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human cell lines uc msc  (ATCC)


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    Structured Review

    ATCC human cell lines uc msc
    Characterization of ADSC EVs <t>and</t> <t>UC-MSC</t> EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.
    Human Cell Lines Uc Msc, supplied by ATCC, used in various techniques. Bioz Stars score: 95/100, based on 189 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human cell lines uc msc/product/ATCC
    Average 95 stars, based on 189 article reviews
    human cell lines uc msc - by Bioz Stars, 2026-05
    95/100 stars

    Images

    1) Product Images from "Extracellular vesicles modulate skin aging biomarkers in a 3D reconstructed full-thickness skin model"

    Article Title: Extracellular vesicles modulate skin aging biomarkers in a 3D reconstructed full-thickness skin model

    Journal: Frontiers in Cell and Developmental Biology

    doi: 10.3389/fcell.2026.1784998

    Characterization of ADSC EVs and UC-MSC EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.
    Figure Legend Snippet: Characterization of ADSC EVs and UC-MSC EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.

    Techniques Used: Concentration Assay, Western Blot, Marker

    Bulk transcriptomic analysis of a reconstructed skin model after treatment with ADSC EVs and UC-MSC EVs. (a,b) Volcano plots and heatmaps showing commonly DEGs regulated by high and low doses of (a) ADSC EVs and (b) UC-MSC EVs, with 5-8 replicates per group; (c,d) Top GO terms enriched among DEGs modulated by (c) ADSC EVs and (d) UC-MSC EVs, as identified in the GO database.
    Figure Legend Snippet: Bulk transcriptomic analysis of a reconstructed skin model after treatment with ADSC EVs and UC-MSC EVs. (a,b) Volcano plots and heatmaps showing commonly DEGs regulated by high and low doses of (a) ADSC EVs and (b) UC-MSC EVs, with 5-8 replicates per group; (c,d) Top GO terms enriched among DEGs modulated by (c) ADSC EVs and (d) UC-MSC EVs, as identified in the GO database.

    Techniques Used:

    Comprehensive profiling of miRNA and protein cargo in EVs. (a) Venn diagram illustrating miRNAs in the two groups of EVs; (b) Top abundantly expressed miRNAs identified in each EV types; (c) Overlap between predicted target genes of shared miRNAs and DEGs from T-skin™ transcriptomic analysis; (d) Identified proteins in the two groups of EVs revealed by Venn plot; (e) Top expressed proteins in the two types of EVs; (f) Selected DEPs that upregulated in ADSC EVs compared to UC-MSC EVs.
    Figure Legend Snippet: Comprehensive profiling of miRNA and protein cargo in EVs. (a) Venn diagram illustrating miRNAs in the two groups of EVs; (b) Top abundantly expressed miRNAs identified in each EV types; (c) Overlap between predicted target genes of shared miRNAs and DEGs from T-skin™ transcriptomic analysis; (d) Identified proteins in the two groups of EVs revealed by Venn plot; (e) Top expressed proteins in the two types of EVs; (f) Selected DEPs that upregulated in ADSC EVs compared to UC-MSC EVs.

    Techniques Used:



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    Characterization of ADSC EVs <t>and</t> <t>UC-MSC</t> EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.
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    ATCC human blca cell lines umuc3
    Characterization of ADSC EVs <t>and</t> <t>UC-MSC</t> EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.
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    Characterization of ADSC EVs <t>and</t> <t>UC-MSC</t> EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.
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    ATCC human blca cell lines
    Glutamine metabolism profiling and single-cell landscape in bladder cancer. ( A ) Schematic of targeted metabolomics workflow comparing <t>BLCA</t> tumor cells and <t>normal</t> <t>urothelial</t> cells. ( B ) Boxplot showing differential amino acid expression between tumor and normal cell lines. ( C ) UMAP clustering of single-cell transcriptomes from BLCA tissue samples. ( D ) Cell type annotation based on canonical markers. ( E ) Differential gene expression analysis (DEGs) across cell clusters. ( F ) Glutamine metabolism module score visualized by UMAP. ( G ) Urothelial cells colored by high/low glutamine score group. ( H ) Volcano plot of DEGs between high- and low-score groups. ( I ) GO enrichment analysis of DEGs showing involvement in metabolic and apoptotic pathways. Error bars indicate mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001
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    ATCC human bca cell lines
    Glutamine metabolism profiling and single-cell landscape in bladder cancer. ( A ) Schematic of targeted metabolomics workflow comparing <t>BLCA</t> tumor cells and <t>normal</t> <t>urothelial</t> cells. ( B ) Boxplot showing differential amino acid expression between tumor and normal cell lines. ( C ) UMAP clustering of single-cell transcriptomes from BLCA tissue samples. ( D ) Cell type annotation based on canonical markers. ( E ) Differential gene expression analysis (DEGs) across cell clusters. ( F ) Glutamine metabolism module score visualized by UMAP. ( G ) Urothelial cells colored by high/low glutamine score group. ( H ) Volcano plot of DEGs between high- and low-score groups. ( I ) GO enrichment analysis of DEGs showing involvement in metabolic and apoptotic pathways. Error bars indicate mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001
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    ATCC human bc cell lines
    Glutamine metabolism profiling and single-cell landscape in bladder cancer. ( A ) Schematic of targeted metabolomics workflow comparing <t>BLCA</t> tumor cells and <t>normal</t> <t>urothelial</t> cells. ( B ) Boxplot showing differential amino acid expression between tumor and normal cell lines. ( C ) UMAP clustering of single-cell transcriptomes from BLCA tissue samples. ( D ) Cell type annotation based on canonical markers. ( E ) Differential gene expression analysis (DEGs) across cell clusters. ( F ) Glutamine metabolism module score visualized by UMAP. ( G ) Urothelial cells colored by high/low glutamine score group. ( H ) Volcano plot of DEGs between high- and low-score groups. ( I ) GO enrichment analysis of DEGs showing involvement in metabolic and apoptotic pathways. Error bars indicate mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001
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    Image Search Results


