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fibroblast basal media  (ATCC)


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    Structured Review

    ATCC fibroblast basal media
    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.
    Fibroblast Basal Media, 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+media/bio_rxiv__64898__2026__05__05__723030-209-12-15?v=ATCC
    Average 97 stars, based on 477 article reviews
    fibroblast basal media - by Bioz Stars, 2026-06
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    Images

    1) Product Images from "Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures"

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

    Journal: bioRxiv

    doi: 10.64898/2026.05.05.723030

    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.
    Figure Legend 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.

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



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    ATCC fibroblast basal media
    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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    Regenerative effect of iHF-MSCs. A – B Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HaCaTs. Scale bars = 200 μm. C – D Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HDFs. Scale bars = 200 μm. E In vitro wound closure assay in HaCaTs. Scale bars = 200 μm. F In vitro wound closure assay in HDFs. Scale bars = 200 μm. G HUVEC-mediated tube formation assay. Scale bars = 200 μm. Data are presented as mean ± SD. N = 3 independent experiments, with 5 replicates per group. Statistical significance: γ p < 0.05, γγ p < 0.01, γγγ p < 0.001 compared to the negative control; * p < 0.05, ** p < 0.01 and *** p < 0.001 compared to HF-MSCs ## p < 0.01 compared to C18; $ p < 0.05, $$ p < 0.01 and $$$ p < 0.001 compared to the positive control. One-way ANOVA with the Bonferroni post-hoc test in B , D , E – II , F-II , G-II , G-III; Student’s t-test in G-IV . HF-MSCs: hair follicle-derived mesenchymal stromal cells. C18: clone 18. C26: clone 26. HaCaTs: HaCaT keratinocytes. HDFs: adult dermal <t>fibroblasts.</t> HUVECs: human umbilical vein endothelial cells
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    ATCC fibroblast basal media fbm
    Regenerative effect of iHF-MSCs. A – B Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HaCaTs. Scale bars = 200 μm. C – D Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HDFs. Scale bars = 200 μm. E In vitro wound closure assay in HaCaTs. Scale bars = 200 μm. F In vitro wound closure assay in HDFs. Scale bars = 200 μm. G HUVEC-mediated tube formation assay. Scale bars = 200 μm. Data are presented as mean ± SD. N = 3 independent experiments, with 5 replicates per group. Statistical significance: γ p < 0.05, γγ p < 0.01, γγγ p < 0.001 compared to the negative control; * p < 0.05, ** p < 0.01 and *** p < 0.001 compared to HF-MSCs ## p < 0.01 compared to C18; $ p < 0.05, $$ p < 0.01 and $$$ p < 0.001 compared to the positive control. One-way ANOVA with the Bonferroni post-hoc test in B , D , E – II , F-II , G-II , G-III; Student’s t-test in G-IV . HF-MSCs: hair follicle-derived mesenchymal stromal cells. C18: clone 18. C26: clone 26. HaCaTs: HaCaT keratinocytes. HDFs: adult dermal <t>fibroblasts.</t> HUVECs: human umbilical vein endothelial cells
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    ATCC fibroblast growth media
    Regenerative effect of iHF-MSCs. A – B Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HaCaTs. Scale bars = 200 μm. C – D Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HDFs. Scale bars = 200 μm. E In vitro wound closure assay in HaCaTs. Scale bars = 200 μm. F In vitro wound closure assay in HDFs. Scale bars = 200 μm. G HUVEC-mediated tube formation assay. Scale bars = 200 μm. Data are presented as mean ± SD. N = 3 independent experiments, with 5 replicates per group. Statistical significance: γ p < 0.05, γγ p < 0.01, γγγ p < 0.001 compared to the negative control; * p < 0.05, ** p < 0.01 and *** p < 0.001 compared to HF-MSCs ## p < 0.01 compared to C18; $ p < 0.05, $$ p < 0.01 and $$$ p < 0.001 compared to the positive control. One-way ANOVA with the Bonferroni post-hoc test in B , D , E – II , F-II , G-II , G-III; Student’s t-test in G-IV . HF-MSCs: hair follicle-derived mesenchymal stromal cells. C18: clone 18. C26: clone 26. HaCaTs: HaCaT keratinocytes. HDFs: adult dermal <t>fibroblasts.</t> HUVECs: human umbilical vein endothelial cells
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    Image Search Results


    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

    Regenerative effect of iHF-MSCs. A – B Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HaCaTs. Scale bars = 200 μm. C – D Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HDFs. Scale bars = 200 μm. E In vitro wound closure assay in HaCaTs. Scale bars = 200 μm. F In vitro wound closure assay in HDFs. Scale bars = 200 μm. G HUVEC-mediated tube formation assay. Scale bars = 200 μm. Data are presented as mean ± SD. N = 3 independent experiments, with 5 replicates per group. Statistical significance: γ p < 0.05, γγ p < 0.01, γγγ p < 0.001 compared to the negative control; * p < 0.05, ** p < 0.01 and *** p < 0.001 compared to HF-MSCs ## p < 0.01 compared to C18; $ p < 0.05, $$ p < 0.01 and $$$ p < 0.001 compared to the positive control. One-way ANOVA with the Bonferroni post-hoc test in B , D , E – II , F-II , G-II , G-III; Student’s t-test in G-IV . HF-MSCs: hair follicle-derived mesenchymal stromal cells. C18: clone 18. C26: clone 26. HaCaTs: HaCaT keratinocytes. HDFs: adult dermal fibroblasts. HUVECs: human umbilical vein endothelial cells

    Journal: Stem Cell Research & Therapy

    Article Title: Immortalized human hair follicle-derived mesenchymal-like stromal cells for the long-term production of scalable Immunomodulatory and regenerative secretome

    doi: 10.1186/s13287-025-04775-8

    Figure Lengend Snippet: Regenerative effect of iHF-MSCs. A – B Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HaCaTs. Scale bars = 200 μm. C – D Effect of iHF-MSC-derived unlicensed conditioned media on the proliferation of HDFs. Scale bars = 200 μm. E In vitro wound closure assay in HaCaTs. Scale bars = 200 μm. F In vitro wound closure assay in HDFs. Scale bars = 200 μm. G HUVEC-mediated tube formation assay. Scale bars = 200 μm. Data are presented as mean ± SD. N = 3 independent experiments, with 5 replicates per group. Statistical significance: γ p < 0.05, γγ p < 0.01, γγγ p < 0.001 compared to the negative control; * p < 0.05, ** p < 0.01 and *** p < 0.001 compared to HF-MSCs ## p < 0.01 compared to C18; $ p < 0.05, $$ p < 0.01 and $$$ p < 0.001 compared to the positive control. One-way ANOVA with the Bonferroni post-hoc test in B , D , E – II , F-II , G-II , G-III; Student’s t-test in G-IV . HF-MSCs: hair follicle-derived mesenchymal stromal cells. C18: clone 18. C26: clone 26. HaCaTs: HaCaT keratinocytes. HDFs: adult dermal fibroblasts. HUVECs: human umbilical vein endothelial cells

    Article Snippet: HF-MSCs were infected using lentiviral particles that contains simian virus 40 (SV40) T antigen to obtain iHF-MSCs as described in Sect. . HDFs were cultured in fibroblasts basal media (ATCC, PCS-201-030) supplemented with the fibroblast growth kit-low serum (ATCC, PCS-201-041) and 1% (v/v) penicillin–streptomycin (P/S).

    Techniques: Derivative Assay, In Vitro, Wound Closure Assay, Tube Formation Assay, Negative Control, Positive Control