Materials availability
All materials used for Visium Spatial Gene Expression and other techniques are commercially available.
Research ethics
Ethical approval for this study was in accordance with the Danish National Guidelines for animal care and were approved by the Danish Animal Experiments Inspectorate under the Danish Veterinary and Food Administration (License no. 2020-15-0201-00617). The collection and usage of human tissue were approved by the Central Denmark Region Committees on Biomedical Research Ethics (Approval number: 1-10-72-211-17) and the Danish Data Protection Agency. All participants provided written informed consent.
Experiment design and in vivo animal studies details
A time course study was implemented to thoroughly investigate the UUO model. Experiments were performed using male C57Bl/6NRj mice(Janvier Labs, France) 8-9 weeks of age. A total of 28 mice were randomly distributed into four groups: Sham, 1dUUO, 3dUUO, and 7dUUO (n=3 in each group for the spatial transcriptomic analysis and n=4 in each group for the validation analysis). During the experiments, 3-4 mice per cage were housed under standard conditions (pathogen free) of a 12:12 h light-dark cycle at a temperature of 21 ± 2°C and a humidity of 55 ± 5%. All animals had ad libitum access to tap water and standard rodent chow (Altromin, Germany). Animals were allowed to acclimatize to the cages for 3–4 days before surgery.
UUO was performed as previously described (Jensen et al., 2019). In short, mice underwent laparotomy, had the left ureter isolated and the mid-portion was obstructed with 5-0 SofsilkTM black-braided silk ligature (S-182, COVIDIENTM, Denmark). Sham-operated animals underwent the same events without occlusion. Temgesic® (Indivior Europe Limited, Sweden) was administrated subcutaneously and was supplied in the drinking water. Mice were euthanized via cardiac puncture performed as a terminal procedure under sevoflurane anesthesia.
Spatial RNA-seq Library Construction
Kidneys were harvested and immediately embedded in Tissue-Tek® O.C.T. compound/cryomolds (4565, Sakura, Japan), followed by freezing in a methylbutane (Sigma-Aldrich, Germany) and liquid nitrogen-bath according to 10x Genomics tissue preparation guide (Cat#CG000240 Rev C). Frozen sections (10 µm) were mounted onto Visium Spatial Gene Expression slides (Cat#PN-1000185, 10x Genomics), subjected to methanol fixation and Hematoxylin-Eosin (H&E) staining as per 10x Genomics protocol (Cat#CG000160 Rev B). H&E slides were imaged at 10x magnification using Olympus VS120-S6-W slide upright widefield scanner (Olympus, Japan). Samples were permeabilized for 24 minutes, with adjustments to the permeabilization time based on the company's instructions (10x Genomics, Cat#1000193 & Cat#CG000238 Rev D). Spatial RNA-seq libraries were prepared according to the manufacturer's guidelines. The resulting libraries were pooled and sequenced on DNBSEQ-G400/T7 sequencers (MGI tech), followed by de-multiplexing using the library index sequences.
Sequencing Data Alignment and Data Cleanup
Sequencing data in FASTQ format were aligned to the reference genome (refdata-gex-mm10-2020-A) using Space Ranger software (10X Genomics, version 1.3.0). Resulting spatial barcode-gene expression matrices were filtered to include only data from tissue sections and spots with over 5,000 captured mRNA molecules.
Data Normalization and Annotation
The Seurat package was utilized for data normalization and annotation of each cryosection. Raw count inputs were normalized using the SCTransform normalization method. Following normalization, principal component analysis (PCA) was performed for linear dimension reduction, selecting the first 30 principal components (PCs) to create a shared nearest neighbor (SNN) network. The same PCs were employed for visualization using UMAP and t-SNE algorithms.
Clusters within each cryosection were annotated by calculating cluster-specific markers using the "FindAllMarkers" function. This determined log fold change, percentage of expression within and outside the target cluster, and p-values for the Wilcoxon rank-sum test. A log fold change threshold of 0.25 and an adjusted p-value of 0.05 were considered significant. By comparing identified marker genes with canonical region marker genes, each cluster was annotated.
Merging Data and Unsupervised Clustering
Individual Seurat objects for each cryosection were combined into a single, integrated dataset. Unsupervised clustering was conducted, involving PCA to identify major axes of variation within the data. The first 30 PCs were selected for further analysis, generating an SNN network to reveal the data's underlying structure. These 30 PCs were used for visualization, applying dimensionality reduction techniques such as UMAP and t-SNE to represent and interpret high-dimensional data more comprehensibly.
