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fibroblast basal medium  (ATCC)


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    Structured Review

    ATCC fibroblast basal medium
    Fibroblast Basal Medium, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 477 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/fibroblast+basal+medium/pm42123500-210-1-5?v=ATCC
    Average 97 stars, based on 477 article reviews
    fibroblast basal medium - by Bioz Stars, 2026-06
    97/100 stars

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    Viability of <t>NHDF</t> <t>cells</t> exposed to different concentrations of GQDs, as determined by the MTT assay, at 24 h (A), 48 h (B), and 72 h (C). Control cells were assigned 100% viability. The experiments were conducted in quadruplicate, and the data are expressed as median ± interquartile range (IQR). * p < 0.05, ** p < 0.01, *** p < 0.001 by Kruskal – Wallis test with Dunn’s multiple comparison test.
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    A: UMAP representing 1024-dimensional DINOv2 features from six cell lines showing clustering by cell identity, confirming capture of meaningful morphological differences. B: effect of common image impairments on the prediction accuracy of a linear classifier trained to predict cell identity based on DINOv2 features (see Methods). Different types of impairments were used (x axis) and the bar plot shows the drop in classifier performance (y axis) for each impairment and cell line (identified by the color). The stars denote two cell lines for which defocused blurred images were not available. Importantly, random rotations result in negligible drops in accuracy, which is key to the use of these morphological features, as cell position cannot be controlled. C: morphological appearance of Hs 675.T colon <t>fibroblast</t> grown on a flow cell with a fibronectin-coated bottom surface, and a top surface with capture spots for transcriptomic analysis. Note that in this experiment we performed transcriptomic analysis at each timepoint in different lanes, which requires cell lysis. Therefore, in this case the pictures depict representative images at each time point, not longitudinal images of the same cells. D: volcano plots displaying the results of pseudo-bulk differential expression analysis between consecutive timepoints. E: UMAP visualization and clustering of 1024-dimensional embeddings extracted by DINOv2 applied to individual cell images at the 24 hours timepoint. The pictures display representative images of each cluster. F: single-cell differential expression analysis between the cell morphology-derived clusters identified in panel E.
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    ATCC fbm medium
    A: UMAP representing 1024-dimensional DINOv2 features from six cell lines showing clustering by cell identity, confirming capture of meaningful morphological differences. B: effect of common image impairments on the prediction accuracy of a linear classifier trained to predict cell identity based on DINOv2 features (see Methods). Different types of impairments were used (x axis) and the bar plot shows the drop in classifier performance (y axis) for each impairment and cell line (identified by the color). The stars denote two cell lines for which defocused blurred images were not available. Importantly, random rotations result in negligible drops in accuracy, which is key to the use of these morphological features, as cell position cannot be controlled. C: morphological appearance of Hs 675.T colon <t>fibroblast</t> grown on a flow cell with a fibronectin-coated bottom surface, and a top surface with capture spots for transcriptomic analysis. Note that in this experiment we performed transcriptomic analysis at each timepoint in different lanes, which requires cell lysis. Therefore, in this case the pictures depict representative images at each time point, not longitudinal images of the same cells. D: volcano plots displaying the results of pseudo-bulk differential expression analysis between consecutive timepoints. E: UMAP visualization and clustering of 1024-dimensional embeddings extracted by DINOv2 applied to individual cell images at the 24 hours timepoint. The pictures display representative images of each cluster. F: single-cell differential expression analysis between the cell morphology-derived clusters identified in panel E.
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    A: UMAP representing 1024-dimensional DINOv2 features from six cell lines showing clustering by cell identity, confirming capture of meaningful morphological differences. B: effect of common image impairments on the prediction accuracy of a linear classifier trained to predict cell identity based on DINOv2 features (see Methods). Different types of impairments were used (x axis) and the bar plot shows the drop in classifier performance (y axis) for each impairment and cell line (identified by the color). The stars denote two cell lines for which defocused blurred images were not available. Importantly, random rotations result in negligible drops in accuracy, which is key to the use of these morphological features, as cell position cannot be controlled. C: morphological appearance of Hs 675.T colon <t>fibroblast</t> grown on a flow cell with a fibronectin-coated bottom surface, and a top surface with capture spots for transcriptomic analysis. Note that in this experiment we performed transcriptomic analysis at each timepoint in different lanes, which requires cell lysis. Therefore, in this case the pictures depict representative images at each time point, not longitudinal images of the same cells. D: volcano plots displaying the results of pseudo-bulk differential expression analysis between consecutive timepoints. E: UMAP visualization and clustering of 1024-dimensional embeddings extracted by DINOv2 applied to individual cell images at the 24 hours timepoint. The pictures display representative images of each cluster. F: single-cell differential expression analysis between the cell morphology-derived clusters identified in panel E.
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    Image Search Results


