human renal epithelial cells  (ATCC)

Journal: Aging Cell

Article Title: Transcriptional Profiling at Single‐Cell Resolution Reveals Diversity and Regulatory Networks of Primary and Secondary Senescent Cells

doi: 10.1111/acel.70540

Figure Lengend Snippet: Transcriptional heterogeneity and lineage‐resolved progression in primary senescence at single‐cell level. (A) Experimental overview. Renal epithelial cells were irradiated (IR; 10 Gy, 10 days) to induce primary senescence, with quiescent controls (QUI; 0.01% serum, 3 days) processed for scRNA‐seq. (B) Expression levels of senescence and SASP‐related genes in senescent relative to the controls (QUI, n = 3; IR, n = 3). (C) Secreted IL‐6 levels in CM measured using ELISA (QUI, n = 6; IR, n = 6). Data are presented as the means ± the standard error of the mean (unpaired two‐tailed t ‐test; * p < 0.05, ** p < 0.01, *** p < 0.001). (D) UMAP of primary dataset showing clusters grouped into non‐senescent (C4 and C9), intermediate (C0, C1, C3, and C7), and fully senescent states (C5, C6, and C8) (left). Each bar represents either IR or QUI, and each colored segment's height indicates the fraction of one of the three senescence states within that group (middle). Stacked bar chart showing the proportions of IR and QUI cells across each cluster (right). (E) Feature plots showing expression levels of proliferation and senescence‐associated genes. (F) Heatmap of pathway activity across clusters scored via gene set variation analysis, with Z ‐score normalization. (G) UMAP trajectory analysis using Slingshot identifying three senescence progression lineages. Trajectory lines overlaid on UMAP. Cell clusters are colored by pseudotime progression. (H, I) Boxplots of normalized pathway scores for DNA repair (H) and SASP‐related gene sets (I) across clusters (Kruskal–Wallis test, with pairwise Wilcoxon rank‐sum test; adjusted p‐values as shown). (J) Enriched pathways of non‐senescent, intermediate, and fully senescent states in the primary SnCs. p‐values were calculated using a hypergeometric distribution. (K) TradeSeq‐based heatmap of temporally regulated top 500 genes along the pseudotime trajectory for lineage 3 ( p < 0.05), with representative late‐pseudotime genes highlighted.

Article Snippet: Human renal epithelial cells (ATCC; PCS‐400‐011) were cultured in Renal Epithelial Cell Basal Medium (ATCC; PCS‐400‐030) supplemented with the Renal Epithelial Cell Growth Kit (ATCC; PCS‐400‐040), which maintains the cultures at a final serum concentration of 0.5% and incubated at 37°C in 10% CO 2 and 3% O 2 .

Techniques: Single Cell, Irradiation, Expressing, Enzyme-linked Immunosorbent Assay, Two Tailed Test, Activity Assay

Journal: Aging Cell

Article Title: Transcriptional Profiling at Single‐Cell Resolution Reveals Diversity and Regulatory Networks of Primary and Secondary Senescent Cells

doi: 10.1111/acel.70540

Figure Lengend Snippet: SASP‐driven secondary senescence shows distinct transcriptional states. (A) Experimental overview: Proliferative renal epithelial cells were treated with CM from quiescent cells (QCMT) or primary senescent cells (SCMT) and separately processed for scRNA‐seq. (B) qPCR validation of senescence/SASP‐associated genes and expressed as fold changes in SCMT versus QCMT (QCMT, n = 4; SCMT, n = 3). Data are presented as the mean ± standard error of the mean. * p < 0.05, ** p < 0.01, *** p < 0.001 (two‐tailed unpaired t ‐test) (C) Secreted IL‐6 levels in CM measured by ELISA (QCMT, n = 12; SCMT, n = 8). (D) UMAP of secondary SnCs showing clusters grouped into non‐senescent (C2 and C6), intermediate (C0, C1, and C4), and fully senescent clusters (C3, C5, and C7) (left). Each bar represents either QCMT or SCMT, and each colored segment's height indicates the fraction of one of the three senescence states within that group (middle). Stacked bar chart showing the proportions of QCMT and SCMT cells across each cluster (right). (E) Feature plots of representative proliferation and senescence‐associated genes across clusters. (F) Heatmap of pathway activities across clusters ( Z ‐score normalized). (G) UMAP trajectory analysis using Slingshot identifies four lineages with distinct terminal clusters, including a senescence‐resistant endpoint. Trajectory lines indicate senescence progression, and clusters are colored by pseudotime. (H, I) Boxplots of DNA repair (H) and SASP‐related gene set scores (I) across clusters (Kruskal–Wallis two‐sided test with pairwise Wilcoxon rank‐sum test; adjusted p‐values as shown). (J) Enriched pathways categorized into non‐senescent, intermediate, and fully senescent states. p‐values were calculated using a hypergeometric distribution. (K) Heatmap displaying temporally regulated the top 500 genes identified through tradeSeq along the pseudotime trajectory for lineage 4 in secondary senescence (hypergeometric distribution; p < 0.05).

