Relationship between Macrophages and Tissue Microenvironments in Diabetic Kidneys

Abstract

Diabetic nephropathy (DN) is the leading cause of end-stage kidney disease. Increasing evidence has suggested that inflammation is a key microenvironment involved in the development and progression of DN. Studies have confirmed that macrophage accumulation is closely related to the progression to human DN. Macrophage phenotype is highly regulated by the surrounding microenvironment in the diabetic kidneys. M1 and M2 macrophages represent distinct and sometimes coexisting functional phenotypes of the same population, with their roles implicated in pathological changes, such as in inflammation and fibrosis associated with the stage of DN. Recent findings from single-cell RNA sequencing of macrophages in DN further confirmed the heterogeneity and plasticity of the macrophages. In addition, intrinsic renal cells interact with macrophages directly or through changes in the tissue microenvironment. Macrophage depletion, modification of its polarization, and autophagy could be potential new therapies for DN.

Keywords: cell–cell interaction; diabetic nephropathy; fibrosis; inflammation; macrophages; microenvironment; single-cell RNA sequencing; therapeutics.

Figure 1

Macrophages have high plasticity to adapt in different microenvironments in diabetic kidneys. Kidney macrophages can be derived from the differentiation of blood monocytes or exist as resident cells in tissues from early embryonic development. After injury, kidney cells release cytokines and growth factors which, together with diabetic milieu, such as high glucose and AGEs, form a diseased microenvironment in the diabetic kidneys. This microenvironment induces monocyte infiltration and activation into inflammatory macrophages (M1). Tissue resident macrophages are also activated to form M1 macrophages. When the disease progresses to the late stage, the kidney microenvironment changes, and these macrophages change their phenotype from M1 and M2. However, this M1-to-M2 switch is a reversible and dynamic process.

Figure 2

scRNAseq analysis revealing the dynamic transition in macrophage activation in the diabetic kidney. As described in our study [25], we utilized a single–cell transcriptome–based annotation tool MacSpectrum, which infers the macrophage activation status by estimating two indices: the macrophage polarization index (MPI) to annotate the degree of inflammation, and the activation–induced macrophage differentiation index (AMDI) to annotate the degree of terminal maturation (a). With this approach, the macrophages from control (WT) and diabetic kidneys from OVE26 mice (OVE) were mapped onto the MacSpectrum plot as “M1–like”, “M2–like”, “transitional”, and “pre–activation” phenotypes, and (b,c) show that macrophages from diabetic mice shifted to more inflammation (high MPI) and less differentiation (low AMDI). Consistent with this, (d) shows that macrophages shifted to the right (high MPI) and low (low AMDI) corner (Figure generated from the data published in the paper [25]).

Figure 2

scRNAseq analysis revealing the dynamic transition in macrophage activation in the diabetic kidney. As described in our study [25], we utilized a single–cell transcriptome–based annotation tool MacSpectrum, which infers the macrophage activation status by estimating two indices: the macrophage polarization index (MPI) to annotate the degree of inflammation, and the activation–induced macrophage differentiation index (AMDI) to annotate the degree of terminal maturation (a). With this approach, the macrophages from control (WT) and diabetic kidneys from OVE26 mice (OVE) were mapped onto the MacSpectrum plot as “M1–like”, “M2–like”, “transitional”, and “pre–activation” phenotypes, and (b,c) show that macrophages from diabetic mice shifted to more inflammation (high MPI) and less differentiation (low AMDI). Consistent with this, (d) shows that macrophages shifted to the right (high MPI) and low (low AMDI) corner (Figure generated from the data published in the paper [25]).