    Viability of NHDF cells exposed to different concentrations of GQDs, as determined by the MTT assay, at 24 h (A), 48 h (B), and 72 h (C). Control cells were assigned 100% viability. The experiments were conducted in quadruplicate, and the data are expressed as median ± interquartile range (IQR). * p < 0.05, ** p < 0.01, *** p < 0.001 by Kruskal – Wallis test with Dunn’s multiple comparison test.

    Journal: Science and Technology of Advanced Materials

    Article Title: Characterization and evaluation of the ability of graphene quantum dots to affect α-synuclein aggregation in synucleinopathy models

    doi: 10.1080/14686996.2026.2662693

    Figure Lengend Snippet: Viability of NHDF cells exposed to different concentrations of GQDs, as determined by the MTT assay, at 24 h (A), 48 h (B), and 72 h (C). Control cells were assigned 100% viability. The experiments were conducted in quadruplicate, and the data are expressed as median ± interquartile range (IQR). * p < 0.05, ** p < 0.01, *** p < 0.001 by Kruskal – Wallis test with Dunn’s multiple comparison test.

    Article Snippet: NHDF cells (PromoCell, C-23210, Heidelberg, Germany) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Sigma, D0819) supplemented with 10% fetal bovine serum (FBS; Sigma, Lot: 0001653683), 1 μg mL −1 penicillin-streptomycin (Sigma, Lot: 0000191002), and 2 mM L-glutamine (Sigma, RNBL6712).

    Techniques: MTT Assay, Control, Comparison

    Ic 5 0 values of GQDs in NHDF cells at 24 h (A), 48 h (B), and 72 h (C) are shown in panels A, B, and C, respectively. Data were analyzed using log-linear regression in GraphPad prism. Individual data points are shown in green (mean ± sd, where applicable). The blue curves represent the best-fit log-linear regression models. Each panel represents an independent dataset or experimental condition analyzed under the same fitting parameters.

    Journal: Science and Technology of Advanced Materials

    Article Title: Characterization and evaluation of the ability of graphene quantum dots to affect α-synuclein aggregation in synucleinopathy models

    doi: 10.1080/14686996.2026.2662693

    Figure Lengend Snippet: Ic 5 0 values of GQDs in NHDF cells at 24 h (A), 48 h (B), and 72 h (C) are shown in panels A, B, and C, respectively. Data were analyzed using log-linear regression in GraphPad prism. Individual data points are shown in green (mean ± sd, where applicable). The blue curves represent the best-fit log-linear regression models. Each panel represents an independent dataset or experimental condition analyzed under the same fitting parameters.

    Article Snippet: NHDF cells (PromoCell, C-23210, Heidelberg, Germany) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Sigma, D0819) supplemented with 10% fetal bovine serum (FBS; Sigma, Lot: 0001653683), 1 μg mL −1 penicillin-streptomycin (Sigma, Lot: 0000191002), and 2 mM L-glutamine (Sigma, RNBL6712).

    Techniques:

    Expression profiles of DDR-related proteins in NHDF cells following 24 h exposure to GQDs at 90 µg mL −1 . (A) Antibody array layout showing antigen-specific antibody spots; ‘nbs1’ control spots were used for data normalization, and ‘NEG’ spots served as negative controls for baseline signal measurement. (B) Representative images of the original antibody arrays. (C) heat map illustrating the relative expression levels of DDR-related proteins, with color intensity indicating normalized expression values. Data represent four independent experiments ( n = 4). (D) semi-quantitative analysis of DDR-related proteins expression using antibody microarray in NHDF cells treated with GQDs at 90 µg mL −1 for 24 h. Data are expressed as mean ± standard deviation (sd) ( n = 4) relative to control cells. Statistical significance was assessed using one-way ANOVA, followed by Sidak’s multiple comparisons test: ns: not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs. control. (e) GQDs induced differential DDR pathway modulation. Pathway–gene interaction heatmap visualized the relationship between key signaling pathways (rows) and DDR-related proteins (columns). Color scale denotes expression changes, with red indicating up-regulation, blue indicating down-regulation.

