Single-Cell Transcriptomics Reveal Disrupted Kidney Filter Cell-Cell Interactions after Early and Selective Podocyte Injury
2021; Elsevier BV; Volume: 192; Issue: 2 Linguagem: Inglês
10.1016/j.ajpath.2021.11.004
ISSN1525-2191
AutoresAbbe R. Clark, Jamie L. Marshall, Yiming Zhou, Mónica S. Montesinos, Haiqi Chen, Lan Nguyễn, Fei Chen, Anna Greka,
Tópico(s)Renal and related cancers
ResumoThe health of the kidney filtration barrier requires communication among podocytes, endothelial cells, and mesangial cells. Disruption of these cell-cell interactions is thought to contribute to disease progression in chronic kidney diseases (CKDs). Podocyte ablation via doxycycline-inducible deletion of an essential endogenous molecule, CTCF [inducible podocyte-specific CTCF deletion (iCTCFpod−/−)], is sufficient to drive progressive CKD. However, the earliest events connecting podocyte injury to disrupted intercellular communication within the kidney filter remain unclear. Single-cell RNA sequencing of kidney tissue from iCTCFpod−/− mice after 1 week of doxycycline induction was performed to generate a map of the earliest transcriptional effects of podocyte injury on cell-cell interactions at single-cell resolution. A subset of podocytes had the earliest signs of injury due to disrupted gene programs for cytoskeletal regulation and mitochondrial function. Surviving podocytes up-regulated collagen type IV ɑ5, causing reactive changes in integrin expression in endothelial populations and mesangial cells. Intercellular interaction analysis revealed several receptor-ligand-target gene programs as drivers of endothelial cell injury and abnormal matrix deposition. This analysis reveals the earliest disruptive changes within the kidney filter, pointing to new, actionable targets within a therapeutic window that may allow us to maximize the success of much needed therapeutic interventions for CKDs. The health of the kidney filtration barrier requires communication among podocytes, endothelial cells, and mesangial cells. Disruption of these cell-cell interactions is thought to contribute to disease progression in chronic kidney diseases (CKDs). Podocyte ablation via doxycycline-inducible deletion of an essential endogenous molecule, CTCF [inducible podocyte-specific CTCF deletion (iCTCFpod−/−)], is sufficient to drive progressive CKD. However, the earliest events connecting podocyte injury to disrupted intercellular communication within the kidney filter remain unclear. Single-cell RNA sequencing of kidney tissue from iCTCFpod−/− mice after 1 week of doxycycline induction was performed to generate a map of the earliest transcriptional effects of podocyte injury on cell-cell interactions at single-cell resolution. A subset of podocytes had the earliest signs of injury due to disrupted gene programs for cytoskeletal regulation and mitochondrial function. Surviving podocytes up-regulated collagen type IV ɑ5, causing reactive changes in integrin expression in endothelial populations and mesangial cells. Intercellular interaction analysis revealed several receptor-ligand-target gene programs as drivers of endothelial cell injury and abnormal matrix deposition. This analysis reveals the earliest disruptive changes within the kidney filter, pointing to new, actionable targets within a therapeutic window that may allow us to maximize the success of much needed therapeutic interventions for CKDs. Chronic kidney diseases (CKDs) affect 700 million people globally, yet specific therapies are lacking.1Webster A.C. Nagler E.V. Morton R.L. Masson P. Chronic kidney disease.Lancet. 2017; 389: 1238-1252Google Scholar Many kidney diseases originate in the glomerulus, the filtration unit of the kidney. The glomerulus consists of (i) podocytes, specialized postmitotic cells with elaborate foot processes that interdigitate forming slit diaphragms and wrapping around glomerular capillaries; (ii) endothelial cells, that lie opposite podocytes on a shared glomerular basement membrane (GBM); (iii) mesangial cells, that form a matrix that provides structural support for the glomerulus; and (iv) parietal epithelial cells (PECs), that line the Bowman capsule.2Scott R.P. Quaggin S.E. Review series: the cell biology of renal filtration.J Cell Biol. 2015; 209: 199-210Google Scholar