imaged with a Philips scanner. After the coverslip is removed, a decrosslinking step is performed.

The spatial gene expression process, including probe hybridization, probe ligation, probe release and extension were performed according to the manufacturer's instructions. cDNA quality was evaluated using Agilent High sensitivity DNA Kit (Agilent, #5067-4626) and spatial gene libraries were constructed using the Visium Spatial Library Construction Kit (10X Genomics, PN-1000184). Spatial data processing was carried out using SpaceRanger software v1.2.2 (10X Genomics). Spatial raw base call (BCL) files were demultiplexed and mapped to the reference genome GRCh38. Loupe Browser from 10X Genomics was used to align the slide's barcoded spot patterns and select spots in the tissue. The resulting count matrices were processed in Seurat v4.1.0 for log2 normalization, scaling, and dimension reduction.

Consents to use their samples and to publish clinical data have been obtained from the patients.

## Deconvolution analysis

The scRNA-seq dataset (n = 49 226 after quality control) from Kuppe et al.⁸ was used to perform the deconvolution for the two Visium samples. The dataset covered 36 different cell types. Cell type annotation from the original article were conserved, except for the stromal and myeloid populations, for which we used the annotation we defined in the study. The spatial sections were decomposed using cell2location version 0.1⁵, implemented in Python3. The CKD atlas was used to compute reference cell type signatures with patient ID as categorical covariate and default parameters. Only cells coming from CKD and not healthy tissue were kept, because we only analyzed sections from patients with CKD. The spatial mapping of cell types was performed by supplying each Visium section to Cell2location and setting *Ncell-s_per_location* to 15 and *detection alpha* to 200 after manual examination of the tissue. 30,000 epochs were used.

After the deconvolution of the spatial sections was performed, Non-Negative Matrix Factorization (NMF) was applied to the deconvolution output using the Cell2location function *run_colocation* with default parameters. NMF is a dimension reduction technique that decomposes the data matrix into a product of two lower-dimensional matrices. In this case, the data matrix was the *q05_cell_abundance_w_sf* cell abundance matrix obtained from the Cell2location deconvolution, and the factorization was performed to identify underlying patterns and structure in the data. The algorithm was run three times with different random initializations to ensure that the final result was robust and not influenced by the initial conditions. After the NMF decomposition was completed, the resulting component matrices were used to generate a heatmap and clustering analysis to identify patterns in the data and relationships between different cell type.

## Bulk RNAseq analysis from the Neptune cohort

The renal biopsy sample was manually dissected and isolated into tubulointerstitial and glomerular compartments per established protocol⁹⁸. The tubulointerstitial dataset was used for further analysis. For RNA-sequencing (RNA-seq) profiles, mRNA samples were prepared using the Illumina TruSeq mRNA Sample Prep v2 kit. Multiplex amplification was used to prepare cDNA with a paired-end read length of 100 bases using an Illumina HiSeq2000. RNAseq was performed by the University of Michigan Advanced Genomics Core (https://brcf.medicine.umich.edu/cores/advanced-genomics/). Quality of the sequencing data were assessed using the FastQC tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Read counts were extracted from the fastq files using HTSeq (version 0.11). RNA-seq profiles from different batches wereloo-transformed and batch corrected using ComBat⁹⁹. The CXCL-iFibro expression score was defined as the mean expression of genes that compose the CXCL-iFibro signature.

## Isolation and culture of kidney primary fibroblasts

Fresh samples from PKD explant received after surgery were cut into fragment of approximatively 1mm³. Fragments were harvested in either non-coated or type I Collagen coated (9 µg/mL, Institut De Bio-technologie Jacques Boy, #207050357) Petri dishes. Cells were cultured in DMEM (Gibco, #41966-029) supplemented with 10% heat inactivated FBS (Biosera, #FB-1003-500) and 1% streptomycin and penicillin (Sigma, #p4333) for 2-3 weeks at 37 °C, in an incubator delivering 5% CO2 and 1.5% of O2. Media was renewed every 3 days for 2-3 weeks, until cells reached 50% confluency to perform first passage. All experiments with fibroblasts were performed with fibroblasts from passage 5 to 10, to avoid cellular senescence.

## Isolation of CD14 + PBMC

PBMC were obtained from healthy donor peripheral blood obtained from "Etablissement Francais du sang". PBMC were isolated using Lymphoprep (STEMCELL #07861), and CD14 + PBMC were isolated using a specific isolation kit (Miltenyi #130-050-201).

## Characterization of fibroblasts phenotype by immunofluorescence

2 × 10⁴ fibroblasts were cultured on glass coverslip coated or not with type I collagen, as described above. On the next day, cells were approximatively at 50% confluency. After PBS wash, cells were fixed in paraformaldehyde 4% for 15 min. After several washes with PBS, cells were stained according to the immunofluorescence protocol described below.

## Characterization of fibroblasts phenotype by RNA sequencing

For RNAseq experiments, fibroblasts cultured on collagen- or plastic-dishes, as described above, were collected. RNAs were then extracted using Qiagen miRNeasy Kit (Qiagen, #217004) according to the manufacturer's instructions. RNA integrity and quality were analyzed using the Agilent RNA 6000 nano Kit (Agilent Technologies, #S067-151I). cDNA libraries were prepared using the TruSeq Stranded mRNA Kit (Illumina, #20020594) followed by sequencing on NovaSeq (Illumina). Reads were mapped on the human reference genome (hg38; Gencode release 26) and quantified using STAR (version 2.5.3a) with parameters "outFilterMultimapNmax = 20; alignSJoverhangMin = 8; alignSJDBoverhangMin = 1; outFilterMismatchNmax = 999; outFilterMismatchNoverLmax = 0.04; alignIntronMin =;20; alignIntronMax = 1000000; alignMatesGapMax = 1000000; outMultimapperOrder = Random." Only genes with one read in at least 5% of all samples were kept for further analyses. Normalization was conducted with DESeq2 R package and raw read matrix was log2 transformed using the *rlog* function from DESeq2 package. Differentially expressed genes depending on the culture condition were identified with DESeq2 R package, after subsetting the count matrix to inflammatory genes (as defined in the Hugo Gene Nomenclature Committee (HNGC) https://www.genenames.org/) or to matrisome-core genes described in³⁹.

## Transwell migration assay

3 × 10⁴ fibroblasts were plated on the lower chamber of transwell 24 wells plate (5 µm pore size, Corning HTS Transwell 24 wells #CLS3421) coated or not with type I Collagen in the lower part of the transwell, in 500 µL of DMEM supplemented with 10% heat-inactivated FBS and 1% Penicillin streptomycin at 1.5% O2. After 24 h, medium was changed for DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin. Then 1.5 × 10⁵ CD14 + PBMC in 150 µL of DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were plated in the upper chamber and incubated at 37 °C for 6 h. After incubation, CD14 + PBMC were harvested in the upper and lower chamber separately and incubated with 0.5 µl of 10 µm carboxylated beads (Polyscience #18133) and DAPI (3 µM). CD14 + PBMC counting
was performed by Flow Cytometry on a LSRII (BD biosciences) using precision beads for normalization and expressed as percentage of migration, being the ratio of the CD14+ cell number in the lower chamber by the total number of cells.

### Coculture of fibroblasts and CD14 + PBMC

For all antibodies used in our study, see also Table S14 listing antibody references and dilutions.

For macrophage analysis, $3 \times 10^4$ fibroblasts were plated in 24 wells plate coated or not with type I collagen for 24 h. Then $1.5 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. After 24 h adherent and non-adherent CD14+ monocytes were harvested, washed and stained first with LIVE/DEAD dye (1:1000, Thermo Fischer, #L34955) for 10 min at room temperature (RT) in PBS to exclude dead cells. Cells suspensions were then incubated for 20 min at RT with antibody mix containing anti-CD14-BV510 (1:50, BD biosciences 563079), anti-CD16-BV650 (1:50, BD biosciences 563692), anti-CD206-BV711 (1:50, BioLegend 321136), anti-FOLR2-PE (1:50, BioLegend 391704), anti-TREM2-biotinilated (1:50, R&D BAF1828) followed by incubation with streptavidin-PECy5 (1:100, BioLegend 405205) for 15 min. Isotype controls were BV510 mouse IgG1k (1:50, BD biosciences 56294, BV650 mouse IgG1k (1:50, BD biosciences 563231), BV711 mouse IgG1k (1:50, BD biosciences 56344), mouse IgG1k (1:50, BioLegend 400112), Goat IgG control (1:50, R&D AB-108-C). Cells were analyzed by LSRFortessaTM analyzer (BD biosciences). Data were examined using FlowJo 10.5.2.

For fibroblast phenotype analysis, $2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated or not with type I collagen for 24 h. Then $1 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. After 24 h of coculture, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below.

### WNT/β-catenin pathway stimulation

$2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated with type I collagen for 24 h. Cells were then stimulated with either control (DMSO), Wnt agonist 1 at the dose of 700 nM (Sigma-Aldrich 681665, CAS 853220-52-7), or Wnt agonist 2 at the dose of 10 µM (Sigma-Aldrich 681667, SKL2001) for 24 h. Wnt agonist 1 is a cell-permeable pyrimidine compound that acts as a potent and selective activator of Wnt signaling without inhibiting the activity of GSK-3β. It is shown to mimic the effect of Wnt and induce β-catenin and TCF (T-cell fate)-dependent transcriptional activity. Wnt agonist 2 is a cell-permeable imidazolyl-isoxazolamide compound that upregulates β-catenin-regulated transcription by disrupting β-catenin and Axin interaction, thereby preventing β-catenin phosphorylation (Ser33/Ser37/Thr41/Ser45) and proteasomal degradation, without affecting the activities of GSK-3α/β. After that, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below.

