Depiction of immune heterogeneity of peripheral blood from patients with type II diabetic nephropathy based on mass cytometry

Abstract

Diabetic nephropathy (DN) is the most prominent cause of chronic kidney disease and end-stage renal failure. However, the pathophysiology of DN, especially the risk factors for early onset remains elusive. Increasing evidence has revealed the role of the innate immune system in developing DN, but relatively little is known about early immunological change that proceeds from overt DN. Herein, this work aims to investigate the immune-driven pathogenesis of DN using mass cytometry (CyTOF). The peripheral blood mononuclear lymphocytes (PBMC) from 6 patients with early-stage nephropathy and 7 type II diabetes patients without nephropathy were employed in the CyTOF test. A panel that contains 38 lineage markers was designed to monitor immune protein levels in PBMC. The unsupervised clustering analysis was performed to profile the proportion of individual cells. t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the differences in DN patients' immune phenotypes. Comprehensive immune profiling revealed substantial immune system alterations in the early onset of DN, including the significant decline of B cells and the marked increase of monocytes. The level of CXCR3 was dramatically reduced in the different immune cellular subsets. The CyTOF data classified the fine-grained differential immune cell subsets in the early stage of DN. Innovatively, we identified several significant changed T cells, B cell, and monocyte subgroups in the early-stage DN associated with several potential biomarkers for developing DN, such as CTLA-4, CXCR3, PD-1, CD39, CCR4, and HLA-DR. Correlation analysis further demonstrated the robust relationship between above immune cell biomarkers and clinical parameters in the DN patients. Therefore, we provided a convincible view of understanding the immune-driven early pathogenesis of DN. Our findings exhibited that patients with DN are more susceptible to immune system disorders. The classification of fine-grained immune cell subsets in this present research might provide novel targets for the immunotherapy of DN.

Keywords: diabetic nephropathy; high-dimensional mass cytometry; immune disorder; peripheral blood mononuclear cell (PBMC); type II diabetes mellitus.

Figure 1

Peripheral immunity signature traits in the early-stage diabetic nephropathy patients. (A) Heatmap analysis for the overall proportions of major immune cell subsets in diabetic nephropathy (B) Pie chart characterizing the differences of major immune cell subsets proportions between T2D-DN and T2D patients. (C) Distributions of major immune cell subsets (D) The key immune cell markers for immune cell subsets are analyzed by t-SNE algorithm. (E) Statistical frequency differences of immune cell subsets between T2D-DN and T2D patients. (F) Heatmap analysis for the expressions of functional immune cell markers in the measurable immune cell subsets. Data are expressed as means ± SEM, n = 6 in T2D-DN group and n = 7 in T2D group.

Figure 2

Identification of immune heterogeneity of T cell subsets. (A) Heatmap exhibiting the expressions of 38 immune cell markers in the T cell subsets (left) and 8 marked definitions of T cell subsets (right). (B) Distributions of functional traits are expressed in the different T cell subsets. (C) Island maps exhibit the measurable T cell subsets distribution. (D) Pie chart characterizing the differences of primary T cell subsets proportions between T2D-DN and T2D patients. (E) Statistical frequency differences of T cell subsets between T2D-DN and T2D patients. (F) Significantly changed T cell subsets between T2D-DN and T2D patients. (G) CTLA-4, (H) PD-1, (I) CD39, and (J) CXCR3 in the specific T cell subsets between T2D-DN and T2D patients. Data are expressed as means ± SEM, n = 6 in T2D-DN group and n = 7 in T2D group., *P < 0.05, **P < 0.01, ****P < 0.0001 vs. T2D group.

Figure 3

Depiction of features of B cell subsets between T2D-DN and T2D patients. (A) Heatmap exhibiting the expressions of immune cell markers in the B cell subsets. (B) Island maps display the measurable B cell subsets distribution. (C) Statistical frequency differences of B cell subsets between T2D-DN and T2D patients. (D) Significantly changed B cell subsets between T2D-DN and T2D patients. (E) CD39, (F) CXCR3 and (G) CCR4 in the specific B cell subsets between T2D-DN and T2 patients. Data are expressed as means ± SEM, n = 6 in T2D-DN group and n = 7 in T2D group., *P < 0.05, **P < 0.01, ***P < 0.001 vs. T2D group.

Figure 4

Immunological heterogeneity of myeloid cell subsets between T2D-DN and T2D patients. (A) Heatmap exhibiting the expressions of immune cell markers in the myeloid cell subsets (left) and six marked definitions of myeloid cell subsets (right). (B) Island maps exhibit the measurable myeloid cell subsets distribution. (C) Pie chart characterizing the differences of significant myeloid cell subsets proportions between T2D-DN and T2D patients. (D) Statistical frequency differences of myeloid cell subsets between T2D-DN and T2D patients. (E) Significant changed myeloid cell subsets between T2D-DN and T2D patients. (F-H) The level of CXCR3 (F), HLA-DR (G), and CD39 (H) in the specific myeloid cell subsets between T2D-DN and T2D patients. Data are expressed as means ± SEM, n = 6 in T2D-DN group and n = 7 in T2D group., *P < 0.05, **P < 0.01 vs. T2D group.

Figure 5

Correlation analysis between identified immune populations and renal clinical parameters as well as diabetic markers in the T2D-DN patients. Rows correspond to renal clinical parameters and diabetic markers, and columns correspond to the specific immune cell subsets and biomarkers. Blue and red colors denote positive and negative associations, respectively. The intensity of the colors represents the degree of association between the immune populations and clinical parameters assessed by Pearson correlation analysis. Stars means P < 0.05.