    Characterization of ADSC EVs and UC-MSC EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.

    Journal: Frontiers in Cell and Developmental Biology

    Article Title: Extracellular vesicles modulate skin aging biomarkers in a 3D reconstructed full-thickness skin model

    doi: 10.3389/fcell.2026.1784998

    Figure Lengend Snippet: Characterization of ADSC EVs and UC-MSC EVs. (a) Size and concentration measurement by NTA; (b) Morphology visualization with Cryo-TEM with EVs indicated by red arrows (scale bar: 100 nm); (c) EV related biomarkers in the EV groups and cell lysate (CL) detected with Western blot, including positive markers TSG101, HSP70, CD63 and negative marker calnexin.

    Article Snippet: The EVs used in this study were produced by EchoBiotech (Beijing, China), using commercial human cell lines UC-MSC (ATCC #PCS-500-010) and ADSC (ATCC #PCS-500-011).

    Techniques: Concentration Assay, Western Blot, Marker

    Bulk transcriptomic analysis of a reconstructed skin model after treatment with ADSC EVs and UC-MSC EVs. (a,b) Volcano plots and heatmaps showing commonly DEGs regulated by high and low doses of (a) ADSC EVs and (b) UC-MSC EVs, with 5-8 replicates per group; (c,d) Top GO terms enriched among DEGs modulated by (c) ADSC EVs and (d) UC-MSC EVs, as identified in the GO database.

    Journal: Frontiers in Cell and Developmental Biology

    Article Title: Extracellular vesicles modulate skin aging biomarkers in a 3D reconstructed full-thickness skin model

    doi: 10.3389/fcell.2026.1784998

    Figure Lengend Snippet: Bulk transcriptomic analysis of a reconstructed skin model after treatment with ADSC EVs and UC-MSC EVs. (a,b) Volcano plots and heatmaps showing commonly DEGs regulated by high and low doses of (a) ADSC EVs and (b) UC-MSC EVs, with 5-8 replicates per group; (c,d) Top GO terms enriched among DEGs modulated by (c) ADSC EVs and (d) UC-MSC EVs, as identified in the GO database.

    Article Snippet: The EVs used in this study were produced by EchoBiotech (Beijing, China), using commercial human cell lines UC-MSC (ATCC #PCS-500-010) and ADSC (ATCC #PCS-500-011).

    Techniques:

    Comprehensive profiling of miRNA and protein cargo in EVs. (a) Venn diagram illustrating miRNAs in the two groups of EVs; (b) Top abundantly expressed miRNAs identified in each EV types; (c) Overlap between predicted target genes of shared miRNAs and DEGs from T-skin™ transcriptomic analysis; (d) Identified proteins in the two groups of EVs revealed by Venn plot; (e) Top expressed proteins in the two types of EVs; (f) Selected DEPs that upregulated in ADSC EVs compared to UC-MSC EVs.

    Journal: Frontiers in Cell and Developmental Biology

    Article Title: Extracellular vesicles modulate skin aging biomarkers in a 3D reconstructed full-thickness skin model

    doi: 10.3389/fcell.2026.1784998

    Figure Lengend Snippet: Comprehensive profiling of miRNA and protein cargo in EVs. (a) Venn diagram illustrating miRNAs in the two groups of EVs; (b) Top abundantly expressed miRNAs identified in each EV types; (c) Overlap between predicted target genes of shared miRNAs and DEGs from T-skin™ transcriptomic analysis; (d) Identified proteins in the two groups of EVs revealed by Venn plot; (e) Top expressed proteins in the two types of EVs; (f) Selected DEPs that upregulated in ADSC EVs compared to UC-MSC EVs.