### Nuclear translocation of β-catenin in fibroblasts upon co-culture with CD14 + PBMC evaluated by western blots

$5 \times 10^4$ fibroblasts were seeded in 24-well plates coated with type I collagen in DMEM supplemented with 10% heat-inactivated FBS and 1% Penicillin streptomycin at 1.5% $O_2$ overnight for complete adherence. Then, the medium was removed and $2,5 \times 10^5$ CD14+ monocytes resuspended in 500 µl of DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. At that time, cells were stimulated with either DMSO or β-catenin/TCF Inhibitor III, iCRT3 (Sigma Aldrich, 219332) or PORCN inhibitor, C59 (Sigma Aldrich, 5004960001) at the dose of 20 µM. 24 h after co-culture, adherent and non-adherent cells were harvested, and the fibroblasts were separated

from the monocytes by negative selection using a specific isolation kit (Miltenyi #130-050-201). Nuclear and cytoplasmic fractionation was performed by following the manufacturer's protocol for NE-PER™ Nuclear and Cytoplasmic Extraction reagents (ThermoFisher #78833). Briefly, fibroblasts were collected in 1,5 ml microcentrifuge tubes and washed with PBS, and ice-cold cytoplasmic reagents (CER I and II) were added. Cells were vigorously vortexed at the highest speed and centrifuged at 16,000 g for 10 min; then the supernatant containing the cytoplasmic extracts was transferred to a new ice-cold tube. The cell pellet was resuspended with a Nuclear extraction reagent (NER), vortexed and the extract was obtained after centrifugation. All the steps were performed on ice and the samples were next sonicated for 15 min (cycles of 30 s ON/30 s OFF) and centrifuged during 10 min at 13.000 × g at 4 °C. The western blot was then performed as detailed in #Protein extraction and western blot. EIF4A1 and Histone-H3 were used as the respective cytoplasmic and nuclear makers for monitoring fraction quality by Western blot. Primary antibodies used were EIF4A1 (1:1000, Cell signaling #2490), Histone H3 (1:10000, abcam #ab1791) and β-catenin (1:1000, cell signaling #9562).

### β-catenin/TCF interaction inhibition during fibroblasts-CD14 + PBMC coculture

$2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated or not with type I collagen for 24 h. Then $1 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat-inactivated FBS and 1% Penicillin streptomycin were added. At that time, cells were stimulated with either DMSO or β-catenin/TCF Inhibitor III, iCRT3 (Sigma Aldrich, 219332), at the dose of 20 µM. For fibroblast analysis, after 24 h of coculture, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below. For macrophage analysis, after 24 h of coculture and stimulation, adherent and non-adherent CD14+ monocytes were harvested, washed, and stained as described above (coculture of fibroblasts and CD14 + PBMC section).

### Immunohistochemistry

**Experiments.** 3-µm sections of paraffin-embedded human kidneys underwent antigen retrieval by microwave heating in a solution of EnVision FLEX Target Retrieval Solution high-pH (Dako, K800421) for 20 min. Staining was performed using the Lab Vision Autostainer (Thermo Fisher Scientific). Sections were incubated with the following primary antibodies: FAP (1:100, abcam ab207178), SFRP1 (1:200, Abcam ab126613), SFRP4 (1:200 abcam ab154167), RAMPI (1:400, EMD Millipore MABS1904). Antigen detection was performed using HRP-DAB revelation system, using Vectastain elite ABC kit, Rabbit IgG. Slides were counterstained with Mayer hematoxylin freshly prepared (Dako, #S3309) and submitted to serial gradients of xylen and mounted with coverslip in an automatic device (Sakura, Tissue-Tek DRS). Images were acquired by a Philips scanner.

**Quantification.** For each slide, staining of fibroblasts markers was evaluated as a histological score (H-score) defined by staining intensity (from 0 to 4) multiplied by the percentage of stained interstitial cells (from 0% to 100%). The whole section was considered, and quantification was performed at 20x magnification. Fibrosis was evaluated as the percentage of fibrotic tissue per field. Fibrosis percentage was evaluated blindly by two independent researchers with very good concordance.

### Immunofluorescence

#### Experiments

**In vitro.** Fibroblasts were cultured on coverslips and fixed in 4% paraformaldehyde for 15 min, permeabilized in PBS containing Triton X-100 0.1%, BSA 3%, then incubated overnight at 4 °C with primary
was performed by Flow Cytometry on a LSRII (BD biosciences) using precision beads for normalization and expressed as percentage of migration, being the ratio of the CD14+ cell number in the lower chamber by the total number of cells.

### Coculture of fibroblasts and CD14 + PBMC

For all antibodies used in our study, see also Table S14 listing antibody references and dilutions.

For macrophage analysis, $3 \times 10^4$ fibroblasts were plated in 24 wells plate coated or not with type I collagen for 24 h. Then $1.5 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. After 24 h adherent and non-adherent CD14+ monocytes were harvested, washed and stained first with LIVE/DEAD dye (1:1000, Thermo Fischer, #L34955) for 10 min at room temperature (RT) in PBS to exclude dead cells. Cells suspensions were then incubated for 20 min at RT with antibody mix containing anti-CD14-BV510 (1:50, BD biosciences 563079), anti-CD16-BV650 (1:50, BD biosciences 563692), anti-CD206-BV711 (1:50, BioLegend 321136), anti-FOLR2-PE (1:50, BioLegend 391704), anti-TREM2-biotinilated (1:50, R&D BAF1828) followed by incubation with streptavidin-PECy5 (1:100, BioLegend 405205) for 15 min. Isotype controls were BV510 mouse IgG1k (1:50, BD biosciences 56294, BV650 mouse IgG1k (1:50, BD biosciences 563231), BV711 mouse IgG1k (1:50, BD biosciences 56344), mouse IgG1k (1:50, BioLegend 400112), Goat IgG control (1:50, R&D AB-108-C). Cells were analyzed by LSRFortessaTM analyzer (BD biosciences). Data were examined using FlowJo 10.5.2.

For fibroblast phenotype analysis, $2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated or not with type I collagen for 24 h. Then $1 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. After 24 h of coculture, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below.

### WNT/β-catenin pathway stimulation

$2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated with type I collagen for 24 h. Cells were then stimulated with either control (DMSO), Wnt agonist 1 at the dose of 700 nM (Sigma-Aldrich 681665, CAS 853220-52-7), or Wnt agonist 2 at the dose of 10 µM (Sigma-Aldrich 681667, SKL2001) for 24 h. Wnt agonist 1 is a cell-permeable pyrimidine compound that acts as a potent and selective activator of Wnt signaling without inhibiting the activity of GSK-3β. It is shown to mimic the effect of Wnt and induce β-catenin and TCF (T-cell fate)-dependent transcriptional activity. Wnt agonist 2 is a cell-permeable imidazolyl-isoxazolamide compound that upregulates β-catenin-regulated transcription by disrupting β-catenin and Axin interaction, thereby preventing β-catenin phosphorylation (Ser33/Ser37/Thr41/Ser45) and proteasomal degradation, without affecting the activities of GSK-3α/β. After that, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below.

### Nuclear translocation of β-catenin in fibroblasts upon co-culture with CD14 + PBMC evaluated by western blots

$5 \times 10^4$ fibroblasts were seeded in 24-well plates coated with type I collagen in DMEM supplemented with 10% heat-inactivated FBS and 1% Penicillin streptomycin at 1.5% $O_2$ overnight for complete adherence. Then, the medium was removed and $2,5 \times 10^5$ CD14+ monocytes resuspended in 500 µl of DMEM supplemented with 1% heat inactivated FBS and 1% Penicillin streptomycin were added. At that time, cells were stimulated with either DMSO or β-catenin/TCF Inhibitor III, iCRT3 (Sigma Aldrich, 219332) or PORCN inhibitor, C59 (Sigma Aldrich, 5004960001) at the dose of 20 µM. 24 h after co-culture, adherent and non-adherent cells were harvested, and the fibroblasts were separated

from the monocytes by negative selection using a specific isolation kit (Miltenyi #130-050-201). Nuclear and cytoplasmic fractionation was performed by following the manufacturer's protocol for NE-PER™ Nuclear and Cytoplasmic Extraction reagents (ThermoFisher #78833). Briefly, fibroblasts were collected in 1,5 ml microcentrifuge tubes and washed with PBS, and ice-cold cytoplasmic reagents (CER I and II) were added. Cells were vigorously vortexed at the highest speed and centrifuged at 16,000 g for 10 min; then the supernatant containing the cytoplasmic extracts was transferred to a new ice-cold tube. The cell pellet was resuspended with a Nuclear extraction reagent (NER), vortexed and the extract was obtained after centrifugation. All the steps were performed on ice and the samples were next sonicated for 15 min (cycles of 30 s ON/30 s OFF) and centrifuged during 10 min at 13.000 × g at 4 °C. The western blot was then performed as detailed in #Protein extraction and western blot. EIF4A1 and Histone-H3 were used as the respective cytoplasmic and nuclear makers for monitoring fraction quality by Western blot. Primary antibodies used were EIF4A1 (1:1000, Cell signaling #2490), Histone H3 (1:10000, abcam #ab1791) and β-catenin (1:1000, cell signaling #9562).