Podocyte injury, in particular, leads to many highly prevalent, progressive kidney diseases, including diabetic kidney disease (DKD), focal segmental glomerulosclerosis (FSGS), and nephrotic syndrome (both idiopathic and genetic). The canonical pattern of injury results in the loss of interdigitating podocyte foot processes, known as foot process effacement, caused by a rearrangement of the actin cytoskeleton. This effacement leads to a disruption of the slit diaphragm, the physical barrier that functions as a filter, followed by podocyte detachment or death.3Greka A. Human genetics of nephrotic syndrome and the quest for precision medicine.Curr Opin Nephrol Hypertens. 2016; 25: 138-143Google Scholar,4Kriz W. Shirato I. Nagata M. LeHir M. Lemley K.V. The podocyte's response to stress: the enigma of foot process effacement.Am J Physiol Ren Physiol. 2013; 304: F333-F347Google Scholar On the other hand, in addition to intact podocytes, the formation and maintenance of the glomerular filtration barrier require intraglomerular communication, tightly controlled by a series of autocrine and paracrine signaling mechanisms. For example, vascular endothelial growth factor A (VEGFA) is a prosurvival signal for endothelial cells secreted by podocytes, and platelet-derived growth factor B (PDGFB) is a prosurvival signal for mesangial cells secreted by endothelial cells.5Eremina V. Cui S. Gerber H. Ferrara N. Haigh J. Nagy A. Ema M. Rossant J. Jothy S. Miner J.H. Quaggin S.E. Vascular endothelial growth factor a signaling in the podocyte-endothelial compartment is required for mesangial cell migration and survival.J Am Soc Nephrol. 2006; 17: 724-735Google Scholar Disruption of these cell-cell interactions are frequently observed in a host of glomerular diseases, including FSGS and DKD.6Dimke H. Maezawa Y. Quaggin S.E. Crosstalk in glomerular injury and repair.Curr Opin Nephrol Hypertens. 2015; 24: 231-238Google Scholar Therefore, identifying the earliest disruptive changes to the glomerulus may offer novel targets and the opportunity to optimize therapeutic success for the treatment of kidney diseases. Single-cell RNA sequencing (scRNAseq) has revolutionized the ability to study individual cell types of complex tissues and cell states after specific perturbations.7Kolodziejczyk A.A. Kim J.K. Svensson V. Marioni J.C. Teichmann S.A. The technology and biology of single-cell RNA sequencing.Mol Cell. 2015; 58: 610-620Google Scholar,8Stuart T. Satija R. Integrative single-cell analysis.Nat Rev Genet. 2019; 20: 257-272Google Scholar Recent scRNAseq studies in kidney have provided insight into the transcriptional profiles of kidney cell types in healthy mice and human samples as well as in some disease states, including DKD and lupus nephritis.9Park J. Shrestha R. Qiu C. Kondo A. Huang S. Werth M. Li M. Barasch J. Suszták K. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease.Science. 2018; 360: 758-763Google Scholar, 10Wilson P.C. Wu H. Kirita Y. Uchimura K. Ledru N. Rennke H.G. Welling P.A. Waikar S.S. Humphreys B.D. The single-cell transcriptomic landscape of early human diabetic nephropathy.Proc Natl Acad Sci. 2019; 116: 19619-19625Google Scholar, 11Young M.D. Mitchell T.J. Vieira Braga F.A. Tran M.G.B. Stewart B.J. Ferdinand J.R. et al.Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors.Science. 2018; 361: 594-599Google Scholar, 12Clark A.R. Greka A. The power of one: advances in single-cell genomics in the kidney.Nat Rev Nephrol. 2020; 16: 73-74Google Scholar However, detailed studies of glomerular cell states have been limited by the small number of cells available for analysis. Furthermore, the earliest cell type–specific changes that occur in all glomerular cells on podocyte injury have not been monitored and are yet to be defined. Podocyte ablation via podocyte-specific inducible deletion of an essential endogenous protein, CTCF, leads to progressive proteinuric kidney disease and CKD.13Christov M. Clark A.R. Corbin B. Hakroush S. Rhee E.P. Saito H. Brooks D. Hesse E. Bouxsein M. Galjart N. Jung J.Y. Mundel P. Jüppner H. Weins A. Greka A. Inducible podocyte-specific deletion of CTCF drives progressive kidney disease and bone abnormalities.JCI Insight. 