Correlation Analysis
To assess relationships between different regions within each cryosection, the average gene expression for all genes in each region was calculated. Pairwise correlation coefficients between all pairs of regions were computed based on their average gene expression profiles using the 'cor' function from the 'stats' package. A Euclidean distance-based dissimilarity matrix was constructed from the resulting correlation matrix. Hierarchical clustering was then performed on the dissimilarity matrix using the complete linkage method. This approach identified patterns and relationships between distinct regions across cryosections based on their gene expression similarities, providing insights into the underlying biological processes and molecular signatures.
Pairwise Comparison of Gene Expression
Differentially expressed genes (DEGs) between two groups were identified using the 'FindMarkers' function in the Seurat package. This function calculates the log fold change in gene expression, the percentage of expression within and outside the target cluster, and p-values using the likelihood-ratio test. DEGs were considered significant if they met the following criteria: a log fold change threshold of 0.25 and an adjusted p-value of 0.05.
Gene Set Analysis
To investigate the functional relevance of gene expression patterns in our dataset, we employed two distinct strategies for gene set analysis. The gene sets were collected from the Molecular Signatures Database (MSigDBr version 7.5.1; http://bioinf.wehi.edu.au/software/MSigDB/) using the msigdbr package, enabling us to identify enriched pathways and cellular processes across different regions and time points.
The first strategy involved performing Gene Set Variation Analysis (GSVA) on the average expression for each cluster. The average expression was calculated for each region in different cryosections. GSVA was conducted using the GSVA R-package (version 1.38.2), which converts the gene-by-cluster matrix into a gene-set-by-cluster matrix, providing a pathway-centric perspective on the data.
The second strategy employed the 'AddModuleScore' function from the Seurat package to calculate module scores for individual gene sets. This approach enabled the computation of module scores for each spot in the dataset, offering a more granular view of gene set activity across the spatial transcriptomic landscape. Together, these complementary strategies allowed for a comprehensive understanding of the biological processes and molecular pathways underlying the observed gene expression patterns in our study.
Single Cell-based Spatial Deconvolution
To deconvolute the spatial sequencing data, we utilized two publicly available datasets 50, 51 as a reference for cell type-specific gene expression patterns. These datasets were downloaded, normalized, subjected to unsupervised clustering, re-annotated, and merged using the Seurat package. We identified signature genes for each cell type by employing the 'FindAllMarkers' function in Seurat (logfc.threshold = 0.25, min.pct = 0.1).
For the deconvolution of spatial spots, we used the SPOTlight package (version 0.1.7) to estimate the cellular composition of each spot. The top 100 pre-calculated marker genes were selected for this analysis. SPOTlight deconvolution scores were subsequently utilized to compute the correlations between the reference cell types and the spatial spots. To reduce the influence of background noise, cells with a deconvolution score below a threshold of 0.05 were considered as noise and removed from further analysis.
Cell-Cell Communication Analysis
To investigate potential intercellular communication networks between cell clusters, we employed the CellChat R package (version 1.1.3), which quantitatively measures networks based on the law of mass action. This approach considers the average expression levels of ligands in one cell group, receptors in another cell group, and their respective cofactors. We began by importing the normalized expression matrix from the scRNA-seq data and created a CellChat object using the 'createCellChat' function. Next, we preprocessed the data by identifying overexpressed genes and interactions with the 'identifyOverExpressedGenes', 'identifyOverExpressedInteraction', and 'projectData' functions. Subsequently, we employed the 'computeCommunProb', 'filterCommunication' (min.cells = 10), and 'computeCommunProbPathway' functions to calculate potential ligand-receptor interactions. Lastly, we utilized the 'aggregateNet' function in CellChat to compute the aggregated cell-cell communication network, providing a comprehensive view of the potential interactions and signaling pathways between distinct cell populations within the studied tissue sections.
Data Visualization
A range of visualization techniques were employed to effectively represent our findings from spatial RNAseq, scRNAseq, and snRNAseq data. We generated UMAP and spatial plots, violin plots, and gene expression dot plots using the 'DimPlot', 'SpatialDimPlot', 'SpatialPlot', 'VlnPlot', and 'DotPlot' functions within the Seurat package. Additionally, heatmaps were created utilizing the ‘heatmap’ function in the stats package (version 4.0.3). Genes responded in different regions at different time points were analyzed and visualized with UpsetR package.