Article Snippet: Human renal epithelial cells (ATCC; PCS‐400‐011) were cultured in Renal Epithelial Cell Basal Medium (ATCC; PCS‐400‐030) supplemented with the Renal Epithelial Cell Growth Kit (ATCC; PCS‐400‐040), which maintains the cultures at a final serum concentration of 0.5% and incubated at 37°C in 10% CO 2 and 3% O 2 .

Techniques: Biomarker Discovery, Two Tailed Test, Enzyme-linked Immunosorbent Assay

Journal: Materials Today Bio

Article Title: Ferrostatin-1-loaded black phosphorus quantum dots (BPQD@Fer-1) nanodelivery system attenuates T cell-mediated rejection after kidney transplantation

doi: 10.1016/j.mtbio.2026.103292

Figure Lengend Snippet: Schematic illustration of the construction of BPQD@Fer-1 nanoparticles and their application in alleviating TCMR in kidney transplantation. Top panel: Synthesis of black phosphorus quantum dots loaded with Ferrostatin-1 (BPQD@Fer-1) via liquid-phase exfoliation of bulk BP in NMP. The nanoparticles exhibit intrinsic ROS-scavenging capabilities by neutralizing free radicals (e.g., ⋅O 2 −and ⋅OH) through electron (e − ) transfer. Middle panel: In vivo application in a murine kidney transplantation model. Donor kidneys are subjected to cold ischemia and subsequently transplanted. Intravenously administered BPQD@Fer-1 selectively accumulate in the tubular epithelial cells of the kidney allograft. Bottom panel: Intracellular mechanisms and immune microenvironment remodeling. (Left, TCMR group): Severe oxidative stress upregulates intracellular ROS and lipid peroxidation (LPO), triggering ferroptosis in tubular epithelial cells. This leads to the massive release of damage-associated molecular patterns (DAMPs), including high mobility group box 1 (HMGB1), calreticulin (CRT), lactate dehydrogenase (LDH), and adenosine triphosphate (ATP), which subsequently recruit and activate CD8 + T cells, resulting in the upregulation of cytotoxic and pro-inflammatory cytokines (GzmB, IL-2, TNF-α, and IFN-γ). (Right, BPQD@Fer-1 group): The nanoparticles efficiently scavenge ROS, suppress LPO, and block the ferroptotic cascade. The consequent inhibition of DAMPs release restricts CD8 + T cell-mediated cytotoxicity and downregulates the inflammatory cytokine storm, ultimately preserving the kidney allograft.

Article Snippet: Human renal tubular epithelial cell line (HK-2) and rat renal tubular epithelial cell line (NRK52E) were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China).

Techniques: Transplantation Assay, In Vivo, Blocking Assay, Inhibition, Preserving

