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. 2024 Aug 9;11(38):2309752. doi: 10.1002/advs.202309752

Identification of a Novel ECM Remodeling Macrophage Subset in AKI to CKD Transition by Integrative Spatial and Single‐Cell Analysis

Yi‐Lin Zhang 1, Tao‐Tao Tang 1, Bin Wang 1, Yi Wen 1, Ye Feng 1,2, Qing Yin 1, Wei Jiang 1, Yue Zhang 1, Zuo‐Lin Li 1, Min Wu 1, Qiu‐Li Wu 1, Jing Song 1, Steven D Crowley 3, Hui‐Yao Lan 4, Lin‐Li Lv 1,✉, Bi‐Cheng Liu 1,✉
PMCID: PMC11481374  PMID: 39119903

Abstract

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) is a critical clinical issue. Although previous studies have suggested macrophages as a key player in promoting inflammation and fibrosis during this transition, the heterogeneity and dynamic characterization of macrophages are still poorly understood. Here, we used integrated single‐cell RNA sequencing and spatial transcriptomic to characterize the spatiotemporal heterogeneity of macrophages in murine AKI‐to‐CKD model of unilateral ischemia‐reperfusion injury. A marked increase in macrophage infiltration at day 1 was followed by a second peak at day 14 post AKI. Spatiotemporal profiling revealed that injured tubules and macrophages co‐localized early after AKI, whereas in late chronic stages had spatial proximity to fibroblasts. Further pseudotime analysis revealed two distinct lineages of macrophages in this transition: renal resident macrophages differentiated into the pro‐repair subsets, whereas infiltrating monocyte‐derived macrophages contributed to chronic inflammation and fibrosis. A novel macrophage subset, extracellular matrix remodeling‐associated macrophages (EAMs) originating from monocytes, linked to renal fibrogenesis and communicated with fibroblasts via insulin‐like growth factors (IGF) signalling. In sum, our study identified the spatiotemporal dynamics of macrophage heterogeneity with a unique subset of EAMs in AKI‐to‐CKD transition, which could be a potential therapeutic target for preventing CKD development.

Keywords: AKI, CKD, macrophage, single‐cell RNA‐seq, spatial transcriptomic

This study sheds new light on the heterogeneous roles of macrophages in the complex and cumbersome pathological process of AKI to CKD. Integrating high‐throughput spatial and single‐cell transcriptomic data, the study identifies distinct macrophage lineages, with renal resident macrophages promoting repair and monocyte‐derived ECM remodeling macrophages (EAMs) contributing to renal fibrogenesis. These findings pave the way for the development of innovative therapeutic strategies to halt disease progression.

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1. Introduction

Over 13 million people around the world are affected by acute kidney injury (AKI) each year.[ 1 ] Patients with AKI are at an increased risk of developing chronic kidney disease (CKD).[ 2 ] After AKI, a rapid innate immune response with inflammation is observed in the acute phase of kidney injury which involves both damage and repair processes, while persistent inflammation leads to interstitial fibrosis.[ 3 , 4 , 5 ] Clearly, inflammation plays a key role in these processes of injury and repair during the AKI to CKD transition, but the dynamic response of immune cells and their precise contributions to this process requires clarification.

Macrophages are key regulators of immune surveillance, playing critical roles in the initiation, maintenance and resolution of tissue injury.[ 6 , 7 , 8 ] In general, they have been classified into classical M1 and alternative M2 subtypes according to their activation states.[ 9 ] Under this oversimplified, dichotomous paradigm, studies indicated that M1 macrophages generate pro‐inflammatory cytokines, exacerbating tissue damage,[ 10 , 11 ] whereas M2 macrophages promote regeneration of injured tissue, angiogenesis and matrix deposition by secreting anti‐inflammatory cytokines.[ 12 ] The fine balance between M1 and M2 macrophages during immune responses affects the extent of tissue injury and repair after an acute event.[ 13 ] However, this binary classification system fails to capture the dynamics of diverse plastic macrophage phenotypes and their contributions to the progression of disease. At present, different macrophage phenotypes and their activation are poorly understood in the context of AKI to CKD transition due to a lack of comprehensive knowledge regarding cell‐specific and spatiotemporal alterations in gene expression.

Recent single‐cell transcriptomic analysis has revealed unprecedented molecular details on macrophage heterogeneity.[ 14 , 15 , 16 , 17 ] For example, Arg1‐positive macrophages expand following early injury and promote fibrosis in human CKD.[ 14 , 16 , 18 ] Another study identified S100A8/A9‐positive macrophages that initiate and amplify the inflammatory response during AKI.[ 15 ] Therefore, macrophage heterogeneity closely correlates with kidney inflammation and fibrosis. However, the spatiotemporal dynamics of macrophage heterogeneity from AKI to CKD remain incompletely understood. Thus, there is an urgent need to identify distinct macrophage subtypes with temporal and spatial resolution to better understand this process. Here, we integrate high‐throughput spatial and single‐cell transcriptomic data that describe the spatial archetypes and macrophage heterogeneity at multiple time points during the AKI to CKD transition. We identified two major cell lineages coexisting in the ischemic kidney. Renal resident macrophages differentiated into the pro‐repair macrophage (Mac) subsets participating in wound healing, whereas monocyte derived extracellular matrix (ECM) remodeling‐associated macrophages (EAMs) transitioned toward a pro‐inflammatory Mac and contributed to chronic inflammation and kidney fibrosis. Among these clusters, EAMs communicate with fibroblasts and drive kidney fibrogenesis via Igf1‐Igf1r interactions. Thus, our data identify a new subset of macrophages with ECM remodeling capacity during the AKI to CKD transition, which could be a potential therapeutic target for preventing this disastrous disorder.

2. Results

2.1. Single‐Cell Transcriptomic Atlas of UIR Mice

We adopted a 35‐min warm unilateral ischemia‐reperfusion injury (UIR) technique to create the mouse AKI to CKD transition model and performed scRNA‐seq to clarify the kidney cell landscape at baseline and days 1, 3, 14, and 28 post‐UIR (Figure 1a; Figure S1a, Supporting Information). We acquired a total of 74766 cells from all samples for single‐cell RNA sequencing (scRNA‐seq), with an average of 1833 genes per cell. Using the Seurat R program, cell clusters were integrated, grouped, and displayed in uniform manifold approximation and projection (UMAP) plots (Figure S1b,c, Supporting Information).[ 19 ]

Figure 1.

Figure 1

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Major cell dynamics after UIR. a) The experimental workflow. Experimental AKI‐to‐CKD transition was induced in mice by UIR. Samples were collected from the injured kidneys at days 0, 1, 3, 14, and 28 post‐UIR for 10 × chromium single‐cell and visium spatial transcriptomic procedures. b) After unsupervised clustering, 2D uniform manifold approximation and projection (UMAP) visualization of the 60010 cells identified 13 major cell types. PT, proximal tubule; M/DC, monocyte, macrophage and DC; T/NK, T/NK cell; B, B cell; Neu, neutrophil; Fib, fibroblast; Pro, proliferation cell; EC, endothelial cell; Epi, epithelial cell; CD, collecting duct; DCT, Distal convoluted tubule; Pod, podocyte; LH, loop of Henle. c) Connected bar plots showing the proportionate abundance of each cell clusters in each samples. Immune cells are enlarged to facilitate data visualization.

Unsupervised clustering revealed 13 major cell clusters, encompassing proximal tubule cells (PT), fibroblasts (Fib), podocytes (Pod), and several innate immune cell types including monocyte/dendritic cells/macrophages (M/DC) (Figure 1b). Cell clusters were annotated based on the expression of curated marker genes (Figure 1b; Figure S1d, Supporting Information) and data integration with previous cell atlas resources.[ 20 , 21 , 22 ] The most abundant cell population was PT (61.9% of total cells) which highly expressed marker genes such as Slc34a1, Slc5a12, Slc7a13, and Slc22a7. After UIR, the proportion of PT cells declined dramatically at day 1 compared to sham (69.6% vs. 90.5%) but then gradually rebounded at day 3 (84.3%), followed by a decline in the chronic phase of UIR (74.5% and 75.9% at days 14 and 28, respectively) (Figure 1c). On the contrary, the proportion of immune cells rapidly increased at day 1 (11.7%) and then reaching a peak at day 14 (16.9%) post‐UIR, which was consistent with previous reports.[ 23 ] Of these, macrophages (C1qa, C1qa, and Cd74 high) emerged as the largest, accounting 21.4% of the immune cell population, followed by T/NK cells (14.4%; Ccl5, Trbc2, and Nkg7 high), neutrophils (13.6%; S100a8 and S100a9 high), and B cells (3.6%; Cd79a high) (Figure 1c). We also discovered a proliferative cluster with enriched expression of cell‐cycle marker genes such as Mki67, Pcna, Top2a, which was predominant at days 1 and 3 with a proportion of 4.8% and 2.7%, respectively (Figure 1c). This percentage decreased to 0.5% and 0.4% on days 14 and 28, respectively after injury (Figure 1c). Notably, the proliferative cluster shared marker expression with PT and Mac. Collectively, our data clearly showed the cellular atlas, especially the macrophage dynamics during AKI to CKD transition, which dominate the immune cell population.

2.2. Spatiotemporal Dynamics of Main Cell Types after UIR

Next, we performed spatial transcriptomic analysis on the frozen kidneys from baseline and at 1, 3, 14, and 28 days post‐UIR to uncover the spatiotemporal dynamics of renal cells during AKI progression. Masson staining revealed collagen deposition at day 3 after ischemia, which increased over time (Figure  2a,b). Unsupervised clustering of transcriptomic spots from all samples based on their cell type compositions identified four clusters, which were defined as major histomorphological regions, including cortex, outer medulla (including outer and inner stripes), and inner medulla (Figure 2c,d). Injury scoring spots, calculated from the expression of inflammation and fibrosis specific genes in all samples, were present (Injury score, Table  1 and Methods) and further revealed the diffuse exacerbations of kidney injury during chronic stages (Figure 2e). Notably, we identified the outer stripe of the outer medulla as the site of the highest degree of injury (Figure 2c,e), consistent with the susceptibility of outer stripe to ischemic injury.[ 24 ]

Figure 2.