    Journal: Science and Technology of Advanced Materials

    Article Title: Characterization and evaluation of the ability of graphene quantum dots to affect α-synuclein aggregation in synucleinopathy models

    doi: 10.1080/14686996.2026.2662693

    Figure Lengend Snippet: Expression profiles of DDR-related proteins in NHDF cells following 24 h exposure to GQDs at 90 µg mL −1 . (A) Antibody array layout showing antigen-specific antibody spots; ‘nbs1’ control spots were used for data normalization, and ‘NEG’ spots served as negative controls for baseline signal measurement. (B) Representative images of the original antibody arrays. (C) heat map illustrating the relative expression levels of DDR-related proteins, with color intensity indicating normalized expression values. Data represent four independent experiments ( n = 4). (D) semi-quantitative analysis of DDR-related proteins expression using antibody microarray in NHDF cells treated with GQDs at 90 µg mL −1 for 24 h. Data are expressed as mean ± standard deviation (sd) ( n = 4) relative to control cells. Statistical significance was assessed using one-way ANOVA, followed by Sidak’s multiple comparisons test: ns: not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 vs. control. (e) GQDs induced differential DDR pathway modulation. Pathway–gene interaction heatmap visualized the relationship between key signaling pathways (rows) and DDR-related proteins (columns). Color scale denotes expression changes, with red indicating up-regulation, blue indicating down-regulation.

    Article Snippet: NHDF cells (PromoCell, C-23210, Heidelberg, Germany) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Sigma, D0819) supplemented with 10% fetal bovine serum (FBS; Sigma, Lot: 0001653683), 1 μg mL −1 penicillin-streptomycin (Sigma, Lot: 0000191002), and 2 mM L-glutamine (Sigma, RNBL6712).

    Techniques: Expressing, Ab Array, Control, Microarray, Standard Deviation, Protein-Protein interactions

    Expression profiles of cytokine-related factors in NHDF cells following 24 h treatment with GQDs at 90 µg mL −1 . A human cytokine antibody array containing 40 cytokines was used, with ‘pos’ positive control spots applied for data normalization. (A) Each antibody was spotted in quadruplicate in a horizontal layout. (B) Representative fluorescence images of the cytokine antibody arrays. (C) Upregulated cytokines and downregulated cytokines were identified in NHDF cells treated with GQDs (90 µg mL −1 , 24 h). The array detected two significantly upregulated cytokines compared to control cells. Eight cytokines were significantly downregulated, intercellular adhesion molecule, C-X-C motif chemokine 9, interleukins/, and metalloproteinase inhibitors. Statistical analysis was receptors performed using unpaired Student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.005, **** p < 0.0001 vs. control. (D) Kyoto encyclopedia of genes and genomes (KEGG) showing immune pathway modulation following GQD exposure. Horizontal bar plot illustrating KEGG pathway enrichment based on log2 Fold change (GQDs/control). Negative values indicate pathway suppression and positive values indicate pathway activation following GQD exposure. GQDs markedly suppressed inflammatory and adhesion-related pathways while enhancing Th2-associated (IL-5) immune signaling.

    Journal: Science and Technology of Advanced Materials

    Article Title: Characterization and evaluation of the ability of graphene quantum dots to affect α-synuclein aggregation in synucleinopathy models