### β-catenin/TCF interaction inhibition during fibroblasts-CD14 + PBMC coculture

$2 \times 10^4$ fibroblasts were plated in 24 wells plate with 13 mm glass coverslips coated or not with type I collagen for 24 h. Then $1 \times 10^5$ CD14 + PBMC in DMEM supplemented with 1% heat-inactivated FBS and 1% Penicillin streptomycin were added. At that time, cells were stimulated with either DMSO or β-catenin/TCF Inhibitor III, iCRT3 (Sigma Aldrich, 219332), at the dose of 20 µM. For fibroblast analysis, after 24 h of coculture, cells were washed with PBS, then fixed with 4% PFA for 15 min. After three washes with PBS, cells were stained following immunofluorescence protocol described below. For macrophage analysis, after 24 h of coculture and stimulation, adherent and non-adherent CD14+ monocytes were harvested, washed, and stained as described above (coculture of fibroblasts and CD14 + PBMC section).

### Immunohistochemistry

**Experiments.** 3-µm sections of paraffin-embedded human kidneys underwent antigen retrieval by microwave heating in a solution of EnVision FLEX Target Retrieval Solution high-pH (Dako, K800421) for 20 min. Staining was performed using the Lab Vision Autostainer (Thermo Fisher Scientific). Sections were incubated with the following primary antibodies: FAP (1:100, abcam ab207178), SFRP1 (1:200, Abcam ab126613), SFRP4 (1:200 abcam ab154167), RAMPI (1:400, EMD Millipore MABS1904). Antigen detection was performed using HRP-DAB revelation system, using Vectastain elite ABC kit, Rabbit IgG. Slides were counterstained with Mayer hematoxylin freshly prepared (Dako, #S3309) and submitted to serial gradients of xylen and mounted with coverslip in an automatic device (Sakura, Tissue-Tek DRS). Images were acquired by a Philips scanner.

**Quantification.** For each slide, staining of fibroblasts markers was evaluated as a histological score (H-score) defined by staining intensity (from 0 to 4) multiplied by the percentage of stained interstitial cells (from 0% to 100%). The whole section was considered, and quantification was performed at 20x magnification. Fibrosis was evaluated as the percentage of fibrotic tissue per field. Fibrosis percentage was evaluated blindly by two independent researchers with very good concordance.

### Immunofluorescence

#### Experiments

**In vitro.** Fibroblasts were cultured on coverslips and fixed in 4% paraformaldehyde for 15 min, permeabilized in PBS containing Triton X-100 0.1%, BSA 3%, then incubated overnight at 4 °C with primary
antibodies: mouse monoclonal anti-αSMA (1:200, DAKO clone 1A4 ref M0851) rabbit monoclonal anti-SFRP1 (1:50, Abcam ab126613), rabbit monoclonal anti-SFRP4 (1:400, Abcam ab154167), rabbit monoclonal anti-E-Cadherin (1:200, Cell Signaling Technology, 4065) mouse monoclonal anti-β-catenin (1:1000, Bio SB, Ref: BSD 5088). After 3 washes with PBS, cells were incubated in secondary antibody goat anti mouse alexa fluor 488 (1:400, Invitrogen A11001) or goat anti rabbit alexa fluor 555 (1:400, Invitrogen A21428). Nuclei were counterstained with Hoescht before mounting with anti-fade diamond mounting medium (Invitrogen P36970). Images were acquired by an upright widefield Apotome (Zeiss) or LSM 700 Zeiss confocal. At least 10 fields at 40x were acquired.

**For human tissue section.** 3-µm sections of paraffin-embedded kidneys underwent antigen retrieval by microwave heating in a solution of EnVision FLEX Target Retrieval Solution high-pH (Dako, K800421) for 20 min. Sections were incubated overnight at 4 °C with the following primary antibodies: CD68 (1:200, Abcam ab955), αSMA (1:400, Dako clone 1A4 M0851), FOLR2 (1:200, Invitrogen MA5-26933), TREM2 (1:100, R&D MAB17291) or collagen I (1:400, Invitrogen MAI-26771). Antigen detection was performed with alexa-fluor coupled antibodies: alexa fluor 488 goat anti mouse (for αSMA, Collagen I, CD68, FOLR2, Invitrogen A11001), alexa fluor-555 goat anti rabbit (for FAP, SFRP1, SFRP4 Invitrogen A21428), alexa fluor-647 goat anti rabbit (for FAP, SFRP4 Invitrogen A21245) or alexa fluor-555 goat anti rat (for TREM2 Invitrogen A21434). Nuclei were counterstained with Hoescht before mounting with anti-fade diamond mounting medium (Invitrogen P36970). Images were acquired by an upright widefield Apotome (Zeiss) or LSM 700 Zeiss confocal.

## Quantification

**In vitro.** For quantification of MFI of markers of interests, at least ten fields at 40x were acquired. Region of Interest (ROI) was manually defined as the contour of an individual cell. At least 100 cells were quantified per condition per independent experiment, and the average of MFI per condition was then compared. For β-catenin nuclear MFI, nucleus was defined as a ROI using the Hoescht staining by performing binarization and thresholding. Then, MFI of β-catenin staining was quantified in this ROI.

**For human tissue section.** For quantification of SFRP1, FAP, CD68, FOLR2 per mm², each 40x magnification acquired image was segmented in images of 18.600 µm² (136 × 136 µm images). Positive cells per field were then manually counted. Then, the positive number of cells per mm² could be calculated. For SFRP1 or FAP colocalization with Collagen I, images were segmented in 80 × 80 µm images. The percentage of stained area was quantified after binarization of the staining using thresholding. Correlation between the percentage of the stained area of interstitial SFRP1 or FAP in these small images is then a reflect of colocalization.

## Protein extraction and western blot

**Protein extraction.** Cells cultured in a 6 well plate coated or not with Collagen I were washed with cold sterile PBS (Gibco #14190) and collected after the addition of 150 µl of Laemml buffer (BioRad, #1610737) supplemented with DTT (Thermoscientific, #11896744) and scratching. The solution was next boiled at 95 °C for 5 min. Samples were next sonicated for 10 min (cycles of 30 s ON/ 1 min OFF) and centrifuged during 10 min at 13.000 x g at 4 °C. The protein extract was then short-term stored at -80 °C.

**Western blot.** A volume of 15 µl of proteins was loaded onto a NuPAGE Novex 4–12% bis tris midi protein gel ten wells (Invitrogen, #WG1403BOX). The gel was transfer onto a 0,45 µm nitrocellulose membrane (GE Healthcare #10600002) in 1X TGS buffer (Biorad #161-

0772) at 100 V for 2 h at 4 °C. Membrane was next blocked during 30 min in TBS-Tween 0.1% complemented with 5% BSA (Euromedex #04-100-812-C) before blotting with primary antibodies diluted in TBS-Tween 0.1% complemented with 5% BSA at 4 °C overnight. Primary antibodies used were FAP (1:1000, abcam ab207178), SFRP1 (1:500, Abcam ab126613), SFRP4 (1:1000 abcam ab154167), αSMA (1:1000, Dako clone 1A4 M0851), actin (1:10.000; Sigma #A5441). The next day, incubation with secondary antibody anti-mouse (Jackson ImmunoResearch Laboratories, INC., #115-035-003) or anti-rabbit (Jackson ImmunoResearch Laboratories, INC., #115-035-003) in TBS-Tween 0.1% + 5% BSA for 1 h at RT was done. The membrane was incubated for 1 min at RT with ECL (ratio 1:1) (Western Lightning Plus-ECL, PerkinElmer, #NEL105001EA). The detection of the signal was done in a Chemidoc device for detecting chemiluminescence. The protein bands were analyzed by ImageJ software for protein quantification.

## Statistical analysis

The graphical representation of the data and statistical analyses were done using R environment (https://cran.r-project.org). Bar plots or scatter plots are represented with mean ± standard error of the mean (SEM). Statistical tests used agree with data distribution. To assess the normality of the distribution of variables, we first applied the Shapiro-Wilk test. According to normality Shapiro-Wilk test, parametric or non-parametric two-tailed tests were applied. The correlation coefficient and its significance between two independent variables were evaluated by Spearman's correlation test. Survival curves were established using the Kaplan-Meier method and compared with the Log-rank test using survival R package. Stratification of patients for survival analyses was performed using an iterative method. Patients were separated in two groups according to their expression of CXCL1iFibro transcriptomic signature or FOLR2. Each value of expression was tested iteratively. The threshold with the lowest p-value was selected as the threshold differentiating low and high expression levels of CXCL1iFibro transcriptomic signature or FOLR2. Univariate analysis was performed using the Wald test. A multivariate Cox model was applied, using as variable all parameter with a significance of *p* < 0.1 in univariate analysis. All applied statistical tests are indicated in the legends. Differences were statistically significant when *p*-values were ≤ 0.05.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

New generated data, as well as source data and codes have been deposited in Figshare. Raw counts for bulk RNAseq for primary human fibroblasts can be downloaded on Figshare under the https://doi.org/10.6084/m9 figshare.24049380 (https://figshare.com/search?q=10.6084%2Fm9 figshare.24049380). Spatial transcriptomics processed data for the two patients with kidney fibrosis can be downloaded on Figshare under the https://doi.org/10.6084/m9 figshare.24049410 (https://figshare.com/search?q=10.6084%2Fm9 figshare.24049410). Raw data have been deposited on the European Genome Phenome Archive (EGA) and can be requested through the Data Access Committee EGAC00001000581. Publicly available single cell RNAseq data for 12 patients with kidney disease from Kuppe et al.⁸ including matrix count and annotations were downloaded from Zenodo data archive (https://zenodo.org/record/4059315, DOI: 10.5281/zenodo.4059315). Publicly available bulk RNAseq data from 15 mice undergoing unilateral ureteral obstruction (UUO) were downloaded on the Gene Expression Omnibus under the number GSE118339³⁸. The publicly available single cell RNAseq dataset from mice UUO was downloaded in the National Center for Biotechnology Information Gene Expression Omnibus database (accession number GSE140023). Source data are
antibodies: mouse monoclonal anti-αSMA (1:200, DAKO clone 1A4 ref M0851) rabbit monoclonal anti-SFRP1 (1:50, Abcam ab126613), rabbit monoclonal anti-SFRP4 (1:400, Abcam ab154167), rabbit monoclonal anti-E-Cadherin (1:200, Cell Signaling Technology, 4065) mouse monoclonal anti-β-catenin (1:1000, Bio SB, Ref: BSD 5088). After 3 washes with PBS, cells were incubated in secondary antibody goat anti mouse alexa fluor 488 (1:400, Invitrogen A11001) or goat anti rabbit alexa fluor 555 (1:400, Invitrogen A21428). Nuclei were counterstained with Hoescht before mounting with anti-fade diamond mounting medium (Invitrogen P36970). Images were acquired by an upright widefield Apotome (Zeiss) or LSM 700 Zeiss confocal. At least 10 fields at 40x were acquired.