Figure 2

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Spatiotemporal dynamics of main cell types after UIR. a) H&E staining of Visium Spatial Gene Expression samples. b) Representative Masson images at each timepoint. c,d) UMAP of spatial transcriptomics spots based on cell‐type compositions and the injury score in spatial transcriptomics. e)Injury scores in each time points. The arrows point to the areas with the highest injury score. f,g,h) The proportions of macrophages, neutrophils and multiple cells were deconvoluted from the scRNA‐seq data using the cell2location algorithm. Max, maximum; min, minimum. i) The proportion of macrophages and neutrophils infiltrated into each area according to the time‐point after UIR. j) Median relevance of cell‐type abundance in predicting other cell‐type abundances within a location.

Table 1.

Scoring genes.

Functions Fibrosis[ 25 ] ECM[ 26 , 27 ] Inflammation[ 28 , 29 ] Injury[ 25 , 28 , 29 ] Cytokines[ 30 , 31 , 32 , 33 , 34 ] DNA repair[ 35 ] Phagocytosis[ 36 ] Angiogenesis[ 37 ] Wound healing[ 38 ]
Genes Pdgfb Fn1 Cd74 Pdgfb Tnf Dctn6 Irf8 Npr3 Hps4
Tgfb1 Spp1 Tnfrsf12a Tgfb1 Tnfrsf1a Bub3 Lyst Tmem215 Arhgef19
Col3a1 Ecm1 Cxcl1 Col3a1 Tnfrsf1b Cenpb Hck Ecscr Arhgap24
Acta2 Mmp12 Cxcl10 Acta2 Il1b Cenpa Abr Hspg2 Cldn1
Mmp2 Mmp14 Cxcl16 Mmp2 Il6 Mki67 Lepr Arhgap24 Fkbp10
Pdgfa Mmp9 Il1b Pdgfa Il10 Ndc80 Sirpb1a Ang6 Gp9
Vim Mmp19 Cxcl2 Vim Icam1 Cenpf Elmo3 Ephb3 Ppara
Nkd2 Ctsb Ccl3 Nkd2 Vcam1 Pmf1 Met Ephb4 Bloc1s4
Tnc Ctsd Tyrobp Tnc Ccl5 Incenp Mertk Fgf9 Scnn1b
Gpnmb Ctsz C3 Gpnmb Cxcl2 Nuf2 Rab11fip2 Meis1 Scnn1g
Cd9 Ctsl Ccl2 Cd9 Ccl2 Smc6 Tafa4 Amotl2 Enpp4
Tnfsf12 Ctss Il34 Tnfsf12 Il1a Csnk1a1 Itgb2 Ang5 Cav3
Tl2r Arg1 Ccl5 Tl2r Il18 Nde1 Itgb1 Dicer1 Evpl
Tnf Pf4 Ccl7 Tnf Ccl12 Cdt1 Itgam Rnf213 Lyst
Igf1 Thbs1 Ccl8 Igf1 Ccl7 Hells Itgal Sema4a Stard13
Timp2 Ccl12 Timp2 Ccl8 Top2a Abca7 Sox17 Nrg1
Anxa5 Lcn2 Anxa5 Cx3cl1 Spdl1 Lrp1 Tmem100 Hbegf
Havcr1 Cd74 ll34 Pkhd1 Tlr4 Ereg Ptpn6
S100a9 Tnfrsf12a Pdgfa Cenpq Anxa1 Plcd3 Serpind1
S100a8 Cxcl1 Pdgfb Ska3 Bltp1 Hbegf Gp1bb
Timp1 Cxcl10 Pdgfd Ercc6l Ldlr Bcas3 Tubb1
Cd40 Cxcl16 Tgfb2 Cbx5 Lep Adam8 Ndnf
Ifit1 Il1b Ifng Nek2 Sh3bp1 Atp5b Myh9
Il6 Cxcl2 Il1rn Dync1li1 Myo7a Ndnf Acvrl1
Tnf Ccl3 Il2 Kat7 Megf10 Myh9 Proz
Ifng Tyrobp Il4 Spag5 Gata2 Plxnd1 Plek
Vcam1 C3 Il5 Kif2b Gas6 Gab1 Chmp4b
Icam Ccl2 Il7 Cenps Tusc2 Egfl7 Ajuba
Ccl2 Il34 Il9 Sycp3 Washc5 Acvrl1 C9
Cxcl3 Ccl5 Il12b Nup43 Syt7 Bmpr1a Clec10a
Ccr2 Ccl7 Il13 Trp53bp1 Axl Amotl1 Mertk
Cxcr2 Ccl8 Il15 Cenpv Mst1r Lepr Vps4a
Ccn2 Ccl12 Cxcl9 Ctcf Cebpe Gdf2 Nog
Trem2 Lcn2 Cxcl10 Suv39h1 Anxa3 Kdr Pik3cb
Cx3cr1 Havcr1 Ccl3 Ckap5 Elmo2 Jun Kdr
Lyz2 S100a9 Ccl4 Clasp1 Elmo1 Itgav Itgb3
Fcgr3 S100a8 Ltb Rassf2 Mex3b Itga5 Itga2b
Ccl4 Timp1 Il16 Spc25 Elane Itga2b Sytl4
Cd72 Cd40 Hmgb1 Cenpo Cdc42se2 Rbpj Ins2
Ifit1 Grn Cenpp Slc11a1 Cemip2 Ins1
Il6 Tnfsf10 Cenpw Ncf2 Ovol2 Il1a
Tnf Cklf Sycp1 P2ry6 Naa15 Igf1
Ifng Timp1 Smc3 Tyro3 Lep Tlr4
Vcam1 Aimp1 Zfp330 Pld4 Rora Lox
Icam Cmtm3 Clasp2 Pik3ca Wars1 Dst
Ccl2 Tnfsf12 Knl1 Cnn2 Col24a1 Procr
Cxcl3 Cmtm7 Septin6 Myd88 Rhoj Ptprj
Ccr2 Nampt Dctn5 Anxa11 Tnfsf12 Dtnbp1
Cxcr2 Tgfb1 Cenpl Eif2ak1 Ramp2 Fermt3
Ccn2 Tnfsf14 Zw10 Tub Foxc1 Rap2b
Trem2 Tnfsf13b Ppp2r5c Vav1 Gata2 Cx3cl1
Cx3cr1 Ccl1 Cenph Pip5k1c Dll4 Fer1l5
Lyz2 Il15 Hnrnpu Dnm2 Ecm1 F10
Fcgr3 Il16 Mis18a Bcr Parva Papss2
Ccl4 Il17a Cenpm Cryba1 Arhgap22 Gata4
Cd72 Il17b Dapk3 Tm9sf4 Fn1 Gata2
Il17c Birc5 Ncf4 Flt4 Gata1
Il17d Mis18bp1 Dock1 Flt1 Gas6
Il17f Ska1 Icam5 Flna Pdpn
Il18 Kansl1 Jmjd6 Ccn2 Fn1
Il19 Zfp276 Rap1gap Fgfr2 Flna
Ccl11 Nudcd2 Tulp1 Fgfr1 Bloc1s3
Il2 Ska2 Slamf1 Fgf6 Fgg
Il20 Dsn1 Unc13d Fgf2 Fgf7
Il21 Spout1 Prtn3 Fgf1 Fgf2
Il22 Cdca8 Fcgr2b Htatip2 Fgf1
Il23a Zfp207 Nod2 Ptk2 Hgfac
Il24 Nup85 4933434E20Rik Epo Erbb2
Il25 Hsf1 Mesd Tspan12 Epb41l4b
Ccl12 Ss18l1 Adgrb1 Vhl Syt11
Il27 Cenpc1 Gulp1 Gna13 Syt7
Ebi3 Aurkb Rab5a Prok1 Slc7a11
Ifnl2 Smc5 Abl2 Pnpla6 Aqp1
Ifnl3 Cenpx Abl1 Ephb2 Gna13
Il3 Ahctf1 Myo1g Ang3 B4galt1
Il31 Dync1i1 Cd302 Prok2 Fntb
Il33 Anapc16 Pecam1 Adm2 Axl
Il34 Champ1 Cdc42se1 Pten Ccm2l
Il4 Cenpt Ticam2 Hand1 Hps5
Il5 Phf6 Coro1c Sox18 Serping1
Il6 H3f3a Coro1a Nus1 Ext1
Il7 Ppp2r5a Pla2g5 Epas1 Ptk7
Il9 Kat5 Epgn Ddr1
Lif Cfdp1 Cspg4 Cxadr
Lect1 Smc1a Pank2 Grhl3
Lect2 Lrwd1 Thsd7a Treml1
Lta Stag2 Setd2 BC004004
Csf1 Septin7 Pdcd10 P2ry12
Mif Nup37 Cib1 Mpig6b
Osm Sgo2b Syk Jaml
Spp1 Rcc2 Grem1 Carmil2
Pdgfa Sgo1 Esm1 Anxa8
Pdgfb Pinx1 Ang2 Kng1
Retn Ngdn Tnfaip2 Mmrn1
Kitl Cenpn Casp8 Lnpk
Thpo Rec8 Ptk2b Pdcd10
Tgfa Seh1l Wasf2 Fgb
Tgfb1 Dscc1 Ubp1 F11
Tgfb2 Sgo2a Hmox1 Arhgap35
Tgfb3 Mad2l1 Tnfrsf12a Myoz1
Tnfsf11 Smc1b Adgra2 Gpx1
Tnfsf12 Mad1l1 Rhob Myof
Tnfsf13 Cenpe Il18 Hmox1
Tnfsf14 Septin2 Zc3h12a Gm21974
Ccl17 Bub1 Mmrn2 P2rx1
Tnfsf15 Kntc1 Ptprb Stxbp1
Tnfsf4 Cenpk Klf5 Stxbp3
Tnfsf8 Bod1 Tie1 Smad4
Tnfsf9 Tex14 Eif2ak3 Scrib
Tnf Plk1 Hs6st1 Slc11a1
Xcl1 Cenpi Prkd1 Prcp
Fas Sycp2l Pofut1 Prss56
Tnfrsf17 Rangap1 Nox1 Tspan32
Cxcr5 Fbxo28 Apold1 Msx2
Cd4 Dnmt3a Gpr15 Tyro3
Cd27 Dnmt3b Pknox1 Mmp12
Tnfrsf8 Fmr1 Angpt2 Vps4b
Cd40 Dctn3 Ncl Itgb6
Ccr1 Itgb3bp Kctd10 Gp6
Ccr3 Nsmce1 Calcrl Tfpi
Ccr4 Spc24 Ccbe1 Gpr4
Ccr5 Kif2c Nr2e1 Ubash3a
Ccr6 Uvrag Mmp2 Nbeal2
Ccl19 Ppp2cb Nrp2 Celsr1
Ccr7 Ppp2ca Pdcl3 Krt6a
Ccr8 Aurkc Angpt1 Npr2
Csf1r Suv39h2 Amot Notch2
Csf2ra Daxx Srpx2 Nf1
Csf2rb Phf2 Ninj1 Plg
Csf3r Meikin Ccl12 Plau
Cx3cr1 Tpr Med1 Serpinb2
Epor Ppp1cc Dab2ip Sdc4
Flt3 Ndel1 Pik3ca Sdc1
Ccr10 Cenpu Nppc Cnn2
Clcf1 Nup107 Notch1 P2ry1
Ccl2 Knstrn Nos3 Adipor2
Xcr1 Zwilch Nf1 Pard3
Cxcr3 Zwint Sema3e Macf1
Ifngr1 Cbx3 Clic4 Tfpi2
Il1r1 Ppp2r1a Plcd1 Serpine2
Il2ra Nup133 Plau Elk3
Il2rb Gpatch11 Serpine1 Fzd6
Il2rg Rad21 Efnb2 Tgfb2
Il3ra Smc4 Pgf Ap3b1
Il4ra Meaf6 Rtl1 F13a1
Il5ra Mis12 Pik3r6 Tsku
Ccl20 Bub1b Sirt1 Fgfr1op2
Il6ra Dynlt3 Elk3 Egfr
Il6st Stag3 Fzd8 Bnc1
Il7r Nsl1 Mir126b Mia3
Cxcr1 Hjurp Tgfbr1 Adamts13
Cxcr2 Cbx1 Adam15 Smpd1
Il9r Oip5 Plxdc1 Hpse
Il10ra Wdhd1 Smad5 Serpinc1
