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imaging mass cytometry imc data  (fluidigm)


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    fluidigm imaging mass cytometry imc data
    Imaging Mass Cytometry Imc Data, supplied by fluidigm, used in various techniques. Bioz Stars score: 98/100, based on 621 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/imaging mass cytometry imc data/product/fluidigm
    Average 98 stars, based on 621 article reviews
    imaging mass cytometry imc data - by Bioz Stars, 2026-03
    98/100 stars

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    ( A ) The imaging mass cytometry <t>(IMC)</t> <t>data</t> collection and analysis workflow outlines the steps involved in gaining comprehensive insights into the spatial distribution of immune cells and relevant cell types within undecidualized (UnD) and decidualized (DD) EM-like lesions of WT, CNR1 k/o, and CNR2 k/o mice. ( B, C ) Representative images showing the single-cell segmentation performed following the acquisition of two regions of interest (ROI) per section (three biological samples per genotype) and segmentation quality of the data after segmentation analysis was conducted, respectively. ( D ) Non-linear dimensionality reduction after batch effect correction showed distinct expression patterns of immune cells and cell state markers between UnD and DD lesions. DD lesions from the CNR1 k/o and CNR2 k/o mice showed expression pattern that was significantly different from the DD lesions of WT mice, as well as compared to UnD lesions among different genotypes. ( E ) Uniform manifold approximation and projection (UMAP) dimensionality reduction highlighted key cell types and differences in composition between UnD and DD lesions. DD lesions exhibited increased stroma and fibroblasts, decreased epithelial cells, and heightened macrophage infiltration compared to UnD lesions.
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    ( A ) The imaging mass cytometry <t>(IMC)</t> <t>data</t> collection and analysis workflow outlines the steps involved in gaining comprehensive insights into the spatial distribution of immune cells and relevant cell types within undecidualized (UnD) and decidualized (DD) EM-like lesions of WT, CNR1 k/o, and CNR2 k/o mice. ( B, C ) Representative images showing the single-cell segmentation performed following the acquisition of two regions of interest (ROI) per section (three biological samples per genotype) and segmentation quality of the data after segmentation analysis was conducted, respectively. ( D ) Non-linear dimensionality reduction after batch effect correction showed distinct expression patterns of immune cells and cell state markers between UnD and DD lesions. DD lesions from the CNR1 k/o and CNR2 k/o mice showed expression pattern that was significantly different from the DD lesions of WT mice, as well as compared to UnD lesions among different genotypes. ( E ) Uniform manifold approximation and projection (UMAP) dimensionality reduction highlighted key cell types and differences in composition between UnD and DD lesions. DD lesions exhibited increased stroma and fibroblasts, decreased epithelial cells, and heightened macrophage infiltration compared to UnD lesions.
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    ( A ) The imaging mass cytometry (IMC) data collection and analysis workflow outlines the steps involved in gaining comprehensive insights into the spatial distribution of immune cells and relevant cell types within undecidualized (UnD) and decidualized (DD) EM-like lesions of WT, CNR1 k/o, and CNR2 k/o mice. ( B, C ) Representative images showing the single-cell segmentation performed following the acquisition of two regions of interest (ROI) per section (three biological samples per genotype) and segmentation quality of the data after segmentation analysis was conducted, respectively. ( D ) Non-linear dimensionality reduction after batch effect correction showed distinct expression patterns of immune cells and cell state markers between UnD and DD lesions. DD lesions from the CNR1 k/o and CNR2 k/o mice showed expression pattern that was significantly different from the DD lesions of WT mice, as well as compared to UnD lesions among different genotypes. ( E ) Uniform manifold approximation and projection (UMAP) dimensionality reduction highlighted key cell types and differences in composition between UnD and DD lesions. DD lesions exhibited increased stroma and fibroblasts, decreased epithelial cells, and heightened macrophage infiltration compared to UnD lesions.

    Journal: eLife

    Article Title: Endocannabinoids and their receptors modulate endometriosis pathogenesis and immune response

    doi: 10.7554/eLife.96523

    Figure Lengend Snippet: ( A ) The imaging mass cytometry (IMC) data collection and analysis workflow outlines the steps involved in gaining comprehensive insights into the spatial distribution of immune cells and relevant cell types within undecidualized (UnD) and decidualized (DD) EM-like lesions of WT, CNR1 k/o, and CNR2 k/o mice. ( B, C ) Representative images showing the single-cell segmentation performed following the acquisition of two regions of interest (ROI) per section (three biological samples per genotype) and segmentation quality of the data after segmentation analysis was conducted, respectively. ( D ) Non-linear dimensionality reduction after batch effect correction showed distinct expression patterns of immune cells and cell state markers between UnD and DD lesions. DD lesions from the CNR1 k/o and CNR2 k/o mice showed expression pattern that was significantly different from the DD lesions of WT mice, as well as compared to UnD lesions among different genotypes. ( E ) Uniform manifold approximation and projection (UMAP) dimensionality reduction highlighted key cell types and differences in composition between UnD and DD lesions. DD lesions exhibited increased stroma and fibroblasts, decreased epithelial cells, and heightened macrophage infiltration compared to UnD lesions.

    Article Snippet: Bulk mRNA sequencing data is provided in the supplementary files (Supplementary Data 4) and IMC data generated in this study has been deposited in Mendeley Data ( https://doi.org/10.17632/2ptns5yhzh.2 ).

    Techniques: Imaging, Mass Cytometry, Expressing