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fibroblast basal media  (ATCC)
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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.
Fibroblast Basal Media, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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fbm medium  (ATCC)
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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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pcs  (ATCC)
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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.
Pcs, supplied by ATCC, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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 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