Il12rb1 UNG Vegfd Apoh
Il13ra1 SMUG1 Anpep Cfh
Il13ra2 MBD4 Robo4 F9
Ccl21a TDG Hif3a F8
Il15ra OGG1 Adgrg1 F5
Tnfrsf9 MUTYH Egf F3
Kit NTHL1 Edn2 F2
Lifr MPG Unc5b F13b
Ltbr NEIL1 Mmp19 Naca
Mst1r NEIL2 Col15a1 Cd44
Tnfrsf11b NEIL3 Scg2 Cd40lg
Tgfbr1 APEX1 Ang Anxa5
Tgfbr2 APEX2 Mcam Wnt7a
Tgfbr3 LIG3 Lemd3 Wnt5a
Ccl22 XRCC1 Tek Wnt3a
Tnfrsf4 PNKP Vav3 Vwf
Il1r2 APLF Cfh Pip5k1c
Cxcr4 HMCES Itgb1bp1 F2rl2
Tnfrsf25 PARP1 Angptl3 F2rl3
Tnfrsf14 PARP2 Apln Chmp5
Tnfrsf18 PARP3 Xbp1 F12
Tnfrsf11a PARG Wnt7b Ccn1
Tnfrsf10b PARPBP Wnt7a Tpm1
Osmr MGMT Otulin Lrg1
Ccr9 ALKBH2 Cyp1b1 Chmp1b
Tnfrsf13b ALKBH3 Pde3b Tspan9
Il17ra TDP1 Vezf1 Gp1ba
Il20ra TDP2 Mydgf Arl8b
Il20rb SPRTN Cald1 Anxa6
Ccr2 MSH2 Anxa2 C3
Ccl24 MSH3 Bsg Tor1a
Ackr3 MSH6 Angptl6 Hif1a
Cxcr6 MLH1 Tbx1 Ctsg
Il11ra1 PMS2 Tal1 Crp
Cntfr MSH4 Bmp4 Timp1
Il12rb2 MSH5 Epha2 Thbd
Il23r MLH3 Hif1a Tgfb1
Mpl PMS1 Nrp1 Trim72
Il21r PMS2P3 Thy1 Comp
Pdgfra HFM1 Tgfa Col1a1
Pdgfrb XPC Col8a2 Col5a1
Ccl25 RAD23B Col8a1 Col3a1
Met CETN2 Vegfb Ccr2
Egfr RAD23A Col4a2 Hrg
Ifngr2 XPA Col4a1 Hnf4a
Ifnar2 DDB1 Col18a1 Drd5
Ifnar1 DDB2 Ccr2 Vegfa
Il10rb RPA1 Ccn3 Cflar
Il22ra1 RPA2 Hrg Yap1
Il22ra2 RPA3 Vegfc Entpd2
Ifnlr1 TFIIH Vegfa Plec
Ccl26 ERCC3 Meox2 Lilrb4a
Ccl27a ERCC2 Efna1 Eng
Ccl3 GTF2H1 Aggf1 F2r
Ccl4 GTF2H2 Eng Myh10
Ccl5 GTF2H3 Srpk2 Slc4a1
Ccl6 GTF2H4 Nrxn1 F7
Ccl7 GTF2H5 Nrxn3 Ano6
Ccl8 GTF2E2 Hand2 Snai2
Ccl9 CDK7 Fap Dsp
Cx3cl1 CCNH Fmnl3 Klkb1
Cxcl1 MNAT1 Ackr3 Entpd1
Cxcl10 ERCC5 Rasip1 Pf4
Cxcl11 ERCC1 S1pr1 Gp5
Cxcl12 ERCC4 Ephb1 Cd151
Cxcl13 LIG1 Tbx4 Pou2f3
Cxcl14 ERCC8 Pdcd6 Shh
Cxcl15 ERCC6 Krit1 Fga
Cxcl16 UVSSA Shh Fbln1
Cxcl17 XAB2 Shc1 Map3k1
Cxcl2 MMS19 Shb Dcbld2
Cxcl3 RAD51 Ccl2 Plpp3
Pf4 RAD51B Apela Cav1
Cxcl5 RAD51D Map3k7 Srf
Ppbp HELQ Mapk14 Mrtfa
Cxcl9 SWI5 Aimp1 Vkorc1
Cklf SWSAP1 Mfge8 Rab3a
Cmtm1 ZSWIM7 Ywhaz Hps1
Cmtm6 SPIDR Vav2 Dysf
Cmtm7 PDS5B Cav1 Proc
Egf DMC1 Enpep Ppia
Epo XRCC2 Or10j5 Pparg
Fgf1 XRCC3 Dysf Alox15
Fgf2 RAD52 Ptgs2 Adrb2
Csf3 RAD54L Psg22 Adrb1
Csf2 RAD54B Adra2b Adra2c
Gdf15 BRCA1 Actg1 Adra2b
Hgf BARD1 Ednra Adra2a
Ifna ABRAXAS1 Pik3cg Fgf10
Ifna1 PAXIP1 Aplnr Rab27a
Ifna10 SHLD1 Angptl4 Chmp4c
Ifna13 SHLD2 Prkd2 Myh2
Ifna14 SHLD3 Optc Plet1
Ifna16 SEM1 Ccdc134 Bloc1s6
Ifna2 RAD50 Cxcr3 Serpina10
Ifna4 MRE11A Minar1 Eppk1
Ifna5 NBN Epha1 Tmeff2
Ifna6 RBBP8 Jam3 Chmp7
Ifna7 MUS81 Becn1 Nlrp6
Cd40lg EME1 Ang4 Pear1
Ifna8 EME2 Rapgef3 Pak1
Ifnb1 SLX1A Fgf18 C1galt1c1
Ifne SLX1B Rspo3 Mustn1
Ifng GEN1 Ramp1 Cpb2
Ifnk FANCA Vash1 Vangl2
Il1rn FANCB Glul Ubash3b
Il1 FANCC Angpt4 Chmp1a
Il1a BRCA2 Cxcl17 BC024139
Il1b FANCD2 Prkca Gnas
Cd70 FANCE Pecam1 Chmp6
Il1f5 FANCF Pdgfrb Pecam1
Il1f10 FANCG Pdgfa Pdgfra
Il10 FANCI Col22a1 Ppl
Il11 BRIP1 Emc10 Evl
Il12a FANCL Pxdn Pros1
Il12b FANCM C1galt1 Cdkn1a
Il13 PALB2 Fzd5 F2rl1
Txlna RAD51C Prkx Chmp2a
Tnfsf18 SLX4 Hps6
Mst1 FAAP20 Coro1b
il18r1 FAAP24 Ppard
Fasl FAAP100 Fzd7
Flt3l UBE2T Chmp2b
Tslp XRCC6
Ccl27a XRCC5
Il17c PRKDC
Cd40lg LIG4
Tnfsf8 XRCC4
Crlf2 DCLRE1C
Sectm1a NHEJ1
Bmp6 NUDT1
Bmp2 DUT
Cntf RRM2B
Ccl6 PARK7
Ccl1 DNPH1
Vegfa NUDT15
Il25 NUDT18
Ccl19 POLA1
Cxcl5 POLB
Msmp POLD1
Ifna15 POLD2
Ccl11 POLD3
Il36g POLD4
Pf4 POLE
Ppbp POLE2
Pglyrp1 POLE3
Ifna11 POLE4
Gm13283 REV3L
Ccl25 MAD2L2
Cxcl3 REV1
Ccl21c POLG
Csf1 POLH
Csf2 POLI
Ccl22 POLQ
Il34 POLK
Ccl28 POLL
Ccl17 POLM
Ccl20 POLN
Cxcl15 PRIMPOL
Cxcl13 DNTT
Ifne FEN1
Ifna13 FAN1
Il36b TREX1
6030468B19Rik TREX2
Il1f10 EXO1
Ccl24 APTX
Ifnl3 SPO11
Cxcl12 ENDOV
Ifnab DNA2
Il17b DCLRE1A
Il3 DCLRE1B
Il12a EXO5
Il27 UBE2A
Csf3 UBE2B
Tnfsf15 RAD18
Tnfsf13b SHPRH
Ifna12 HLTf
Ifnl2 RNF168
Lta RNF8
Osm RNF4
Ccl9 UBE2V2
Il17f UBE2N
Sectm1b USP1
Mif WDR48
Il23a HERC2
Il21 H2AFX
Ifna14 CHAF1A
Il33 SETMAR
Ccl26 ATRX
Ifnk BLM
Tnfsf13 RMI1
Ccl21a TOP3A
Ccl21b WRN
Xcl1 RECQL4
Cxcl16 ATM
Tnfsf4 MPLKIP
Il36rn RPA4
Il36a PRPF19
Lif RECQL
Cxcl11 RECQL5
Il17a RDM1
Ifna1 NABP2
Ifna2 ATR
Ifna4 ATRIP
Ifna5 MDC1
Ifna6 PCNA
Ifna7 RAD1
Ifna9 RAD9A
Ifnb1 HUS1
Tnfsf11 RAD17
Ifnz CHEK1
Cxcl1 CHEK2
Cxcl14 TP53
Ifna16 RIF1
Cd70 TOPBP1
Gm13277 CLK2
Gm13276 PER1
Gm13275
Gm13272
Gm13271
Il17d
Ifna
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Since each spatial transcriptomics spot randomly captured 2–10 cells, we increased its resolution by inferring the cell‐type compositions of each spot. To accomplish this, we deconvoluted each spot using annotated scRNA‐seq data from the same sample. (Figure 2f–h; Figure S2a, Supporting Information, Table 1 and Methods). Notably, anatomical analysis showed that macrophages accumulated prominently the outer stripe area at day 1, followed by a second peak at day 14 post‐UIR, which was consistent with the scRNA‐seq data (Figure 2i). Similarly, neutrophils also accumulated in the outer stripe area at day 1 (Figure 2h and i; Figure S2a, Supporting Information). Fibroblasts emerged in limited areas and dispersed across the whole kidney with the chronic progression of fibrosis (Figure 2h; Figure S2a, Supporting Information). We next tested whether the abundances of major cell types within spots could predict the spatial dependency of macrophages with other cells during injury and repair. We evaluated colocalization, immediate and extended neighbourhood area sizes using MISTy, an explainable machine learning framework of marker interactions to profile the intra‐ and intercellular relationships (Figure 2j; Figure S2b, Supporting Information). Our findings revealed strong dependencies between macrophages, neutrophils, fibroblasts and PTs indicating that the microenvironment formed by multiple cell types may influence the dynamics of macrophage clusters. It was noted that macrophages co‐localized with neutrophils from days 3, which were remarkably co‐enriched in the outer stripe area, reflecting the recruitment of myeloid cells to injury areas early after AKI (Figure 2j). However, from day 3 onward, macrophages and fibroblasts showed stronger dependencies with each other, reflecting their close interaction in the process of fibrosis (Figure 2j).

