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hek293 cells  (ATCC)


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    ATCC hek293 cells
    Hek293 Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 97 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hek293 cells/product/ATCC
    Average 99 stars, based on 97 article reviews
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    CLS Cell Lines Service GmbH hek293 cells
    Machine learning framework for live-cell single-particle tracking elucidates variations in intracellular trafficking of insulin analogs (A) A schematic representation of the live-cell assay used to investigate intracellular trafficking patterns. Cells are incubated with labeled insulin (see primary structure in (B) and a fluorescent marker probing an endosomal compartment; early endosomes by Rab5a, late endosomes by Rab7a, endolysosomes, and lysosomes with LysoTracker or transferrin to probe recycling pathways. Temporal 2D molecular movies in two channels are collected to compare insulin behavior with the given endolysosomal or recycling compartment. (B) Primary structure of Atto655-labeled human insulin (Atto655 and insulin are not to scale) highlighting the single point mutation from proline to aspartic acid on position B28, of the fast-acting insulin analog, insulin aspart. (C) Representative spinning disk confocal microscope image of dual-labeled <t>HEK293</t> cells (Scale bars, 10 μm). Live-cell imaging produces a set of x , y , and t , localizations for each particle, yielding a dataset of single particle trajectories (inset, Scale bars, 0.5 μm). Here, cells are overlaid with the corresponding single particle trajectories of insulin aspart (red, N = 269) and transferrin (blue, N = 2,911). See representative videos of all conditions in , , , , , , , and . (D) Trajectories are fed to DeepSPT, a deep-learning framework, which quantifies diffusional variation between entities, here the two insulin variants (1), and classifies them according to the most similar endolysosomal diffusion (2). LE, late endosomes; LS, lysosomes. (E) Colocalization fingerprinting, a machine learning pipeline for reliable and automatic identification of colocalization based on temporal consistent proximity, and spatial and diffusional features.
    Hek293 Cells, supplied by CLS Cell Lines Service GmbH, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    293  (ATCC)
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    ATCC 293
    Machine learning framework for live-cell single-particle tracking elucidates variations in intracellular trafficking of insulin analogs (A) A schematic representation of the live-cell assay used to investigate intracellular trafficking patterns. Cells are incubated with labeled insulin (see primary structure in (B) and a fluorescent marker probing an endosomal compartment; early endosomes by Rab5a, late endosomes by Rab7a, endolysosomes, and lysosomes with LysoTracker or transferrin to probe recycling pathways. Temporal 2D molecular movies in two channels are collected to compare insulin behavior with the given endolysosomal or recycling compartment. (B) Primary structure of Atto655-labeled human insulin (Atto655 and insulin are not to scale) highlighting the single point mutation from proline to aspartic acid on position B28, of the fast-acting insulin analog, insulin aspart. (C) Representative spinning disk confocal microscope image of dual-labeled <t>HEK293</t> cells (Scale bars, 10 μm). Live-cell imaging produces a set of x , y , and t , localizations for each particle, yielding a dataset of single particle trajectories (inset, Scale bars, 0.5 μm). Here, cells are overlaid with the corresponding single particle trajectories of insulin aspart (red, N = 269) and transferrin (blue, N = 2,911). See representative videos of all conditions in , , , , , , , and . (D) Trajectories are fed to DeepSPT, a deep-learning framework, which quantifies diffusional variation between entities, here the two insulin variants (1), and classifies them according to the most similar endolysosomal diffusion (2). LE, late endosomes; LS, lysosomes. (E) Colocalization fingerprinting, a machine learning pipeline for reliable and automatic identification of colocalization based on temporal consistent proximity, and spatial and diffusional features.
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    ATCC adherent hek293 cells
    Culturing Adherent <t>HEK</t> <t>293</t> T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.
    Adherent Hek293 Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Culturing Adherent <t>HEK</t> <t>293</t> T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.
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    Culturing Adherent <t>HEK</t> <t>293</t> T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.
    Product, supplied by CLS Cell Lines Service GmbH, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    ATCC jo urn al pr e p roo f experimental procedures cell culture hek293 cells
    Culturing Adherent <t>HEK</t> <t>293</t> T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.
    Jo Urn Al Pr E P Roo F Experimental Procedures Cell Culture Hek293 Cells, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Culturing Adherent <t>HEK</t> <t>293</t> T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.
    Cell Transfection Hek293, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Machine learning framework for live-cell single-particle tracking elucidates variations in intracellular trafficking of insulin analogs (A) A schematic representation of the live-cell assay used to investigate intracellular trafficking patterns. Cells are incubated with labeled insulin (see primary structure in (B) and a fluorescent marker probing an endosomal compartment; early endosomes by Rab5a, late endosomes by Rab7a, endolysosomes, and lysosomes with LysoTracker or transferrin to probe recycling pathways. Temporal 2D molecular movies in two channels are collected to compare insulin behavior with the given endolysosomal or recycling compartment. (B) Primary structure of Atto655-labeled human insulin (Atto655 and insulin are not to scale) highlighting the single point mutation from proline to aspartic acid on position B28, of the fast-acting insulin analog, insulin aspart. (C) Representative spinning disk confocal microscope image of dual-labeled HEK293 cells (Scale bars, 10 μm). Live-cell imaging produces a set of x , y , and t , localizations for each particle, yielding a dataset of single particle trajectories (inset, Scale bars, 0.5 μm). Here, cells are overlaid with the corresponding single particle trajectories of insulin aspart (red, N = 269) and transferrin (blue, N = 2,911). See representative videos of all conditions in , , , , , , , and . (D) Trajectories are fed to DeepSPT, a deep-learning framework, which quantifies diffusional variation between entities, here the two insulin variants (1), and classifies them according to the most similar endolysosomal diffusion (2). LE, late endosomes; LS, lysosomes. (E) Colocalization fingerprinting, a machine learning pipeline for reliable and automatic identification of colocalization based on temporal consistent proximity, and spatial and diffusional features.