    Article Snippet: The EVs used in this study were produced by EchoBiotech (Beijing, China), using commercial human cell lines UC-MSC (ATCC #PCS-500-010) and ADSC (ATCC #PCS-500-011).

    Techniques:

    Glutamine metabolism profiling and single-cell landscape in bladder cancer. ( A ) Schematic of targeted metabolomics workflow comparing BLCA tumor cells and normal urothelial cells. ( B ) Boxplot showing differential amino acid expression between tumor and normal cell lines. ( C ) UMAP clustering of single-cell transcriptomes from BLCA tissue samples. ( D ) Cell type annotation based on canonical markers. ( E ) Differential gene expression analysis (DEGs) across cell clusters. ( F ) Glutamine metabolism module score visualized by UMAP. ( G ) Urothelial cells colored by high/low glutamine score group. ( H ) Volcano plot of DEGs between high- and low-score groups. ( I ) GO enrichment analysis of DEGs showing involvement in metabolic and apoptotic pathways. Error bars indicate mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001

    Journal: Journal of Translational Medicine

    Article Title: Glutamine metabolism reprogramming promotes bladder cancer progression via PYCR1: a multi-omics and functional validation study

    doi: 10.1186/s12967-025-07386-2

    Figure Lengend Snippet: Glutamine metabolism profiling and single-cell landscape in bladder cancer. ( A ) Schematic of targeted metabolomics workflow comparing BLCA tumor cells and normal urothelial cells. ( B ) Boxplot showing differential amino acid expression between tumor and normal cell lines. ( C ) UMAP clustering of single-cell transcriptomes from BLCA tissue samples. ( D ) Cell type annotation based on canonical markers. ( E ) Differential gene expression analysis (DEGs) across cell clusters. ( F ) Glutamine metabolism module score visualized by UMAP. ( G ) Urothelial cells colored by high/low glutamine score group. ( H ) Volcano plot of DEGs between high- and low-score groups. ( I ) GO enrichment analysis of DEGs showing involvement in metabolic and apoptotic pathways. Error bars indicate mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001

    Article Snippet: Human BLCA cell lines (T24, J82, UMUC3, SW780) and a normal urothelial cell line (SV-HUC-1) were obtained from American Type Culture Collection (ATCC).

    Techniques: Expressing, Gene Expression

    Knockdown or inhibition of PYCR1 suppresses proliferation, migration, and tumor growth of BLCA cells in vitro and in vivo. ( A ) Western blot showing PYCR1 expression levels in BLCA cell lines and normal urothelial cell line SV-HUC-1. ( B ) Validation of PYCR1 knockdown efficiency in T24 and J82 cells using three different shRNAs. ( C ) CCK-8 assay showing reduced proliferation of T24 and J82 cells upon PYCR1 knockdown. ( D ) Colony formation assay indicating a significant decrease in clonogenic ability after PYCR1 knockdown. ( E ) Transwell migration assay demonstrating impaired migration in PYCR1 knockdown cells, scale bar = 200 μm. ( F ) Flow cytometry analysis of cell cycle distribution in J82 and T24 cells after PYCR1 knockdown. The G2 phase showed significant accumulation with P = 0.0036 for J82 cells and P = 0.0055 for T24 cells. ( G ) Flow cytometry analysis of apoptosis in T24 and J82 cells following PYCR1 knockdown. ( H ) Schematic diagram of xenograft mouse models used to evaluate the effects of PYCR1 inhibition in vivo ( n = 6 mice per group). ( I ) Tumor volume comparison and IHC staining (H&E, PYCR1, Ki-67) of xenografts from T24 cells treated with PYCR1 inhibitor versus DMSO control. ( J ) Tumor volume comparison and IHC staining of xenografts from T24-NC and T24-shPYCR1 cells. Data are presented as mean ± SD from three independent experiments. ns indicates P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001

    Journal: Journal of Translational Medicine

    Article Title: Glutamine metabolism reprogramming promotes bladder cancer progression via PYCR1: a multi-omics and functional validation study

