Table 1.
Primer sequences and concentrations used for real-time PCR analysis.
Figure 1.
Gating strategy of bovine monocyte subsets based on relative CD14 and CD16 expression.
(A) Three-color immunofluorescence of bovine MNC with mAbs to CD172a, CD14 and CD16 defines three monocyte subsets in peripheral blood. Viable (propidium-iodide-negative) mononuclear cells, based on forward and side scatter characteristics, were gated on CD172a-positive cells. Dot plots of CD14 and CD16 expression display classical monocytes (CD14+CD16−, upper left), intermediate monocytes (CD14+CD16+, upper right) and nonclassical monocytes (CD14-CD16+, lower right). (B) Monocytes were gated in a side scatter/CD172a dot plot, identifying monocytes as CD172a-positive cells. Histograms show that CD172a-positive cells do not express common markers of B cell (CD21), T cell (CD2, CD4 or CD8), NK cell (NKp46) or blood γδ T cell (WC1). Data are shown for cells from one of five tested animals.
Figure 2.
Purification of bovine monocyte subsets.
(A) Bovine cM (CD14+ CD16−), intM (CD14+ CD16+) and ncM (CD14− CD16+) subsets were separated from bovine MNC using a two-step MACS procedure as described in material and methods. Separated subsets were labeled with monoclonal antibodies to CD14 and CD16 to identify their purity. Representative examples from 5 independent experiments are shown. (B) Microscopic images of separated monocyte subsets are shown with magnification 100 after staining with acridine orange. (C) Bovine MNC were labeled with monoclonal antibodies to CD14 and CD16 to identify three monocyte subsets. Gated monocyte subsets were presented in FSC against SSC. The median of FSC and SSC of the individual subsets was measured (n = 6 cows) and presented as mean ± SEM. Statistical analysis of significance were performed using ANOVA and Bonferroni’s correction for normally distributed data. *(P<0,01).
Figure 3.
Flow cytometric analysis of established monocyte surface markers.
MACS-separated monocyte subsets (n = 5 cows) were labeled with antibodies to different monocyte markers or isotype controls. After dead cell exclusion with propidium iodide, data were measured as median fluorescence intensity (MFI) and presented as means ± SEM. Background fluorescence (measured in negative controls) was subtracted. Statistical analysis of significance was performed using ANOVA and Bonferroni’s correction for normally distributed data *(P<0,01).
Table 2.
Fractions of monocyte subsets of MNC or CD172a+ monocytes and cell numbers in bovine peripheral blood.
Figure 4.
Phagocytosis and ROS-production capacity of bovine monocyte subsets.
(A) MACS-separated cM, intM and ncM subsets (1×105/well) were incubated with heat killed FITC-labeled S. aureus or E. coli, which have been opsonized with bovine serum for 45 minutes. Control cells were incubated only in medium. The percentage of FITC-positive cells within viable (PI-negative) cells were determined flow cytometrically for the three monocyte subsets and are denoted as mean value ± SEM (n = 3 animals). (B) Flow cytometric analysis of ROS-formation by bovine monocyte subsets. Bovine MNC (n = 4 animals) were stimulated with PMA or heat killed and serum opsonized E. coli in the presence of DHR-123. The ncM subset was identified after labeling the cells with antibodies to CD172a and CD14 as CD172a positive and CD14 low cells. In a combination of CD14 and MHC-II monoclonal antibodies, the intM and cM subsets could be identified as both CD14 high cells with higher MHC-II expression on the intM subset. Cells were gated on viable (propidium-iodide-negative) CD172a-positive cells or CD14 high cells. ROS generation was calculated as the median green fluorescence intensity of gated monocyte subsets after subtracting the MFI of non-stimulated cells and presented as mean ± SEM. Differences between groups were calculated using the one-way ANOVA and were considered significant (*) if p<0.05.
Figure 5.
Chemokine and cytokine mRNA expression of bovine monocyte subsets.
Bovine MACS-separated cM, intM and ncM (n = 3 animals) were stimulated with LPS (1 µg/ml) for 3 h. Messenger RNA copy numbers of the chemokines CXCL1 and CXCL8 and the cytokines IL-1β, Arginase1, iNOS and TNF-α were determined in 8 ng total RNA by quantitative RT-PCR. Samples were tested in duplicates. Differences between treatments and between groups (one-way ANOVA) were considered significant (*) at p<0.05.
Figure 6.
Inflammasome activation in bovine monocyte subsets.
Analysis of inflammasome activation was done by measuring the secreted IL-1β in medium supernatants of monocyte subsets after combined stimulation with LPS and ATP. MACS-separated bovine monocyte subsets (n = 5 animals) were primed for 5 h with LPS (1 µg/mL) and 5 mmol/L ATP was added to the culture for an additional hour. IL-1β in cell supernatants was measured by ELISA. Differences between groups (one-way ANOVA) were considered significant (*) at p<0.05.
Figure 7.
The effect of whole blood stimulation with different cytokines or CCL5 on monocyte subsets.
Whole blood samples (n = 7 animals) were incubated with the cytokines IFNγ, IL-4/IL-13, TNF-α or IL-1β or the chemokine CCL5 for 4 h. Leukocytes were stained with monoclonal antibodies to CD172a, CD14 and CD16. After gating on vital (PI-negative) MNC, agate was made on CD172a-positive cells. CD172a-positive cells were presented in CD14 and CD16 dot plot. Mean fluorescence intensity of CD16 (A) and the percentage (B) of the three monocyte subsets was calculated and presented as mean ± SEM. Differences between groups were calculated using the one-way ANOVA and were considered significant (*) if p<0.05.