2018; 3Google Scholar Historically, CKD mouse models were generated by inducing kidney injury by exogenous toxins or surgical interventions, suboptimal systems that fall short of recapitulating sequential mechanistic changes. Deletion of CTCF, an essential endogenous molecule, leads to rapid podocyte loss, severe progressive albuminuria, bone mineral metabolism changes, kidney failure, and premature death.13Christov M. Clark A.R. Corbin B. Hakroush S. Rhee E.P. Saito H. Brooks D. Hesse E. Bouxsein M. Galjart N. Jung J.Y. Mundel P. Jüppner H. Weins A. Greka A. Inducible podocyte-specific deletion of CTCF drives progressive kidney disease and bone abnormalities.JCI Insight. 2018; 3Google Scholar The inducible podocyte-specific CTCF deletion (iCTCFpod−/−) model provides the unique opportunity to study changes in intraglomerular cell-cell interactions as a consequence of induced podocyte injury. Podocyte CTCF expression is undetectable at the earliest timepoint, at 1 week of doxycycline-mediated Cre induction in iCTCFpod−/− mice.13Christov M. Clark A.R. Corbin B. Hakroush S. Rhee E.P. Saito H. Brooks D. Hesse E. Bouxsein M. Galjart N. Jung J.Y. Mundel P. Jüppner H. Weins A. Greka A. Inducible podocyte-specific deletion of CTCF drives progressive kidney disease and bone abnormalities.JCI Insight. 2018; 3Google Scholar Significant and progressive podocyte loss, as measured by histologic analysis, starts at 2 weeks after Cre induction. In the current study, a detailed analysis was performed at the 1-week time point to identify the earliest changes in intraglomerular cell-cell interactions driven by podocyte injury that ultimately lead to CKD. This study was approved by the Animal Care and Use Committee at Brigham and Women's Hospital, Harvard Medical School. All animal studies were performed in accordance with guidelines established and approved by the Animal Care and Use Committee at Brigham and Women's Hospital, Harvard Medical School. iCrepod-Ctcfwt/fl mice were generated as previously described13Christov M. Clark A.R. Corbin B. Hakroush S. Rhee E.P. Saito H. Brooks D. Hesse E. Bouxsein M. Galjart N. Jung J.Y. Mundel P. Jüppner H. Weins A. Greka A. Inducible podocyte-specific deletion of CTCF drives progressive kidney disease and bone abnormalities.JCI Insight. 2018; 3Google Scholar and inbred to generate iCrepod-Ctcffl/fl (iCTCFfl/fL) and iCrepod-Ctcfwt/wt [wild-type (WT)] mice. Both male and female mice were used in this study. Doxycycline (4 g/L) (Sigma D9891) was continuously administered in drinking water that contained sucrose (50 g/L) (VWR BDH0308) to both iCTCFfl/fL (generates iCTCFpod−/− mice) and WT littermate control mice (6 to 8 weeks of age at the start of doxycycline use) to drive Cre expression specifically in podocytes. iCTCFpod−/− and WT mice were sacrificed after 1 week of doxycycline treatment, and kidneys were quickly dissected and washed with ice-cold Hanks' balanced salt solution (catalog number 14170112, Thermo Scientific, Waltham, MA). After removing the kidney capsules, glomeruli were enriched at 4°C using the sieving technique14Kreisberg J.I. Hoover R.L. Karnovsky M.J. Isolation and characterization of rat glomerular epithelial cells in vitro.Kidney Int. 1978; 14: 21-30Google Scholar with 180-mm, 75-mm and 53-mm sieves. Glomerular-enriched fractions collected from the 53-mm sieve were rinsed with ice-cold 1× Hanks' balanced salt solution and placed on ice. Glomerular-enriched fractions were centrifuged at 350 × g for 5 minutes at room temperature. After removing most of the Hanks' balanced salt solution, 1 mL of liberase TH digestion buffer (catalog number 5401135001, Sigma-Aldrich, St. Louis, MO) that contained 50 U/mL of DNase I (catalog number 90083, Thermo Scientific) was added to the glomerular pellet and incubated at 37°C for 60 minutes on an orbital shaker (56 × g). The suspension was passed through a 27-gauge needle twice after 20 minutes. Digested glomerular fractions were added to 9 mL of RPMI 1640 medium (catalog number 11875119, Thermo Scientific) containing 10% fetal bovine serum (catalog number 16000044, Thermo Scientific) and centrifuged at 500 × g for 5 minutes at room temperature. Then 1 mL of red blood cell lysis buffer (catalog number A1049201, Thermo Scientific) was added to the glomerular pellet and mixed for 1 minute at room temperature. Next 9 mL of RPMI 1640 