Introduction
Acute kidney injury (AKI) afflicts 1.2 million hospitalized patients annually in the US; 20–50% of AKI survivors progress to chronic kidney disease (CKD), increasing their risk for dialysis-dependency, cardiovascular events, and mortality (Chawla et al., 2014; Lewington et al., 2013; Strausser et al., 2018). Other than general supportive care, there are no targeted therapies to treat AKI or to prevent AKI to CKD transition. A better understanding of the molecular events underpinning the AKI to CKD transition is needed to develop therapeutic strategies to interrupt this devastating disease process.
Clinical and preclinical studies have identified damage to proximal tubular (PT) epithelial cells after severe AKI as a critical mechanism driving transition to CKD (Strausser et al., 2018; Chawla et al., 2011; Liu et al., 2017; Cippà et al., 2018; Ferenbach and Bonventre, 2015; Humphreys, 2018). PT cells are most severely affected by acute ischemic and toxic injuries due to their high metabolic and energy-intensive transporter activities required to maintain normal homeostasis of body fluids (Ferenbach and Bonventre, 2015; Humphreys, 2018; Gewin, 2018). In the renal repair process, damaged PT cells adopt heterogeneous molecular states (Kirita et al., 2020). They reactivate genes normally active during renal development (Rudman-Melnick et al., 2020; Kang et al., 2016; Kumar et al., 2015), alter their dependency on metabolic fuels (Legouis et al., 2020), change their morphology, and proliferate to replenish the areas of denuded epithelium in the proximal tubule (Ferenbach and Bonventre, 2015; Witzgall et al., 1994). When the initial damage to kidneys is mild, PT cells subsequently return to their original state by redifferentiation, with resolution of inflammation and fibrosis (Ferenbach and Bonventre, 2015; Kirita et al., 2020; Rudman-Melnick et al., 2020; Legouis et al., 2020; Witzgall et al., 1994; Berger et al., 2014; Kusaba et al., 2014). However, if damage is more extensive, prolonged, or recurrent, the damaged cells fail to redifferentiate, leading to persistent inflammation, fibrosis, and eventual cell death. The molecular pathways that govern proximal tubular heterogeneity and cell fate during failed renal repair after severe injury are poorly understood. This knowledge gap prevents the development of therapies based on underlying disease mechanisms.
One of the critical pathways involved in AKI pathogenesis and proximal tubular cell death is ferroptosis, a distinct non-apoptotic form of regulated cell death (Stockwell et al., 2017; Dixon et al., 2012; Yang et al., 2014; Kagan et al., 2017; Doll et al., 2017; Alim et al., 2019; Zhao et al., 2020; Linkermann et al., 2014). An imbalance between the generation of lipid peroxides and their detoxification induces overwhelming accumulation of lipid peroxides (ferroptotic stress), triggering ferroptosis (Stockwell et al., 2017; Alim et al., 2019). The glutathione/glutathione peroxidase 4 (GPX4) axis is the central defense pathway to prevent ferroptotic stress and ferroptosis (Stockwell et al., 2017; Dixon et al., 2012; Yang et al., 2014; Friedmann Angeli et al., 2014). Global genetic deletion of
Here, using complementary single-cell transcriptomic and mouse genetic approaches, we identify the role of a molecularly distinct, damage-associated, PT cell state that is dynamically and differentially regulated during successful versus failed repair. Furthermore, we provide mechanistic evidence that ferroptotic stress in PT cells enhances this damage-associated state, in addition to triggering cell death, thereby promoting failed renal repair and the AKI-to-CKD transition.
Results
Tubular epithelial cells exhibit heterogeneous molecular states after severe injury
To identify cellular mechanisms that promote maladaptive repair after severe kidney injury, we first developed and optimized mouse models for ‘successful’ versus ‘failed’ renal repair after ischemia-reperfusion-induced injury (IRI). This was achieved by extending renal ischemic times from 20 min for successful recovery to 30 min for failed recovery (Figure 1—figure supplement 1 and Figure 1—figure supplement 2). After mild injury, histologic examination showed that inflammation and macrophage accumulation resolved within 21 days (ischemic time 20 min; Figure 1—figure supplement 1, D and E). By contrast, after severe injury there was progressive epithelial damage and fibrosis, and the accumulation of F4/80+ macrophages persisted around the damaged epithelial cells for at least 6 months (ischemic time 30 min; Figure 1—figure supplement 1, D and E; and Figure 1—figure supplement 2E).
We used this failure-to-repair model (unilateral IRI, ischemic time 30 min) to generate a single-cell transcriptome map of failed renal repair (Figure 1A). Kidneys were harvested at 6 hr and 1, 7, and 21 days after IRI. High-quality transcriptome data from a total of 18,258 cells from injured kidneys (IRI) and homeostatic uninjured kidneys (Homeo) were obtained (Figure 1, B and C). Using a Seurat integration algorithm that normalizes data and removes potential batch effects (Stuart et al., 2019; Hafemeister and Satija, 2019), we integrated the transcriptome data from each condition and performed unsupervised clustering analysis of the integrated dataset. Uniform manifold approximation and projection (UMAP) resolved 21 separate clusters, representing distinct cell types (Figure 1B; Figure 1—figure supplement 3B and Figure 1—figure supplement 4A). The cellular identity of each cluster was determined based on known cell-type-specific markers (Park et al., 2018; Ransick et al., 2019). We successfully identified known cell-type-specific damage-induced genes such as
Figure 1.
Single-cell RNA sequencing (scRNA-seq) identifies dynamic cellular state transitions of tubular epithelial cells after severe IRI.
(A) Drop-seq strategy. uIRI, unilateral IRI. A schematic illustration of epithelial cell states is shown. (B) and (C) Integrated single-cell transcriptome map. Unsupervised clustering identified 21 distinct clusters in the UMAP plot. Arrowheads indicate damage-associated tubular epithelial cells. The dotted area (PT cell clusters; PT and DA-PT) was used for the downstream analyses in (D)–(G). (D) UMAP plots showing the expression of indicated genes in PT cell clusters (PT and DA-PT in (B)). Differentiated/mature PT cell markers:
Figure 1—figure supplement 1.
Characterization of severe and mild unilateral IRI models.
(A) Experimental workflow for the mild and severe IRI models. Left kidneys from wild-type C57BL/6J mice were subjected to mild (20 min) and severe (30 min) ischemia. Contralateral kidneys (CLK) were used as controls. uIRI, unilateral IRI. Kidneys were harvested on day 21 post-IRI. (B) Severe IRI results in renal atrophy. Relative size of post-IRI kidney compared to contralateral kidney (CLK) was quantified. N = 4–5. (C) Hematoxylin-eosin staining of IRI kidneys on day 21 (D21). Note that severe-IRI resulted in tubular dilatation, flattening of tubular epithelial cells, cast formation, and inflammatory cell infiltration. (D) Immunofluorescence for KIM1, NGAL, F4/80, and αSMA. Severe IRI resulted in persistent expression of proximal and distal tubular injury markers. Kidney injury molecule 1, KIM1 (encoded by
Figure 1—figure supplement 2.