    doi: 10.1186/s12967-025-07386-2

    Figure Lengend Snippet: Knockdown or inhibition of PYCR1 suppresses proliferation, migration, and tumor growth of BLCA cells in vitro and in vivo. ( A ) Western blot showing PYCR1 expression levels in BLCA cell lines and normal urothelial cell line SV-HUC-1. ( B ) Validation of PYCR1 knockdown efficiency in T24 and J82 cells using three different shRNAs. ( C ) CCK-8 assay showing reduced proliferation of T24 and J82 cells upon PYCR1 knockdown. ( D ) Colony formation assay indicating a significant decrease in clonogenic ability after PYCR1 knockdown. ( E ) Transwell migration assay demonstrating impaired migration in PYCR1 knockdown cells, scale bar = 200 μm. ( F ) Flow cytometry analysis of cell cycle distribution in J82 and T24 cells after PYCR1 knockdown. The G2 phase showed significant accumulation with P = 0.0036 for J82 cells and P = 0.0055 for T24 cells. ( G ) Flow cytometry analysis of apoptosis in T24 and J82 cells following PYCR1 knockdown. ( H ) Schematic diagram of xenograft mouse models used to evaluate the effects of PYCR1 inhibition in vivo ( n = 6 mice per group). ( I ) Tumor volume comparison and IHC staining (H&E, PYCR1, Ki-67) of xenografts from T24 cells treated with PYCR1 inhibitor versus DMSO control. ( J ) Tumor volume comparison and IHC staining of xenografts from T24-NC and T24-shPYCR1 cells. Data are presented as mean ± SD from three independent experiments. ns indicates P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001

    Article Snippet: Human BLCA cell lines (T24, J82, UMUC3, SW780) and a normal urothelial cell line (SV-HUC-1) were obtained from American Type Culture Collection (ATCC).

    Techniques: Knockdown, Inhibition, Migration, In Vitro, In Vivo, Western Blot, Expressing, Biomarker Discovery, CCK-8 Assay, Colony Assay, Transwell Migration Assay, Flow Cytometry, Comparison, Immunohistochemistry, Control

    PYCR1 knockdown impairs proline synthesis and suppresses the PI3K/AKT/mTOR signaling pathway in BLCA cells. ( A ) Quantification of intracellular proline levels in T24 and J82 cells after PYCR1 knockdown. ( B ) Correlation analysis showing positive associations between PYCR1 expression and SLC1A5 (left) and P5CS (right) in BLCA samples. ( C ) Western blot analysis confirming that knockdown of PYCR1 reduces protein expression levels of P5CS and SLC1A5. ( D ) Heatmap of differentially expressed genes in BLCA cells following PYCR1 knockdown from RNA-seq data. ( E - F ) Pathway enrichment analyses of downregulated ( E ) and upregulated ( F ) genes after PYCR1 knockdown, indicating involvement in PI3K-AKT and immune-related signaling pathways. ( G ) Western blot analysis validating the downregulation of PI3K, AKT, and mTOR pathway components upon PYCR1 knockdown in T24 and J82 cells. ( H ) Western blot analysis of PI3K/AKT/mTOR pathway activation following PYCR1 overexpression with or without LY294002 treatment in T24 and J82 cells. PYCR1 overexpression increased phosphorylation of PI3K, AKT, and mTOR, which was reversed by the PI3K inhibitor. Vinculin served as the loading control. Data are presented as mean ± SD from three independent experiments. * P < 0.05, ** P < 0.01; oe: Overexpression

    Journal: Journal of Translational Medicine

    Article Title: Glutamine metabolism reprogramming promotes bladder cancer progression via PYCR1: a multi-omics and functional validation study

    doi: 10.1186/s12967-025-07386-2

    Figure Lengend Snippet: PYCR1 knockdown impairs proline synthesis and suppresses the PI3K/AKT/mTOR signaling pathway in BLCA cells. ( A ) Quantification of intracellular proline levels in T24 and J82 cells after PYCR1 knockdown. ( B ) Correlation analysis showing positive associations between PYCR1 expression and SLC1A5 (left) and P5CS (right) in BLCA samples. ( C ) Western blot analysis confirming that knockdown of PYCR1 reduces protein expression levels of P5CS and SLC1A5. ( D ) Heatmap of differentially expressed genes in BLCA cells following PYCR1 knockdown from RNA-seq data. ( E - F ) Pathway enrichment analyses of downregulated ( E ) and upregulated ( F ) genes after PYCR1 knockdown, indicating involvement in PI3K-AKT and immune-related signaling pathways. ( G ) Western blot analysis validating the downregulation of PI3K, AKT, and mTOR pathway components upon PYCR1 knockdown in T24 and J82 cells. ( H ) Western blot analysis of PI3K/AKT/mTOR pathway activation following PYCR1 overexpression with or without LY294002 treatment in T24 and J82 cells. PYCR1 overexpression increased phosphorylation of PI3K, AKT, and mTOR, which was reversed by the PI3K inhibitor. Vinculin served as the loading control. Data are presented as mean ± SD from three independent experiments. * P < 0.05, ** P < 0.01; oe: Overexpression

    Article Snippet: Human BLCA cell lines (T24, J82, UMUC3, SW780) and a normal urothelial cell line (SV-HUC-1) were obtained from American Type Culture Collection (ATCC).

    Techniques: Knockdown, Expressing, Western Blot, RNA Sequencing, Protein-Protein interactions, Activation Assay, Over Expression, Phospho-proteomics, Control