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anti tomm20 rabbit polyclonal antibodies  (Proteintech)


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    Structured Review

    Proteintech anti tomm20 rabbit polyclonal antibodies
    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    Images

    1) Product Images from "Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy"

    Article Title: Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy

    Journal: Nature Communications

    doi: 10.1038/s41467-025-68150-0

    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: TOMM20 protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
    Figure Legend Snippet: A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: TOMM20 protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.

    Techniques Used: Fluorescence, Microscopy, Staining, Cell Counting



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    Proteintech anti tomm20 rabbit polyclonal antibodies
    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: <t>TOMM20</t> protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.
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    PEDV Nsp14 interacts with NDP52 and <t>TOM20.</t> ( A, B ) The Ni-NTA agaroses were bound by His-Nsp14 and incubated with IPEC-J2 WCLs at 4°C overnight. The resulting complexes were separated using SDS-PAGE and subjected to silver staining or WB analysis with the specific antibodies. The asterisk marked the bait protein His-Nsp14, the red arrow denoted NDP52, and the green arrow pointed to TOM20. ( C, D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, with the HA-tagged empty vector serving as a negative control. IP was performed with anti-HA magnetic beads, and WB was conducted with the specific antibodies. ( E, F ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, and fluorescence signals were visualized with the specific antibodies using confocal microscopy at 36 hpt. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( G, H ) IPEC-J2 cells were infected with PEDV (0.2 MOI) for 36 h. Fluorescence signals were visualized with the specific antibodies using confocal microscopy. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm.
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    PEDV Nsp14 interacts with NDP52 and <t>TOM20.</t> ( A, B ) The Ni-NTA agaroses were bound by His-Nsp14 and incubated with IPEC-J2 WCLs at 4°C overnight. The resulting complexes were separated using SDS-PAGE and subjected to silver staining or WB analysis with the specific antibodies. The asterisk marked the bait protein His-Nsp14, the red arrow denoted NDP52, and the green arrow pointed to TOM20. ( C, D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, with the HA-tagged empty vector serving as a negative control. IP was performed with anti-HA magnetic beads, and WB was conducted with the specific antibodies. ( E, F ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, and fluorescence signals were visualized with the specific antibodies using confocal microscopy at 36 hpt. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( G, H ) IPEC-J2 cells were infected with PEDV (0.2 MOI) for 36 h. Fluorescence signals were visualized with the specific antibodies using confocal microscopy. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm.
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    PEDV Nsp14 interacts with NDP52 and <t>TOM20.</t> ( A, B ) The Ni-NTA agaroses were bound by His-Nsp14 and incubated with IPEC-J2 WCLs at 4°C overnight. The resulting complexes were separated using SDS-PAGE and subjected to silver staining or WB analysis with the specific antibodies. The asterisk marked the bait protein His-Nsp14, the red arrow denoted NDP52, and the green arrow pointed to TOM20. ( C, D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, with the HA-tagged empty vector serving as a negative control. IP was performed with anti-HA magnetic beads, and WB was conducted with the specific antibodies. ( E, F ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, and fluorescence signals were visualized with the specific antibodies using confocal microscopy at 36 hpt. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( G, H ) IPEC-J2 cells were infected with PEDV (0.2 MOI) for 36 h. Fluorescence signals were visualized with the specific antibodies using confocal microscopy. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm.
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    Image Search Results


    A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: TOMM20 protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.

    Journal: Nature Communications

    Article Title: Automatic optimization of flat-field corrections by evaluation and enhancement (EVEN) in multimodal optical microscopy

    doi: 10.1038/s41467-025-68150-0

    Figure Lengend Snippet: A three-channel fluorescence microscopy measurement of stained HEK293 cells measured by Ph2 objective is automatically optimized by EVEN (prediction dataset 2, red: peroxisomal proteins (anti-GFP nanobody); green: TOMM20 protein; blue: peroxisomal proteins (eGFP)). a Raw multi-channel image. The inset shows the 2 × 2 tile section of the image used in this figure, with dashed white lines marking tile borders. Multiple corrections are obtained by applying BaSiC, CIDRE, Fourier methods, and then optimizing the multi-channel image with EVEN. EVEN selects CIDRE for the red and green channel, and Fourier for the blue channel. b Steps to analyse the measurements of stained cells: multi-channel images are converted to greyscale by summing the single channels (that contain signals from different components of the cytoplasm) and are analysed with automatic cells segmentation using Cellpose . The greyscale image is obtained for the raw measurement, the single-channel corrections and the EVEN optimization. c Intensity sum (along y) of the greyscale inset for each method. The black dashed line indicates the border between neighbouring tiles. The corrected images show higher intensities at the edges of the tiles and the enhancement of sample features. EVEN and CIDRE show the greatest intensity recovery between tiles. d Top row: multi-channel images obtained with single-method corrections and EVEN optimization; the white dashed boxes highlight two regions significantly improved by EVEN. Bottom row: Cellpose prediction on the greyscale sum of the three channels for each method. After correction of uneven illumination, Cellpose can outline a greater number of cells, especially at the borders of neighbouring tiles. White dashed boxes highlight three regions where EVEN optimization provides better identification of the cells compared to non-optimized images. Bottom labels show, for each image, the normalized EVEN score summed over three channels and the cell count in the zoomed region. While counts are not strictly correlated with segmentation performance, good correction of uneven illumination enhances downstream analysis and generally increases the number of detected cells. Further quantification is provided in Supplementary Fig. . Scale bar: 180 µm, size of a single tile.