    Journal: iScience

    Article Title: Diverse intracellular trafficking of insulin analogs by machine learning-based colocalization and diffusion analysis

    doi: 10.1016/j.isci.2025.114516

    Figure Lengend Snippet: Machine learning framework for live-cell single-particle tracking elucidates variations in intracellular trafficking of insulin analogs (A) A schematic representation of the live-cell assay used to investigate intracellular trafficking patterns. Cells are incubated with labeled insulin (see primary structure in (B) and a fluorescent marker probing an endosomal compartment; early endosomes by Rab5a, late endosomes by Rab7a, endolysosomes, and lysosomes with LysoTracker or transferrin to probe recycling pathways. Temporal 2D molecular movies in two channels are collected to compare insulin behavior with the given endolysosomal or recycling compartment. (B) Primary structure of Atto655-labeled human insulin (Atto655 and insulin are not to scale) highlighting the single point mutation from proline to aspartic acid on position B28, of the fast-acting insulin analog, insulin aspart. (C) Representative spinning disk confocal microscope image of dual-labeled HEK293 cells (Scale bars, 10 μm). Live-cell imaging produces a set of x , y , and t , localizations for each particle, yielding a dataset of single particle trajectories (inset, Scale bars, 0.5 μm). Here, cells are overlaid with the corresponding single particle trajectories of insulin aspart (red, N = 269) and transferrin (blue, N = 2,911). See representative videos of all conditions in , , , , , , , and . (D) Trajectories are fed to DeepSPT, a deep-learning framework, which quantifies diffusional variation between entities, here the two insulin variants (1), and classifies them according to the most similar endolysosomal diffusion (2). LE, late endosomes; LS, lysosomes. (E) Colocalization fingerprinting, a machine learning pipeline for reliable and automatic identification of colocalization based on temporal consistent proximity, and spatial and diffusional features.

    Article Snippet: HEK293 cells were obtained from Cell Line Service (CLS, now Cytion, received authenticated at split level 27) and cultured in Dulbecco’s Modified Eagle Medium (DMEM) with 10% Fetal Bovine Serum (FBS) at 37°C with 5% CO 2 .

    Techniques: Single-particle Tracking, Incubation, Labeling, Marker, Mutagenesis, Microscopy, Live Cell Imaging, Single Particle, Diffusion-based Assay

    Culturing Adherent HEK 293 T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.

    Journal: Biofabrication

    Article Title: Collagen hydrogel tube microbioreactors for cell and tissue manufacturing

    doi: 10.1088/1758-5090/ae2718

    Figure Lengend Snippet: Culturing Adherent HEK 293 T Cells in ColTubes. Phase-contrast and Live/Dead staining images of adherent HEK 293 cells (A) and suspension 293 T 17SF cells (B) in ColTubes.

    Article Snippet: For a typical cell culture, adherent HEK293 cells (#CRL-1573, ATCC) loaded in 20 μ l ColTubes were suspended in 2 ml DMEM supplemented with 10% fetal bovine serum (FBS) in a 6-well plate and incubated at 37 °C with 5% CO2 and 21% O2.

    Techniques: Staining, Suspension