A flow cytometry approach reveals heterogeneity in conventional subsets of murine renal mononuclear phagocytes

Abstract

Mononuclear phagocytes (MNPs) participate in inflammation and repair after kidney injury, reflecting their complex nature. Dissection into refined functional subunits has been challenging and would benefit understanding of renal pathologies. Flow cytometric approaches are limited to classifications of either different MNP subsets or functional state. We sought to combine these two dimensions in one protocol that considers functional heterogeneity in each MNP subset. We identified five distinct renal MNP subsets based on a previously described strategy. In vitro polarization of bone marrow-derived macrophages (BMDM) into M1- and M2-like cells suggested functional distinction of CD86 + MHCII + CD206- and CD206 + cells. Combination of both distinction methods identified CD86 + MHCII + CD206- and CD206 + cells in all five MNP subsets, revealing their heterologous nature. Our approach revealed that MNP composition and their functional segmentation varied between different mouse models of kidney injury and, moreover, was dynamically regulated in a time-dependent manner. CD206 + cells from three analyzed MNP subsets had a higher ex vivo phagocytic capacity than CD86 + MHCII + CD206- counterparts, indicating functional uniqueness of each subset. In conclusion, our novel flow cytometric approach refines insights into renal MNP heterogeneity and therefore could benefit mechanistic understanding of renal pathology.

Figure 1

Markers F4/80, CD11b, and CD11c distinguish five distinct MNP subsets in the murine kidney. (A) A gating strategy for five renal MNP subsets was adopted from Kawakami et al. and representative FACS plots are shown of a naïve mouse and 7 weeks old Col4a3^−/− mice with Alport syndrome as an example for diseased state. (B) Quantification of cell numbers in the five MNP subsets for naïve (n = 8) and Col4a3^−/− mice (n = 11). Shown are pooled data from two independent experiments. y = ln(y) transformed cell numbers were compared by unpaired t-test *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. (C) Representative histograms for expression of surface markers F4/80, Ly6C, CX3CR1 and C103 in all five MNP subsets. (D) Quantification of geometric mean fluorescence intensity (MFI) of surface markers. MFI were compared by Kruskal–Wallis with post-hoc Dunn’s multiple comparisons test *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Figure 2

CD86, MHCII, and CD206 identify M1- and M2-like cells in in vitro stimulated BMDM. (A) We applied gating strategies for M1/M2 that are commonly used in literature (i–iii) and an in-house approach (iv) to in vitro stimulated BMDM. (B) For in vitro polarization BMDM were differentiated for 5 days with M-CSF and then stimulated with LPS or IL-4 and IL-13. After 48 h cells were analyzed by flow cytometry and M1- (C) and M2-like cells (D) were quantified with each strategy from (A). (E) Duplicates were subjected to qPCR to determine expression of M1 (cd86, socs3, tnfa) and M2 (mrc1, cd200r1) genes. (n = 10 from 3 independent experiments). Kruskal–Wallis with post-hoc Dunn’s multiple comparisons test (C and D) or one way ANOVA with post-hoc Dunnett's (E) multiple comparisons test against medium *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Figure 3

Combination of phenotypical MNP subset analysis and functional distinction grants additional dimension to flow cytometric analysis. Flow cytometry of renal leukocytes (pre-gated on CD45 + Ly6G-, different cells are depicted schematically) is often either used to distinguish MNP subsets with specific surface markers (left box) or functionally distinct cells (right box). We propose a combined analysis of both dimensions for comprehensive understanding of functional and phenotypical state on a single cell level.

Figure 4

Different models of kidney injury possess a fingerprint-like MNP subset composition collectively but also in CD206 + and CD86 + MHCII + CD206- cells. (A) MNP subsets were quantified among total leukocytes (mid row) in different models of kidney injury (means of n = 9 for 7 days UUO sham control, n = 6 for IRI, n = 9 for UUO, n = 11 for Alport; cell number is given in cells per mg kidney). Our approach additionally allowed the quantification of functionally distinct CD206 + (top row) and CD86 + MHCII + CD206- (bottom row) cells in each MNP subset. (B) Quantification of cell numbers from (A) as bar graph. (C) Percent CD86 + and MHCII + cells among CD206 + cells. One-way ANOVA followed by Tukey’s multiple comparisons test of y = ln(y) transformed data *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Figure 5

CD206 + and CD86 + MHCII + CD206- cells in MNP subsets are dynamically regulated after IRI. MNP composition was monitored over a 10 day time course in a kidney injury model of IRI (n = 9 for sham, n = 10 for 3 h, n = 10 for 1d, n = 12 for 3d, n = 6 for 7d, n = 7 for 10d): Data are depicted as pie charts with their size corresponding to the amount of total cells (A, cell number is given as mean in cells per mg kidney) or plotted on a time axis grouped into CD206 + and CD86 + MHCII + CD206- cells (B) or MNP subsets (C, red = CD86 + MHCII + CD206-, green = CD206 +). Cell numbers for each time point in (C) were compared by unpaired t test of y = ln(y) transformed data *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Figure 6

CD206 + and CD86 + MHCII + CD206- cells in MNP subsets are dynamically regulated after UUO. MNP composition was monitored over a 10 day time course in a kidney injury model of UUO (n = 6 for sham, n = 10 for 3 h, n = 8 for 1d, n = 8 for 3d, n = 9 for 7d, n = 9 for 10d): Data are depicted as pie charts with their size corresponding to the amount of total cells (A, cell number is given as mean in cells per mg kidney) or plotted on a time axis grouped into CD206 + and CD86 + MHCII + CD206- cells (B) or MNP subsets (C, red = CD86 + MHCII + CD206-, green = CD206 +). Cell numbers for each time point in (C) were compared by unpaired t-test of y = ln(y) transformed data *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Figure 7

Ex vivo phagocytic capacity is variable between MNP subsets in different kidney injury models. CD11b + CD11c + -sorted cells from kidneys from naïve C57BL6/J (n = 11), 24 h after IRI (n = 7) and 3 days after UUO (n = 3) were fed PE + latex-beads for 2 h and phagocytic cells were identified as PE + events. Phagocytic cells were further dissected with our gating strategy for MNP subsets and CD206 + and CD86 + MHCII + CD206- cells. The data of each replicate were plotted either as (A) relative change compared to CD86 + MHCII + CD206- cells [%phagocytic cells/%phagocytic cells in CD86 + MHCII + CD206-] or (B) as relative change compared to MNP subset 1 [%phagocytic cells/%phagocytic cells in MNP1]. Data from two to three independent experiments are shown. Kruskal–Wallis test followed by Dunn’s multiple comparisons tests against M1 (A) or Friedman with post-hoc Dunn’s multiple comparisons (B) *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.