medium (10% fetal bovine serum) was added, and the suspension was centrifuged at 500 × g for 5 minutes at room temperature. The media was removed from the pellet and 200 mL of Accumax (catalog number 07921, Stem Cell Technologies, Vancouver, BC, Canada) was added and incubated for 20 minutes at 37°C. Then 1.8 mL 1× phosphate-buffered saline (PBS) (catalog number 14190250, Thermo Scientific) plus 0.04% bovine serum albumin (BSA) (catalog number A1933, Sigma-Aldrich) was added, and the suspension was centrifuged at 500 × g for 8 minutes. The digested glomeruli were washed with 750 mL of 1× PBS plus 0.04% BSA and filtered using a 40-m Flowmi Tip Strainer (catalog number BAH136800040, Sigma-Aldrich). Then 1.25 mL 1× PBS plus 0.04% BSA was added, and the suspension was centrifuged at 500 × g for 8 minutes. The pellet was resuspended in a small volume of 1× PBS plus 0.04% BSA. Single cells were processed through the 10× Chromium 3′ Single Cell Platform using the Chromium Single Cell 3′ Library, Gel Bead, and Chip Kits (10× Genomics, Pleasanton, CA), following the manufacturer's protocol. Briefly, 10,000 cells were added to each channel of a chip to be partitioned into Gel Beads in Emulsion in the Chromium instrument, followed by cell lysis and barcoded reverse transcription of RNA in the droplets. Breaking of the emulsion was followed by amplification, fragmentation, and addition of adapter and sample index. Libraries were pooled together and sequenced on Illumina HiSeq. All hybridization chain reaction (HCR) v3 reagents (probes, hairpins, and buffers) were purchased from Molecular Technologies (Pasadena, CA). Thin sections of tissue (10 m) were mounted in 24-well glass bottom plates (catalog number 82050-898, VWR, Radnor, PA) coated with a 1:50 dilution of (3-aminopropyl)triethoxysilane (catalog number 440140, Sigma-Aldrich). The following solutions were added to the tissue: 10% formalin (catalog number 100503-120, VWR) for 15 minutes, two washes of 1× PBS (catalog number AM9625, Thermo Fisher Scientific), ice-cold 70% EtOH at −20°C for 2 hours (to overnight), three washes of 5× saline sodium citrate with 0.2% Tween-20 (SSCT) (catalog number 15557044, Thermo Fisher Scientific), hybridization buffer (Molecular Technologies) for 10 minutes, probes in hybridization buffer overnight, four washes of wash buffer (Molecular Technologies) for 15 minutes, three washes of 5× SSCT, amplification buffer (Molecular Technologies) for 10 minutes, and three washes of 15 minutes with 5× SSCT (1:10,000 DAPI, catalog number TCA2412-5MG, VWR) in the second wash. Hairpins were heat denatured in amplification buffer overnight. Samples were stored and imaged in 5× SSCT. Imaging was performed on a spinning disk confocal (Yokogawa W1 on Nikon Eclipse Ti) operating NIS-elements AR software. Image analysis and processing was performed on ImageJ Fiji software version 2.1.0/1.53c (NIH, Bethesda, MD; http://imagej.nih.gov/ij). A Cellranger toolkit (version 2.1.1) was used to perform demultiplexing using the cellranger mkfastq command and the cellranger count command for alignment to the mouse transcriptome, cell barcode partitioning, collapsing unique molecular identifier (UMI) to transcripts, and gene-level quantification. Cells were filtered to include cells expressing a minimum of 500 genes and a maximum of 4000 genes. Furthermore, the percentage of reads mapping to mitochondrial genes was capped at 12%. DoubletFinder was used to identify potential doublets.15McGinnis C.S. Murrow L.M. Gartner Z.J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors.Cell Syst. 2019; 8: 329-337.e4Google Scholar Clusters with >75% of cells classified as high-confidence doublets were removed from further analysis. The remaining cells classified as high-confidence doublets were also removed from further analysis. The analysis involved downsampling the number of cells to account for this difference. For example, if there were fewer WT cells in a given cell type, a random sample of iCTCF cells equal to the number of WT cells was used for the differential expression analysis. The default settings in the Seurat R package16Stuart T. Butler A. Hoffman P. Hafemeister C. Papalexi E. Mauck 3rd, W.M. Hao Y. Stoeckius M. Smibert P. Satija R. Comprehensive integration of single-cell