Neutrophil Extracellular Traps (NETs) are a contributing factor of vascular thrombosis and alveolar damage in COVID-19 patients. As enoxaparin is currently used to inhibit vascular thrombosis, this study aimed to investigate whether enoxaparin also reduced inflammation and NETs in COVID-19 patients. Patients with COVID-19 infection were classified into three groups: mild, moderate, and severe (n = 10 for all groups). Plasma was collected from patients and healthy donors (n = 10). Neutrophils isolated from healthy controls were incubated with COVID-19 or healthy plasma, and with or without enoxaparin pretreatment in vitro. Neutrophils and plasma isolated from patients treated with enoxaparin were also investigated. The levels of inflammatory cytokines and NET products such as dsDNA, NE, MPO–DNA and Histone–DNA complexes in plasma and supernatants were measured using immunofluorescence staining and ELISA kits. The expression of inflammatory signaling genes by neutrophils (RELA, SYK, ERK and PKC) was measured using real-time qPCR. The levels of NET products were elevated in the plasma of COVID-19 patients, particularly in the severe group (p < 0.01). Moreover, plasma from the severe group enhanced NET formation (p < 0.01) from neutrophils in vitro. Enoxaparin pretreatment in vitro decreased plasma-induced NETs in a dose-dependent manner and down-regulated the expression of inflammatory genes (p < 0.05). Patients treated with prophylactic enoxaparin showed lower inflammatory cytokine levels and expression of inflammatory genes (p < 0.05). Increased NETs were associated with the severity of COVID-19 infection, particularly in patients with severe pneumonia, and could be used as biomarkers to assess disease severity. Enoxaparin pretreatment inhibited NETs and reduced the expression of inflammatory cytokines, and these effects mostly persisted in patients treated with prophylactic enoxaparin.

Background/Objectives: Untargeted ¹H NMR metabolomics is a robust and reproducible approach used to study the metabolism in biological samples, providing unprecedented insight into altered cellular processes associated with human diseases. Metabolomics is increasingly used alongside other techniques to detect an instantaneous altered cellular function, for example, the role of neutrophils in the inflammatory response. However, in some clinical settings, blood samples may be limited, restricting the amount of cellular material available for a metabolomic analysis. In this study, we wanted to establish an optimal 1D ¹H NMR metabolomic pipeline for use with human neutrophil samples with low amounts of input material. Methods: We compared the effect of different neutrophil isolation protocols on metabolite profiles. We also compared the effect of the absolute cell counts (100,000 to 5,000,000) on the identities of metabolites that were detected with an increasing number of scans (NS) from 256 to 2048. Results/Conclusions: The variance in the neutrophil profile was equivalent between the isolation methods, and the choice of isolation method did not significantly alter the metabolite profile. The minimum number of cells required for the detection of neutrophil metabolites was 400,000 at an NS of 256 for the spectra acquired with a cryoprobe (700 MHz). Increasing the NS to 2048 increased metabolite detection at the very lowest cell counts (<400,000 neutrophils); however, this was associated with a significant increase in the analysis time, which would be rate-limiting for large studies. The application of a correlation-reliability-score-filtering method to the spectral bins preserved the essential discriminatory features of the PLS-DA models whilst improving the dataset robustness and analytical precision.