To represent the average module scores across various groups, we employed line plots generated with the ggplot2 package. The deconvolution pie plots were constructed using the ‘spatial_scatterpie’ function in the SPOTlight package. Lastly, to visualize ligand-receptor signal scores, we created dot plots using the ‘netVisual_bubble’ function available in the CellChat package. These diverse visualization approaches allowed for a comprehensive and accessible representation of our data and findings.
Immunolabeling
For immunolabeling, the mouse kidneys were fixed by retrograde perfusion via the left ventricle using 4% paraformaldehyde in 0.01 M phosphate-buffered saline (PBS). Afterwards, the kidneys were removed and immersed in 4% PFA for 1h, rinsed in PBS, dehydrated using alcohol grades and xylol, and embedded in paraffin. Kidney sections (2 µm) were deparaffinized, rehydrated and rinsed in tap water.
Concisely, for immunohistochemistry, the sections were blocked using 35% Hydrogen peroxide in methanol and subjected to antigen retrieval using citrate buffer (pH 6). Sections were overnight incubated at 4°C with the following primary antibodies: Arg-1 (1:200), α-SMA (1:1000), CD68 (1:900), EGF (1:500), FBP-1 (1:500), FN-1 (1:50), KIM-1 (1:500), LDHA-1 (1:25), PCK-1 (1:500), PDGFR-β (1:300), and PDHA-1 (1:100). After a new series of PBS washing, sections were incubated with the appropriate secondary antibodies: AF488 (1:400), AF568 (1:400) or HRP (1:400). For a detailed list of primary and secondary antibodies, see Supplementary Data 5. Nuclei were counterstained with either DAPI or Hematoxylin. Slides were mounted with either Fluorescent Mounting Medium (S3023, Agilent Technologies, Denmark) or DPX (1.00579, Sigma-Aldrich, Germany).
In addition, kidney sections were subjected to Picro Sirius Red (365548, Signa-Aldrich, Germany) staining. Hydrated sections were kept in 0.1% Picro-Sirius red solution for 30 minutes, rinsed in tap water, and followed to dehydration and mounting (DPX). Scanned images were done using slide scanner (Olympus VS120, Olympus, Japan) at 10x, 20x or 40x magnification. Individual images were taken with an upright widefield fluorescent microscope (Olympus BX63, Olympus, Japan) at 20x or 40x magnification.
Semi-quantitative immunohistochemistry analysis
Semi-quantitative analyses of PDHA1, LDHA-1, PCK-1, FBP-1, and FN were done in human samples. Six random images were taken from control (n=3) and CKD (n=3) kidneys tissues, and the mean intensity of DAB signal for each image was calculated using ImageJ 121. All tissues were evaluated by a single investigator in a blind manner.
CRISPR gene editing
Human CCL7 CRISPR gRNAs were designed with the deep-learning based tool CRISPRon122 and synthesized by Synthego, USA. Human mammary fibroblasts were cultured in Dulbecco's Modified Eagle Medium (DMEM, GibcoTM, USA), supplemented with 10% Fetal Bovine Serum (FBS, Sigma-Aldrich, Germany), 1% Penicillin Streptomycin (GibcoTM, USA), and bFGF (10 ng/ml). For nucleofection, we first formed CRISPR ribonucleoprotein (RNP) by mixing 6 µg Alt-R™ S.p. Cas9 Nuclease V3 (1081059, IDT, Belgium) and 3.2 µg gRNA, and incubated the RNP room temperature (RT) for 15 min. Hereafter, we resuspended 2 x 105 cells in 20µL Opti-MEM (GibcoTM, USA) and gently mixed the cells with the CRISPR RNP complex before transferring to a 16-well NucleocuvetteTM (Lonza, Switzerland) strip. We used the optimized nucleofection program P3-CM138 for fibroblasts. Immediately after nucleofection, the cells were transferred to a 6-well plate, where they were incubated for 72 h at 37°C under 5% CO2. To determine the CRISPR editing efficiency, we performed Sanger sequencing analysis of the cells (Eurofins Genomics, Germany) and used TIDE/ICE to determine the editing efficiency.
MTT-based cell proliferation assay
For MTT assay (11465007001, Roche), 10,000 cells were seeded per well in a 96-well plate overnight. On the following day, 10 µl MTT labeling reagent (5 mg/ml) was added to the cells and incubated at 37°C under 5% CO2 protected from light. After 3 hours, 100 µl of the solubilization buffer was added to the cells and incubated overnight. The absorbance was measured at 570 nm on a GloMax (Promega).