data.Cell. 2019; 177: 1888-1902.e21Google Scholar version 3.0 were used for normalization (NormalizeData) of the gene expression counts, identifying variable genes (FindVariableGenes), finding integration anchors (FindIntegrationAnchors), and integrating the samples (IntegrateData). Unwanted variation occurred because of the number of UMIs and ratio of reads mapping to mitochondrial genes (ScaleData). Dimensionality reduction was performed using principal component analyses (RunPCA) on the highly variable genes. The PCElbowPlot() function was used to distinguish principal components for further analysis. For clustering all cells, the first 40 principal components sufficiently captured all of the variance. Molecularly distinct clusters were identified using the default parameters (FindClusters) and a resolution of 0.4 (FindNeighbors). The data were processed and scaled as described above after subsetting glomerular cells. A total of 30 principal components were used for downstream clustering at a resolution of 0.3 (FindNeighbors). Cluster-enriched or marker genes were computed using the Wilcoxon rank sum test (FindAllMarkers) for differential expression of genes in the cluster cells versus all other cells and selecting those genes that pass the adjusted P value (false discovery rate) cutoff of 0.05 as cluster representative. Cluster identity was assigned by comparing data-driven genes with a list of literature-curated genes for mature kidney cell types. Pairwise differential expression analysis in Seurat (FindMarkers) was used with the log fold change threshold set to 0.01 and default parameters to analyze differential expression between iCTCFpod−/− and WT cells in a specific cluster. Within FindMarkers, ident.1 was set to cluster-specific iCTCFpod−/− cells, and ident.2 was set to the corresponding cluster-specific WT cells. Genes with an adjusted P < 0.05 were considered significant. Overrepresentation analysis was used to determine whether any known biological functions or processes were overrepresented or enriched in the list(s) of differentially expressed genes.17Boyle E.I. Weng S. Gollub J. Jin H. Botstein D. Cherry J.M. Sherlock G. GO::TermFinder--open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes.Bioinformatics. 2004; 20: 3710-3715Google Scholar The R package clusterProfiler,18Yu G. Wang L.-G. Han Y. He Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS. 2012; 16: 284-287Google Scholar specifically the enrichGO function, was used for the overrepresentation analysis. Default parameters were used, with the exceptions of ont = BP, pvaluecutoff = 0.25, and qvaluecutoff = 0.25. The R package enrichplot (GitHub, https://github.com/YuLab-SMU/enrichplot, last accessed August 30, 2021) was then used, specifically the emapplot function, to visualize the enrichment results by plotting the top enrichment terms. iTALK19Wang Y. Wang R. Zhang S. Song S. Jiang C. Han G. Wang M. Ajani J. Futreal A. Wang L. iTALK: an R package to characterize and illustrate intercellular communication.bioRxiv. 2019; : 507871Google Scholar was used to map receptors to ligands that were differentially expressed in iCTCFpod−/− podocytes. Specifically, rawParse was used to calculate the mean expression of each gene, using scaled data from Seurat. FindLR using datatype = meancount was used to identify ligand and receptor pairs. Ligand and receptor pairs of interest were plotted using default parameters of LRPlot. The thickness of the lines indicates the relative mean expression of the ligand, and the size of the arrowhead indicates the relative mean expression of the receptor. The R package NicheNet20Browaeys R. Saelens W. Saeys Y. NicheNet: modeling intercellular communication by linking ligands to target genes.Nat Methods. 