Journal: Materials Today Bio

Article Title: Ferrostatin-1-loaded black phosphorus quantum dots (BPQD@Fer-1) nanodelivery system attenuates T cell-mediated rejection after kidney transplantation

doi: 10.1016/j.mtbio.2026.103292

Figure Lengend Snippet: Single-cell landscape reveals ferroptosis-associated epithelial states and enhanced T-cell activation in TCMR. (A) UMAP visualization of all single cells colored by sample origin (left panel) and cell cluster (right panel). (B) Dot plot showing the expression of canonical marker genes used for cell type annotation across major renal epithelial, immune, and stromal populations. Dot size represents the percentage of cells expressing each gene, and color intensity indicates scaled average expression. (C) UMAP plot annotated by cell type. (D) UMAP plots split by experimental condition (CTRL and TCMR), illustrating comparable global cellular architecture across conditions. (E) Boxplot showing ferroptosis module scores in epithelial cells from CTRL and TCMR groups. Each dot represents a single cell. (F) Ferroptosis scores across epithelial subtypes, including proximal tubule, thick ascending limb, collecting duct principal and intercalated cells, thin limb, and PEC subsets, stratified by condition (CTRL vs TCMR). (G) Heatmap showing average expression of ferroptosis-related genes across epithelial cell types and conditions. Expression values are scaled by gene to highlight relative differences. (H) Boxplot of DAMPs module scores in epithelial cells stratified by ferroptosis state (Ferro_High vs Ferro_Low). (I) Dot plot visualizing the expression profiles of DAMPs-associated genes in epithelial cells, stratified by their ferroptosis states. Dot diameter corresponds to the proportion of cells expressing the target gene, while the color gradient indicates the average expression intensity. (J) Boxplots delineating the variations in T-cell activation scores among the three defined ferroptosis subgroups (Ferro_High, Ferro_Med, and Ferro_Low). (K) Dot plot displaying the expression dynamics of T-cell-related markers across different ferroptosis cohorts. The cellular fraction expressing each gene is represented by dot size, with the mean expression level denoted by color scaling. (L) Boxplot showing cytotoxicity scores in T cells across ferroptosis groups.

Article Snippet: Human renal tubular epithelial cell line (HK-2) and rat renal tubular epithelial cell line (NRK52E) were purchased from Procell Life Science & Technology Co., Ltd. (Wuhan, China).

Techniques: Single Cell, Activation Assay, Expressing, Marker

Journal: Human Mutation

Article Title: Cross‐Cohort Transcriptomic Integration Identifies IFIT2 as a Translational Diagnostic Biomarker and Functional Driver of Inflammation‐Linked Tubular Injury in Chronic Kidney Disease

doi: 10.1155/humu/8282277

Figure Lengend Snippet: Induction and knockdown of IFIT2 in renal tubular epithelial cells. (A–B) IFN‐ γ –induced IFIT2 expression in HK‐2 and RPTEC cells. (C–D) TGF‐ β 1–induced IFIT2 expression in HK‐2 and RPTEC cells. (E–F) Validation of IFIT2 knockdown efficiency by qPCR. Data are presented as mean ± SD. ∗ p < 0.05, ∗∗ p < 0.01, and ∗∗∗ p < 0.001.

Article Snippet: Human renal proximal tubular epithelial cells, including the HK‐2 cell line (ATCC, RRID: CVCL_0302) and primary RPTEC cells (ATCC, RRID: CVCL_K278), were used in this study.

Techniques: Knockdown, Expressing, Biomarker Discovery

Journal: Human Mutation

Article Title: Cross‐Cohort Transcriptomic Integration Identifies IFIT2 as a Translational Diagnostic Biomarker and Functional Driver of Inflammation‐Linked Tubular Injury in Chronic Kidney Disease

doi: 10.1155/humu/8282277

Figure Lengend Snippet: IFIT2 knockdown attenuates IFN‐ γ –induced injury and apoptosis in renal tubular epithelial cells. (A–B) CCK‐8 assay showing that IFIT2 knockdown alleviates IFN‐ γ –induced reduction of cell viability in HK‐2 and RPTEC cells. (C–F) Annexin V/PI flow cytometry analysis showing that IFIT2 knockdown reduces IFN‐ γ –induced apoptosis in (C, E) HK‐2 and (D, F) RPTEC cells. Data are presented as mean ± SD from three independent experiments. ∗∗∗ p < 0.001.

Article Snippet: Human renal proximal tubular epithelial cells, including the HK‐2 cell line (ATCC, RRID: CVCL_0302) and primary RPTEC cells (ATCC, RRID: CVCL_K278), were used in this study.

Techniques: Knockdown, CCK-8 Assay, Flow Cytometry