Therefore, spatial transcriptomic data showed that macrophages and neutrophils infiltrated into the injured area at early AKI and an increased co‐localization of macrophages with fibroblasts was present from early stages to chronicity.

2.3. The Heterogeneity of Macrophages and Their Temporal Dynamics following UIR

In order to gain a higher resolution of the dynamics of myeloid cell subtypes during AKI to CKD, we performed sub‐clustering analysis specifically on myeloid cells, partitioning them into 11 clusters, including monocyte (cluster 1), 6 macrophage populations (cluster 2–7) and dendritic cells (Figure  3a,b). A detailed analysis revealed 7 monocyte/macrophage populations with distinct gene expression profiles (Figure 3a,c; Figure S3a, Supporting Information). Monocytes displayed the highest level of accumulation in the kidney at day 1 (Figure 3e), indicating early recruitment of monocytes to the kidney after injury. Clusters 2 and 3 expressed high levels of typical monocyte‐recruiting marker genes such as Ccr2, Cx3cr1, Ccl2, Ccl3, Ccl4 (Figure S3a, Supporting Information) and cluster 2 was “freshly” recruited into the kidney at day 1 (Figure 3e), indicating their derivation from bone marrow‐derived monocytes.

Figure 3.

Figure 3

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Sub‐clustering of monocyte/macrophage. a) Heatmap of top 20 marker genes in each sub‐clusters. The color scheme is based on the distribution of z‐score. b) UMAP plot of all monocyte/macrophages. c) Dot plot showing expression of marker genes. d) The score comparison of typical functions among different monocyte/macrophage clusters (all time points). e) Connected bar plots displaying the proportional abundance in each time points.

Cluster 2 macrophages were marked by a high expression of genes encoding extracellular matrix (ECM) components (Fn1, Spp1, Ecm1), profibrotic genes (Tgfb1, Tgfbi, Igf1), metalloproteinases (Mmp12, Mmp14, Mmp9 and Mmp19), and cathepsin‐encoding genes (Ctsb, Ctsd, Ctsz, Ctsl and Ctss) (Figure 3a,c; Figure S3a, Supporting Information), signatures associated with remodeling of the ECM. Notably, cluster 2 also expressed Trem2, a master regulator in sensing tissue damage, phagocytosing of apoptotic debris, and activating robust immune remodeling.[ 39 ] Hence we named these cells as “ECM remodeling associated macrophages (EAMs)”.

We classified cluster 3 as “pro‐inflammatory Mac” due to their expression of inflammation‐related marker genes (Il1b, Ccl3, and Ccl4, Tnf, Cxcl10, Ccl12, and Cxcl2) (Figure 3a,c; Figure S3a, Supporting Information) and the highest inflammation score (Figure 3d; Figure S3c, Supporting Information). Gene ontology (GO) terms for cluster 3 was enriched in terms associated with acute inflammatory response, proinflammatory ability, and chemotaxis (Figure S3b, Supporting Information). Additionally, the proportion of this population gradually increased with AKI progression and became predominant in the late phase of UIR (days 14 and 28) (Figure 3e), indicating their potential contribution to unresolved inflammation after AKI.

Cluster 6 macrophages were present in the healthy kidney and persisted in the kidney early after UIR (Figure 3e). This population represented the homeostatic tissue‐resident macrophages according to their low expression of Itgam and Fcgr1 and intermediate expression of Adgre1. Correspondingly, cluster 6 exhibited abundant expression of angiogenesis and wound healing genes (Figure 3a,c; Figure S3a, Supporting Information). Among the six macrophage clusters, cluster 6 had the highest wound repair and angiogenesis scores (Figure 3d; Figure S3c, Supporting Information) which was defined as “pro‐repair Mac”. Notably, the proportions of the pro‐repair cluster dropped drastically in the late chronic stages (Figure 3e), indicating depletion of pro‐repair macrophages may lead to maladaptive kidney repair after AKI.

In addition, we also identified a subset of “proliferative Mac” (cluster 5), characterized by high expression of cell‐cycle‐related genes (Mki67, Top2a, Pcna, Nusap1) (Figure 3a,c; Figure S3a, Supporting Information), which might represent the proliferative states of macrophages for replenishment after AKI. Finally, cluster 4 and 7 constituted a large proportion in homeostasis‐state and expressed low levels of macrophage terminal maturation genes such as Csf1r, Cebpb, and Itga4 (Figure 3a,c; Figure S3a, Supporting Information), indicating a pre‐activation or immature tissue‐resident state.

Overall, the sub‐clustering analysis illustrated the dynamic heterogeneity of macrophages in the process of AKI to CKD transition. Importantly, these data revealed that pro‐inflammatory Mac contributed to the chronic inflammation, while pro‐repair Mac emerged early after AKI which continuingly declined in the chronic stage. The EAMs emerged early and persistently presented in the injured kidney, which closely correlated with ECM hemostasis throughout the entire disease process.

2.4. Dynamic Function of EAMs during AKI to CKD Transition

Given the unique characteristic of EAMs, we further investigated the functional significance of this cluster in the process of injury and repair after AKI. Compared to other macrophage clusters, this cluster showed dense accumulation in the outer stripe area with the highest degree of injury (Figure S2c, Supporting Information) and showed enrichment of ECM and phagocytosis related genes (Figure  4a; Figure S3a, Supporting Information). One day after UIR, EAMs showed high expression of genes that promote ECM deposition as well as metalloproteinases (Figure 4a). GO terms were enriched in “extracellular region” and “collagen‐containing extracellular matrix” (Figure 4e). Notably, spatial transcriptomic analysis revealed the increasing expression of Tgfbi and Mmp9 in the injured outer stripe area at day 1 post‐UIR (Figure 4b), supporting that this cluster promoted ECM deposition in the injured area, thereby facilitating tissue repair and remodeling early after AKI. By day 3 after UIR, the expression of phagocytosis and fat metabolism genes (Fabp5, Trem2, Ctsb, Ctsd, Apoe, Lipa, Lpl, Apoc2, Fabp4, Plin2, Tbxas1, Abhd12, Pla2g7, Npc2, Soat1, Gde1, Sptssa, Sh3glb1) were specifically upregulated in EAMs (Figure 4b,c). Visualization of these markers in our spatial transcriptomic dataset suggested enrichment of lipid‐related genes in the injured outer stripe region from day 3, highlighting its role in lipid metabolism (Figure 4d). During late chronic stages (days 14 and 28), EAMs expressed genes with well‐known pathogenic functions in fibrosis, such as Igf1, Mmp12, Tgfb1, Tl2r, Tnf, Timp2, Anxa5, Pdgfa, and exhibited the highest fibrosis score at day 14 (Figure 4b). High levels of Mmp12, Mmp9 were observed in this population, which could diminish matrix degradation and augment fibrotic responses. Additionally, remarkable expression of Igf1, encoding a member of the insulin‐like growth factor (IGF) family of proteins known to promote myofibroblast survival,[ 40 ] was gradually increased and specifically expressed in macrophages (Figure 4g). Collective scoring analysis showed that this population was characterized with a high ECM score at early stages after ischemia (days 1 and 3), as well as a high fibrosis score at chronic stages (days 14 and 28) (Figure 4f).

Figure 4.

Figure 4

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Characteristics of ECM remodeling macrophages. a) Expression of representative ECM‐related genes in EAMs after UIR. b) Gene expression of Tgfbi and Mmp9 in spatial transcriptomics dataset. c) Expression of representative lipid metabolism‐related genes. d) Gene expression of Fabp5 and Pla2g7 in spatial transcriptomics dataset. e) Gene Ontology terms enriched from the differentially expressed genes of EAMs compared to all other Mac clusters in each time points. f) Inflammation, fibrosis and ECM scores of EAMs in each time points. g) Gene expression of Igf1 and Mmp12 in spatial transcriptomics dataset.