2019; 17: 159-162Google Scholar was used to predict ligand-receptor interactions that might drive gene expression changes in the cell type of interest. All podocyte clusters and all endothelial clusters were combined for this analysis. All default parameters were used with the exception of setting a lower cutoff threshold of 0.11 for prepare_ligand_target_visualization. Images were processed using ImageJ2 software (NIH, Bethesda, MD; http://imagej.nih.gov/ij). For ImageJ files, version 2.1.0/1.53c was used (http://imagej.net/Contributors (last accessed August 2, 2020). Raw ND2 files were background subtracted using the Rolling Ball method (rolling = 50 sliding stack). Mean intensities of the Z-stack images were then projected, and image channels were split and saved separately. CellProfiler version 3.1.5 (Broad Institute of MIT and Harvard, Cambridge, MA)21Carpenter A.E. Jones T.R. Lamprecht M.R. Clarke C. Kang I.H. Friman O. Guertin D.A. Chang J.H. Lindquist R.A. Moffat J. Golland P. Sabatini D.M. CellProfiler: image analysis software for identifying and quantifying cell phenotypes.Genome Biol. 2006; 7: R100Google Scholar was used for cell segmentation based on the fluorescence intensity of DAPI channel and for measuring integrated fluorescence intensity in the rest of the channels. Four WT and four iCTCFpod−/− mice were evaluated for statistical analysis. All glomeruli from three 40× images were quantified. A Welch-corrected two-tailed t-test was performed. Current single-cell protocols for whole kidney identify <2.5% glomerular cells,9Park J. Shrestha R. Qiu C. Kondo A. Huang S. Werth M. Li M. Barasch J. Suszták K. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease.Science. 2018; 360: 758-763Google Scholar and although a purified glomerular preparation using magnetic beads enriches for this population,22Karaiskos N. Rahmatollahi M. Boltengagen A. Liu H. Hoehne M. Rinschen M. Schermer B. Benzing T. Rajewsky N. Kocks C. Kann M. Müller R.-U. A single-cell transcriptome atlas of the mouse glomerulus.J Am Soc Nephrol. 2018; 29: 2060-2068Google Scholar it fails to capture other cell types of the kidney that may be of interest. A sieving method was used to simultaneously enrich glomeruli and capture additional kidney cell types to extend these findings and develop a detailed understanding of cell-cell interactions within the glomerulus in the context of the entire cellular landscape of the kidney, before and after podocyte injury. To identify the early transcriptional effects of podocyte ablation in a cell type–specific manner, scRNAseq was performed with kidney tissue from WT and iCTCFpod−/− mice collected by serial sieving after 1 week of doxycycline treatment (Figure 1A). Kidney tissue from four WT animals (four biological replicates) and four iCTCFpod−/− animals yielded a total of 14,783 WT and 14,727 iCTCFpod−/− cells profiled after filtering (Supplemental Figure S1, A and B; Supplemental Tables S1 and S2). Data were normalized to remove effects due to the number of UMIs and percentage of mitochondrial reads. After integration of WT and iCTCFpod−/− samples, a low resolution of clustering was used to detect nine clusters (Figure 1B). Biological replicates of WT and iCTCFpod−/− samples were distributed among all clusters (Supplemental Figure S2, A-C). All cell types of the glomerulus, as well as additional kidney cell types, were identified using established and data-derived markers (Figure 1C; Supplemental Table S3; Supplemental Figure S3A). Glomerular cells, representing a total 82.1% of all recovered cells [36.9% podocytes, 42.1% glomerular endothelial cells (GECs), 2.7% mesangial cells, and 0.36% PECs] were isolated and reclustered (Figure 1D). Four clusters of podocytes, five clusters of GECs, and one cluster each of mesangial cells and PECs, expressing canonical cell-type markers, were identified (Figure 1E; Supplemental Figure S3B). Biological replicates of WT and iCTCFpod−/− samples were distributed throughout all clusters (Supplemental Figure S2, D-F; Supplemental Table S4). As anticipated, given that podocyte-specific CTCF deletion leads to histologically detectable podocyte loss at 2 weeks,13Christov M. Clark A.R. Corbin B. Hakroush S. Rhee E.P. Saito H. Brooks D. Hesse E. Bouxsein M. Galjart N. Jung J.Y. Mundel P. Jüppner H. Weins A. Greka A. Inducible podocyte-specific deletion of CTCF drives progressive kidney disease and bone abnormalities.JCI Insight. 