**For human tissue section.** 3-µm sections of paraffin-embedded kidneys underwent antigen retrieval by microwave heating in a solution of EnVision FLEX Target Retrieval Solution high-pH (Dako, K800421) for 20 min. Sections were incubated overnight at 4 °C with the following primary antibodies: CD68 (1:200, Abcam ab955), αSMA (1:400, Dako clone 1A4 M0851), FOLR2 (1:200, Invitrogen MA5-26933), TREM2 (1:100, R&D MAB17291) or collagen I (1:400, Invitrogen MAI-26771). Antigen detection was performed with alexa-fluor coupled antibodies: alexa fluor 488 goat anti mouse (for αSMA, Collagen I, CD68, FOLR2, Invitrogen A11001), alexa fluor-555 goat anti rabbit (for FAP, SFRP1, SFRP4 Invitrogen A21428), alexa fluor-647 goat anti rabbit (for FAP, SFRP4 Invitrogen A21245) or alexa fluor-555 goat anti rat (for TREM2 Invitrogen A21434). Nuclei were counterstained with Hoescht before mounting with anti-fade diamond mounting medium (Invitrogen P36970). Images were acquired by an upright widefield Apotome (Zeiss) or LSM 700 Zeiss confocal.

## Quantification

**In vitro.** For quantification of MFI of markers of interests, at least ten fields at 40x were acquired. Region of Interest (ROI) was manually defined as the contour of an individual cell. At least 100 cells were quantified per condition per independent experiment, and the average of MFI per condition was then compared. For β-catenin nuclear MFI, nucleus was defined as a ROI using the Hoescht staining by performing binarization and thresholding. Then, MFI of β-catenin staining was quantified in this ROI.

**For human tissue section.** For quantification of SFRP1, FAP, CD68, FOLR2 per mm², each 40x magnification acquired image was segmented in images of 18.600 µm² (136 × 136 µm images). Positive cells per field were then manually counted. Then, the positive number of cells per mm² could be calculated. For SFRP1 or FAP colocalization with Collagen I, images were segmented in 80 × 80 µm images. The percentage of stained area was quantified after binarization of the staining using thresholding. Correlation between the percentage of the stained area of interstitial SFRP1 or FAP in these small images is then a reflect of colocalization.

## Protein extraction and western blot

**Protein extraction.** Cells cultured in a 6 well plate coated or not with Collagen I were washed with cold sterile PBS (Gibco #14190) and collected after the addition of 150 µl of Laemml buffer (BioRad, #1610737) supplemented with DTT (Thermoscientific, #11896744) and scratching. The solution was next boiled at 95 °C for 5 min. Samples were next sonicated for 10 min (cycles of 30 s ON/ 1 min OFF) and centrifuged during 10 min at 13.000 x g at 4 °C. The protein extract was then short-term stored at -80 °C.

**Western blot.** A volume of 15 µl of proteins was loaded onto a NuPAGE Novex 4–12% bis tris midi protein gel ten wells (Invitrogen, #WG1403BOX). The gel was transfer onto a 0,45 µm nitrocellulose membrane (GE Healthcare #10600002) in 1X TGS buffer (Biorad #161-

0772) at 100 V for 2 h at 4 °C. Membrane was next blocked during 30 min in TBS-Tween 0.1% complemented with 5% BSA (Euromedex #04-100-812-C) before blotting with primary antibodies diluted in TBS-Tween 0.1% complemented with 5% BSA at 4 °C overnight. Primary antibodies used were FAP (1:1000, abcam ab207178), SFRP1 (1:500, Abcam ab126613), SFRP4 (1:1000 abcam ab154167), αSMA (1:1000, Dako clone 1A4 M0851), actin (1:10.000; Sigma #A5441). The next day, incubation with secondary antibody anti-mouse (Jackson ImmunoResearch Laboratories, INC., #115-035-003) or anti-rabbit (Jackson ImmunoResearch Laboratories, INC., #115-035-003) in TBS-Tween 0.1% + 5% BSA for 1 h at RT was done. The membrane was incubated for 1 min at RT with ECL (ratio 1:1) (Western Lightning Plus-ECL, PerkinElmer, #NEL105001EA). The detection of the signal was done in a Chemidoc device for detecting chemiluminescence. The protein bands were analyzed by ImageJ software for protein quantification.

## Statistical analysis

The graphical representation of the data and statistical analyses were done using R environment (https://cran.r-project.org). Bar plots or scatter plots are represented with mean ± standard error of the mean (SEM). Statistical tests used agree with data distribution. To assess the normality of the distribution of variables, we first applied the Shapiro-Wilk test. According to normality Shapiro-Wilk test, parametric or non-parametric two-tailed tests were applied. The correlation coefficient and its significance between two independent variables were evaluated by Spearman's correlation test. Survival curves were established using the Kaplan-Meier method and compared with the Log-rank test using survival R package. Stratification of patients for survival analyses was performed using an iterative method. Patients were separated in two groups according to their expression of CXCL1iFibro transcriptomic signature or FOLR2. Each value of expression was tested iteratively. The threshold with the lowest p-value was selected as the threshold differentiating low and high expression levels of CXCL1iFibro transcriptomic signature or FOLR2. Univariate analysis was performed using the Wald test. A multivariate Cox model was applied, using as variable all parameter with a significance of *p* < 0.1 in univariate analysis. All applied statistical tests are indicated in the legends. Differences were statistically significant when *p*-values were ≤ 0.05.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

New generated data, as well as source data and codes have been deposited in Figshare. Raw counts for bulk RNAseq for primary human fibroblasts can be downloaded on Figshare under the https://doi.org/10.6084/m9 figshare.24049380 (https://figshare.com/search?q=10.6084%2Fm9 figshare.24049380). Spatial transcriptomics processed data for the two patients with kidney fibrosis can be downloaded on Figshare under the https://doi.org/10.6084/m9 figshare.24049410 (https://figshare.com/search?q=10.6084%2Fm9 figshare.24049410). Raw data have been deposited on the European Genome Phenome Archive (EGA) and can be requested through the Data Access Committee EGAC00001000581. Publicly available single cell RNAseq data for 12 patients with kidney disease from Kuppe et al.⁸ including matrix count and annotations were downloaded from Zenodo data archive (https://zenodo.org/record/4059315, DOI: 10.5281/zenodo.4059315). Publicly available bulk RNAseq data from 15 mice undergoing unilateral ureteral obstruction (UUO) were downloaded on the Gene Expression Omnibus under the number GSE118339³⁸. The publicly available single cell RNAseq dataset from mice UUO was downloaded in the National Center for Biotechnology Information Gene Expression Omnibus database (accession number GSE140023). Source data are
provided with this paper and under the https://doi.org/10.6084/m9.
figshare.24049305 (https://figshare.com/search?q=10.6084%2Fm9.
figshare.24049305%20). Uncropped blots are available at https://doi.
org/10.6084/m9 figshare.24049938 (https://figshare.com/search?q=
10.6084%2Fm9. figshare.24049938). Source data are provided with
this paper.

Code availability

All the codes used for this study are available on Figshare under the
https://doi.org/10.6084/m9. figshare.24049350 (https://figshare.com/
search?q=10.6084%2Fm9. figshare.24049350). They can be also
accessible on GitHub (https://github.com/StressAndCancerLab/).

References

1. Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395, 709–733 (2020).

2. Duffield, J. S. Cellular and molecular mechanisms in kidney fibrosis. *J. Clin. Invest.* **124**, 2299–2306 (2014).

3. Humphreys, B. D. et al. Fate tracing reveals the pericyte and not epithelial origin of myofibroblasts in kidney fibrosis. *Am. J. Pathol.* **176**, 85–97 (2010).

4. Kramann, R., DiRocco, D. P. & Humphreys, B. D. Understanding the origin, activation and regulation of matrix-producing myofibroblasts for treatment of fibrotic disease. *J. Pathol.* **231**, 273–289 (2013).

5. Kramann, R. & Humphreys, B. D. Kidney pericytes: roles in regeneration and fibrosis. *Semin. Nephrol.* **34**, 374–383 (2014).

6. Kramann, R. et al. Perivascular Gli1+ progenitors are key contributors to injury-induced organ fibrosis. *Cell Stem Cell* **16**, 51–66 (2015).

7. Chang-Panesso, M., Kadyrov, F. F., Machado, F. G., Kumar, A. & Humphreys, B. D. Meis1 is specifically upregulated in kidney myofibroblasts during aging and injury but is not required for kidney homeostasis or fibrotic response. *Am. J. Physiol. Renal Physiol.* **315**, F275–F290 (2018).

8. Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. *Nature* **589**, 281–286 (2021).

9. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. *Nature* **608**, 766–777 (2022).

10. Sugimoto, H., Mundel, T. M., Kieran, M. W. & Kalluri, R. Identification of fibroblast heterogeneity in the tumor microenvironment. *Cancer Biol. Ther.* **5**, 1640–1646 (2006).

11. Su, S. et al. CD10(+)GPR77(+) Cancer-associated fibroblasts promote cancer formation and chemoresistance by sustaining cancer stemness. *Cell* **172**, 841–856 e816 (2018).

12. Ishii, G., Ochiai, A. & Neri, S. Phenotypic and functional heterogeneity of cancer-associated fibroblast within the tumor microenvironment. *Adv. Drug Deliv. Rev.* **99**, 186–196 (2016).

13. Ohlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. *J. Exp. Med.* **214**, 579–596 (2017).

14. Bartoschek, M. et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. *Nat. Commun.* **9**, 5150 (2018).

15. Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. *Cancer Cell* **33**, 463–479.e410 (2018).

16. Givel, A. M. et al. miR200-regulated CXCL12beta promotes fibroblast heterogeneity and immunosuppression in ovarian cancers. *Nat. Commun.* **9**, 1056 (2018).

17. Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. *Cancer Discov.* **9**, 1102–1123 (2019).

18. Bonneau, C. et al. A subset of activated fibroblasts is associated with distant relapse in early luminal breast cancer. *Breast Cancer Res.* **22**, 76 (2020).

19. Pelon, F. et al. Cancer-associated fibroblast heterogeneity in axillary lymph nodes drives metastases in breast cancer through complementary mechanisms. *Nat. Commun.* **11**, 404 (2020).

20. Wu, S. Z. et al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer. *Embo. J.* **39**, e104063 (2020).

21. Biffi, G. & Tuveson, D. A. Diversity and biology of cancer-associated fibroblasts. *Physiol. Rev.* **101**, 147–176 (2021).

22. Galbo, P. M. Jr, Zang, X. & Zheng, D. Molecular features of cancer-associated fibroblast subtypes and their implication on cancer pathogenesis, prognosis, and immunotherapy resistance. *Clin. Cancer Res.* **27**, 2636–2647 (2021).

23. Hu, H. et al. Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. *Cancer Cell* **39**, 1531–1547.e1510 (2021).

24. Luo, H. et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. *Nat. Commun.* **13**, 6619 (2022).

25. Cremasco, V. et al. FAP delineates heterogeneous and functionally divergent stromal cells in immune-excluded breast tumors. *Cancer Immunol Res* **6**, 1472–1485 (2018).

26. Biffi, G. et al. IL1-Induced JAK/STAT signaling is antagonized by TGFβ to shape CAF heterogeneity in pancreatic ductal adenocarcinoma. *Cancer Discov.* **9**, 282–301 (2019).

27. Davidson, S. et al. Single-Cell RNA sequencing reveals a dynamic stromal niche that supports tumor growth. *Cell Rep.* **31**, 107628 (2020).

28. Dominguez, C. X. et al. Single-cell RNA sequencing reveals stromal evolution into LRRC15(+) myofibroblasts as a determinant of patient response to cancer immunotherapy. *Cancer Discov.* **10**, 232–253 (2020).

29. Kieffer, Y. et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. *Cancer Discov.* **10**, 1330–1351 (2020).

30. Sebastian, A. et al. Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer. *Cancers* **12**, 1307 (2020).

31. Hutton, C. et al. Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity. *Cancer Cell* **39**, 1227–1244.e1220 (2021).

32. Obradovic, A. et al. Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer. *Clin Cancer Res* **28**, 2094–2109 (2022).

33. Peltier, A., Seban, R. D., Buvat, I., Bidard, F. C. & Mechta-Grigoriou, F. Fibroblast heterogeneity in solid tumors: from single cell analysis to whole-body imaging. *Semin. Cancer Biol.* **86**, 262–272 (2022).

34. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. *Nat. Genet.* **53**, 1334–1347 (2021).

35. Krishnamurty, A. T. et al. LRRC15(+) myofibroblasts dictate the stromal setpoint to suppress tumour immunity. *Nature* **611**, 148–154 (2022).

36. Bhattacharjee, S. et al. Tumor restriction by type I collagen opposes tumor-promoting effects of cancer-associated fibroblasts. *J. Clin. Invest.* **131**, e146987 (2021).

37. Nicolas, A. M. et al. Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer. *Cancer Cell* **40**, 168–184.e113 (2022).

38. Pavkovic, M. et al. Multi omics analysis of fibrotic kidneys in two mouse models. *Sci. Data* **6**, 92 (2019).

39. Naba, A. et al. Characterization of the extracellular matrix of normal and diseased tissues using proteomics. *J. Proteome Res.* **16**, 3083–3091 (2017).
provided with this paper and under the https://doi.org/10.6084/m9.
figshare.24049305 (https://figshare.com/search?q=10.6084%2Fm9.
figshare.24049305%20). Uncropped blots are available at https://doi.
org/10.6084/m9 figshare.24049938 (https://figshare.com/search?q=
10.6084%2Fm9. figshare.24049938). Source data are provided with
this paper.

Code availability

All the codes used for this study are available on Figshare under the
https://doi.org/10.6084/m9. figshare.24049350 (https://figshare.com/
search?q=10.6084%2Fm9. figshare.24049350). They can be also
accessible on GitHub (https://github.com/StressAndCancerLab/).

References

1. Collaboration GBDCKD. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395, 709–733 (2020).

2. Duffield, J. S. Cellular and molecular mechanisms in kidney fibrosis. *J. Clin. Invest.* **124**, 2299–2306 (2014).

3. Humphreys, B. D. et al. Fate tracing reveals the pericyte and not epithelial origin of myofibroblasts in kidney fibrosis. *Am. J. Pathol.* **176**, 85–97 (2010).

4. Kramann, R., DiRocco, D. P. & Humphreys, B. D. Understanding the origin, activation and regulation of matrix-producing myofibroblasts for treatment of fibrotic disease. *J. Pathol.* **231**, 273–289 (2013).

5. Kramann, R. & Humphreys, B. D. Kidney pericytes: roles in regeneration and fibrosis. *Semin. Nephrol.* **34**, 374–383 (2014).

6. Kramann, R. et al. Perivascular Gli1+ progenitors are key contributors to injury-induced organ fibrosis. *Cell Stem Cell* **16**, 51–66 (2015).

7. Chang-Panesso, M., Kadyrov, F. F., Machado, F. G., Kumar, A. & Humphreys, B. D. Meis1 is specifically upregulated in kidney myofibroblasts during aging and injury but is not required for kidney homeostasis or fibrotic response. *Am. J. Physiol. Renal Physiol.* **315**, F275–F290 (2018).

8. Kuppe, C. et al. Decoding myofibroblast origins in human kidney fibrosis. *Nature* **589**, 281–286 (2021).

9. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. *Nature* **608**, 766–777 (2022).

10. Sugimoto, H., Mundel, T. M., Kieran, M. W. & Kalluri, R. Identification of fibroblast heterogeneity in the tumor microenvironment. *Cancer Biol. Ther.* **5**, 1640–1646 (2006).

11. Su, S. et al. CD10(+)GPR77(+) Cancer-associated fibroblasts promote cancer formation and chemoresistance by sustaining cancer stemness. *Cell* **172**, 841–856 e816 (2018).

12. Ishii, G., Ochiai, A. & Neri, S. Phenotypic and functional heterogeneity of cancer-associated fibroblast within the tumor microenvironment. *Adv. Drug Deliv. Rev.* **99**, 186–196 (2016).

13. Ohlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. *J. Exp. Med.* **214**, 579–596 (2017).

14. Bartoschek, M. et al. Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. *Nat. Commun.* **9**, 5150 (2018).

15. Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. *Cancer Cell* **33**, 463–479.e410 (2018).

16. Givel, A. M. et al. miR200-regulated CXCL12beta promotes fibroblast heterogeneity and immunosuppression in ovarian cancers. *Nat. Commun.* **9**, 1056 (2018).

17. Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. *Cancer Discov.* **9**, 1102–1123 (2019).

18. Bonneau, C. et al. A subset of activated fibroblasts is associated with distant relapse in early luminal breast cancer. *Breast Cancer Res.* **22**, 76 (2020).

19. Pelon, F. et al. Cancer-associated fibroblast heterogeneity in axillary lymph nodes drives metastases in breast cancer through complementary mechanisms. *Nat. Commun.* **11**, 404 (2020).

20. Wu, S. Z. et al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer. *Embo. J.* **39**, e104063 (2020).

21. Biffi, G. & Tuveson, D. A. Diversity and biology of cancer-associated fibroblasts. *Physiol. Rev.* **101**, 147–176 (2021).

22. Galbo, P. M. Jr, Zang, X. & Zheng, D. Molecular features of cancer-associated fibroblast subtypes and their implication on cancer pathogenesis, prognosis, and immunotherapy resistance. *Clin. Cancer Res.* **27**, 2636–2647 (2021).

23. Hu, H. et al. Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. *Cancer Cell* **39**, 1531–1547.e1510 (2021).

24. Luo, H. et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. *Nat. Commun.* **13**, 6619 (2022).

25. Cremasco, V. et al. FAP delineates heterogeneous and functionally divergent stromal cells in immune-excluded breast tumors. *Cancer Immunol Res* **6**, 1472–1485 (2018).

26. Biffi, G. et al. IL1-Induced JAK/STAT signaling is antagonized by TGFβ to shape CAF heterogeneity in pancreatic ductal adenocarcinoma. *Cancer Discov.* **9**, 282–301 (2019).

