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ATCC
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renal epithelial cells ![]() Renal Epithelial Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/normal+human+renal+epithelial+cells/pm42108472-149-5-18?v=ATCC Average 99 stars, based on 1 article reviews
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renal epithelial cell line ![]() Renal Epithelial Cell Line, supplied by ATCC, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/normal+human+renal+epithelial+cells/pm42108472-138-15-26?v=ATCC Average 94 stars, based on 1 article reviews
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pcs 400 010tm ![]() Pcs 400 010tm, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more https://www.bioz.com/product/normal+human+renal+epithelial+cells/us12616681-590-22-18?v=ATCC Average 99 stars, based on 1 article reviews
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Servicebio Inc
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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:
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:
Techniques: Biomarker Discovery, Two Tailed Test, Enzyme-linked Immunosorbent Assay
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:
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:
Techniques: Knockdown, CCK-8 Assay, Flow Cytometry
Journal: Comprehensive Physiology
Article Title: The cardiac circadian clock regulates rhythms in peripheral tissues via Fibulin 5
doi: 10.1002/cph4.70147
Figure Lengend Snippet: Treatment of synchronized kidney and muscle cells with recombinant Fbln5 (10ng/mL) disrupts rhythmic gene expression. A,B) Disrupted expression of Clock and Bmal1 but not C) Per2 in mouse myotube C2C12 cells with Fbln5 treatment. D,E) Disrupted expression of Bmal1 and Per1 but not F) Clock in human renal proximal tubule endothelial cells (RPTEC) with Fbln5. n=3 repetitions with biological triplicate. *p<0.05 Vehicle vs Fbln5. Cells were synchronized with dexamethasone prior to treatment with Fbln5 or vehicle.
Article Snippet: The human
Techniques: Recombinant, Gene Expression, Expressing