    Article Snippet: Additionally, immunolabeling was performed on TOMM20-protein with anti-Tomm20 rabbit polyclonal antibodies (proteintech, USA), dilution 1:200, and goat anti-rabbit IgG secondary antibodies labelled with Abberior STAR Orange (Abberior, Germany) at a dilution of 1:350.

    Techniques: Fluorescence, Microscopy, Staining, Cell Counting

    PEDV Nsp14 interacts with NDP52 and TOM20. ( A, B ) The Ni-NTA agaroses were bound by His-Nsp14 and incubated with IPEC-J2 WCLs at 4°C overnight. The resulting complexes were separated using SDS-PAGE and subjected to silver staining or WB analysis with the specific antibodies. The asterisk marked the bait protein His-Nsp14, the red arrow denoted NDP52, and the green arrow pointed to TOM20. ( C, D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, with the HA-tagged empty vector serving as a negative control. IP was performed with anti-HA magnetic beads, and WB was conducted with the specific antibodies. ( E, F ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, and fluorescence signals were visualized with the specific antibodies using confocal microscopy at 36 hpt. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( G, H ) IPEC-J2 cells were infected with PEDV (0.2 MOI) for 36 h. Fluorescence signals were visualized with the specific antibodies using confocal microscopy. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm.

    Journal: Journal of Virology

    Article Title: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to antagonize host innate immunity and facilitate viral proliferation

    doi: 10.1128/jvi.00498-25

    Figure Lengend Snippet: PEDV Nsp14 interacts with NDP52 and TOM20. ( A, B ) The Ni-NTA agaroses were bound by His-Nsp14 and incubated with IPEC-J2 WCLs at 4°C overnight. The resulting complexes were separated using SDS-PAGE and subjected to silver staining or WB analysis with the specific antibodies. The asterisk marked the bait protein His-Nsp14, the red arrow denoted NDP52, and the green arrow pointed to TOM20. ( C, D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, with the HA-tagged empty vector serving as a negative control. IP was performed with anti-HA magnetic beads, and WB was conducted with the specific antibodies. ( E, F ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, and fluorescence signals were visualized with the specific antibodies using confocal microscopy at 36 hpt. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( G, H ) IPEC-J2 cells were infected with PEDV (0.2 MOI) for 36 h. Fluorescence signals were visualized with the specific antibodies using confocal microscopy. The assessment of interaction was conducted by calculating the Pearson’s correlation coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm.

    Article Snippet: Rabbit anti-TOM20 polyclonal antibodies (pAbs, 11802-1-AP), horseradish peroxidase (HRP)-labeled mouse anti-β-actin monoclonal antibody (mAb, HRP-66009), rabbit anti-NDP52 pAbs (12229-1-AP), and rabbit anti-OPTN pAbs (10837-1-AP) were purchased from Proteintech (Wuhan, China).

    Techniques: Incubation, SDS Page, Silver Staining, Transfection, Plasmid Preparation, Negative Control, Magnetic Beads, Fluorescence, Confocal Microscopy, Software, Infection

    PEDV Nsp14 recruits NDP52 to mitochondria to induce mitophagy. ( A, B ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, followed by IP using protein G magnetic beads pre-incubated with anti-NDP52 or anti-TOM20 pAbs, and subsequent WB analysis. ( C ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h. The cytoplasmic and mitochondrial fractions were isolated for WB analysis with the indicated antibodies. ( D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h. The cells were subjected to confocal microscopy using the specific antibodies and fluorescent reagents. The assessment of co-localization was conducted by calculating the Manders’ overlap coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( E ) IPEC-J2 cells were infected with or without PEDV for 36 h. The cells were subjected to confocal microscopy using the specific antibodies and fluorescent reagents. The assessment of co-localization was conducted by calculating the Manders’ overlap coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( F ) IPEC-J2 cells were transfected with siNC or siNDP52. At 24 hpt, the cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, followed by WB.

