Fig 1.
Monocyte subset maturation and marker selection.
(A) After exiting the bone marrow and entering circulation, monocytes transition from classical to intermediate and then non-classical subsets in a linear fashion, dependent on time and inflammatory activation signals. (B) Selected monocyte markers and corresponding functions.
Fig 2.
Flow cytometric gating strategy.
Peripheral blood samples, collected at screening, were processed by magnetic separation to isolate subpopulations. (A) Cells were divided into CD15+ (polymorphonuclear neutrophils; PMN) and CD15- (peripheral blood mononuclear cells; PBMCs) fractions. The PBMC fraction was further analyzed by flow cytometry, initially gated on forward and side scatter properties to identify monocyte and lymphocyte populations. Gating on the common leukocyte antigen CD45 was employed to refine these populations and exclude red blood cells, and cells were stained with a viability dye to ensure analysis of live cells only. (B) From the viable cell population, monocytes were categorized into three subsets based on their expression of CD14 and CD16. (C) Within each monocyte subset, further analysis focused on six specific markers—CD87, CD11b, CD163, HLA-DR, CD195, and CD192. Mean fluorescence intensity (MFI), measured in arbitrary units (a.u.), quantitatively reflects the density of marker expression on the cell surface, providing a more detailed assessment of cellular activation states to evaluate the level of expression rather than just the presence (frequency) of these markers. Further gating to evaluate a broad lymphocyte population was conducted and is included in the supplemental material.
Table 1.
Study sample characteristics.
Fig 3.
Eighty-four subjects provided a blood sample for cell isolation and immunophenotyping. Two subjects were excluded for poor data quality. Analysis was completed on primary outcomes of interest including self-reported symptoms, medical history, deployment information as well as physiological measures including PFT, FOT, and FMD/NMD. Only a smaller portion (n = 48) of the total sample completed the FMD and NMD procedures.
Fig 4.
Individual marker expression within each monocyte subset.
Heatmap showing individual marker expression (MFI) within each subset, with every column representing a single subject’s expression (repeated for each subset) and every row an individual marker. Red indicates higher standardize frequency (z-score), and blue lower.
Table 2.
Cluster differences in CD surface marker expression.
Table 3.
Subject demographics and self-reported deployment history by cluster.
Table 4.
Self-reported symptoms, functional limitations, and exposures cluster.
Fig 5.
Dendrogram of monocyte cluster analysis based on PCA.
Hierarchical clustering of monocyte surface markers following dimensionality reductions with PCA. Two major clusters emerged with distinct CD expression phenotypes potentially reflective of differing activation states. Four additional subclusters were identified and discussed within the context of monocyte activation states.
Table 5.
Pulmonary function by cluster.
Table 6.
Lung function assessed by oscillometry.
Table 7.
Flow mediated vasodilation by cluster.
Fig 6.
Summary of subcluster analysis.