We thus conclude that EAMs appeared early after AKI and persist into the chronic stages, which participated in the dynamic process of ECM remodeling during AKI to CKD transition.

2.5. Lineage Analysis of Mononuclear Phagocytic System

To assess the potential origin and cellular differentiation of the mononuclear phagocytic system, we next defined cell lineage relationships using pseudotemporal cell ordering by RNA velocity analysis. We retrieved two distinct trajectories (Figure  5a). Starting at 1 day post‐UIR, monocytes differentiated along lineage 1 toward EAMs, consistent with the high expression of Ccr2, Retnla and Ear2 as markers of early activation of macrophages and typical monocyte‐recruiting marker (Figures 3a and 5d). Notably, this cluster differentiated into pro‐inflammatory Mac with the highest injury score at the endpoint of lineage 1 (Figure 5b,d). These analyses indicated a differentiation trajectory from EAMs to high‐inflammation and fibrosis Mac. Lineage 2 was composed of homeostatic tissue‐resident macrophages. We observed an alternative differentiation path from tissue resident cluster 4 toward pro‐repair Mac in UIR kidneys (Figure 5a). Diffusion mapping revealed the highest wound healing score at the endpoint of lineage 2 (Figure 5b). Notably, on day 1 post injury, part of resident cluster 4 transformed into proliferative Mac, indicative of a transitory differentiation state (Figure 5a). This highlighted that kidney injury induces local proliferation and self‐renewing of resident macrophages at the site of injury to regulate wound healing early after AKI.

Figure 5.

Figure 5

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Macrophage trajectories during repair and fibrosis. a) RNA velocity analysis in each sub‐clusters. b) Inflammation, fibrosis, ECM, and wound healing scores along trajectory. c) Gene expression dynamics and Gene Ontology pathway enrichment analysis along pseudotime (using Monocle2). d) Pseudotime‐dependent gene expression along the lineage 1 and corresponding feature plots. e) Flow cytometric analysis of the expression of Itgam/Igf1, Itgam/Trem2, and Mertk/Vegfa in the injured kidneys at days 0, 1, and 14 post‐UIR.

Using Monocle2, we discovered differentially expressed genes along monocyte differentiation trajectories to further characterize EAMs. Module 1 represented upregulated genes during monocyte to pro‐inflammatory Mac differentiation (Figure 5c), which contained typical inflammatory markers such as Il1b, Cxcl10, Ccl4, and Ccl3 at the endpoint of pro‐inflammatory Mac, and displayed ontology terms consistent with inflammatory response and immune system process (Figure 5d). Module 2 included a set of genes that were increased during monocyte‐to‐EAMs differentiation, which contained ECM components or molecules markers involved in ECM interaction and remodeling such as Fn1, Arg1, Ctsd, Pf4, Lgals1, and Lgals3 (Figure 5c,d). These data identified the key signals involved in the process of monocyte differentiation that contributed to fibrosis. Further, the flow cytometry analysis of the phenotypic characteristics of these populations showed that the Itgam+ infiltrating macrophages expressed the pro‐fibrotic marker Igf1 and lipid metabolism marker Trem2. As the inflammation and fibrosis progressed in the UIR‐injured kidney, the expression of Igf1 reached its peak at 14 days. Moreover, we found that Mertk+ resident macrophages exhibited upregulation of Vegfa early at 1 day after UIR surgery, indicating that renal‐resident macrophages play a pro‐repair role such as promoting angiogenesis early after AKI (Figure 5e; Figure S4a–c, Supporting Information).

Collectively, these results support that two major cell lineages exist in the ischemic kidney. Renal resident macrophages differentiated into the pro‐repair Mac subsets as a repair response after injury, while EAMs were differentiated from monocytes which thereafter transitioned toward a pro‐inflammatory Mac and contributed to chronic inflammation and fibrosis. Distinct transcriptional programming models were observed that may determine the macrophage ontogeny.

2.6. Interaction of EAMs with Fibroblast

Finally, we sought to determine the cell communication network involving EAMs. Interestingly, kidneys of the sham group posited only limited interactions between endothelial cells and proximal tubule cells, while increasing cell‐cell communications were observed after UIR and especially in late fibrosis stages (Figure  6a). We found that EAMs could directly interact with endothelial cells, fibroblasts, repairing PT, and neutrophils through the ligand‐receptor pairs such as Grn‐Sort1, Vsir‐Igsf11, Anxa1‐Fpr1, and Anxa1‐Fpr2, respectively (Figure 6b).

Figure 6.

Ligand–receptor interactome of communication between macrophages and other cells. a) Heatmap of the number of relevant ligand‐receptor interaction pairs predicted by CellChatDB between main kidney cell types in cell‐cell interaction study. Scale = number of interactions. b) Dot plot showing expression of ligand‐receptor pairs in EAMs and all other clusters in each time points. c) UMAP scRNA‐seq and heatmap of select ligands expressed by Mac sub‐clusters and cognate receptor expression by fibroblasts on days 14 and 28 post injury. d) Co‐expression pattern of Igf1 and Igf1r in spatial transcriptomics dataset. Spatial feature plots showing the expression pattern of ligand gene Igf1 (red spots), receptor gene Igf1r (blue spots), and co‐expression pattern (purple spots). e) Multicolour immunohistochemistry staining of Igf1+Cd68+ cells (Igf1 and Cd68) and Igf1r+α‐SMA+ cells (Igf1r and α‐SMA) in each injury time points. Scale bars, 10 µm. The heatmaps show the density of Igf1+ Cd68+ cells and Igf1r+ α‐SMA+ cells. Results of nearest neighbor distance used to compute areas in which Igf1+Cd68+ cells (blue) and Igf1r+α‐SMA+ cells (yellow) lie within ≈1 µm of each other. The box plots show the quantification results. n = 15. Ligand–receptor interactome of communication between macrophages and other cells. f) The representative images of IGF1 and CD68 immunofluorescence costaining in normal, AKI, and CKD kidneys and g) its correlation with fibrosis (Masson staining). h) The urinary sediment, supernatant, and serum IGF1 concentrations of healthy control (n = 15), patients with AKI (n = 15) and CKD including CKD 2–3 stage (n = 15) and CKD 3–5 stage (n= 15) and its correlation with GFR‐EPI. *P < 0.05. i) Western blots of Col1a1, a‐SMA, Vim, and PCNA after stimulation with IGF1 (IGF1 10 ng ml−1 24 h and IGF1 10 ng/ml 48 h) in NRK49F cells. j) The representative images of IGF1 and CD68 immunofluorescence costaining in NRK49F cells after stimulation with IGF1 (IGF1 10 ng ml−1 24 h and IGF1 10 ng ml−1 48 h). k) Working model for spatiotemporal dynamics of macrophage heterogeneity in the process of AKI‐to‐CKD transition.

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As revealed using CellChatDB that predicts the ligands from sender‐cells to interact with specific targets of receiver‐cells, several significant ligand‐receptor interactions were involved in ECM regulation. Igf1‐Igf1r, Fn1‐Cd44, Fn1‐Sdc4, Grn‐Sort1, and Tnfsf12‐Tnfrsf12a pairs were found between EAMs and fibroblasts (Figure 6b). We speculated that these pro‐fibrosis communication pairs, associated with pro‐fibrotic signals, are crucial in the activation of fibroblasts and regulation of ECM remodeling. Of these, we identified Igf1 derived from EAMs as the top ligand that can interact with most of the fibroblast targets in the late phase of UIR (days 14 and 28) (Figure 6c).

To confirm the findings from the interactome, the expression of Igf1‐Igf1r pairs were validated in the spatial transcriptomic renal tissue slices. We found that Igf1 and Igf1r were highly co‐expressed in niche cells of intra‐spots in late chronic stages (Figure 6d). Using multicolour immunohistochemistry (mIHC), we observed that Igf1+Cd68+ macrophages and Igf1r+α‐SMA+fibroblasts mainly localized in the tubulointerstitium which was particularly prominent in the chronic phase (Figure 6e; Figure S5a, Supporting Information). Importantly, the density and nearest analysis of HALO Image Analysis Platform revealed the physical proximity between Igf1+Cd68+ macrophages and Igf1r+α‐SMA+fibroblasts (Figure 6e; Figure S5b, Supporting Information), supporting their direct interaction. Additionally, we observed an increase in infiltrating Igf1+Cd68+ macrophages and Igf1r+α‐SMA+ fibroblasts, as well as a decrease in the distance between them, in the tubulointerstitium during chronic stages compared to the sections from the acute stages of kidney injury (Figure 6e). These results suggested the potential cell‐cell interactions between EAMs and fibroblasts. To further investigate the potential pathological significance of infiltrated IGF1+ macrophages in human AKI and CKD, we took advantage of an AKI and CKD cohort. Interestingly, we found the increasing expression of IGF1/CD68 co‐exist with the fibrosis area in kidney tissues of CKD patients (Figure 6f,g). Surprisingly, excretion of IGF1 in both urinary sediment and supernatant significantly increased in patients with CKD and was negatively correlated with GFR‐EPI (Figure 6h). To further verify the above finding link with fibrosis, we detect the influence of IGF1 on fibroblast cells using western blot and immunofluorescence. Impressively, the expression of Col1a1, a‐SMA, Vim and PCNA were substantially increased in fibroblast cell after IGF1 stimulation (Figure 6i,j). Thus, we proposed that IGF1+ macrophages could be a critical player in AKI to CKD transition.

3. Discussion

Chronic kidney disease (CKD) is a global public health problem.[ 41 ] The transition of AKI to CKD is a key risk factor for the development and progression of CKD.[ 42 ] During this process, unresolved inflammation and fibrosis represent the key event that promotes AKI to CKD transition, where macrophages served as a critical driver.[ 8 , 10 , 43 ] However, the spatiotemporal dynamics of macrophages in this pathological process are poorly understood. Here we have delineated a comprehensive map of the macrophage populations during AKI to CKD transition in a mouse model of ischemic kidney injury by integrating spatial transcriptomics with single‐cell gene expression profiles.