    Journal: Journal of Virology

    Article Title: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to antagonize host innate immunity and facilitate viral proliferation

    doi: 10.1128/jvi.00498-25

    Figure Lengend Snippet: PEDV Nsp14 recruits NDP52 to mitochondria to induce mitophagy. ( A, B ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h, followed by IP using protein G magnetic beads pre-incubated with anti-NDP52 or anti-TOM20 pAbs, and subsequent WB analysis. ( C ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h. The cytoplasmic and mitochondrial fractions were isolated for WB analysis with the indicated antibodies. ( D ) IPEC-J2 cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector for 36 h. The cells were subjected to confocal microscopy using the specific antibodies and fluorescent reagents. The assessment of co-localization was conducted by calculating the Manders’ overlap coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( E ) IPEC-J2 cells were infected with or without PEDV for 36 h. The cells were subjected to confocal microscopy using the specific antibodies and fluorescent reagents. The assessment of co-localization was conducted by calculating the Manders’ overlap coefficient using the JaCoP plugin in the ImageJ software. Scale bars = 10 µm. ( F ) IPEC-J2 cells were transfected with siNC or siNDP52. At 24 hpt, the cells were transfected with the pCAGGS-HA-Nsp14 plasmid or HA-tagged empty vector, followed by WB.

    Article Snippet: Rabbit anti-TOM20 polyclonal antibodies (pAbs, 11802-1-AP), horseradish peroxidase (HRP)-labeled mouse anti-β-actin monoclonal antibody (mAb, HRP-66009), rabbit anti-NDP52 pAbs (12229-1-AP), and rabbit anti-OPTN pAbs (10837-1-AP) were purchased from Proteintech (Wuhan, China).

    Techniques: Transfection, Plasmid Preparation, Magnetic Beads, Incubation, Isolation, Confocal Microscopy, Software, Infection

    PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to inhibit IFN-β production and facilitate viral proliferation during infection in IPEC-J2 cells. ( A, B ) IPEC-J2 cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (24, 36, and 48 hpi). The MAVS mRNA and protein levels were detected by RT-qPCR and WB, respectively. ( C through G ) IPEC-J2 cells were transfected with siNC, siNsp14, or siNDP52. At 24 hpt, the cells were infected with 0.2 MOI PEDV. In parallel, Mdivi-1 was applied as a control. The MAVS, TOM20, and PEDV N protein abundance were analyzed by WB ( C ). After poly (I:C) transfection, the mRNA levels of IFN-β were detected by RT-qPCR. The cells transfected with siNC alone served as control ( D ). RT-qPCR was used to evaluate the PEDV mRNA levels ( E ). PEDV infectivity was detected using IFA with the mouse anti-PEDV N protein mAb. The cells were stained with DAPI for nuclear staining. Scale bars = 50 µm ( F ). PEDV titers were measured by assessing TCID 50 ( G ). Data represent means ± SEM from three independent experiments. Statistical analysis was carried out using one-way ANOVA or Student’s t test. ns P > 0.05, *** P < 0.001 and **** P < 0.0001.

    Journal: Journal of Virology

    Article Title: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to antagonize host innate immunity and facilitate viral proliferation

    doi: 10.1128/jvi.00498-25

    Figure Lengend Snippet: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to inhibit IFN-β production and facilitate viral proliferation during infection in IPEC-J2 cells. ( A, B ) IPEC-J2 cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (24, 36, and 48 hpi). The MAVS mRNA and protein levels were detected by RT-qPCR and WB, respectively. ( C through G ) IPEC-J2 cells were transfected with siNC, siNsp14, or siNDP52. At 24 hpt, the cells were infected with 0.2 MOI PEDV. In parallel, Mdivi-1 was applied as a control. The MAVS, TOM20, and PEDV N protein abundance were analyzed by WB ( C ). After poly (I:C) transfection, the mRNA levels of IFN-β were detected by RT-qPCR. The cells transfected with siNC alone served as control ( D ). RT-qPCR was used to evaluate the PEDV mRNA levels ( E ). PEDV infectivity was detected using IFA with the mouse anti-PEDV N protein mAb. The cells were stained with DAPI for nuclear staining. Scale bars = 50 µm ( F ). PEDV titers were measured by assessing TCID 50 ( G ). Data represent means ± SEM from three independent experiments. Statistical analysis was carried out using one-way ANOVA or Student’s t test. ns P > 0.05, *** P < 0.001 and **** P < 0.0001.

    Article Snippet: Rabbit anti-TOM20 polyclonal antibodies (pAbs, 11802-1-AP), horseradish peroxidase (HRP)-labeled mouse anti-β-actin monoclonal antibody (mAb, HRP-66009), rabbit anti-NDP52 pAbs (12229-1-AP), and rabbit anti-OPTN pAbs (10837-1-AP) were purchased from Proteintech (Wuhan, China).