Severe IRI leads to cystic and atrophic kidneys 6 months after severe IRI.
(A) Experimental workflow for mild and severe IRI models with long-term observation. Kidneys were harvested on 6 months post-IRI. (B) Severe IRI (30 min ischemia) results in marked renal atrophy on 6 months post-IRI. Relative size of post-IRI kidney compared to contralateral kidney (CLK) was quantified. N = 5. Unpaired Student’s t-test. (C) and (D) Hematoxylin-eosin staining of IRI kidneys. Note that severe-IRI resulted in flattened and necrotic tubular epithelial cells with massive infiltration of inflammatory cells, which occupied the renal parenchyma. * indicates clusters of inflammatory cells, occupying renal parenchyma. (E) Immunofluorescence for KIM1, NGAL, F4/80, and αSMA. Severe IRI resulted in persistent expression of proximal and distal tubular injury markers (KIM1 and NGAL, respectively). Note that post-severe IRI kidneys exhibit accumulation of F4/80+ myeloid cells (mostly macrophages) and αSMA+ myofibroblasts in renal interstitium. Small cystic structures (#) were encircled by myofibroblasts. Scale bars: 20 μm in (C), 200 μm in (D) and 50 μm in (E).
Figure 1—figure supplement 3.
scRNA-seq identifies major cell types in homeostatic and post-IRI kidneys.
(A) Pearson correlation plot showing the linear relationship between the number of genes (nGene) and unique molecular identifiers (nUMI). Experimental conditions and cell types are color-coded. (B) Dot plot shows the gene expression patterns of cluster-enriched canonical markers. Note that damage-associated tubular epithelial cells (DA-PT, DA-TAL, and DA-DCT) have reduced expression of canonical cellular markers. (C) Tubular injury marker gene expressions are selectively observed in damaged kidneys (IRI) but not in homeostatic control kidneys. ‘C’ indicates cells from the control homeostatic kidneys. ‘I’ indicates cells from IRI kidneys from all time points. Proximal tubule-specific injury markers (
Figure 1—figure supplement 4.
UMAP plots show the expression pattern of anchor genes in homeostatic and post-IRI kidneys.
(A) UMAP plots show the identified cell clusters (resolution was set as 1.0). (B) UMAP plots show the expression pattern of indicated canonical marker genes (anchor genes) of each cluster. We manually combined three clusters of differentiated/mature proximal tubular cells (PT, S1/S2 and PT, S2/S3) into one cluster (PT) to generate a more coarse-grained cell-type annotation and data visualization in other figures. We also combined three clusters of endothelial cells (Endo-1, Endo-2, and Endo-3) into one cluster (Endo) for data visualization in other figures. S1, S1 segment; S2, S2 segment 2; S3, S3 segment of proximal tubule.
Figure 1—figure supplement 5.
Damage-associated PT cells show an inflammatory transcriptional signature.
(A) UMAP of PT clusters (PT, differentiated proximal tubular cell cluster; DA-PT, damage-associated proximal tubular cell cluster). See Figure 1B. (B) Volcano plot showing a distinct transcriptional profile of damage-associated PT cells (PT cells in DA-PT cluster). A Wilcoxon rank-sum test was used for the statistical analysis comparing cells in PT cluster from IRI kidneys and cells in DA-PT cluster from IRI kidneys. Blue and gray data points indicate transcripts that fall below the set threshold for fold change (Log2 fold change, a threshold value of 0.25) and p value (a cutoff value of
Figure 1—figure supplement 6.
Severe IRI reduces expressions of proximal tubular differentiation markers.
(A) UMAP plots showing the expression of indicated genes. Note that damage-associated PT cell state (cells in DA-PT cluster) are enriched with damage-induced genes (
Figure 1—figure supplement 7.
Comparative analyses of damage-associated PT cells and neonatal proximal tubular cells.
(A) and (B) Characterization of mouse neonatal kidney single-cell RNA-seq data. UMAP plots show mouse neonatal kidney cells from GSM2473317 (4693 cells; post-natal day 1) in (A). Dot plots show the gene expression patterns of cluster-enriched canonical markers in (B). (C) UMAP rendering of Top 100 genes characterizing mature and immature early proximal tubular (PT) cells. We obtained the top 100 genes representing mature PT and immature early PT cells by performing differential gene expression analysis using the ‘FindMarkers’ command in Seurat. As expected, the mature PT Top 100 genes are enriched in mature PT cluster. The early PT Top 100 genes are enriched in immature early PT and nephron progenitor clusters. (D) UMAP rendering of mature and early PT genes on adult PT clusters from our dataset (PT and DA-PT). Note that the early PT Top 100 genes are highly enriched in damage-associated PT (DA-PT) population, indicating they are in a less differentiated state. (E) UMAP plots showing gene expressions of early PT genes (
Figure 1—figure supplement 8.
Trajectory analyses predict lineage hierarchy from differentiated mature PT cells to damage-associated PT cells.
(A) UMAP plots showing mature and damage-associated PT cells (PT and DA-PT clusters) underwent IRI. (B) Pseudotime trajectory analysis using Monocle 3. A region occupied with
Based on the cell clustering and gene expression patterns, we noticed that there are at least three epithelial cell states (homeostatic normal, activated, and dedifferentiated cells) in our dataset (See Figure 1A, right panel). Homeostatic normal cells express high expression of ‘anchor’ genes involved in normal cell function and identity (Figure 1—figure supplement 3, B and C). Most of the tubular epithelial cells from IRI kidneys robustly expressed damage-induced genes (ex.
Among these damage-associated epithelial cell clusters, we found a damage-associated proximal tubular cell state (See DA-PT cluster), which shows reduced homeostatic gene expression (ex.
Among the damage-induced genes expressed in this dedifferentiated inflammatory PT cell state, we focused on the enrichment of
To understand the lineage hierarchy of PT cell states, we analyzed PT cells from differentiated and damage-associated PT cell clusters (PT and DA-PT in Figure 1B) using two algorithm tools (Monocle 3 and Velocyto) that allow the computational prediction of cell differentiation trajectories (Cao et al., 2019; La Manno et al., 2018). By placing each cell from the entire dataset in pseudotime we observed a predicted differentiation trajectory originating from PT to DA-PT (Figure 1F and Figure 1—figure supplement 8, A and B). We then performed RNA velocity analysis, which predicts the cell state trajectory based on the ratio between unspliced and spliced mRNA expressions, for these two PT cell states from the post-IRI dataset on day 7. Our RNA velocity analysis showed two trajectories running in opposite directions from the middle of the cluster, a position where genes associated with tubular maturation and damage are both not highly expressed (Figure 1G and Figure 1—figure supplement 8C). One projects toward the area with high levels of damage-induced genes (dedifferentiation path to damage-associated PT cell state) and the other toward the area with high levels of maturation-associated genes (redifferentiation path to differentiated PT cell state). Our computational analyses suggest the potential existence of cellular plasticity at this stage (Day 7 post-IRI; Figure 1G and Figure 1—figure supplement 8C).