27. Davidson, S. et al. Single-Cell RNA sequencing reveals a dynamic stromal niche that supports tumor growth. *Cell Rep.* **31**, 107628 (2020).

28. Dominguez, C. X. et al. Single-cell RNA sequencing reveals stromal evolution into LRRC15(+) myofibroblasts as a determinant of patient response to cancer immunotherapy. *Cancer Discov.* **10**, 232–253 (2020).

29. Kieffer, Y. et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. *Cancer Discov.* **10**, 1330–1351 (2020).

30. Sebastian, A. et al. Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer. *Cancers* **12**, 1307 (2020).

31. Hutton, C. et al. Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity. *Cancer Cell* **39**, 1227–1244.e1220 (2021).

32. Obradovic, A. et al. Immunostimulatory cancer-associated fibroblast subpopulations can predict immunotherapy response in head and neck cancer. *Clin Cancer Res* **28**, 2094–2109 (2022).

33. Peltier, A., Seban, R. D., Buvat, I., Bidard, F. C. & Mechta-Grigoriou, F. Fibroblast heterogeneity in solid tumors: from single cell analysis to whole-body imaging. *Semin. Cancer Biol.* **86**, 262–272 (2022).

34. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. *Nat. Genet.* **53**, 1334–1347 (2021).

35. Krishnamurty, A. T. et al. LRRC15(+) myofibroblasts dictate the stromal setpoint to suppress tumour immunity. *Nature* **611**, 148–154 (2022).

36. Bhattacharjee, S. et al. Tumor restriction by type I collagen opposes tumor-promoting effects of cancer-associated fibroblasts. *J. Clin. Invest.* **131**, e146987 (2021).

37. Nicolas, A. M. et al. Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer. *Cancer Cell* **40**, 168–184.e113 (2022).

38. Pavkovic, M. et al. Multi omics analysis of fibrotic kidneys in two mouse models. *Sci. Data* **6**, 92 (2019).

39. Naba, A. et al. Characterization of the extracellular matrix of normal and diseased tissues using proteomics. *J. Proteome Res.* **16**, 3083–3091 (2017).
40. Chu, T., Wang, Z., Pe'er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. *Nat. Cancer* **3**, 505–517 (2022).

41. Forbes, M. S. et al. Fight-or-flight: murine unilateral ureteral obstruction causes extensive proximal tubular degeneration, collecting duct dilatation, and minimal fibrosis. *Am. J. Physiol. Renal Physiol.* **303**, F120–F129 (2012).

42. Conway, B. R. et al. Kidney single-cell atlas reveals myeloid heterogeneity in progression and regression of kidney disease. *J. Am. Soc. Nephrol.* **31**, 2833–2854 (2020).

43. Grande, M. T. et al. Snail1-induced partial epithelial-to-mesenchymal transition drives renal fibrosis in mice and can be targeted to reverse established disease. *Nat. Med.* **21**, 989–997 (2015).

44. Lovisa, S. et al. Epithelial-to-mesenchymal transition induces cell cycle arrest and parenchymal damage in renal fibrosis. *Nat. Med.* **21**, 998–1009 (2015).

45. Guiteras, R., Flaquer, M. & Cruzado, J. M. Macrophage in chronic kidney disease. *Clin. Kidney J.* **9**, 765–771 (2016).

46. Tang, P. M., Nikolic-Paterson, D. J. & Lan, H. Y. Macrophages: versatile players in renal inflammation and fibrosis. *Nat. Rev. Nephrol.* **15**, 144–158 (2019).

47. Wang, X. et al. The role of macrophages in kidney fibrosis. *Front. Physiol.* **12**, 705838 (2021).

48. Bell, R. M. B. & Conway, B. R. Macrophages in the kidney in health, injury and repair. *Int. Rev. Cell Mol. Biol.* **367**, 101–147 (2022).

49. Vlasschaert, C., Moran, S. M. & Rauh, M. J. The myeloid-kidney interface in health and disease. *Clin. J. Am. Soc. Nephrol.* **17**, 323–331 (2022).

50. Casanova-Acebes, M. et al. Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. *Nature* **595**, 578–584 (2021).

51. Nalio Ramos, R. et al. Tissue-resident FOLR2(+) macrophages associate with CD8(+) T cell infiltration in human breast cancer. *Cell* **185**, 1189–1207.e1125 (2022).

52. Timperi, E. et al. Lipid-associated macrophages are induced by cancer-associated fibroblasts and mediate immune suppression in breast cancer. *Cancer Res.* **82**, 3291–3306 (2022).

53. Zimmerman, K. A. et al. Single-cell RNA sequencing identifies candidate renal resident macrophage gene expression signatures across species. *J. Am. Soc. Nephrol.* **30**, 767–781 (2019).

54. Fu, J. et al. The single-cell landscape of kidney immune cells reveals transcriptional heterogeneity in early diabetic kidney disease. *Kidney Int.* **102**, 1291–1304 (2022).

55. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. *Nat. Biotechnol.* **40**, 661–671 (2022).

56. Doke, T. et al. Single-cell analysis identifies the interaction of altered renal tubules with basophils orchestrating kidney fibrosis. *Nat. Immunol.* **23**, 947–959 (2022).

57. Wu, H. et al. Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies. *Cell Metab.* **34**, 1064–1078.e1066 (2022).

58. O'Sullivan, E. D. et al. Indian Hedgehog release from TNF-activated renal epithelia drives local and remote organ fibrosis. *Sci. Transl. Med.* **15**, eabn0736 (2023).

59. Wu, X. et al. CXCL12/CXCR4: an amazing challenge and opportunity in the fight against fibrosis. *Ageing Res. Rev.* **83**, 101809 (2023).

60. Van Damme, J. et al. Homogeneous interferon-inducing 22K factor is related to endogenous pyrogen and interleukin-1. *Nature* **314**, 266–268 (1985).

61. Lemos, D. R. et al. Interleukin-1beta activates a MYC-dependent metabolic switch in kidney stromal cells necessary for progressive tubulointerstitial fibrosis. *J. Am. Soc. Nephrol.* **29**, 1690–1705 (2018).

62. Lin, H. et al. Discovery of a cytokine and its receptor by functional screening of the extracellular proteome. *Science* **320**, 807–811 (2008).

63. Mak, K. M. & Mei, R. Basement membrane type IV collagen and laminin: an overview of their biology and value as fibrosis biomarkers of liver disease. *Anat. Rec. (Hoboken)* **300**, 1371–1390 (2017).

64. Vu, R. et al. Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication. *Cell Rep.* **40**, 111155 (2022).

65. Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. *Genome Res.* **29**, 1363–1375 (2019).

66. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. *Nature* **566**, 496–502 (2019).

67. Han, H. et al. TRRUST: a reference database of human transcriptional regulatory interactions. *Sci. Rep.* **5**, 11432 (2015).

68. Lin, S. L. et al. Macrophage Wnt7b is critical for kidney repair and regeneration. *Proc. Natl. Acad. Sci. USA* **107**, 4194–4199 (2010).

69. Saha, S. et al. Macrophage-derived extracellular vesicle-packaged WNTs rescue intestinal stem cells and enhance survival after radiation injury. *Nat. Commun.* **7**, 13096 (2016).

70. Gadegbeku, C. A. et al. Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach. *Kidney Int* **83**, 749–756 (2013).

71. Gillies, C. E. et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. *Am. J. Hum. Genet.* **103**, 232–244 (2018).

72. Anders, H. J. & Ryu, M. Renal microenvironments and macrophage phenotypes determine progression or resolution of renal inflammation and fibrosis. *Kidney Int.* **80**, 915–925 (2011).

73. Ricardo, S. D., van Goor, H. & Eddy, A. A. Macrophage diversity in renal injury and repair. *J. Clin. Invest.* **118**, 3522–3530 (2008).

74. Muto, Y. et al. Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. *Nat. Commun.* **13**, 6497 (2022).

75. Henderson, N. C., Rieder, F. & Wynn, T. A. Fibrosis: from mechanisms to medicines. *Nature* **587**, 555–566 (2020).

76. Affo, S. et al. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. *Cancer Cell* **39**, 866–882.e811 (2021).

77. Chang, S. K. et al. Cadherin-11 regulates fibroblast inflammation. *Proc. Natl. Acad. Sci. USA* **108**, 8402–8407 (2011).

78. Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. *Nature* **570**, 246–251 (2019).

79. Mhaidly, R. & Mechta-Grigoriou, F. Role of cancer-associated fibroblast subpopulations in immune infiltration, as a new means of treatment in cancer. *Immunol. Rev.* **302**, 259–272 (2021).

80. Wei, K., Nguyen, H. N. & Brenner, M. B. Fibroblast pathology in inflammatory diseases. *J. Clin. Invest.* **131**, e149538 (2021).

81. Mizoguchi, F. et al. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. *Nat. Commun.* **9**, 789 (2018).

82. Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. *Nat. Immunol.* **20**, 928–942 (2019).

83. Cantero-Navarro, E. et al. Role of macrophages and related cytokines in kidney disease. *Front. Med.* **8**, 688060 (2021).

84. Cassetta, L. et al. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets. *Cancer Cell* **35**, 588–602.e510 (2019).

85. Kawakami, T., Ren, S. & Duffield, J. S. Wnt signalling in kidney diseases: dual roles in renal injury and repair. *J. Pathol.* **229**, 221–231 (2013).
40. Chu, T., Wang, Z., Pe'er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. *Nat. Cancer* **3**, 505–517 (2022).

