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codex multiplexed imaging computational analysis workflow  (Akoya Biosciences)


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    Structured Review

    Akoya Biosciences codex multiplexed imaging computational analysis workflow
    (A) Schematic overview of the <t>CODEX</t> imaging <t>workflow.</t> We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.
    Codex Multiplexed Imaging Computational Analysis Workflow, supplied by Akoya Biosciences, used in various techniques. Bioz Stars score: 99/100, based on 12644 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/codex+multiplexed+imaging+computational+analysis+workflow/PhenoCycler-Fusion+2%2E0/bio_rxiv__2024__02__22__581315-66-4-4
    Average 99 stars, based on 12644 article reviews
    codex multiplexed imaging computational analysis workflow - by Bioz Stars, 2026-11
    99/100 stars

    Images

    1) Product Images from "Unraveling the Complexity of Abdominal Aortic Aneurysm: Multiplexed Imaging Insights into C-Reactive Protein-Related Variations"

    Article Title: Unraveling the Complexity of Abdominal Aortic Aneurysm: Multiplexed Imaging Insights into C-Reactive Protein-Related Variations

    Journal: bioRxiv

    doi: 10.1101/2024.02.22.581315

    (A) Schematic overview of the CODEX imaging workflow. We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.
    Figure Legend Snippet: (A) Schematic overview of the CODEX imaging workflow. We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.

    Techniques Used: Imaging, Construct, Staining, Expressing

    Related Articles

    Imaging:

    Article Title: Unraveling the Complexity of Abdominal Aortic Aneurysm: Multiplexed Imaging Insights into C-Reactive Protein-Related Variations
    Article Snippet: Staining patterns were confirmed using the online database, The Human Protein Atlas ( www.proteinatlas.org ). .. The overview of the CODEX multiplexed imaging computational analysis workflow is illustrated in . ..



    Similar Products

    codex multiplexed imaging computational analysis workflow  (Akoya Biosciences)
    99
    Akoya Biosciences codex multiplexed imaging computational analysis workflow
    (A) Schematic overview of the <t>CODEX</t> imaging <t>workflow.</t> We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.
    Codex Multiplexed Imaging Computational Analysis Workflow, supplied by Akoya Biosciences, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/codex+multiplexed+imaging+computational+analysis+workflow/PhenoCycler-Fusion+2%2E0/bio_rxiv__2024__02__22__581315-66-4-4
    Average 99 stars, based on 1 article reviews
    codex multiplexed imaging computational analysis workflow - by Bioz Stars, 2026-11
    99/100 stars
    		  Buy from Supplier

    Image Search Results


    (A) Schematic overview of the CODEX imaging workflow. We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.

    Journal: bioRxiv

    Article Title: Unraveling the Complexity of Abdominal Aortic Aneurysm: Multiplexed Imaging Insights into C-Reactive Protein-Related Variations

    doi: 10.1101/2024.02.22.581315

    Figure Lengend Snippet: (A) Schematic overview of the CODEX imaging workflow. We constructed 31 antibody panels tagged by oligonucleotides. All antibodies were stained in a single step, and imaging was performed on every three antibodies in a cyclic reveal-imaging-remove manner. All images were preprocessed and layered in one qptiff multidimensional image. (B) Computational analysis pipeline for multiplexed imaging data. After CODEX imaging, we segmented DAPI nuclei using Mesmer’s unsupervised segmentation method. Then, we extracted the mean intensity from each segmented cell, making 415,365 cells × 31 Antibodies matrix. Scatter plots for each antibody pair were visualized for quality control purposes. The area was calculated by binarized image for each ROI. Unsupervised clustering was performed for 415,365 cells using z score normalized mean intensity of 29 antibodies. We used the GPU version of Phenograph, Grapheno, for the unsupervised clustering. Fifty-one distinct phenotypes were identified. The right column contains narrative annotations for each clustering result. The heatmap was visualized by z expression score. Antibodies were also clustered, and same lineage antibodies were also clustered, which shows biological relevance. The left bar graph shows each cluster’s cell composition (%) of three groups: High-CRP, Low-CRP, and normal control. The composition ratio varies according to the phenotype cluster.

    Article Snippet: The overview of the CODEX multiplexed imaging computational analysis workflow is illustrated in .

    Techniques: Imaging, Construct, Staining, Expressing