Proximal tubular cells dynamically alter their cellular states after acute kidney injury
To determine the temporal dynamics of damage-associated PT cell state in adaptive and maladaptive repair and validate the computational analyses, we performed expression analyses of multiple marker genes for this PT cell state in successful and failed renal repair processes. Quantitative RT-PCR analyses for
Figure 2.
Damage-associated PT cells emerge transiently after mild injury but persist after severe injury.
(A) Experimental workflow for the mild and severe IRI models. Left kidneys from wild-type (WT) C57BL/6J (B6) mice were subjected to mild (20 min) and severe (30 min) ischemia (unilateral IRI, uIRI). Contralateral kidneys (CLK) were used as controls. (B) Real-time PCR analyses of indicated gene expression. Whole kidney lysates were used. N = 4–5. (C) Expression analyses of VCAM1 and
Figure 2—figure supplement 1.
Comparative analyses of mild and severe IRI identify distinct temporal dynamics of damage-associated PT cells.
(A) Experimental workflow. Wild type C57BL/6J mice were used. Mild IRI (20 min ischemia) and severe IRI (30 min ischemia). Kidneys were harvested on day 7, day 21 and 6 months post-IRI. (B) Immunostaining for SOX9 and VCAM1. Note that double-positive cells (indicative for damage-associated PT cell state) emerge after mild and severe IRI similarly on day 7, but they are differentially regulated subsequently. SOX9+VCAM1+ cells disappear after mild IRI (20 min ischemia) on day 21, but they persist at least for 6 months after severe IRI (30 min ischemia). Scale bars: 50 μm. * indicates cystic lesions. (C) Quantification of double-positive cells over DAPI+ area in (B). N = 3–8. ***p < 0.001, ****p < 0.0001, one-way ANOVA with post hoc multiple comparisons test. n.s., not significant.
To further characterize the dynamic changes and plasticity of proximal tubular cell state, we employed a
Figure 3.
Lineage-tracing identifies the cellular plasticity of damage-associated PT cells.
(A) Schematic of fate-mapping strategy using
Damage-associated PT cells create a proinflammatory milieu with renal myeloid cells
While an initial inflammatory response is critical for tissue repair, uncontrolled persistent inflammation underlies organ fibrosis (Ferenbach and Bonventre, 2015; Humphreys, 2018; Gewin, 2018). We hypothesized that the accumulation of damage-associated PT cells, which show proinflammatory transcriptional signature (Figure 1—figure supplement 5B,D and E), creates an uncontrolled inflammatory milieu by interacting with resident and infiltrating myeloid cells such as macrophages and monocytes (Ide et al., 2020). To determine the intercellular interactions between damage-associated PT cells and myeloid cells, we used NicheNet, a computational algorithm tool that infers ligand-receptor interactions and downstream target genes (Figure 4, A-D), (Browaeys et al., 2020). We applied NicheNet to predict ligand-receptor pairs in which ligands from damage-associated PT cells interact with receptors in monocyte or macrophages (Figure 4, A and C), (Browaeys et al., 2020). Among the top five predicted ligands expressed in damage-associated PT cells, we confirmed the enrichment of
Figure 4.
Damage-associated PT cells create a proinflammatory milieu with myeloid cells.
(A) Schematic model of intercellular communications between damage-associated PT cells and macrophages. NicheNet was used to predict intercellular interactions using our integrated single-cell map of failed renal repair. (B) Predicted ligands from damage-associated PT cells and receptors in macrophages. (C) Schematic model of intercellular communications between damage-associated PT cells and monocytes. (D) Predicted ligands from damage-associated PT cells and receptors in monocytes. (E) UMAP plots showing the expression of indicated genes. Our integrated single-cell map of mouse failed renal repair is shown (See Figure 1, B and C). Arrowheads indicate damage-associated PT cells (DA-PT cluster). Arrows indicate differentiated PT cells (PT cluster). (F) Real-time PCR analyses of indicated gene expression. Post-IRI kidneys on day 21 that underwent mild (20 min) or severe (30 min) ischemia were used. N = 4–5. *p < 0.05; **p < 0.01; ***p < 0.001, one-way ANOVA with post hoc multiple comparisons test.
Damage-associated PT cells exhibit high ferroptotic stress after severe IRI
Next, we investigated the molecular mechanisms that are critical for cells to traverse between differentiated PT cells and damage-associated PT cells. To this end, we analyzed the transcriptional signature of PT cells in the differentiated/mature cluster to identify critical pathways to maintain this cellular state. We found that genes associated with glutathione metabolic processes and anti-oxidative stress response pathways are overrepresented in the differentiated mouse PT cell cluster (Figure 5A; Figure 1—figure supplement 5F and Figure 5—figure supplement 1). We also found that these pathways are enriched in normal differentiated human PT cells (Figure 5A and Figure 5—figure supplement 2, A and B; GSE131882). Mirroring these findings, oxidative stress-induced signaling pathways related to failed renal repair, such as cellular senescence and DNA damage responses (Kishi et al., 2019; Canaud et al., 2019), were highly enriched in damage-associated PT cells (Figure 5—figure supplement 1). Taken together, we propose that glutathione-mediated anti-oxidative stress responses are critical for maintaining the cellular identity of fully differentiated PT cells, and dysregulation of these pathways underlies the failure of damage-associated PT cells to redifferentiate into normal PT cell state.
Figure 5.
Damage-associated PT cells undergo high ferroptotic stress after severe IRI.
(A) UMAP rendering of glutathione metabolic process in mouse and human kidneys. (B) A scheme showing glutathione-glutathione peroxidase 4 (GPX4) anti-ferroptotic defense pathway.
Figure 5—figure supplement 1.
Gene ontology analyses identify enrichment of anti-oxidative stress defense genes in differentiated/mature PT cells.
UMAP rendering of signaling pathways. Upper panels show the pathways enriched in differentiated PT cells (PT cluster). Lower panels show the pathways enriched in damage-associated PT cells (DA-PT cluster). Arrows indicate differentiated PT cell cluster (PT). Arrowheads indicate DA-PT cell cluster.
Figure 5—figure supplement 2.
Characterization of human normal kidney single-nucleus RNA-seq data.