The microenvironment and cellular communication are known to play fundamental roles in shaping the plasticity, function and localization of macrophages in different stages of kidney disease.[ 44 , 45 , 46 , 47 ] Previously, macrophages are classified as M1/M2 phenotypes in the progression of renal fibrosis. Here, we found that macrophage subsets changed dynamically in a more nuanced manner during the entire process of chronic progression following renal injury. Specifically, the proportions of the pro‐repair cluster dropped dramatically in late chronic stage while the pro‐inflammatory and ECM remodeling clusters increased continually. Stewart BJ et al. found that there was a cross‐talk between epithelial cells and immune cells that may determine the localization of macrophages and neutrophils to the infected regions of the kidney.[ 48 ] However, the exact mechanism is largely unclear. In this study, by using integrative spatial and single‐cell analysis, we clearly demonstrated that macrophages are preferentially recruited to the injured S3 segment of the proximal tubular cells with the strong spatial dependency with neutrophils at the early stages but dependency with fibroblasts at chronic stages, suggesting that they may be involved in modulating the phenotype of renal macrophages in the vicinity of injured areas.

Although the macrophage heterogeneity is strongly shaped by the tissue environment with distinct lineages driving the functional characteristics, recent studies have indicated that monocyte‐derived macrophages recruited to the injured kidney and augment inflammatory responses, whereas tissue‐resident cells tend to mitigate inflammation and restore homeostasis.[ 10 , 44 , 49 , 50 ] However, the fate and dynamics of these cells are poorly understood. Our results suggested that renal resident macrophages exhibited remarkable proliferative characteristics after injury and differentiated into pro‐repair Mac from proliferative and immature status which contributes to tissue repair. In addition, we found ECM‐remodeling Mac was derived from monocytes and exhibited a phenotype shift toward pro‐inflammatory Mac thereafter, which may represent a new critical population associated with unresolved chronic inflammation and fibrosis. Interestingly, emerging data from multiple mouse models of nonalcoholic steatohepatitis (NASH) and idiopathic pulmonary fibrosis (IPF) have also shown significant role of infiltrating macrophages derived from monocytes in fibrotic niches and ECM remodeling activity.[ 51 , 52 , 53 , 54 , 55 ] As such, the emergence of macrophages characterized with high levels of ECM related genes served as a critical player in organ fibrosis.

A notable aspect of the EAMs is its appearance early after the injury and persistence throughout the chronic phase with prominent function of ECM regulation among those diverse subsets of macrophages. Previous studies had suggested that macrophages played an essential role in regulating ECM remodeling via secretion of ECM components, production of ECM‐modulating proteases, and regulating the activation of ECM‐producing fibroblasts or macrophage‐to‐myofibroblast transition (MMT),[ 56 , 57 ] which could promote the survival and functional maintenance of macrophages. However, the precise function of this macrophage subset in the transition of AKI to CKD has not been fully clarified. We found that EAMs expressed high levels of lipid metabolism and phagocytosis genes in addition to ECM‐related component and proteases. This lipid metabolism signature is reminiscent of “Lipid‐Associated Macrophages” (LAM) found in the context of obesity, which highly express genes encoding ECM components and phagocytosis and promote fibrogenesis.[ 51 , 58 ] Macrophage scavenger receptor CD36 mediated the uptake of long‐chain fatty acids which activated fibrogenic signaling and promoted renal fibrosis.[ 59 ] Our results strongly suggest that co‐regulation of ECM and lipid metabolic reprogramming represent a key aspect of EAMs biology during fibrogenesis.

Finally, our study provides a new perspective on the function of IGF‐mediated cellular communication in fibrogenesis. Excessive or sustained production of growth factors is a key mechanism of fibrosis.[ 60 ] Here, we found that EAMs released exaggerated levels of growth factors Igf1 and Tgfb1, especially in the late fibrosis stages. Igf1 is a member of the small family of single‐chain polypeptides and have key roles in diabetes, inflammation, and fibrosis.[ 61 ] Previous studies have suggested that Igf1 could activate fibroblasts and transformed to the myofibroblast which is involved in renal fibrosis.[ 62 ] However, the role of macrophages‐derived IGF1 in renal fibrosis warranted elucidation. In the current study, we found that Igf1 is predominantly expressed in EAMs, while Igf1r is mainly expressed in epithelial cells and fibroblasts. Furthermore, during the fibrotic phase, there is a significant increase in the expression level of IGF1R in fibroblasts. Notably, EAMs had a strong interaction with fibroblasts through the Igf1‐Igf1r signaling axis and IGF1 stimulated fibroblast activation and growth, suggesting its critical role in mediating the cellular interaction between macrophages and fibroblast in renal fibrosis. Impressively, the presence of kidney IGF1/CD68+ macrophage infiltration and the relevance of renal IGF1/CD68 expression to the degree of renal fibrosis were confirmed in human CKD. The urinary excretion of IGF1 in CKD patients also correlated with the severity of kidney fibrosis. Taken together, these data suggested that IGF1 might also serve as a potential prognostic and novel predictive biomarker for fibrosis.

There are several limitations in our study. First, we combined three mice in each time point for analysis. While each of these mice has its individual differences, the protocol of creating UIR model is robust and reproducible. In the future, sample multiplexing approaches will address this limitation. Second, UIR is a unique injury model in which the innate immune response and interaction with the adaptive immune system are involved. Combining data sets of other models in the atlas might prove beneficial in the future. Meanwhile, validation specific cell types in human datasets is in urgent need for identification of their consistency across different species. Additionally, although we stimulated fibroblasts and promoted their activation using IGF1 in vitro, in vivo inhibition of IGF will be needed to evaluate whether the transition from AKI to CKD is prevented.

In conclusion, the present study demonstrated the spatiotemporal dynamics of macrophage heterogeneity in the process of AKI to CKD transition. Renal resident macrophages differentiated into the pro‐repair Mac subsets, while EAMs originated from monocytes persistently present and transformed toward a pro‐inflammatory Mac which contributed to chronic inflammation and fibrosis. Our findings provided a new insight about the diverse role of macrophage during AKI to CKD transition, which will provide a novel therapeutic strategy to prevent renal fibrosis after acute kidney injury.

4. Experimental Section

Renal Unilateral Ischemic Reperfusion Injury Model

The trials used C57BL/6 male mice (6‐8‐week‐old) acquired from Beijing Vital River Laboratory Animal Technology Co., Ltd., Beijing, China. Mice were anesthetized with isoflurane treated to ischemia by clamping the right renal pedicles with non‐traumatic microaneurysm clamps for 35 min, and then reperfused by releasing the clamps. The mice were kept at a constant body temperature of 37 °C. Mice were euthanized at day 1, day 3, day 14, or day 28 after UIR. The same surgical procedures were performed on sham mice, except that the renal pedicles were not constricted. At the end of the experiment, the mice were euthanized by inhaling carbon dioxide after isoflurane anesthesia, and blood and kidney tissues were collected. Blood and urine were used for analysis of renal function and renal injury biomarkers, while organs were used for observation of tissue lesions and relevant indicator tests.

Patient Samples

The study was approved by the Ethical Committee of Zhong Da Hospital (approval number: 2017ZDSYLL107‐Y02), Southeast University and the informed consent were obtained from all participants. Healthy controls (n = 15) and patients with AKI (n = 15), CKD 2–3 stage (n = 15) and CKD 3–5 stage (n = 15) were enrolled in this study. The clinical characteristics of all patients are summarized in Table  2 . Renal biopsy was used for IGF1 and CD68 immunostaining. The concentrations of IGF1 in the plasma and urine samples from the renal biopsy cohort were measured by enzyme‐linked immunosorbent assay (ELISA) using human IGF1 Quantikine kit (Cloud‐Clone, China) according to the manufacturer's instructions. Plasma and urine samples for the healthy controls were from 15 ethically matched volunteers.

Table 2.

Clinical characteristics of all patients.