2018; 3Google Scholar iCTCFpod−/− samples contained a lower percentage of podocytes than WT samples (Figure 1F; Supplemental Table S5). iCTCFpod−/− samples had 13.1% fewer podocytes, 11.9% more GECs, and 1.5% more mesangial cells than WT samples (Supplemental Table S5). PECs contributed <1% to either of the iCTCFpod−/− or WT samples. This finding suggests that disruption of transcriptional programs critical for podocyte survival precedes histologically detectable podocyte injury and loss and highlights the power of scRNAseq in discerning subtle changes that can be missed by histologic analysis. Differential expression analysis was performed in each of 11 clusters comparing iCTCFpod−/− and WT cells (Supplemental Table S6). Genes were considered differentially expressed if found in at least 10% of cells in a given cluster, with a minimum absolute log fold change of 0.1 and an adjusted P < 0.05. Ctcf was differentially expressed in each of the four podocyte clusters, with mean log fold changes of −0.390, −0.189, −0.331, and −0.303, respectively. The numbers of cells in each cluster were downsampled and the differential expression analysis repeated to compare the number of differentially expressed genes among the 11 clusters. The podocyte clusters had the most differentially expressed genes, followed by GEC-1 and GEC-2 (Figure 1G). The remaining three clusters of GECs, along with the mesangial cells and PECs, had far fewer differentially expressed genes (Figure 1G), suggesting that GEC-1 and GEC-2, among all glomerular clusters, were most affected by the sequelae of CTCF deletion–driven podocyte injury. This study first sought to examine how the individual podocyte clusters respond to injury. A Venn diagram of the differentially expressed genes in each of the four podocyte clusters revealed that podocyte 1 had the most uniquely differentially expressed genes of the four podocyte clusters (Figure 2A; Supplemental Table S7). The observation that one of four podocyte clusters was more prominently affected is in agreement with prior histologic data indicating that not all podocytes are affected with equal severity in the face of injury and highlights the power of scRNAseq to molecularly characterize the heterogeneity of cell states within the same cell type.23Greka A. Mundel P. Cell biology and pathology of podocytes.Annu Rev Physiol. 2012; 74: 299-323Google Scholar An overrepresentation analysis was performed to identify gene programs enriched as a consequence of CTCF loss in the podocyte 1 cluster (Supplemental Table S8). The top enriched terms were visualized with an enrichment map to cluster mutually overlapping gene sets (Figure 2B). A prominent group of enriched terms was mitochondrial functions, including ATP synthesis, mitochondrial organization, electron transport chain, and oxidative phosphorylation (Figure 2B). These data extend recent work pointing to mitochondrial dysfunction as a sign of podocyte injury.24Brinkkoetter P.T. Bork T. Salou S. Liang W. Mizi A. Özel C. Koehler S. Hagmann H.H. Ising C. Kuczkowski A. Schnyder S. Abed A. Schermer B. Benzing T. Kretz O. Puelles V.G. Lagies S. Schlimpert M. Kammerer B. Handschin C. Schell C. Huber T.B. Anaerobic glycolysis maintains the glomerular filtration barrier independent of mitochondrial metabolism and dynamics.Cell Rep. 2019; 27: 1551-1566.e5Google Scholar In addition, human genetics have pointed to the importance of mitochondrial functions in podocytes, including several mutations in the CoQ biosynthesis pathway (PDSS1, PDSS2, COQ2, COQ6, and ADCK4) that cause nephrotic syndrome, mainly in children.25Emma F. Bertini E. Salviati L. Montini G. Renal involvement in mitochondrial cytopathies.Pediatr Nephrol. 2012; 27: 539-550Google Scholar,26Akchurin O. Reidy K.J. Genetic causes of proteinuria and nephrotic syndrome: impact on podocyte pathobiology.Pediatr Nephrol. 2015; 30: 221-233Google Scholar It has therefore been postulated that podocyte mitochondrial dysfunction may represent a prominent cell state associated with all diseases that stem from podocyte loss.24Brinkkoetter P.T. Bork T. Salou S. Liang W. Mizi A. Özel C. Koehler S. Hagmann H.H. Ising C. Kuczkowski A. Schnyder S. Abed A. Schermer B. Benzing T. Kretz O. Puelles V.G. Lagies S. Schlimpert M. Kammerer B. Handschin C. Schell C. Huber T.B. Anaerobic glycolysis maintains the glomerular filtration barrier independent of mitochondrial metabolism and dynamics.Cell Rep. 2019; 27: 1551-1566.e5Google Scholar The data provide support for this notion at single-cell resolution, suggesting that mitochondrial dysfunction may represent the earliest injury
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