(A) UMAP plots showing human normal kidney cells from GSE131882 (7,631 cells). Two normal kidney datasets were integrated and analyzed. (B) Dot plot showing the gene expression patterns of cluster-enriched canonical markers. (C) UMAP rendering of signaling pathways. Note that the signaling pathways for anti-oxidative stress, which are enriched in differentiated PT cells in mouse kidneys, are also enriched in normal differentiated PT cells in humans. Blue arrowheads: PT cells, green arrowheads, VCAM1+ PT cells.
Among the cellular stress pathways related to dysregulation of glutathione metabolism, ferroptotic stress and ferroptosis have been implicated in failed repair of human AKI and pathogenesis in mouse models of AKI, (Figure 5B), (Stockwell et al., 2017; Dixon et al., 2012; Yang et al., 2014; Linkermann et al., 2014; Wenzel et al., 2017; Müller et al., 2017). To investigate whether ferroptotic stress underlies the emergence and accumulation of damage-associated PT cells in addition to its known role in inducing cell death during maladaptive repair, we first tested the expression of the canonical anti-ferroptosis defense pathway, glutathione/GPX4 axis (Figure 5B). In agreement with the underrepresentation of glutathione metabolic process in damage-associated PT cells, the genes encoding the glutathione/GPX4 defense pathway were markedly downregulated in this PT cell state (DA-PT) compared to differentiated PT cells (PT), suggesting that damage-associated PT cells are potentially vulnerable to ferroptotic stress (Figure 5C).
We then analyzed the expression of ferroptotic stress biomarkers such as malondialdehyde (MDA, a lipid peroxidation product) and acyl-CoA synthetase long-chain family member 4 (ACSL4), which also regulate cellular sensitivity to ferroptosis (Kagan et al., 2017; Doll et al., 2017; Müller et al., 2017; Kenny et al., 2019; Yuan et al., 2016; Li et al., 2019). A recent pharmacological inhibitor study showed that ACSL4 is a reliable maker for ferroptotic stress in murine model of ischemic AKI (Zhao et al., 2020). We identified significant upregulation of
To address whether the emergence of damage-associated PT cells is specific to IRI injury or appears in other cases of acute kidney injury, we investigated the co-expression of SOX9 and VCAM1 in models of toxic renal injury (aristolochic acid nephropathy, AAN) and obstructive renal injury (unilateral ureteral obstruction, UUO), which lead to severe fibrosis. By immunofluorescence analyses of SOX9 and VCAM1 co-expression, we found the emergence of damage-associated PT cells in both models (Figure 6, A and C). Furthermore, the SOX9-positive tubular epithelial cells in these models showed co-expression of ACSL4, suggesting that ferroptotic stress of damage-associated PT cells is a conserved response to kidney injury across various etiologies (Figure 6, B and C).
Figure 6.
Damage-associated PT cells emerge after injury in mouse and human kidneys.
(A) Immunostaining for SOX9 and VCAM1. Aristolochic acid nephropathy (AAN) and unilateral ureteral obstruction (UUO) models were used. Kidneys from wild-type C57BL/6J mice were harvested on day 26 (D26) for AAN and day 10 (D10) for UUO. Insets: individual fluorescence channels of the dotted box area. (B) Immunostaining for SOX9 and ACSL4. bIRI, bilateral IRI model. Kidneys were harvested on day 3 (D3) for bIRI, day 5 (D5) for AAN, and day 10 (D10) for UUO. Insets: individual fluorescence channels of the dotted box area. (C) Quantification of double-positive cells in total SOX9+ cells from panel (A) and (B). Scale bars, 20 μm. N = 3–4. **p < 0.01; ***p < 0.001; ****p < 0.0001, one-way ANOVA with post hoc multiple comparisons test. (D) UMAP of the human proximal tubular cells from AKI kidneys. (E) Dot plots showing the expression of indicated genes. Note that PT cells in state 3 (DA-PT-like) show increased gene expressions of markers for mouse damage-associated PT cells (
Figure 6—figure supplement 1.
Characterization of human AKI kidney single-cell RNA-seq data.
(A) UMAP plots showing human AKI kidney cells from GSE145927 (43,998 cells). Data from the two non-rejecting AKI tissues were analyzed and integrated. We manually combined two clusters of differentiated proximal tubular cells (PT, state one and PT, state 2) into one cluster (PT) to generate a more coarse-grained cell-type annotation and data visualization. (B) Dot plot showing the gene expression patterns of cluster-enriched canonical markers. Note that DA-PT-like cells (PT, state 3) exhibit reduced expression of homeostatic genes (ex.
We then investigated whether molecularly similar damage-associated PT cells can be observed in human AKI. We analyzed scRNA-seq data from biopsy samples of two transplanted human kidneys with evidence of AKI and acute tubular injury but no evidence of rejection (GSE145927; Figure 6D and Figure 6—figure supplement 1A), (Malone et al., 2020). We found a cell population that is enriched for genes expressed in mouse damage-associated PT cells, including
Genetic induction of ferroptotic stress results in accumulation of inflammatory PT cells after mild injury
Our data suggest that severe injury, which induces more oxidative and ferroptotic stress than mild injury, causes the accumulation of inflammatory damage-associated PT cells and worsens long-term renal outcomes. We hypothesized that ferroptotic stress plays a crucial role in driving the accumulation of inflammatory PT cells and promoting maladaptive repair in addition to triggering cell death (ferroptosis). To test this hypothesis, we generated a mouse model that selectively and conditionally deletes
Figure 7.
Genetic induction of ferroptotic stress to
(A) Experimental workflow for
Figure 7—figure supplement 1.
Genetic deletion of
(A) Experimental workflow for genetic deletion of
Figure 7—figure supplement 2.
Genetic deletion of
(A) Experimental workflow for genetic deletion of
Figure 7—figure supplement 3.
Genetic deletion of
(A) and (B) Immunostaining for KIM1, KRT8, F4/80, and αSMA. Post-IRI kidneys on day 21 were analyzed. Note that
Figure 7—figure supplement 4.
Genetic deletion of
(A) Experimental workflow for genetic deletion of
The post-ischemic cKO kidneys were atrophic and showed severe tubular injury on histological evaluation on day 21 and exhibited marked accumulation of KIM1+KRT8+ injured tubular cells (Figure 7, B-D and Figure 7—figure supplement 2). By contrast, control littermate kidneys that underwent the same ischemic stress exhibited resolution of histological changes and fewer KIM1+KRT8+ cells (Figure 7, C and D, and Figure 7—figure supplement 2). Contralateral kidneys from both genotypes showed neither increased KIM1 nor KRT8 expression (Figure 7—figure supplement 3, A and C). The post-ischemic cKO kidneys also exhibited massive accumulation of F4/80+ macrophages, αSMA+ myofibroblasts, and increased collagen synthesis (Figure 7, E-F; Figure 7—figure supplement 3, B and C). Then, we assessed the number of cell death by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay, which detects ferroptotic cell death in
We then investigated if the number of damage-associated PT cells was increased in the
Figure 8.