41. Forbes, M. S. et al. Fight-or-flight: murine unilateral ureteral obstruction causes extensive proximal tubular degeneration, collecting duct dilatation, and minimal fibrosis. *Am. J. Physiol. Renal Physiol.* **303**, F120–F129 (2012).

42. Conway, B. R. et al. Kidney single-cell atlas reveals myeloid heterogeneity in progression and regression of kidney disease. *J. Am. Soc. Nephrol.* **31**, 2833–2854 (2020).

43. Grande, M. T. et al. Snail1-induced partial epithelial-to-mesenchymal transition drives renal fibrosis in mice and can be targeted to reverse established disease. *Nat. Med.* **21**, 989–997 (2015).

44. Lovisa, S. et al. Epithelial-to-mesenchymal transition induces cell cycle arrest and parenchymal damage in renal fibrosis. *Nat. Med.* **21**, 998–1009 (2015).

45. Guiteras, R., Flaquer, M. & Cruzado, J. M. Macrophage in chronic kidney disease. *Clin. Kidney J.* **9**, 765–771 (2016).

46. Tang, P. M., Nikolic-Paterson, D. J. & Lan, H. Y. Macrophages: versatile players in renal inflammation and fibrosis. *Nat. Rev. Nephrol.* **15**, 144–158 (2019).

47. Wang, X. et al. The role of macrophages in kidney fibrosis. *Front. Physiol.* **12**, 705838 (2021).

48. Bell, R. M. B. & Conway, B. R. Macrophages in the kidney in health, injury and repair. *Int. Rev. Cell Mol. Biol.* **367**, 101–147 (2022).

49. Vlasschaert, C., Moran, S. M. & Rauh, M. J. The myeloid-kidney interface in health and disease. *Clin. J. Am. Soc. Nephrol.* **17**, 323–331 (2022).

50. Casanova-Acebes, M. et al. Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. *Nature* **595**, 578–584 (2021).

51. Nalio Ramos, R. et al. Tissue-resident FOLR2(+) macrophages associate with CD8(+) T cell infiltration in human breast cancer. *Cell* **185**, 1189–1207.e1125 (2022).

52. Timperi, E. et al. Lipid-associated macrophages are induced by cancer-associated fibroblasts and mediate immune suppression in breast cancer. *Cancer Res.* **82**, 3291–3306 (2022).

53. Zimmerman, K. A. et al. Single-cell RNA sequencing identifies candidate renal resident macrophage gene expression signatures across species. *J. Am. Soc. Nephrol.* **30**, 767–781 (2019).

54. Fu, J. et al. The single-cell landscape of kidney immune cells reveals transcriptional heterogeneity in early diabetic kidney disease. *Kidney Int.* **102**, 1291–1304 (2022).

55. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. *Nat. Biotechnol.* **40**, 661–671 (2022).

56. Doke, T. et al. Single-cell analysis identifies the interaction of altered renal tubules with basophils orchestrating kidney fibrosis. *Nat. Immunol.* **23**, 947–959 (2022).

57. Wu, H. et al. Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies. *Cell Metab.* **34**, 1064–1078.e1066 (2022).

58. O'Sullivan, E. D. et al. Indian Hedgehog release from TNF-activated renal epithelia drives local and remote organ fibrosis. *Sci. Transl. Med.* **15**, eabn0736 (2023).

59. Wu, X. et al. CXCL12/CXCR4: an amazing challenge and opportunity in the fight against fibrosis. *Ageing Res. Rev.* **83**, 101809 (2023).

60. Van Damme, J. et al. Homogeneous interferon-inducing 22K factor is related to endogenous pyrogen and interleukin-1. *Nature* **314**, 266–268 (1985).

61. Lemos, D. R. et al. Interleukin-1beta activates a MYC-dependent metabolic switch in kidney stromal cells necessary for progressive tubulointerstitial fibrosis. *J. Am. Soc. Nephrol.* **29**, 1690–1705 (2018).

62. Lin, H. et al. Discovery of a cytokine and its receptor by functional screening of the extracellular proteome. *Science* **320**, 807–811 (2008).

63. Mak, K. M. & Mei, R. Basement membrane type IV collagen and laminin: an overview of their biology and value as fibrosis biomarkers of liver disease. *Anat. Rec. (Hoboken)* **300**, 1371–1390 (2017).

64. Vu, R. et al. Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication. *Cell Rep.* **40**, 111155 (2022).

65. Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D. & Saez-Rodriguez, J. Benchmark and integration of resources for the estimation of human transcription factor activities. *Genome Res.* **29**, 1363–1375 (2019).

66. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. *Nature* **566**, 496–502 (2019).

67. Han, H. et al. TRRUST: a reference database of human transcriptional regulatory interactions. *Sci. Rep.* **5**, 11432 (2015).

68. Lin, S. L. et al. Macrophage Wnt7b is critical for kidney repair and regeneration. *Proc. Natl. Acad. Sci. USA* **107**, 4194–4199 (2010).

69. Saha, S. et al. Macrophage-derived extracellular vesicle-packaged WNTs rescue intestinal stem cells and enhance survival after radiation injury. *Nat. Commun.* **7**, 13096 (2016).

70. Gadegbeku, C. A. et al. Design of the Nephrotic Syndrome Study Network (NEPTUNE) to evaluate primary glomerular nephropathy by a multidisciplinary approach. *Kidney Int* **83**, 749–756 (2013).

71. Gillies, C. E. et al. An eQTL landscape of kidney tissue in human nephrotic syndrome. *Am. J. Hum. Genet.* **103**, 232–244 (2018).

72. Anders, H. J. & Ryu, M. Renal microenvironments and macrophage phenotypes determine progression or resolution of renal inflammation and fibrosis. *Kidney Int.* **80**, 915–925 (2011).

73. Ricardo, S. D., van Goor, H. & Eddy, A. A. Macrophage diversity in renal injury and repair. *J. Clin. Invest.* **118**, 3522–3530 (2008).

74. Muto, Y. et al. Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis. *Nat. Commun.* **13**, 6497 (2022).

75. Henderson, N. C., Rieder, F. & Wynn, T. A. Fibrosis: from mechanisms to medicines. *Nature* **587**, 555–566 (2020).

76. Affo, S. et al. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. *Cancer Cell* **39**, 866–882.e811 (2021).

77. Chang, S. K. et al. Cadherin-11 regulates fibroblast inflammation. *Proc. Natl. Acad. Sci. USA* **108**, 8402–8407 (2011).

78. Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. *Nature* **570**, 246–251 (2019).

79. Mhaidly, R. & Mechta-Grigoriou, F. Role of cancer-associated fibroblast subpopulations in immune infiltration, as a new means of treatment in cancer. *Immunol. Rev.* **302**, 259–272 (2021).

80. Wei, K., Nguyen, H. N. & Brenner, M. B. Fibroblast pathology in inflammatory diseases. *J. Clin. Invest.* **131**, e149538 (2021).

81. Mizoguchi, F. et al. Functionally distinct disease-associated fibroblast subsets in rheumatoid arthritis. *Nat. Commun.* **9**, 789 (2018).

82. Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. *Nat. Immunol.* **20**, 928–942 (2019).

83. Cantero-Navarro, E. et al. Role of macrophages and related cytokines in kidney disease. *Front. Med.* **8**, 688060 (2021).

84. Cassetta, L. et al. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets. *Cancer Cell* **35**, 588–602.e510 (2019).

85. Kawakami, T., Ren, S. & Duffield, J. S. Wnt signalling in kidney diseases: dual roles in renal injury and repair. *J. Pathol.* **229**, 221–231 (2013).
86. Xiao, L. et al. Sustained activation of Wnt/β-catenin signaling drives AKI to CKD progression. *J. Am. Soc. Nephrol.* **27**, 1727–1740 (2016).

87. Zuo, Y. & Liu, Y. New insights into the role and mechanism of Wnt/β-catenin signalling in kidney fibrosis. *Nephrology* **23**, 38–43 (2018).

88. Malik, S. A., Modarage, K. & Goggolidou, P. The Role of Wnt Signalling in Chronic Kidney Disease (CKD). *Genes* **11**, 496 (2020).

89. Schunk, S. J., Floege, J., Fliser, D. & Speer, T. WNT-β-catenin signalling—a versatile player in kidney injury and repair. *Nat. Rev. Nephrol.* **17**, 172–184 (2021).

90. Li, L., Fu, H. & Liu, Y. The fibrogenic niche in kidney fibrosis: components and mechanisms. *Nat. Rev. Nephrol.* **18**, 545–557 (2022).

91. Zhou, D. et al. Fibroblast-specific β-catenin signaling dictates the outcome of AKI. *J. Am. Soc. Nephrol.* **29**, 1257–1271 (2018).

92. Zhou, D. et al. Tubule-derived Wnts are required for fibroblast activation and kidney fibrosis. *J. Am. Soc. Nephrol.* **28**, 2322–2336 (2017).

93. Surendran, K., Schiavi, S. & Hruska, K. A. Wnt-dependent β-catenin signaling is activated after unilateral ureteral obstruction, and recombinant secreted frizzled-related protein 4 alters the progression of renal fibrosis. *J. Am. Soc. Nephrol.* **16**, 2373–2384 (2005).

94. Feng, Y. et al. Wnt/β-catenin-promoted macrophage alternative activation contributes to kidney fibrosis. *J. Am. Soc. Nephrol.* **29**, 182–193 (2018).

95. Ju, W. et al. Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. *Sci. Transl. Med.* **7**, 316ra193 (2015).

96. Barisoni, L. et al. Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE). *Clin. J. Am. Soc. Nephrol.* **8**, 1449–1459 (2013).

