Table 1.
Demographic and clinical parameters of study participants.
Table 2.
Stratification analysis of the clinical measures in patients following regular therapy for SLE.
Fig 1.
Peripheral blood γδ T cells and subsets in SLE patients.
A. The panels in this section show the gating strategy employed for the analysis of γδ T cells and subsets. Peripheral venous blood derived leukocytes were stained with different fluorescent antibodies and after lysis of red blood cells, the remaining cells were gated on living lymphocytes, then gated on CD3+γδTCR+ or CD3+γδTCR+γ9TCR+ cells, and then further gated on CD4-CD8- cells, respectively. B—I. Flow cytometry results are represented as the scatter dot plots of γδ T cells and subsets in healthy controls (HC) or in SLE patients before and after therapy (treatment time is indicated). SLE patients are further grouped as responders (R) and non-responders (NR) based on response to therapy.
Fig 2.
Characterization of different subsets of CD3+γδTCR+ cells in SLE patients.
A. The panels in this section show the gating strategy employed for the analysis of γδ T cells and subsets. Peripheral venous blood derived leukocytes were stained with different fluorescent antibodies and after lysis of red blood cells, the remaining cells were gated on living lymphocytes and further gated on CD3+γδTCR+ cells and CD3+γδTCR+CD4-CD8- cells, and then further gated on IFN-γ+, TNF-α+, IL17+ and CD27+ cells, respectively. The frequencies of different subsets of γδT cells were analyzed by flow cytometry. B—I. Flow cytometry results represented as the scatter dot plots of γδ T cells and subsets in healthy controls (HC) or in SLE patients before and after therapy (treatment time is indicated). SLE patients are further grouped as responders (R) and non-responders (NR) based on response to therapy.
Fig 3.
Characterization of different subsets of CD3+γδTCR+γ9TCR+ cells in SLE patients.
A. The panels in this section show the gating strategy employed for the analysis of γδ T cells and subsets. Peripheral venous blood derived leukocytes were stained with different fluorescent antibodies and after lysis of red blood cells, the remaining cells were gated on living lymphocytes and further gated on CD3+γδTCR+γ9TCR+ cells and CD3+γδTCR+γ9TCR+CD4-CD8- cells, and then further gated on IFN-γ+, TNF-α+, IL17+ and CD27+ cells, respectively. The frequency of different subsets of γ9δT cells were analyzed by flow cytometry. B—I. Flow cytometry results represented as the scatter dot plots of γδ T cells and subsets in healthy controls (HC) or in SLE patients before and after therapy (treatment time is indicated). SLE patients are further grouped as responders (R) and non-responders (NR) based on response to therapy.
Fig 4.
Cytokines profile in SLE patients The levels of serum IL-2, IL-4, IL-6, IL-10, IL-17A, IFN-γ and TNF-α in study participants was analyzed by CBA before and at 4 weeks and 12 weeks after initiation of treatment.
Fig 5.
Correlations between γδT cell subsets size and the clinical indicators.
The levels of the serum C3 and SLEDAI scores were correlated to various serum cytokines and to pan γδT cell numbers and to γδT cell subsets in SLE patients. There is no significant correlation among other parameters tested (data not shown), see text for details.
Fig 6.
Correlations between the different subsets of γδT cells.
The levels of the numbers of different subsets of γδT cell were correlated in SLE patients. Apart from those shown no statically significant correlations were detected.