Genetic induction of ferroptotic stress induces the accumulation of damage-associated PT cells after mild injury.
(A) Schematic representation of experimental workflow. tdTomato-lineage tracing was employed to detect
Pharmacological inhibition of ferroptotic stress prevents the accumulation of inflammatory PT cells and ferroptosis after ischemia-reperfusion injury
We next investigated whether pharmacological inhibition of ferroptosis blunts the dynamic changes seen in proximal tubular cells. We administered liproxstatin-1, an in vivo active ferroptosis inhibitor that scavenges lipid peroxides (Friedmann Angeli et al., 2014), to our cKO mice that underwent mild renal ischemia (Figure 9A). The same volume of vehicle solution (1% dimethyl sulfoxide in phosphate-buffered saline) was administered to cKO and littermate controls (
Figure 9.
Pharmacological inhibition of ferroptotic stress blunts the accumulation of damage-associated PT cells and cell death.
(A) Schematic representation of experimental workflow. All mice (cKO and control littermates) were subjected to the same ischemic stress (ischemic time, 22 min, unilateral IRI) and tamoxifen treatment. The same volume of vehicle was administered to the control groups (control vehicle and cKO vehicle). Kidneys were harvested on day 21 post-IRI. (B) Liproxstatin-1 prevents renal atrophy. Relative size of post-IRI kidneys compared to contralateral kidneys (CLK) was quantified. Control, littermate control. N = 4–5. (C and D) Immunostaining for KIM1 and KRT8. IRI kidneys from cKO are shown. Quantification of immunostained area over the DAPI+ area is shown in (D). N = 4–5. (E and F) Immunostaining for SOX9 and VCAM1. Quantification of VCAM1+EMCN–F4/80– area over the DAPI+ area is shown in (F). Arrowheads indicate damage-associated PT cells. (G) Real-time PCR analyses of indicated gene expression. Whole kidney lysates were used. N = 4–5. (H) and (I) TUNEL staining for evaluating cell death. Quantification of TUNEL-positive nuclei is shown in (I). N = 4–5. Red arrowheads indicate TUNEL+ tubular epithelial cells. Scale bars, 50 μm in (C) and (E); and 20 μm in (H). *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001, unpaired t-test for (D), (F). and (I); One-way ANOVA with post hoc multiple comparisons test for (G). (J) Liproxstatin-1 improves renal repair after IRI.
Figure 9—figure supplement 1.
Liproxstatin-1 potently reduced ferroptotic stress in the absence of GPX4.
(A) Experimental workflow for liproxstatin-1 (Lip-1) treatment to
Figure 9—figure supplement 2.
Liproxstatin-1 potently mitigated ferroptotic stress-induced pathologic changes in the absence of GPX4.
(A) Experimental workflow for liproxstatin-1 (Lip-1) treatment to
Discussion
By using complementary scRNA-seq and mouse genetic approaches in several experimental models of renal injury and repair, our study revealed novel mechanisms regulating proximal tubular cell states that underlie renal repair and regeneration. By detailed characterization of damage-associated PT cells in our single-cell map of failed repair, we identified that this PT state significantly downregulates the canonical anti-ferroptosis defense pathway, making them potentially vulnerable to ferroptotic stress. Genetic induction of ferroptotic stress after mild injury was sufficient to prevent the redifferentiation of damage-associated PT cells into the normal PT cell state, leading to the accumulation and persistence of inflammatory PT cells that promote maladaptive repair. Our data collectively advances our understanding of the ferroptotic cell death pathway by identifying a novel role of ferroptotic stress in promoting and accumulating pathologic cellular state beyond its known role to trigger non-apoptotic regulated cell death (ferroptosis). GPX4 is a key coordinator of proximal tubular cell fate for renal repair and regeneration by preventing both cell death and cell death-independent pathologic changes after IRI.
Unbiased clustering of cells clearly separates damage-associated PT cells from homeostatic and activated differentiated PT cells, indicating that damage-associated PT cells represent a unique cellular status. We also found a molecularly similar PT cell state in kidneys of patients with acute kidney injury. Similar to our current findings, we and others have identified the emergence of molecularly distinct epithelial cells during the process of lung injury and repair (Kobayashi et al., 2020; Choi et al., 2020; Strunz et al., 2020). These novel transient cells are termed as pre-alveolar type-1 transitional cell state (PATS), alveolar differentiation intermediate, and damage-associated transient progenitors. They originate from alveolar type two epithelial cells and differentiate into type one alveolar epithelial cells (Kobayashi et al., 2020; Choi et al., 2020; Strunz et al., 2020). PATS and PATS-like cells in humans accumulate during failed lung repair and fibrosis (Kobayashi et al., 2020), as in the case of maladaptive repair of kidneys. Molecular mechanisms underlying the accumulation of these transitional cell state include hypoxia, inflammation, and DNA damage. All these pathways promote maladaptive renal repair by altering PT cell states (Strausser et al., 2018; Liu et al., 2017; Ferenbach and Bonventre, 2015; Kishi et al., 2019). These data suggest that the emergence of molecularly distinct epithelial cell states and their persistence/accumulation is a general mechanism of maladaptive repair in multiple organs across mice and humans.
The complexity of proximal tubular cell states in renal injury and repair processes has been recently identified at single-cell resolution (Kirita et al., 2020; Rudman-Melnick et al., 2020). A recent study investigated PT cellular heterogeneity using single-nucleus RNA sequencing in a mouse model of bilateral renal IRI. The paper revealed multiple novel PT cellular states, ranging from severely injured cells, cells repairing from injury, and cells undergoing failed repair (Kirita et al., 2020). Interestingly, the damage-associated PT cells reported here shares some of the transcriptional signatures with so-called failed repair proximal tubular cells (FR-PTC), such as
Another study profiled juvenile (4-week-old) mouse kidneys that underwent 30 min unilateral IRI (Rudman-Melnick et al., 2020). Unlike adult kidneys, the kidneys at this stage showed marked regenerative ability and showed successful repair. The study found transient induction of nephrogenic transcriptional signature (ex.
