A New Easy-to-Perform Flow Cytometry Assay for Determining Bacterial- and Viral-Infection-Induced Polymorphonuclear Neutrophil and Monocyte Membrane Marker Modulation in Febrile Patients

Abstract

We developed a flow cytometry (FC) assay enabling the rapid and accurate identification of bacterial and viral infections using whole blood samples. The streamlined flow cytometry assay is designed to be user-friendly, making it accessible even for operators with limited experience in FC techniques. The key components of the assay focus on the expression levels of specific surface markers-CD64 on polymorphonuclear neutrophils (PMN) as a marker for bacterial infection, and CD169 on monocytes (MO) for viral infection. The strong performance indicated by an area under the receiver operating characteristic (ROC) curve of 0.94 for both PMN CD64 positive predictive value (PPV) 97.96% and negative predictive value (NPV) 76.67%, and MO CD169 PPV 82.6% and NPV 86.9%, highlight the assay's robustness in differentiating between bacterial and viral infections accurately. The FC assay includes the assessment of immune system status through HLA-DR and IL-1R2 modulation in MO, providing a useful insight into the patients' immune response. The significant increase in the frequency of MO exhibiting reduced HLA-DR expression and elevated IL-1R2 levels in infected patients (compared to healthy controls) underscores the potential of these markers as indicators of infection severity. Although the overall correlation between HLA-DR and IL-1R2 expression levels was not significant across all patients, there was a trend in patients with more severe disease suggesting that these markers may have the potential to assist in stratifying patient risk. The present FC assay has the potential to become routine in the clinical microbiology laboratory community and to be helpful in guiding clinical decision making.

Keywords: CD169; CD64; HLA-DR; bacterial and viral infection; flow cytometry; immune dysfunction; interleukin-1 receptor 2; monocytes; polymorphonuclear neutrophils.

Figure 1

Gating strategy to analyze PMN CD64 expression level modulation. PMN and non-B lymphocyte regions used to calculate the PMN index. (A) Representative healthy control; (B) microbiologically confirmed bacterial infection patient. PMN and non-B lymphocytes MFI are displayed along with the PMN index.

Figure 2

Gating strategy to analyze MO CD169, MO HLA-DR, and MO IL-1R2 modulation. (A) Selection of PMN, MO, non-B lymphocytes, and B lymphocytes to establish boundary position for each marker; (B) boundary for MO CD169 using PMN; (C) boundary for MO HLA-DR using B-lymphocytes; (D) boundary for MO IL-1R2 using non-B lymphocytes; (E–G) representative healthy control; (H) patient with SARS-CoV-2 infection; (I,J) patients with severe bacterial infection. Percentage of cells above or below the boundary is indicated in each plot.

Figure 3

(A) Good correspondence between MO HLA-DR downmodulation calculated by the present FC assay (percentage of MO with reduced HLA-DR expression level, x-axis) and the Quantibrite™ Anti–HLA-DR/Anti-Monocyte kit (antibody binding capacity, ABC, y-axis). (B) Concomitant modulation of MO HLA-DR and MO IL-1R2 in a representative healthy control subject (left plot) and a critically ill febrile metastatic gastric cancer patient with severe bacterial infection (right plot).

Figure 4

FC assay performance in recognizing patients with bacterial and viral infection according to PMN CD64 and MO CD169 modulation and in stratifying febrile patients into different groups according to MO HLA-DR and MO IL-1R2 expression level. Left plots show the area under the receiver operating characteristic curve (AUC curve). Right plots show data distribution as box plots (median value and interquartile range) and cutoff value (red dotted line). (A) PMN index. (B) MO CD169. (C) MO HLA-DR. (D) MO IL-1R2. HC, healthy controls; BaMC and ViMC, febrile patients with microbiologically confirmed bacterial and viral infection, respectively; WMC, febrile patients without available microbiological confirmation; IP, infected patients (cumulated data of BaMC and ViMC patients); T, tumor patients. Statistically significant differences are shown as ** for p ≤ 0.02, *** for p ≤ 0.001, and **** for p ≤ 0.0001; not statistically significant differences are indicated as n.s.