Journal: Cell reports

Article Title: CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

doi: 10.1016/j.celrep.2020.107523

Figure Lengend Snippet: (A) CytoMAP is designed to extract quantitative information on cellular localization and composition within tissue regions, revealing how local cell microenvironments form global tissue structure, as well as allowing comparison of intra- and inter-sample tissue heterogeneity. (B) The workflow starts with multi-parameter imaging of either thin sections or large 3D tissue volumes. Next, hierarchical gating of cell objects is used to annotatedistinct cell subsets, which are passed into CytoMAP for analysis. CytoMAP segments these spatial datasets into individual neighborhoods and uses clustering algorithms to define similar groups of neighborhoods, or tissue “regions,” which are explored and spatially reconstructed in 2D or 3D space. (C) CytoMAP contains multiple tools to quantify and visualize the tissue architecture, including analysis of spatial correlations between different cell types,investigation of distance relationships of cells with architectural landmarks, analysis of neighborhood heterogeneity within individual tissues or across multiple samples, and quantitative visualization of tissue architecture.

Article Snippet: We implemented CytoMAP as a comprehensive toolbox in MATLAB specifically designed to analyze datasets generated with existing quantitative approaches that already incorporate information on cell phenotype, morphology, and location.

Techniques: Comparison, Imaging

Journal: Cell reports

Article Title: CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

doi: 10.1016/j.celrep.2020.107523

Figure Lengend Snippet: (A) Confocal image and zoom-in image of a LN section from a C57BL/6 mouse, adoptively transferred with 10^6 naive OT-II CD4+ T cells and immunized with OVA plus alum. Overview image scale bar, 200 μm; zoom-in scale bar, 30 μm. Only select channels are shown. (B) Histo-cytometry plots of cell object MFI for different channels demonstrating the gating used for identification of the indicated immune cell populations. (C) Positional plot of cell data from (B) (area matches the zoom-in image in A). CytoMAP was used to calculate the number of cells in 30-μm-radius neighborhoods (denoted by the circles in the bottom left), which were raster scanned as denoted by the arrow. (D) Heatmap of the neighborhood composition (percentage of each cell phenotype per neighborhood) after SOM clustering. Individual clusters, or “regions,” aredenoted by the color bar at the top of the graph. Arrowheads at the bottom highlight specific neighborhoods. (E) Region color-coded positional plot of the neighborhoods from (D). (F) Pseudo-space plot with the neighborhoods sorted based on B cell composition (sorted to the left) and T cell composition (sorted to the right). (G) Dimensionality reduction plots of the neighborhoods in which the standardized numbers of cells and total MFI of all channels were used for the dimensionalityreduction. t-SNE, PCA, UMAP, and PHATE were all calculated for the same input neighborhoods, which are color coded based on region type from (D). For this experiment, an imaging volume of 0.03 mm 3 , 139,399 cells, and 11,328 neighborhoods were analyzed.

Article Snippet: We implemented CytoMAP as a comprehensive toolbox in MATLAB specifically designed to analyze datasets generated with existing quantitative approaches that already incorporate information on cell phenotype, morphology, and location.

Techniques: Cytometry, Imaging

Journal: Cell reports

Article Title: CytoMAP: A Spatial Analysis Toolbox Reveals Features of Myeloid Cell Organization in Lymphoid Tissues

doi: 10.1016/j.celrep.2020.107523

Figure Lengend Snippet:

Article Snippet: We implemented CytoMAP as a comprehensive toolbox in MATLAB specifically designed to analyze datasets generated with existing quantitative approaches that already incorporate information on cell phenotype, morphology, and location.

Techniques: Recombinant, Electron Microscopy, Antibody Labeling, Adjuvant, Software, Plasmid Preparation