It has been largely believed that ferroptotic stress reduces functional renal epithelial cells by intercellular propagation of ferroptotic cell death (synchronized cell death) and induces so-called necroinflammation (Linkermann et al., 2014; Friedmann Angeli et al., 2014; Li et al., 2019; Strunz et al., 2020). Consistent with this notion, we observed the accumulation of TUNEL+ tubular epithelial cells in cKO kidneys. In addition to inducing ferroptosis in some tubular epithelial cells, to our surprise, our genetic knockout studies showed that excess ferroptotic stress in regenerating PT cells drives the accumulation, but not reduction, of damage-associated PT cells that augment renal inflammation. Specific gene-expression signatures indicate that damage-associated PT cells are not merely severely injured cells on the pathway to cell death but a unique functional cell state. The cells are enriched for expression of renal developmental genes such as
In summary, our study broadens the roles of ferroptotic stress from one that is restricted to the induction of regulated cell death (ferroptosis) to include the promotion and accumulation of a pathologic cell state, processes that underlie maladaptive repair. Understanding the molecular mechanisms by which ferroptotic stress controls these processes in vivo would open a new avenue for currently available and prospective anti-ferroptotic reagents to enhance tissue repair/regeneration in multiple organs. Our studies provide a scientific foundation for future mechanistic and translational studies to enhance renal repair and regeneration by modulating anti-ferroptotic stress pathways to prevent AKI to CKD transition in patients.
Materials and methods
Key resources table
| Reagent type | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Genetic reagent ( | C57BL/6J | The Jackson laboratory | RRID:IMSR_JAX:020940 | |
| Genetic reagent ( | The Jackson laboratory | RRID:MGI:4947114 | ||
| Genetic reagent ( | The Jackson laboratory | RRID:IMSR_JAX:007914 | ||
| Genetic reagent ( | The Jackson laboratory | RRID:IMSR_JAX: 027964 | ||
| Antibody | Anti-SOX9 (Rabbit monoclonal) | Abcam | RRID:AB_2665383 | Clone EPR14335 |
| Antibody | Anti-SOX9 (Rabbit monoclonal) | Abcam | RRID:AB_2728660 | Clone EPR14335-78 |
| Antibody | Anti-KIM1 | R and D systems | RRID:AB_2116446 | IF: 1:400 |
| Antibody | Anti-NGAL | Abcam | RRID:AB_2136473 | IF: 1:400 |
| Antibody | Anti-GPX4 (Rabbit monoclonal) | Abcam | RRID:AB_10973901 | Clone EPNCIR144 |
| Antibody | Anti-F4/80 (Rat monoclonal) | Bio-Rad | RRID:AB_2098196 | Clone C1:A3-1 |
| Antibody | Anti-Endomucin (Rat monoclonal) | Abcam | RRID:AB_10859306 | Clone V.7C7.1 |
| Antibody | Anti-KRT8 | DSHB | RRID:AB_531826 | IF: 1:200 |
| Antibody | Anti-αSMA | Sigma | RRID:AB_476856 | Clone 1A4 |
| Reagent, commercial | LTL | Vector laboratories | RRID:AB_2336558 | IF: 1:200 |
| Antibody | Anti-MDA | Abcam | RRID:AB_305484 | IF: 1:200 |
| Antibody | Anti-ACSL4 | Abcam | RRID:AB_2714020 | Clone: EPR8640 |
| Antibody | Anti-VCAM1 | CST | RRID:AB_2799146 | Clone: D8U5V |
| Commercial assay, kit | RNAScope probe-Mm-Cdh6 | Advance Cell Diagnosis | ||
| Commercial assay, kit | RNAscope Intro Pack 2.5 HD Reagent Kit Brown Mm | Advance Cell Diagnosis | ||
| software, algorithm | ImageJ | NIH, | RRID:SCR_003070 | https://imagej.nih.gov/ij/ |
| Software, algorithm | GraphPad Prism | RRID:SCR_002798 | https://www.graphpad.com/scientific-software/prism/ | |
| Software, algorithm | Seurat | RRID:SCR_016341 | Stuart et al., 2019
| |
| Software, algorithm | Monocle 3 | RRID:SCR_018685 | Cao et al., 2019 https://cole-trapnell-lab.github.io/monocle3/ | |
| Software, algorithm | Velocyto.R | La Manno et al., 2018
| ||
| Software, algorithm | NicheNet | Browaeys et al., 2020
| ||
| Software, algorithm | RStudio | RRID:SCR_000432 | http://www.rstudio.com/ | |
| Commercial reagnet | Liberase | Roche | 0.3 mg/ml | |
| Commercial reagnet | Hyaluronidase | Sigma | 10 μg/mL | |
| Commercial reagnet | Trypsin | Corning | 0.25% | |
| Chemical compound, drug | Tamoxifen | Sigma | 100 mg/kg | |
| Chemical compound, drug | Liproxstatin-1 | Selleckchem | 10 mg/kg | |
| Chemical compound, drug | Aristolochic acid | Sigma | 6 mg/kg | |
| Commercial assay, kit | TUNEL staining | Abcam |
Animals
All animal experiments were approved by the Institutional Animal Care and Use Committee at Duke University and performed according to the IACUC-approved protocol (A051-18-02 and A014-21-01) and adhered to the NIH Guide for the Care and Use of Laboratory Animals. The following mouse lines were used for our study;
Mouse models of renal injury and repair
Adult male mice aged between 8 and 16 weeks were used for all the models described below. The mice were euthanized, and kidneys were harvested for analyses. For the unilateral IRI (uIRI) model, ischemia was induced by the retroperitoneal approach on the left kidney for 20 min (mild IRI), 22 min (mild IRI in cKO studies), or 30 min (severe IRI) by an atraumatic vascular clip (Roboz, RS-5435, Gaithersburg, MD), as previously reported (Nezu et al., 2017; Fu et al., 2018). Mice were anesthetized with isoflurane and provided preemptive analgesics (buprenorphine SR). The body temperature of mice was monitored and maintained on a heat-controlled surgical pad. For the bilateral IRI (bIRI) model, ischemia was induced by the retroperitoneal approach on both kidneys for 22 min. The mice were received intraperitoneal injections of 500 μl of normal saline at the end of surgery. For the unilateral ureteral obstruction (UUO) model, the left ureter was tied at the level of the lower pole of the kidney, and the kidneys were harvested on day 10. For the aristolochic acid nephropathy (AAN) model, we used acute and chronic models, as we previously described (Ren et al., 2020). For the acute AAN model, three doses of 6 mg/kg body weight aristolochic acid (Sigma, A9451) in phosphate-buffered saline (PBS) were administered daily intraperitoneally to the male mice. For the chronic AAN model, six doses of 6 mg/kg body weight aristolochic acid in phosphate-buffered saline (PBS) were administered on alternate days over 2 weeks intraperitoneally to the male mice. The same volume of PBS was injected to control animals (Ren et al., 2020; Dickman et al., 2011). Contralateral kidneys (CLK), sham-treated kidneys, and vehicle-injected kidneys were used as controls depending on the models used. The numbers and dates of treatment are indicated in the individual figure legends and experimental schemes. Operators were blinded to mouse genotypes when inducing surgical injury models.