    doi: 10.1080/14686996.2026.2662693

    Figure Lengend Snippet: Expression profiles of cytokine-related factors in NHDF cells following 24 h treatment with GQDs at 90 µg mL −1 . A human cytokine antibody array containing 40 cytokines was used, with ‘pos’ positive control spots applied for data normalization. (A) Each antibody was spotted in quadruplicate in a horizontal layout. (B) Representative fluorescence images of the cytokine antibody arrays. (C) Upregulated cytokines and downregulated cytokines were identified in NHDF cells treated with GQDs (90 µg mL −1 , 24 h). The array detected two significantly upregulated cytokines compared to control cells. Eight cytokines were significantly downregulated, intercellular adhesion molecule, C-X-C motif chemokine 9, interleukins/, and metalloproteinase inhibitors. Statistical analysis was receptors performed using unpaired Student’s t-test. * p < 0.05, ** p < 0.01, *** p < 0.005, **** p < 0.0001 vs. control. (D) Kyoto encyclopedia of genes and genomes (KEGG) showing immune pathway modulation following GQD exposure. Horizontal bar plot illustrating KEGG pathway enrichment based on log2 Fold change (GQDs/control). Negative values indicate pathway suppression and positive values indicate pathway activation following GQD exposure. GQDs markedly suppressed inflammatory and adhesion-related pathways while enhancing Th2-associated (IL-5) immune signaling.

    Article Snippet: NHDF cells (PromoCell, C-23210, Heidelberg, Germany) were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Sigma, D0819) supplemented with 10% fetal bovine serum (FBS; Sigma, Lot: 0001653683), 1 μg mL −1 penicillin-streptomycin (Sigma, Lot: 0000191002), and 2 mM L-glutamine (Sigma, RNBL6712).

    Techniques: Expressing, Ab Array, Positive Control, Fluorescence, Control, Activation Assay

    A: UMAP representing 1024-dimensional DINOv2 features from six cell lines showing clustering by cell identity, confirming capture of meaningful morphological differences. B: effect of common image impairments on the prediction accuracy of a linear classifier trained to predict cell identity based on DINOv2 features (see Methods). Different types of impairments were used (x axis) and the bar plot shows the drop in classifier performance (y axis) for each impairment and cell line (identified by the color). The stars denote two cell lines for which defocused blurred images were not available. Importantly, random rotations result in negligible drops in accuracy, which is key to the use of these morphological features, as cell position cannot be controlled. C: morphological appearance of Hs 675.T colon fibroblast grown on a flow cell with a fibronectin-coated bottom surface, and a top surface with capture spots for transcriptomic analysis. Note that in this experiment we performed transcriptomic analysis at each timepoint in different lanes, which requires cell lysis. Therefore, in this case the pictures depict representative images at each time point, not longitudinal images of the same cells. D: volcano plots displaying the results of pseudo-bulk differential expression analysis between consecutive timepoints. E: UMAP visualization and clustering of 1024-dimensional embeddings extracted by DINOv2 applied to individual cell images at the 24 hours timepoint. The pictures display representative images of each cluster. F: single-cell differential expression analysis between the cell morphology-derived clusters identified in panel E.

    Journal: bioRxiv

    Article Title: Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

    doi: 10.64898/2026.05.05.723030

    Figure Lengend Snippet: A: UMAP representing 1024-dimensional DINOv2 features from six cell lines showing clustering by cell identity, confirming capture of meaningful morphological differences. B: effect of common image impairments on the prediction accuracy of a linear classifier trained to predict cell identity based on DINOv2 features (see Methods). Different types of impairments were used (x axis) and the bar plot shows the drop in classifier performance (y axis) for each impairment and cell line (identified by the color). The stars denote two cell lines for which defocused blurred images were not available. Importantly, random rotations result in negligible drops in accuracy, which is key to the use of these morphological features, as cell position cannot be controlled. C: morphological appearance of Hs 675.T colon fibroblast grown on a flow cell with a fibronectin-coated bottom surface, and a top surface with capture spots for transcriptomic analysis. Note that in this experiment we performed transcriptomic analysis at each timepoint in different lanes, which requires cell lysis. Therefore, in this case the pictures depict representative images at each time point, not longitudinal images of the same cells. D: volcano plots displaying the results of pseudo-bulk differential expression analysis between consecutive timepoints. E: UMAP visualization and clustering of 1024-dimensional embeddings extracted by DINOv2 applied to individual cell images at the 24 hours timepoint. The pictures display representative images of each cluster. F: single-cell differential expression analysis between the cell morphology-derived clusters identified in panel E.

    Article Snippet: Human primary subcutaneous pre-adipocytes were obtained from ATCC (#PCS-210-010) and maintained in fibroblast basal media (ATCC, #PCS-201-030) (proliferation media) supplemented with Fibroblast Growth Kit low serum from (ATCC, #PCS-201-041).

    Techniques: Lysis, Quantitative Proteomics, Single Cell, Derivative Assay