Number GFR‐EPI IGF1‐sediment(ng/ml) IGF1‐supernatant(ng/ml) CKD stage Age Gender Collection Date Total protein Albumin BUN Scr(umol/l) TG TC GLU Pro Urine Protein(g/l) 24UTP ACR
1 41.15 0.135 0.196 CKD 3 71 Male 2023/9/25 63.9 38.4 8.4 127 0.67 3.54 4.22 0 – – –
2 65.9 0.019 0.052 CKD 2 64 Female 2023/9/25 47 26.8 11 82 1.68 8.54 4.55 2 3.78 6.804 3188.2
3 108.13 0.035 0.019 CKD 2 61 Male 2023/9/23 43.5 25.1 3.3 70 0.89 6.01 4.18 2 1.43 1.144 –
4 65.94 0.035 0.055 CKD 3 59 Male 2023/10/8 68.7 44.8 7.4 106 1.7 5.87 8.4 0 0.08 0.136 –
5 47.6 0.017 0.019 CKD 3 60 Male 2023/10/7 41.4 19.5 15.5 138 1.15 4.53 4.37 3 2.47 4.446 3524.9
6 75.44 0.065 0.113 CKD 2 68 Male 2023/10/8 57.5 37.8 11.4 90 1.99 5.11 5.79 0 0.09 0.18 80.76
7 86.4 0.102 0.147 CKD 2 75 Female 2023/10/7 49 33.8 8.1 59 2.52 4.07 5.1 0 0.28 0.532 214.78
8 81.46 0.023 0.114 CKD 2 59 Male 2023/10/7 35.6 21.3 14.7 89 1.07 4.9 3.71 4 4.48 7.616 134.53
9 110.66 0.198 0.046 CKD 1 53 Female 2019‐05‐13 47.4 22.7 3.6 44 4.43 9.74 5.14 2 0.395 0.592 1674
10 77.31 0.172 0.093 CKD 2 77 Female 2021‐04‐08 63 35.9 7.1 66 5.47 5.96 9.38 1 0.298 0.775 1818.5
11 60.8 0.166 0.052 CKD 2 72 Male 2019‐07‐03 71.7 43.5 8.4 105 2.23 2.45 7.49 0 0.156 0.312 137.08
12 44.28 0.181 0.042 CKD 3 49 Male 2019‐06‐28 64.5 40.9 9.8 156 2.48 4.32 9.54 0 0.081 0.182 7.15
13 73.69 0.174 0.089 CKD 2 43 Male 2019‐05‐30 76.9 44.3 5.4 106 2.17 3.51 5.09 0 0.109 1.164 85.22
14 92.64 0.161 0.119 CKD 1 63 Female 2019‐03‐06 43 23.2 3.1 61 2.51 7.61 5.51 3 2.25 2.7 177.22
15 115.57 0.079 0.137 CKD 1 39 Female 2019‐01‐07 74.6 44 6.2 52 1.12 5 4.64 2 0.432 0.518 212.42
16 52.39 0.114 0.148 AKI 54 Male 2023/9/20 66.3 39.5 15.5 132 1.28 4.14 5.86 0 – – 10.07
17 8.3 0.037 0.029 AKI 67 Male 2023/9/22 64.4 44 18.8 562 2.05 4.72 4.74 0 0.35 0.84 92.47
18 71.5 0.04 0.035 AKI 62 Male 2023/10/10 53.7 35.6 10.9 112 2.19 5.4 5.44 1 0.26 0.676 259
19 10.02 0.097 0.111 AKI 79 Male 2023/10/26 62.1 35.7 21 355 1.39 4.81 4.37 0 0.62 0.558 >296.18
20 49.4 0.087 0.021 AKI 25 Male 2023/11/16 78.1 52 7.7 164 1.94 4.8 4.8 0 – – 14.74
21 12.6 0.053 0.018 AKI 27 Male 2023/10/26 60.3 31.6 24.2 502 3.02 4.3 4.34 3 0.17 0.391 –
22 60.3 0.133 0.027 AKI 53 Male 2020‐10‐ 61.8 40.5 7.3 118 1.04 4.16 4.94 Trace 0.117 0.176 141.97
23 61.5 0.064 0.112 AKI 36 Male 2019‐12‐ 46.6 23.7 11.2 129 5.52 7.83 6.14 3 18.31 23.803 161.97
24 12.5 0.084 0.213 AKI 35 Male 2019‐2‐ 60.5 37.6 16.7 481 1.46 4.11 4.76 1 0.253 0.557 162.5
25 35.8 0.083 0.037 AKI 40 Male 2019‐2‐ 45.7 21.5 12.8 196 2.63 13.01 5.28 3 20.679 16.543 –
26 12.13 0.112 0.076 AKI 75 Male 2023‐10‐ 47.1 24.5 38.6 394 1.91 3.45 13.8 1 1.03 1.03 506.83
27 37.95 0.031 0.031 AKI 69 Female 2023‐10‐ 56.9 33.5 13.6 125 2 2.99 5.46 0 0.34 0.442 23.73
28 52.86 0.123 0.093 AKI 58 Male 2023‐9‐ 61.8 41.7 4.7 128 4.4 4.67 4.55 0 – – 10.89
29 33.36 0.098 0.127 AKI 62 Male 2023‐6‐ 64.1 38.9 12.8 183 1.35 2.3 3.62 0 0.03 0.03 6.79
30 26.27 0.144 0.136 AKI 86 Male 2023‐6‐ 71.9 37.4 29.5 194 1.81 3.09 4.52 0 0.08 0.32 28.16
31 5.58 0.013 0.032 CKD 5 76 Female 45 193 58.2 32.1 19.4 586 1.58 1.55 4.43 2 – – –
32 6.82 0.365 0.458 CKD 5 37 Male 45 209 56.9 37.1 18.1 787 1.46 3.14 3.93 0 0.7 0.98 –
33 10.12 0.16 0.211 CKD 5 70 Female 45 206 56.3 32.6 24.3 371 2.46 4.5 8.85 2 2.44 4.392 –
34 7.7 0.238 0.299 CKD 5 67 Female 45 206 71.7 42.6 14.6 473 1.86 4.27 5.21 1 – – –
35 9.88 0.291 0.397 CKD 5 74 Male 45 209 51 27 21.5 467 1.17 2.77 4.54 1 – – 563.35
36 20.34 0.149 0.114 CKD 4 55 Male 45 193 67 40 13.7 287 0.88 2.21 4.51 ± – – 199.94
37 9.65 0.371 0.541 CKD 5 74 Male 45 225 62.8 38.2 24.1 476 1.14 3.31 3.91 0 0.16 0.208 46.91
38 28.26 0.134 0.411 CKD 4 41 Female 2018‐04‐11 66.8 36.1 6 187 1.55 3.82 4.62 3 1.619 3.886 –
39 22.72 0.176 0.373 CKD 4 67 Male 2018‐04‐13 50.9 25.6 20.6 244 1.33 3.29 4.51 1 0.568 1.136 –
40 13.01 0.243 0.462 CKD 5 73 Female 2018‐05‐21 61.9 27.1 22.9 295 2.48 4.95 6.15 2 1.808 2.17 –
41 5.26 0.299 0.291 CKD 5 40 Female 2018‐05‐29 47.2 23.8 24.8 756 2.39 5.43 3.23 3 1672 0.334 1449.9
42 25.45 0.345 0.313 CKD 4 66 Female 2023‐11‐ 44.4 24 6.6 177 0.75 3.56 4.01 3 4.76 3.332 –
43 22.9 0.198 0.031 CKD 4 46 Female 2023‐11‐ 63.3 36.8 10.2 217 1.09 4.88 4.14 3 1.4 3.36 1459
44 21.27 0.221 0.045 CKD 4 78 Male 2023‐10‐ 50.6 28.6 12.8 242 0.78 3.11 3.81 0 0.51 1.071 376.23
45 12.53 0.124 0.211 CKD 5 88 Female 2023‐10‐ 40 21.2 3.6 280 1.07 3.79 4 3 0.02 0.024 8055.2
46 107.42 0.021 0.031 Healthy 25 Female 2023/8/26
47 101.23 0.032 0.022 Healthy 28 Male 2023/8/26
48 114.31 0.121 0.113 Healthy 22 Male 2023/8/26
49 104.22 0.093 0.087 Healthy 35 Male 2023/8/26
50 117.21 0.168 0.031 Healthy 32 Male 2023/8/26
51 102.21 0.127 0.089 Healthy 29 Female 2023/8/27
52 108.44 0.018 0.066 Healthy 30 Male 2023/8/27
53 117.23 0.012 0.033 Healthy 43 Male 2023/8/27
54 114.76 0.021 0.031 Healthy 57 Female 2023/8/27
55 102.57 0.034 0.012 Healthy 32 Female 2023/8/27
56 103.91 0.111 0.064 Healthy 22 Female 2023/8/27
57 106.37 0.092 0.092 Healthy 47 Female 2023/10/17
58 102.96 0.081 0.084 Healthy 60 Male 2023/10/17
59 106.22 0.083 0.044 Healthy 51 Female 2023/10/17
60 109.54 0.024 0.051 Healthy 31 Male 2023/10/17
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Morphological Studies

A normal process was used to obtain formalin‐fixed, paraffin‐embedded mouse kidney slices (4‐m thickness). A standard protocol was followed for hematoxylin and eosin (H&E), Periodic Acid‐Schiff (PAS), and masson staining. A conventional protocol for immunofluorescence staining was followed.

Single‐Cell Suspension Preparation

Each sample included three mice. Anesthetized mice were perfused with pre‐chilled 1 × PBS via the left cardiac ventricle. Samples were minced into 1 mm3 cubes before being digested with the Multi Tissue dissociation kit (Miltenyi, 130‐110‐203). Using 21 G and 26 1/2 G syringes, the tissue was homogenized. In 3 ml of 1640 medium (Gibco, USA), 0.25 g of tissue was digested with 1350 µl of collagenase I, 375 µl of collagenase IV, and 180 µl of hyaluronidase and incubated for 40 min at 37 °C. 10% FBS inhibited the response. The solution was then filtered using a 70 µm cell strainer. After centrifugation at 400 g for 5 min, the cell pellet was treated on ice for 5 min with 1 ml of RBC lysis solution (Miltenyi, 130‐094‐183). Countstar (Alit Biotech, Rigel S2) was used to determine cell number and viability. This approach produced a single‐cell suspension with a viability of more than 90%.

Cell Culture and Cell Treatment

The fibroblastic clone of NRK (mixed culture of normal rat kidney) cell line (NRK49F cell) was obtained from the American Type Culture Collection (Manassas, VA) and cultured in DMEM/F12 with 10% FBS in a 37 °C incubator with 5% CO2. Cells were pretreated with 10 ng mL−1 IGF1 (UA BIO, UA040099) for 24/48 h. Some cells were detected by immunofluorescence and WB analysis.

Western Blot Analysis

Nearly 15 mg of total kidney tissue was homogenized in SDS lysis buffer, sonicated, and heated at 95 °C. Lysates were cleared by centrifuging (15 000 × g at 4 °C for 15 min). 15 µL of total lysate was loaded onto 11% SDS‐PAGE and subjected to electrophoresis (140 V, room temperature). Proteins were transferred onto PVDF membranes at 100 V on ice for 1 h. Membranes were incubated in 5% bovine standard solution (BSA) prepared in Tris‐buffered saline containing Tween‐20 (TBST) for 1 h at room temperature on an orbital rocker. Membranes were probed with: anti‐Col1a1 (dilution 1:1000; ab270993; Abcam), anti‐aSMA (dilution 1:1000; ab7817; Abcam), anti‐Vim (dilution 1:2500; ab92547; Abcam), anti‐PCNA (dilution 1:1000; ab29; Abcam). After primary antibody incubation blots were washed three times with TBST, HRP‐conjugated secondary antibodies (#7074, CST, dilution 1:2000) were probed for 1 h at room temperature prepared in TBST. Finally, blots were washed with TBST for 5 min each at room temperature. After applying the ECL color reagent and performing dark chamber exposure imaging, the gray value of the images was analyzed using ImageJ software v1.8.0 (NIH) and The final relative quantification values are the ratio of net band to net loading control. GraphPad Prism 9 was used to obtain statistical figures.

Immunofluorescence Staining

Immunofluorescence analysis was performed on kidney sections (2‐µm thickness) and NRK49F cells. Slides were incubated with antibodies against anti‐aSMA (dilution 1:1000; ab7817; Abcam), anti‐CD68 (1:200, GB113109, Servicebio), anti‐PCNA (dilution 1:1000; ab29; Abcam). and incubated with secondary antibodies (ab150114 and ab150077, Abcam). Nuclei were stained with DAPI (Sigma–Aldrich) according to the manufacturer's instructions. Under a confocal microscope (FV3000, Olympus), 10 fields of view were randomly assigned, and the number of CDK12‐positive cells was counted in a blinded manner.

scRNA‐Seq by 10 × Genomics

The cell suspension was fed onto the Chromium single cell controller (10x Genomics, GCG‐SR‐1) to form single‐cell gel beads in the emulsion according to the manufacturer's procedure using the Single Cell G Chip Kit (10x Genomics, 1 000 120). Reverse transcription was carried out on an S1000TM Touch Thermal Cycler (Bio Rad) at 53 °C for 45 min, 85 °C for 5 min, and 4 °C hold. The cDNA was produced, amplified, and tested for quality using an Agilent 4200 (done by CapitalBio Technology, Beijing). Single Cell 3′ Library and Gel Bead Kit V3.1 were used to create single‐cell RNA‐seq libraries. The libraries were finally sequenced on an Illumina Novaseq 6000 sequencer with at least 100 000 reads per cell and pair‐end 150 bp (sPtrEa1t5e0gy) (done by CapitalBio Technology, Beijing).

