificity of 90.9%. Data reveal the potential of plasma fingerprints analysis by MALDI-TOF-mass spectrometry as a simple, speedy and readily transferrable method for sepsis patient stratification that would contribute to therapeutic decision-making based on their immunological status.

Sepsis constitutes a major cause of death in intensive care units. Patients present a dysregulated response to infection, which progresses through different pro/anti-inflammatory phases. The lack of tools to monitor their response constrains the therapeutic approaches. Here, we evaluated a test based on plasma protein fingerprints acquired by MALDI-TOF-mass spectrometry and supervised/unsupervised algorithms to discriminate the different immunological stages of sepsis in lipopolysaccharide-induced murine models with encouraging results. Moreover, our predictive models through machine learning algorithms were able to discriminate the different groups with a sensitivity of up to 95.7% and a specificity of 90.9% depending on the selected peaks number. Potential individual biomarkers associated with each phase were also analysed. Our data reveal the potential of plasma peptidome analysis by MALDI-TOF-mass spectrometry as a highly relevant strategy for sepsis patient stratification that could contribute to therapeutic decisions, depending on the immunological phase that the patient is undergoing.