Serial intravital 2-photon microscopy
All experimental procedures involving in vivo imaging were approved by local authorities (Animal Experiments Inspectorate, Denmark, permit number: 2020-15-0201-00443) and reported according to ARRIVE guidelines. To enable conditional expression of tdTomato in endothelial cells, Cdh5-CreERT2-tdTomato mice were generated by crossing of Cdh5(PAC)-CreERT2 mice 123 to PC-G5-tdT mice (Strain # 024477, The Jackson Laboratory, USA). A total of 6 male Cdh5CreERT2-tdToamto mice 8-10 weeks of age were randomized into either a sham group (n = 3) or an UUO group (n = 3). For conditional tdTomato-expression in endothelial cells, mice were treated with 2 doses of tamoxifen (0.2 mg/g body weight in corn oil) by oral gavage whereas the second dose was administered 2 days after the first. A minimum of 10 days wash out was granted prior to start of the experiment 124.
On the first imaging day, mice underwent a combined surgery for UUO/sham procedure and implantation of a winged abdominal imaging window (wAIW). Mice were anesthetized with isoflurane (3.5% for induction, 1.5–1.75% for maintenance, 1.2-1.8 L/min flow rate, 50% oxygen in medical air) and placed on a 37 ± 0.5°C heating pad with rectal thermometer. Analgesia was managed with buprenorphine (0.1 mg/ kg BW, Temgesic) via i.p. injection and eye ointment prevented cornea dehydration. After shaving and disinfecting the left flank (Chlorhexidine 0,5% in 70% Ethanol), a 1 cm dorsoventral incision was made above the left kidney and gently repositioned via maneuvering of connective tissue. Next, the left ureter was isolated and obstructed using 5-0 SofsilkTM black-braided silk ligature. This was followed by a wAIW implantation as described previously 62, 63, 70. In short, a cover-slipped titanium ring was glued to the kidney using cyanoacryl glue and then secured by tightening of a purse-string suture around it that connected the abdominal wall and the skin. The first Intravital imaging session could be initiated 20-25 minutes after occlusion of the ureter (day 0). Further serial intravital imaging was conducted on day 1 and 3 after UUO/sham surgery. Post-operative and imaging analgesia was ensured by s.c. administration of Meloxicam (1-3mg/kg BW) and Temgesic® supplementation in the drinking water (7.5 µg/ml) until the end of the experiment.
Imaging acquisition protocol
In vivo imaging was conducted using an upright Olympus FVMPE-RS 2-photon microscope (Olympus, Japan) operated with Fluoview FV31S software (Olympus, Japan). The microscope was equipped with a MaiTaiHP DS-OL excitation laser (Spectra Physics, United States), an XLPLN25xWMP2 objective, water immersion, (Olympus, Japan, NA 1.05; WD 2.00mm), and the following detection cubes:Ch1: λEm = 705/45nm (multialkali PMT), Ch2: λEm = 610/35nm (multialkali PMT), Ch3: λEm = 540/40nm (GaAsP), Ch4: λEm = 480/40nm (GaAsP), with an IR cut filter of 690nm (Figure 7a, Supplementary Figure 15), as previously described 63. The wAIW implant was slotted into a custom 3D-printed frame ensuring image stabilization on an upright microscope setup 62. During imaging, anesthesia was maintained with a low-flow (30–50ml/min, 1.0–1.5%) isoflurane vaporizer (SomnoSuite, Kent Scientific, USA), which was further equipped with a heating blanket placed beneath the animal to ensure physiological body temperature throughout the experiment.
Prior to every imaging session, a retro-orbital injection of 1μl/g body weight of a 2.5mg/ml conjugated Albumin-Alexa Fluor 594 dye solution 63, along with 1.5μl/g body weight of a 1mg/ml conjugated wheat germ agglutinin-Alexa flour 488 (Alexa488-WGA) 91.
We first identified 2-4 fields of view (FOVs), which were imaged using a dual-excitation wavelength of 750nm and 940nm (640 x 640, dwell time: 4μs, line averaging x 2, Galvano laser). Starting from the kidney capsule, we acquired Z-stacks of every FOV, with a step size of 2μm and depth of 60μm. Occasionally, a superficial glomerulus was encountered, in each case the image depth was extended to encompass the entire glomerulus, if possible. Lastly, capillary blood flow was measured using line scans.
Correlative microscopy
Following the last imaging session (day 3), mice were anesthetized with isoflurane and perfusion-fixated with PBS followed by 4% PFA. The cortical renal area which was still attached to the wAIW was separated as a ≈ 2mm thick tissue section, and post-fixated with 4% PFA for 1h.