Pharmacological inhibition of ferroptosis
Mice were randomly assigned to vehicle (1% dimethyl sulfoxide in phosphate-buffered saline) and liproxstatin-1 (10 mg/kg, Selleckchem, S7699, Friedmann Angeli et al., 2014) groups. Liproxstatin-1 and vehicle were administered daily by intraperitoneal injections starting from 1 hr before renal ischemia. All the mice were subjected to the same ischemic stress (22 min ischemic time, unilateral IRI model) and tamoxifen treatment. The mice were euthanatized, and kidneys were harvested on day 21 after IRI.
Droplet-based scRNA-seq
Mice were transcardially perfused with ice-cold PBS, and the kidneys were harvested. The kidneys were dissociated with liberase TM (0.3 mg/mL, Roche, Basel, Switzerland, #291963), hyaluronidase (10 μg/mL, Sigma, H4272), DNaseI (20 μg/mL) at 37°C for 40 min, followed by incubation with 0.25% trypsin EDTA at 37°C for 30 min. Trypsin was inactivated using 10% fetal bovine serum in PBS. Cells were then resuspended in PBS supplemented with 0.01% bovine serum albumin. Our protocol yielded high cell viability (>95%) and very few doublets, enabling us to avoid the use of flow cytometry-based cell sorting. After filtration through a 40 μm strainer, cells at a concentration of 100 cells/μl were run through microfluidic channels along with mRNA capture beads and droplet-generating oil, as previously described (Kobayashi et al., 2020; Macosko et al., 2015). cDNA libraries were generated and sequenced using HiSeq X Ten with 150 bp paired-end sequencing. Each condition contains the cells from three mice to minimize potential biological and technical variability.
Data preprocessing, unsupervised clustering, and cell type annotation of Drop-Seq data
Analysis of the scRNA-seq of mouse kidneys was performed by processing FASTQ files using dropSeqPipe v0.3 and mapped on the GRCm38 genome reference with annotation version 91. Unique molecular identifier (UMI) counts were then further analyzed using an R package Seurat v.3.06 for quality control, dimensionality reduction, and cell clustering (Stuart et al., 2019). The scRNA-seq matrices were filtered by custom cutoff (genes expressed in >3 cells and cells expressing more than 500 and less than 3000 detected genes were included) to remove potential empty droplets and doublets. Relationships between the number of UMI/cell and genes/cell were comparable across the condition (Figure 1—figure supplement 3A). After quality control filtration and normalization using SCTransform (Hafemeister and Satija, 2019), UMI count matrices from post-IRI kidneys and homeostatic kidneys were integrated using Seurat’s integration and label transfer method, which corrects potential batch effects (Stuart et al., 2019). The integrated dataset was used for all the analyses. To remove an additional confounding source of variation, the mitochondrial mapping percentage was regressed out. The number of principal components (PC) for downstream analyses were determined using elbow plot to identify knee point, and we included the first 25 PCs for the downstream analyses. A graph-based clustering approach in Seurat was used to cluster the cells in our integrated dataset. The resolution was set at 1.0 for the mouse integrated dataset. Cluster-defining markers for each cluster were obtained using the Seurat FindAllMarkers command (genes at least expressed in 25% of cells within the cluster, log fold change> 0.25) with the Wilcoxon Rank Sum test (Supplementary file 1). Based on the marker genes and manual curation of the gene expression pattern of canonical marker genes in UMAP plots (Figure 1—figure supplement 4), we assigned a cell identity to each cluster. Ambiguous clusters were shown as unknown. We manually combined 3 clusters of differentiated proximal tubular cells (PT, S1/S2 and PT, S2/S3; Figure 1—figure supplement 4) into one cluster (PT) to generate a more coarse-grained cell-type annotation and data visualization. We also combined three clusters of endothelial cells (Endo-1, Endo-2, and Endo-3; Figure 1—figure supplement 4) into one cluster (Endo) for data visualization.
Data preprocessing, unsupervised clustering, and cell type annotation of mouse neonatal kidneys
The RDS files for mouse neonatal kidneys (postnatal day 1) were obtained from Gene Expression Omnibus (GEO accession number: GSE94333, GSM2473317), (Adam et al., 2017). Data were analyzed as in our mouse kidney dataset using Seurat and SCTransform (Stuart et al., 2019; Hafemeister and Satija, 2019). We included the first 17 PCs for the downstream analyses of mouse neonatal kidneys. A graph-based clustering approach in Seurat was used to cluster the cells. The resolution was set at 0.8. Based on the marker genes and manual curation of the gene expression pattern of canonical marker genes in UMAP plots (Figure 1—figure supplement 7), we assigned a cell identity to each cluster. The anchor genes for assigning cell identity were obtained from previous single-cell transcriptome analyses of the developing mouse kidneys (Adam et al., 2017; Combes et al., 2019b).
Data preprocessing, unsupervised clustering, and cell type annotation of human kidneys
The RDS files for human kidneys were obtained from Gene Expression Omnibus (GEO accession number: GSE131882 and GSE145927), (Malone et al., 2020; Wilson et al., 2019). Normal human kidney data was originated from two macroscopically normal nephrectomy samples without renal mass (GSE131882; GSM3823939 and GSM3823941), (Wilson et al., 2019). Human AKI kidney data was originated from two biopsy-samples of transplant kidneys with evidence of AKI and acute tubular injury but no evidence of rejection (GSE145927; GSM4339775 and GSM4339778), (Malone et al., 2020). Data were integrated and analyzed as in the mouse kidney analyses using Seurat’s integration method and SCTransform (Stuart et al., 2019; Hafemeister and Satija, 2019). We included the first 25 PCs for the downstream analyses of human normal and AKI kidneys. A graph-based clustering approach in Seurat was used to cluster the cells. The resolution was set at 0.5 for normal human kidneys and 1.0 for the human AKI kidneys. Based on the marker genes and manual curation of the gene expression pattern of canonical marker genes in UMAP plots (Figure 5—figure supplement 2 and Figure 6—figure supplement 1), we assigned a cell identity to each cluster. The anchor genes for assigning cell identity were obtained from previous single-cell transcriptome analyses of the human kidneys (Malone et al., 2020; Wilson et al., 2019; Stewart et al., 2019).