97. Hao, Y. et al. Integrated analysis of multimodal single-cell data. *Cell* **184**, 3573–3587.e3529 (2021).

98. Cohen, C. D., Frach, K., Schlondorff, D. & Kretzler, M. Quantitative gene expression analysis in renal biopsies: a novel protocol for a high-throughput multicenter application. *Kidney Int.* **61**, 133–140 (2002).

99. Zee, J. et al. Kidney biopsy features most predictive of clinical outcomes in the spectrum of minimal change disease and focal segmental glomerulosclerosis. *J. Am. Soc. Nephrol.* **33**, 1411–1426 (2022).

## Acknowledgements

We are grateful to Geraldine Gentric and Agathe Peltier for fruitful discussions and critical reading of the manuscript. We thank Coralie Guerin and Lea Guyonnet at the cytometry core facility. F.M.-G. is a permanent scientist at Inserm. C.C. was supported by the Fondation Chercher et Trouver, R.M. by the Simone and Cino del Duca Foundation, the SIGN’IT 2019 program for the Foundation ARC and the post-doctoral grant from Foundation de France (00119142/WB-2021-36276), and HC by the Foundation for Medical Research (FRM, grant number 13683). Y.K. and C.R. were supported by the Institut National du Cancer INCa (CaLYS INCa_11692; CAFHeros INCa_16101). The experimental work was supported by grants from the Ligue Nationale Contre le Cancer (Labelisation), Inserm, Institut Curie (Incentive and Cooperative Program Tumor Micro-environment PIC TME/T-MEGA, PIC3i CAFi), INCa (STROMAE INCa-DGOS-9963, CaLYS INCa_11692, ChemoCAF INCa_16086, CAF-Heros INCa_16101) and the Foundation “Chercher et Trouver”. F.M.-G. acknowledges both the “French Pink Ribbon Association” and the “Simone and Cino del Duca Foundation” for attribution of their respective Grand Prix, and the FRM for the Rozen Price. F.M.-G. is very grateful to all her funders for providing support throughout the years. Under the remit of the U.K. QUOD Consortium supported by NHS Blood and Transplant, sections were obtained by David A. Ferenbach from renal biopsies from preimplantation renal transplants. This study was also

supported by the George M. O’Brien Michigan Kidney Translational Core Center, funded by NIH/NIDDK grant 2P30-DK081943. The Nephrotic Syndrome Study Network Consortium (NEPTUNE), U54-DK-083912, is a part of the National Institutes of Health (NIH) Rare Disease Clinical Research Network (RDCRN), supported through a collaboration between the Office of Rare Diseases Research, NCATS, and the National Institute of Diabetes, Digestive, and Kidney Diseases. Additional funding and/or programmatic support for this project has also been provided by the University of Michigan, the NephCure Kidney International and the Halpin Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Art from Figs. 2 and 4 have been created with Biorender.com (F.M.-G. account).

## Author contributions

F.M.-G. conceived the project and proposed it to C.C., who both designed the concept of experiments. C.C. performed experiments and acquired data, together with R.M. and C.R. M.R. and A.S. built cohorts of patients, provided human samples and expertise in pathology analyses. R.L. and A.V.-S. performed the spatial transcriptomics. C.C., H.C., and Y.K. performed all bioinformatic and statistical analyses. D.A. and M.O.T. provided human kidney samples. S.E., W.J., and M.K. acquired and analyzed NEPTUNE data. F.M.-G. supervised the entire project and wrote the paper with C.C. with suggestions from the authors.

## Competing interests

F.M.-G. received research support from Innate-Pharma, Institut Roche, Roche, and Bristol-Myers-Squibb (BMS). Other authors declare no potential conflict of interest.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-44886-z.

**Correspondence** and requests for materials should be addressed to Fatima Mechta-Grigoriou.

**Peer review information** *Nature Communications* thanks Hui Lan and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher’s note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
86. Xiao, L. et al. Sustained activation of Wnt/β-catenin signaling drives AKI to CKD progression. *J. Am. Soc. Nephrol.* **27**, 1727–1740 (2016).

87. Zuo, Y. & Liu, Y. New insights into the role and mechanism of Wnt/β-catenin signalling in kidney fibrosis. *Nephrology* **23**, 38–43 (2018).

88. Malik, S. A., Modarage, K. & Goggolidou, P. The Role of Wnt Signalling in Chronic Kidney Disease (CKD). *Genes* **11**, 496 (2020).

89. Schunk, S. J., Floege, J., Fliser, D. & Speer, T. WNT-β-catenin signalling—a versatile player in kidney injury and repair. *Nat. Rev. Nephrol.* **17**, 172–184 (2021).

90. Li, L., Fu, H. & Liu, Y. The fibrogenic niche in kidney fibrosis: components and mechanisms. *Nat. Rev. Nephrol.* **18**, 545–557 (2022).

91. Zhou, D. et al. Fibroblast-specific β-catenin signaling dictates the outcome of AKI. *J. Am. Soc. Nephrol.* **29**, 1257–1271 (2018).

92. Zhou, D. et al. Tubule-derived Wnts are required for fibroblast activation and kidney fibrosis. *J. Am. Soc. Nephrol.* **28**, 2322–2336 (2017).

93. Surendran, K., Schiavi, S. & Hruska, K. A. Wnt-dependent β-catenin signaling is activated after unilateral ureteral obstruction, and recombinant secreted frizzled-related protein 4 alters the progression of renal fibrosis. *J. Am. Soc. Nephrol.* **16**, 2373–2384 (2005).

94. Feng, Y. et al. Wnt/β-catenin-promoted macrophage alternative activation contributes to kidney fibrosis. *J. Am. Soc. Nephrol.* **29**, 182–193 (2018).

95. Ju, W. et al. Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. *Sci. Transl. Med.* **7**, 316ra193 (2015).

96. Barisoni, L. et al. Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE). *Clin. J. Am. Soc. Nephrol.* **8**, 1449–1459 (2013).

97. Hao, Y. et al. Integrated analysis of multimodal single-cell data. *Cell* **184**, 3573–3587.e3529 (2021).

98. Cohen, C. D., Frach, K., Schlondorff, D. & Kretzler, M. Quantitative gene expression analysis in renal biopsies: a novel protocol for a high-throughput multicenter application. *Kidney Int.* **61**, 133–140 (2002).

99. Zee, J. et al. Kidney biopsy features most predictive of clinical outcomes in the spectrum of minimal change disease and focal segmental glomerulosclerosis. *J. Am. Soc. Nephrol.* **33**, 1411–1426 (2022).

## Acknowledgements

We are grateful to Geraldine Gentric and Agathe Peltier for fruitful discussions and critical reading of the manuscript. We thank Coralie Guerin and Lea Guyonnet at the cytometry core facility. F.M.-G. is a permanent scientist at Inserm. C.C. was supported by the Fondation Chercher et Trouver, R.M. by the Simone and Cino del Duca Foundation, the SIGN'it 2019 program for the Foundation ARC and the post-doctoral grant from Foundation de France (00119142/WB-2021-36276), and HC by the Foundation for Medical Research (FRM, grant number 13683). Y.K. and C.R. were supported by the Institut National du Cancer INCa (CaLYS INCa_11692; CAFHeros INCa_16101). The experimental work was supported by grants from the Ligue Nationale Contre le Cancer (Labelisation), Inserm, Institut Curie (Incentive and Cooperative Program Tumor Micro-environment PIC TME/T-MEGA, PIC3i CAFi), INCa (STROMAE INCa-DGOS-9963, CaLYS INCa_11692, ChemoCAF INCa_16086, CAF-Heros INCa_16101) and the Foundation "Chercher et Trouver". F.M.-G. acknowledges both the "French Pink Ribbon Association" and the "Simone and Cino del Duca Foundation" for attribution of their respective Grand Prix, and the FRM for the Rozen Price. F.M.-G. is very grateful to all her funders for providing support throughout the years. Under the remit of the U.K. QUOD Consortium supported by NHS Blood and Transplant, sections were obtained by David A. Ferenbach from renal biopsies from preimplantation renal transplants. This study was also

supported by the George M. O'Brien Michigan Kidney Translational Core Center, funded by NIH/NIDDK grant 2P30-DK081943. The Nephrotic Syndrome Study Network Consortium (NEPTUNE), U54-DK-083912, is a part of the National Institutes of Health (NIH) Rare Disease Clinical Research Network (RDCRN), supported through a collaboration between the Office of Rare Diseases Research, NCATS, and the National Institute of Diabetes, Digestive, and Kidney Diseases. Additional funding and/or programmatic support for this project has also been provided by the University of Michigan, the NephCure Kidney International and the Halpin Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Art from Figs. 2 and 4 have been created with Biorender.com (F.M.-G. account).

## Author contributions

F.M.-G. conceived the project and proposed it to C.C., who both designed the concept of experiments. C.C. performed experiments and acquired data, together with R.M. and C.R. M.R. and A.S. built cohorts of patients, provided human samples and expertise in pathology analyses. R.L. and A.V.-S. performed the spatial transcriptomics. C.C., H.C., and Y.K. performed all bioinformatic and statistical analyses. D.A. and M.O.T. provided human kidney samples. S.E., W.J., and M.K. acquired and analyzed NEPTUNE data. F.M.-G. supervised the entire project and wrote the paper with C.C. with suggestions from the authors.

## Competing interests

F.M.-G. received research support from Innate-Pharma, Institut Roche, Roche, and Bristol-Myers-Squibb (BMS). Other authors declare no potential conflict of interest.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-44886-z.

**Correspondence** and requests for materials should be addressed to Fatima Mechta-Grigoriou.

**Peer review information** *Nature Communications* thanks Hui Lan and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024