For correlative imaging of αSMA-positivity in tdTomato-positive endothelial cells, the tissue was first
washed in PBS (3 x 5min), blocked in 1% BSA/2% SEA (37527, Thermo Fisher Scientific, USA)/PBS for 1h, and then incubated in αSMA (1:1200) primary antibody overnight at 4°C. After washing in PBS (3 x 5min), Cy5 (1:600) was used as secondary antibody with an incubation time of 4h at room temperature. After washing in PBS, the tissue sections were embedded between two coverslips separated by a 1-2mm thick spacer (SunJim Lab, Taiwan) in PBS and imaged. Based on tissue landmarks, such as recognizable tubule shapes and fluorescence patterns, the same tissue regions as imaged in vivo were identified 70. Dual track imaging at 800 nm and 940 nm excitation wavelength were acquired to excite Cy5 and tdTomato, respectively. aSMA ex-vivo imaging acquisition setting was the same as done for in vivo experiments.
Macrophage validation and detection
Non-induced male mouse C57BL/6-Tg(Cdh5-cre/ERT2)1Rha (22 weeks-old) was used as a control to validate the macrophage activity after wAIW. Prior to imaging, a retro-orbital injection of WGA-conjugated Alexa-488 and Albumin-conjugated Alexa-594 dye were administrated consecutively for 3-days shortly before each imaging sessions. We imaged 2-3 FOVs using excitation wavelength of 800nm (512 x 512, dwell time: 4µs, line averaging x2, Galvano laser). Starting from the capsule, Z-series with step size of 1µm and depth of 60µm were captured.
At day 3, the mouse was anesthetized with isoflurane and perfusion-fixated with PBS followed by 4% PFA. The area of interest that was still attached to the wAIW was preserved by sectioning it from the rest of the kidney and proceeded for correlative imaging of Alexa 488-WGA-incorporating macrophages by staining with macrophage pan-marker CD68. The cortical area was washed in PBS (3 x 5min) and kept in 4% PFA for 1h (RT). Thereafter, the same piece was placed in a blocking buffer for 1h, incubated in anti-CD68 (1:450) overnight at 4°C. After washing in PBS (3 x 5min), AF594 (1:500) was used as secondary antibody for 4h at RT. The stained tissue was then embedded as described for aSMA ex-vivo experiments and imaged as described above for CD68 in-vivo set-up.
In vivo image processing
Denoising of images was conducted using the BM3D algorithms125 as previously described62. In short, a denoising pipeline was created utilizing both FIJI and Matlab. FIJI facilitated batch data input/output, handled multichannel images, and merged denoised outputs. Matlab executed BM3D algorithms, processing one channel at a time by reading temporary files written on disk by FIJI and producing the denoised data in separate temporary files.
Registration of serial imaging data was performed using a combination of a manual landmark-based volumetric registration within the FIJI plugin BigWarp using rigid rotation transforms126, and intensity-based medical image registration using Elastix127.
Glycocalyx and NADH quantification
To assess the endothelial glycocalyx, we quantified the intensity of the glycocalyx as described elsewhere 128. In short, a region of interest (ROI) was manually drawn transversally to the glycocalyx in five different spots on each FOV on the 940nm track. The max intensity of channel 3 (λEm: 540/40nm) in each ROI was recorded. Glycocalyx intensity was recorded as paired observation analyzing the same capillaries on days 0, 1, and 3 after UUO.
For quantification of endogenous tubule epithelial NADH signal, 750 nm excitation track imaging data was used. A ROI was manually drawn around the epithelium of each tubule, and the average intensity of channel 4 (lEm: 480/40 nm) was measured.
Image segmentation of endothelial cells, macrophages and tubules
In order to quantify endothelial cells, tdTomato-labeled cells were segmented from the registered 940nm tracks using a machine-learning approach using the software Ilastik69 (v5.1.0) with pixel classification. To train Ilastik, labels were assigned to Z-stack images, designating structures/cells as either tdTomato-positive or tdTomato-negative cells. Following exportation of the 2D Ilastik predictions, a threshold was applied using the Otsu method129, whereafter the percentage of the tdTomato-positive area was assessed relative to the total FOV area, as visualized in Supplementary Figure 17 and 18.