Differential gene expression analyses and Gene ontology (GO) enrichment analyses
To predict the cellular functions based on enriched gene signature, we performed gene-ontology enrichment analyses. Differentially expressed genes obtained using FindMarkers command in Seurat were used for identifying signaling pathways and gene ontology through Enricher (Supplementary file 2 and 3; Figure 1—figure supplement 5B), (Kuleshov et al., 2016). To visualize the overrepresented signaling pathways, scaled data in the integrated Seurat object were extracted. Then, mean values of the scaled score of gene members in each GO class were calculated and shown in UMAP (Kobayashi et al., 2020). The gene member lists of signaling pathways were obtained from AmiGO 2 (AmiGO Hub et al., 2009). Log2 fold changes and
RNA velocity analyses
To infer future states of individual cells, we performed RNA velocity analyses (La Manno et al., 2018) using single-time point dataset of post-IRI kidney on day 7. The aligned BAM files were used as input for Velocyto to obtain the counts of unspliced and spliced reads in loom format. Cell barcodes for the clusters of interests (PT and DA-PT) were extracted and utilized for velocyto run command in velocyto.py v0.17.15, as well as for generating RNA velocity plots using velocyto.R v0.6 in combination with an R package SeuratWrappers v0.2.0 (Stuart et al., 2019; https://github.com/satijalab/seurat-wrappers). Twenty-five nearest neighbors in slope calculation smoothing were used for RunVelocity command.
Pseudotime trajectory analyses
To infer the dynamic cellular process during injury and repair, we performed single-cell trajectory analyses. We first extracted the clusters of interests (PT and DA-PT) from our integrated Seurat object of mouse kidneys and utilized for Monocle 3 (version 0.2.3.0) analyses with default parameters to identify a pseudotime trajectory with SeuratWrappers v0.2.0 (Cao et al., 2019; Trapnell et al., 2014). We set the starting states in two different approaches. We used the UMAP space area occupied by cells from the earliest time point of IRI kidneys (6 hr post-IRI, Figure 1F) and the area occupied by the cells with high expression of genes that are highly expressed in differentiated PT cells, such as
Intercellular communication analyses using NicheNet
To predict the intercellular communication process between damage-associated PT (DA-PT) cells and myeloid cells (monocytes and macrophages), we performed NicheNet analyses based on the analytical pipeline (Browaeys et al., 2020; https://github.com/saeyslab/nichenetr/blob/master/vignettes/seurat_wrapper.md) using an R package nichenetr (version 1.0.0) with default parameters (Browaeys et al., 2020). Based on high enrichment of chemokines and cytokines in DA-PT cells and the observed positive association between the numbers of macrophages and DA-PT cells in severely injured kidneys, we surmised that they have a close molecular interaction. We used NicheNet to predict the ligand-receptor pairs that are most likely to explain the target gene expression in renal myeloid cells after IRI. We defined DA-PT cells as the ‘sender/niche’ cell population and myeloid cells as the ‘receiver/target’ cell population in our integrated Seurat object for these analyses. We defined the differentially expressed genes in monocytes or macrophages in IRI-kidneys compared to homeostatic kidneys as the gene sets of interest that were affected by predicted ligand-receptor interactions.
Tissue collection and histology
Kidneys were prepared as described previously (Nezu et al., 2017; Ide et al., 2020). For cryosections (7 μm), the tissues were fixed with 4% paraformaldehyde in PBS at 4°C for 4 hr and then processed through a sucrose gradient. Kidneys were embedded in OCT compound for sectioning. For paraffin sections (5 μm), the tissues were fixed with 10% neutral buffered formalin overnight at 4°C and processed at Substrate Services Core and Research Support at Duke. Sections were blocked (animal-free blocker with 0.5% triton x-100) for 30 min and incubated with the primary antibodies overnight at 4°C. Primary antibodies used were as follows: SOX9 (Abcam, Cambridge, UK, ab196450 or ab185966, 1:200), KIM1 (R and D Systems, Minneapolis, MN, AF1817, 1:400), NGAL (Abcam, ab70287, 1:400), F4/80 (Bio-rad, Hercules, CA, MCA497G, 1:200), α-SMA (Sigma, C6198, 1:200), LTL (Vector, Burlingame, CA, B-1325 or FL-1321, 1:200), KRT8 (DSHB, TROMA-I, 1:200), MDA (Abcam, ab6463, 1:200), ACSL4 (Abcam, ab204380 or ab155282, 1:200), EMN (Abcam, 106100, 1:200), VCAM1 (CST, 39036S or 33901S, 1:100), and GPX4 (Abcam, ab125066, 1:200). Alexa Fluor-labeled secondary antibodies were used appropriately for immunofluorescence. ImmPRES HRP reagent kit was used for immunohistochemistry (Vector, MP-7401). Nuclei were stained with DAPI (1:400, Sigma). Heat-induced antigen retrieval was performed using pH 6.0 sodium citrate solution (eBioscience). Experiments for RNAScope in situ hybridization (Advanced Cell Diagnostics, ACD, Newark, CA) was performed as recommended by the manufacturer. Mm-Cdh6 (ACD, 519541) was used. Images were captured using Axio imager and 780 confocal microscopes (Zeiss, Oberkochen, Germany). Paraffin-sections were stained with hematoxylin and eosin (H and E). The kidney injury score was calculated as we previously reported (Ren et al., 2020). TUNEL staining was performed following the manufacturer’s instruction (Abcam, ab206386). To ensure the TUNEL signal’s specificity, we used sections treated with DNase I as a positive control and a section treated without terminal deoxynucleotidyl transferase as a negative control, as recommended by the manufacturer. Sections were counterstained with methyl green. More than three randomly selected areas from at least three kidneys were imaged and quantified using ImageJ (Ide et al., 2020). The stitched large area was used for quantification to alleviate the selection bias in the acquisition of images. All representative images were from more than three kidneys tested.
RNA extraction and real-time quantitative PCR
Total RNA was extracted from kidneys using the TRIzol reagent (Invitrogen, 15596026). Three μg of total RNA was then reverse transcribed with Maxima H minus cDNA synthesis master mix (Invitrogen, M1662). Equivalent amounts of diluted cDNA from each sample were analyzed with Real-time PCR with the primers listed below using the Powerup SYBR Green reagent (Invitrogen, A25776) on a QuantStudio three real-time PCR systems (Thermo). 18S rRNA expression was used to normalize samples using the ΔΔCT-method.
Statistical analysis
Statistical analyses were conducted using GraphPad Prism software. Two-tailed unpaired Student’s t-test was used for two groups, and one-way analysis of variance (ANOVA) followed by Sidak multiple comparison test was used for more than two groups. All results are represented as means ± SE. A p value less than 0.05 was considered statistically significant.
Additional protocols are available in the supplementary method.
Primers used for quantitative PCR.
Genotyping primers.
Cre, Rv:
2 Department of Cell Biology, Duke University School of Medicine Durham United States
3 Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill Chapel Hill United States
4 Department of Pathology, Duke University School of Medicine Durham United States
5 Regeneration Next, Duke University Durham United States
6 Duke Cancer Institute, Duke University School of Medicine Durham United States
Icahn School of Medicine at Mount Sinai United States
National Institutes of Health United States
© 2021, Ide et al. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.