Analysis of Single‐Cell RNA‐seq Data

Alignment and quality control: CellRanger was used to quantify raw fastq files that were aligned to the 10 mm (Ensembl GRCm38.93) reference genome. Seurat was used to control data quality, preprocess data, and do dimensional reduction analysis. Each time point is a pool of kidney from 3 mice. Following the compilation of gene‐cell data matrices from n = 12 UIR samples and n = 3 control samples, all 15 matrices were pooled, and poor‐quality cells with 200 expressed genes and mitochondrial gene percentages greater than 25 were eliminated. The remaining high‐quality cell barcodes were exported.

Dimension‐Reduction and Cell Clustering: The entire Seurat process was run again on the remaining 60010 high quality single cells to generate the final dataset used for all downstream analysis. The top 2000 highly variable genes were retrieved from each sample using the FindVariableFeatures function and then analyzed using principal component analysis (PCA). The main cell types were identified with the standard Seurat software at a resolution of 0.6. The data with a resolution of 0.6 were chosen for the target cell sub‐clustering analysis to demonstrate changes in the subtypes in each sample. Then, Seurat's RunUMAP function with dimension settings (1:30) was used for 2D visualization.

Identification of marker genes and DEGs: The FindAllMarkers function in Seurat was used to identify DEGs in cell clusters, and a list of marker genes was used for manual annotation of main cell types to the 13 found clusters in the final dataset. The heatmaps, and dot plots for the cell‐specific markers were generated using the Seurat DoHeatmap/DotPlot function.

Enrichment analysis: KOBAS software was used to perform GO and KEGG pathway enrichment with Benjamini‐Hochberg multiple testing adjustment. The R package was used to visualize the results.

Monocyte/Macrophage sub‐clustering analysis: The whole Seurat pipeline was run again, but this time just the barcodes of cells tagged as Mac/DC cells were used. The pipeline was run with the same settings as described before, giving 11 subclusters, including 7 macrophage subclusters. The subclusters were manually annotated using canonical marker genes and known functions, as detailed in previously published reports.

The scoring and comparing of gene sets in scRNA‐seq data: Gene sets comprising relevant markers were created based on previously published data on various macrophage lineages and phenotypic functions (Table 1). The gene set scores of each cell were produced using the Seurat package's “AddModuleScore” function and the “CellCycleScoring” function.

scRNA‐seq Trajectory Analysis

Monocle2: Pseudotime trajectories were validated using the same cells as input using the Monocle packages. The differentialGeneTest function of Monocle2 with q < 0.01 was used to identify highly variable genes along pseudotime. The BEAM and plot_genes_branched_heatmap functions were used to evaluate individual branches.

RNA velocity: The Python‐based Velocyto command‐line tool and the Velocyto. R package were used as directed to compute RNA velocity. Using spliced and unspliced data, we used Velocyto to calculate the single‐cell trajectory/directionality. The SeuratWrappers package was used to load this subset into R. RNA velocity was calculated using a gene‐relative model with kNN cell pooling (k = 25). When viewing RNA velocity on the UMAP embedding, the parameter n was set at 200.

Ligand–receptor interactions: The CellChat library to predict cellular communication networks from single‐cell RNA seq data to examine cellular cross‐talk between different cell types was used.[ 63 ] The Python software CellChat with database v1.1.3 to predict ligand‐receptor interactions was used. Only receptors and ligands expressed in >5% of the cells were considered.

Statistics & reproducibility: Unless otherwise specified, data were expressed as means ± SEM unless otherwise stated. Statistical analyses were indicated in the respective “Methods” section and Figure legends. According to the normalcy distribution, appropriate parametric or non‐parametric tests were performed. P < 0.05 was regarded as statistically significant. To establish sample size, no statistical procedure was applied. There were no data removed from the analyses.

Spatial Transcriptome Sequencing

Cryosections of 10‐mm thickness were put on the Thermocycler Adaptor with the active surface facing up for 1 min at 37 °C, then fixed for 30 min in −20 °C with methyl alcohol before staining with H&E.

The Visum spatial gene expression slide and Reagent Kit (10x Genomics, PN‐1000184) were used to process the Visum spatial gene expression. 70 µl Permeabilization enzyme were added and incubated for 30 min at 37 °C. Each well was rinsed with 100l SSC before adding 75 µl reverse transcription Master Mix for cDNA synthesis.

RT Master Mix from the wells at the end of first‐strand synthesis was removed and added 75 µl 0.08 M KOH for 5 min at room temperature before removing the KOH from the wells and washing with 100 ul EB buffer. For second‐strand synthesis, 75 µl Second Strand Mix to each well was added. The cDNA amplication was carried out on a Bio Rad S1000TM Touch Thermal Cycler. Visum spatial libraries were created using the Visum spatial Library construction kit (10x Genomics, PN‐1000184). The libraries were finally sequenced using an Illumina Novaseq6000 sequencer with at least 100 000 reads per spot and a pair‐end 150 bp (PE150) reading method (done by CapitalBio Technology, Beijing).

Spatial Transcriptome Sequencing Data Analysis

The ST‐seq data was bioinformatically processed using the R package Seurat (version 3.2.0). In brief, default values were used for normalization (“SCTransform”), dimensionality reduction (“RunPCA”), graph‐based clustering (“FindNeighbors” and “FindClusters” algorithms), UMAP visualization, and DEGs analysis. Cell2location with the default parameters to determine the cell‐type composition of each spot was used.[ 64 ] The model basis was initialized using all markers, and unit variance normalization was performed.

MistyR (v1.2.1) was used to investigate the relationships between tissue organization and the spatial distribution of macrophages and other cells. The abundances of cell types determined with cell2location were used as predictors. To account only for the impacts of cell state activation, predictor cell state scores were masked to 0 whenever their score was less than 0.

mIHC

All the Formalin‐fixed and paraffin‐embedded (FFPE) tissues used within the experimental operation were sectioned as slides of 4 µm thickness. The slides were deparaffinized in xylene for 30 min and rehydrated in absolute ethyl alcohol for 5 min (twice), 95% ethyl alcohol for 5 min, and 75% ethyl alcohol for 2 min sequentially. Wash the slides with distilled water 3 times. A microwave oven was used for heat‐induced epitope retrieval, and during epitope retrieval, the slides were immersed in boiling EDTA buffer (ZLI‐9079, zsbio, Beijing, China) for 15 min. Antibody Diluent/Block from Alpha X Bio was used for blocking. The mIHC experiments were performed by AlphaXPainter X30 (Alpha X Bio, Beijing, China). Primary antibodies for anti‐Igf1 (1:500, Abcam), anti‐Igf1r (1:200, Abcam), anti‐α‐SMA (1:500, Abcam), and anti‐CD68 (1:200, GB113109, Servicebio) were utilized on mice kidneys. 2–3 experienced pathologists assessed each part. All the primary antibodies were incubated for 1 h at 37 °C. Then slides were incubated with Alpha X Ploymer HRP Ms+Rb (Alpha X Bio, Beijing, China) for 10 min at 37 °C. Alpha X 7‐Color IHC Kit (AXT37100031, Alpha X Bio, Beijing, China) was used for visualization. After each staining cycle, heat‐induced epitope retrieval was performed to remove all the antibodies including primary & secondary antibodies. The slides were counter‐stained with DAPI for 5 min and enclosed in Antifade Mounting Medium (I0052; NobleRyder, Beijing, China). ZEISS AXIOSCAN 7 was used to scan multispectral pictures. Halo (v.3.4; Indica Labs) or QuPath (v.0.2.0) were used to count cells of interest.

Conflict of Interest

The authors declare no conflict of interest.

Author Contributions

Y.‐L.Z., T.‐T.T., and B.W. contributed equally to this work. Y.‐L.Z., T.‐T.T., and B.W. conceptualized and designed the experiments, interpreted experimental results, and wrote the manuscript, with contributions from all authors; Y.‐L.Z. conducted most of the experiments, with help from Y.W., Y.F., Q.Y., W.J., Y.Z., Z.‐L.L., M.W., Q.‐L.W., and J.S.; W.J. carried out animal experiments; S.D.C., H.‐Y.L., L.‐L.L., and B.‐C.L. conceptualized the study, provided supervision, interpreted all results, and wrote the manuscript. All authors read and approved the final paper.

Supporting information

Supporting Information

ADVS-11-2309752-s001.docx (3.4MB, docx)

Acknowledgements

This work was supported by grants from the National Key Research and Development Program of China (2022YFC2502500), National Natural Science Foundation of China (82230022, 82030024, 81720108007) to Bi‐Cheng Liu. This research was supported by additional grants from the National Natural Science Foundation of China (82241045, 82122011) to Lin‐Li Lv and National Natural Science Foundation of China (82200772), Natural Science Foundation of Jiangsu Province grant (BK20220828) to Tao‐Tao Tang.

Zhang Y.‐L., Tang T.‐T., Wang B., Wen Y., Feng Y., Yin Q., Jiang W., Zhang Y., Li Z.‐L., Wu M., Wu Q.‐L., Song J., Crowley S. D., Lan H.‐Y., Lv L.‐L., Liu B.‐C., Identification of a Novel ECM Remodeling Macrophage Subset in AKI to CKD Transition by Integrative Spatial and Single‐Cell Analysis. Adv. Sci. 2024, 11, 2309752. 10.1002/advs.202309752

Contributor Information

Lin‐Li Lv, Email: lvlinli@seu.edu.cn.

Bi‐Cheng Liu, Email: liubc64@seu.edu.cn.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information

ADVS-11-2309752-s001.docx (3.4MB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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