    Techniques: Infection, Quantitative RT-PCR, Transfection, Control, Quantitative Proteomics, Staining

    PEDV-induced mitophagy and MAVS degradation had no effect on viral proliferation in Vero cells. ( A ) Vero cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (12, 18, and 24 hpi). The MAVS and TOM20 protein levels were analyzed by WB. ( B ) Vero cells were infected with PEDV at different MOIs (0.1, 0.2, and 0.4 MOI) or mock-infected for 24 h. WCLs were analyzed by WB. ( C ) Vero cells were infected with or without PEDV (0.2 MOI) for 18 h and fixed for TEM (scale bars = 5 µm). ( D ) Vero cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (12, 18, and 24 hpi). The MAVS mRNA levels were detected by RT-qPCR. ( E ) The cytotoxicity of Mdivi-1 on Vero cells was detected using the cell viability assay. ( F, G ) Vero cells were transfected with siNC, siNsp14, or siNDP52. At 24 hpt, the cells were infected with 0.2 MOI PEDV. In parallel, Mdivi-1 was applied as a control. The MAVS, TOM20, and PEDV N protein abundance were analyzed by WB ( F ). PEDV titers were measured by assessing TCID 50 ( G ). The cells transfected with siNC alone served as a control. Data represent means ± SEM from three independent experiments. Statistical analysis was carried out using one-way ANOVA or Student’s t test. ns P > 0.05 and **** P < 0.0001.

    Journal: Journal of Virology

    Article Title: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to antagonize host innate immunity and facilitate viral proliferation

    doi: 10.1128/jvi.00498-25

    Figure Lengend Snippet: PEDV-induced mitophagy and MAVS degradation had no effect on viral proliferation in Vero cells. ( A ) Vero cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (12, 18, and 24 hpi). The MAVS and TOM20 protein levels were analyzed by WB. ( B ) Vero cells were infected with PEDV at different MOIs (0.1, 0.2, and 0.4 MOI) or mock-infected for 24 h. WCLs were analyzed by WB. ( C ) Vero cells were infected with or without PEDV (0.2 MOI) for 18 h and fixed for TEM (scale bars = 5 µm). ( D ) Vero cells were infected with or without 0.2 MOI PEDV and collected at the indicated time points (12, 18, and 24 hpi). The MAVS mRNA levels were detected by RT-qPCR. ( E ) The cytotoxicity of Mdivi-1 on Vero cells was detected using the cell viability assay. ( F, G ) Vero cells were transfected with siNC, siNsp14, or siNDP52. At 24 hpt, the cells were infected with 0.2 MOI PEDV. In parallel, Mdivi-1 was applied as a control. The MAVS, TOM20, and PEDV N protein abundance were analyzed by WB ( F ). PEDV titers were measured by assessing TCID 50 ( G ). The cells transfected with siNC alone served as a control. Data represent means ± SEM from three independent experiments. Statistical analysis was carried out using one-way ANOVA or Student’s t test. ns P > 0.05 and **** P < 0.0001.

    Article Snippet: Rabbit anti-TOM20 polyclonal antibodies (pAbs, 11802-1-AP), horseradish peroxidase (HRP)-labeled mouse anti-β-actin monoclonal antibody (mAb, HRP-66009), rabbit anti-NDP52 pAbs (12229-1-AP), and rabbit anti-OPTN pAbs (10837-1-AP) were purchased from Proteintech (Wuhan, China).

    Techniques: Infection, Quantitative RT-PCR, Viability Assay, Transfection, Control, Quantitative Proteomics

    A proposed model depicts that PEDV Nsp14 induces mitophagy to degrade MAVS for suppressing IFN-β production and facilitating viral proliferation. Mechanistically, PEDV Nsp14 mediates the interaction between NDP52 and TOM20, which leads to the recruitment of NDP52 to mitochondria. This process induces mitophagy, which degrades MAVS and inhibits the IFN-β production, ultimately promoting virus proliferation.

    Journal: Journal of Virology

    Article Title: PEDV Nsp14 induces mitophagy-mediated degradation of MAVS to antagonize host innate immunity and facilitate viral proliferation

    doi: 10.1128/jvi.00498-25

    Figure Lengend Snippet: A proposed model depicts that PEDV Nsp14 induces mitophagy to degrade MAVS for suppressing IFN-β production and facilitating viral proliferation. Mechanistically, PEDV Nsp14 mediates the interaction between NDP52 and TOM20, which leads to the recruitment of NDP52 to mitochondria. This process induces mitophagy, which degrades MAVS and inhibits the IFN-β production, ultimately promoting virus proliferation.

    Article Snippet: Rabbit anti-TOM20 polyclonal antibodies (pAbs, 11802-1-AP), horseradish peroxidase (HRP)-labeled mouse anti-β-actin monoclonal antibody (mAb, HRP-66009), rabbit anti-NDP52 pAbs (12229-1-AP), and rabbit anti-OPTN pAbs (10837-1-AP) were purchased from Proteintech (Wuhan, China).

    Techniques: Virus