Macrophages were identified through their phagocytic activity, engulfing cells labelled with WGA-Alexa Fluor 488 and Albumin-Alexa Fluor 594. Segmentation was performed as described above. Here, labels were assigned to 750nm registered Z-stack images to distinguish structures or cells as either macrophages or non-macrophages. Subsequently, 2D predictions underwent thresholding using the Otsu method whereafter the percentage of the macrophage-positive area was assessed relative to the total FOV area, as visualized in Supplementary Figure 19 and 20.
Lastly, glomerular leakage, we quantified Albumin-Alexa Fluor 594 uptake in renal tubules. To achieve this, we segmented renal tubules in the registered 750nm Z-stack images using Ilastik69. Here, the simple segmentations were exported and converted to 8-bit. The resulting mask had pixel values normalized to either 0 or 1, with 1 representing positive pixels corresponding to the tubules and 0 representing background/everything else. Hereafter the mask was multiplied by channel 2 (λEm = 610/35nm), and pixels with positive signals were retained after thresholding. Whereafter the average intensity of channel 2 (Ch2: λEm = 610/35nm) within the Albumin-uptaking tubules was measured.
Line scanning/Blood flow measurement
Capillary blood flow was measured via line scan imaging66, 67 Here, a line was drawn on the measured blood vessel (Figure 7d). This line was imaged repeatedly 200 times at a speed of 4μs per pixel. Blood velocity was determined by tracking the movement distance of blood cells over time in the line scan. For each line scan, the average velocity was calculated based on duplicate measurements. Subsequently, the diameter of the blood vessel was measured, enabling the calculation of blood flow.
Human tissue samples
Human fresh kidney tissue was obtained from ten male patients undergoing nephrectomy at Department of Urology at Aarhus University Hospital, Aarhus Denmark. Functional and macroscopically healthy cortical tissue samples were collected from five patients undergoing nephrectomy due to kidney cancer. They had an eGFR over 60 ml/min per 1.73 m2 and it was assumed that the removed kidney was well-functioning (judged from the CT-scan) since a 99mTc-MAG3 Renography is not routinely offered to this patient group. Fibrotic cortical tissue samples were collected from five patients undergoing nephrectomy of a non-functioning kidney due to obstruction of either benign or malign cause. They had an eGFR below 60 ml/min per 1.73 m2, and they all had a 99mTc-MAG3 Renography showing < 15% renal function to the obstructed kidney. eGFR is calculated using the Modification of Diet in Renal Disease (MDRD) formula. Patient demographics are presented in Supplementary Data 5. Kidney tissue samples were processed for QPCR or immunohistochemistry analysis.
Quantitative PCR (qPCR)
RNA from human cortical tissue was isolated using the NucleoSpin RNA II mini kit (740955.50, Macherey-Nagel, Germany). The RNA concentration was measured with spectrophotometry and samples were stored at −80°C until use. cDNA was synthesized using the RevertAid First Strand Synthesis Kit (K1622, Thermo Fisher Scientific, USA). QPCR was performed using 100 ng cDNA, which served as the template for PCR amplification using the Brilliant SYBR Green qPCR master Mix (600831, Agilent Technologies, Denmark), according to the manufacturer’s instructions. Primers are listed as it follows: haSMA-f’ ACTGGGACGACATGGAAAAG, -r’ TACATGGCTGGGACATTGAA; hCOL1A1-f’ CCTGGATGCCATCAAAGTCT, -r’ AATCCATCGGTCATGCTCTC; hFN-f’ CAGTGGGAGACCTCGAGAAG, -r’ GTCCCTCGGAACATCAGAAA; hRPL22-f’ GGAGCAAGAGCAAGATCACC, -r’ TGTTAGCAACTACGCGCAAC.
Data and Code Availability
The processed and raw data generated during this study have been made publicly accessible through the Gene Expression Omnibus (accession number: GSE226709). Additionally, we have created a dedicated webpage (https://dreamapp.biomed.au.dk/STOP_CKD/) for the visualization and exploration of our analyzed RNA sequencing data. Furthermore, the two-photon imaging data generated in this study is likewise available here. This interactive platform has been developed and customized using the ShinyCell package, allowing users to engage with the data more effectively.
Statistics
All data are expressed as mean ± SD if not specified otherwise in the legends. Unless otherwise stated, statistical analysis was performed using either two-way or one-way ANOVA analysis followed by Tukey post-test or, when appropriate, an unpaired two-tailed t-test/Mann Whitney U test with p value < 0.05 being considered statistically significant. Statistical analyses were performed using GraphPad Prism version 9.0 (GraphPad Software Inc, USA) or as described in the Methods above.