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media  (ATCC)


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    Structured Review

    ATCC media
    Media, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 758 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/basal+media/bio_rxiv__64898__2026__05__07__723543-48-5-11?v=ATCC
    Average 99 stars, based on 758 article reviews
    media - by Bioz Stars, 2026-07
    99/100 stars

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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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    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

    Fabrication and surface characterization of silver nanoparticle (AgNP)-functionalized PLA scaffolds. A) Schematic illustration of the scaffold fabrication and surface modification workflow: hexagonal honeycomb G-code was used to 3D-print PLA scaffolds, which were subsequently dip-coated in AgNO₃ solution, with or without prior incubation in polydopamine hydrochloride (PDA), followed by Plasma Electroless Reduction (PER) under H₂ gas to yield PLA+AgNP and PLA+PDA+AgNP constructs, respectively. B) Representative scanning electron microscopy (SEM) images of PLA+AgNP (top row) and PLA+PDA+AgNP (bottom row) scaffolds fabricated across a range of AgNO₃ concentrations (0–25 mM), with corresponding optical images of the scaffold surface shown as insets. Scale bars = 5 µm. C) SEM micrographs of L929 fibroblasts (top row) and human mesenchymal stem cells (hMSCs, bottom row) adhered to unmodified PLA HC, PLA HC+AgNP (0.7 mM AgNO₃), and PLA HC+PDA+AgNP (0.7 mM AgNO₃) scaffolds. Black arrows indicate representative cell–scaffold interactions. Scale bars = 15 µm.

    Journal: bioRxiv

    Article Title: Plasma-Enabled Multiscale Coupling of Architecture and Biointerfaces Drives Osteogenesis in 3D-Printed Gyroid Scaffolds

    doi: 10.64898/2026.04.16.718992

    Figure Lengend Snippet: Fabrication and surface characterization of silver nanoparticle (AgNP)-functionalized PLA scaffolds. A) Schematic illustration of the scaffold fabrication and surface modification workflow: hexagonal honeycomb G-code was used to 3D-print PLA scaffolds, which were subsequently dip-coated in AgNO₃ solution, with or without prior incubation in polydopamine hydrochloride (PDA), followed by Plasma Electroless Reduction (PER) under H₂ gas to yield PLA+AgNP and PLA+PDA+AgNP constructs, respectively. B) Representative scanning electron microscopy (SEM) images of PLA+AgNP (top row) and PLA+PDA+AgNP (bottom row) scaffolds fabricated across a range of AgNO₃ concentrations (0–25 mM), with corresponding optical images of the scaffold surface shown as insets. Scale bars = 5 µm. C) SEM micrographs of L929 fibroblasts (top row) and human mesenchymal stem cells (hMSCs, bottom row) adhered to unmodified PLA HC, PLA HC+AgNP (0.7 mM AgNO₃), and PLA HC+PDA+AgNP (0.7 mM AgNO₃) scaffolds. Black arrows indicate representative cell–scaffold interactions. Scale bars = 15 µm.

    Article Snippet: The cells were incubated in mesenchymal stem cell complete media (Basal media: ATCC, USA: Cat no: PCS-500-030 and growth kit: ATCC, USA: Cat no: PCS-500-041) for five days.

    Techniques: Modification, Incubation, Clinical Proteomics, Construct, Electron Microscopy