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. 2017 Sep;14(9):873-876.
doi: 10.1038/nmeth.4391. Epub 2017 Aug 7.

histoCAT: Analysis of Cell Phenotypes and Interactions in Multiplex Image Cytometry Data

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Free PMC article

histoCAT: Analysis of Cell Phenotypes and Interactions in Multiplex Image Cytometry Data

Denis Schapiro et al. Nat Methods. .
Free PMC article

Abstract

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.

Conflict of interest statement

Competing Financial Interests

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. From molecular to clinical information: multi-scale analysis of the tissue ecosystem.
(a) Spatially resolved, high-dimension molecular measurements are aggregated using image masks to define regions corresponding to each cell. (b) Visualization of images, (c) cytometry analysis, and (d) analysis of neighbors and cellular interaction networks facilitate “round-trip” analysis through layers of information. (e) Using molecular, cellular, and spatial signatures experimental cohorts can be compared and contrasted.
Figure 2
Figure 2. Round-trip analysis of unique cell types in high-dimension images of breast cancer.
(a) Left: Two representative multi-parametric images are displayed in the miCAT image window using user-defined color channels (red, E-cadherin; green, vimentin; blue, histone H3; cyan, Ki-67; magenta, cytokeratin 7; yellow, CD68). Up to six colors can be defined. Scale bar = 100 µm. Right: High-dimension single-cell data, including spatial features and all expressed markers from each segmented cell are extracted from each individual image and visualized in a t-SNE plot. (b) When the entire breast cancer dataset is visualized in one t-SNE plot, distinct colors distinguish cells of each source image. (c) Unsupervised clustering of all cells according to their marker expression throughout the dataset using PhenoGraph defines complex cell phenotypes and enables labeling of each cell phenotype cluster with a distinct color. (d) Bar plot of the total number of cells from each PhenoGraph-defined cell phenotype in the dataset. (e) Cell phenotypes can be further investigated using plotting tools such as heatmaps. (f) All single cells can be colored according to the identified phenotypes within the context of the tissue microenvironment on their original image. In this example, non-cell tissue is not labeled. (g) Quantification of an individual parameter can be heatmapped onto the t-SNE plot, and populations can be identified in a supervised manner using the gating tool. (h) All images containing cells of a subpopulation of interest can then be identified. In this example: red, E-cadherin; green, fibronectin; blue, histone H3; cyan, Ki67; magenta, cytokeratin 7; yellow, CD68. Scale bar = 100 µm. Images can be visualized using (i) pseudo-color or by (j) heatmap representing the intensity of a marker in each cell. (k) Cells of interest can be highlighted on the image (turquoise), and neighboring cells (purple or gray if representing both subpopulations) within a defined pixel range can also be identified and highlighted on (l) the image or (m) the analysis plot of the individual image (red, cell of interest; blue, neighbor; yellow, both subpopulations).
Figure 3
Figure 3. Neighborhood analysis of breast cancer cell phenotypes.
(a) Schematic of neighbor analysis in which the prevalence of a particular cell-to-cell interaction in an image is quantified and significance is determined by comparison to its prevalence in cell-type-randomized controls of the same image. Number of interactions between abundant green cells (green line), between rare clustered red cells (red line), and between abundant green cells and rare red cells (black line). (b) Schematic depicting directional aspects of neighbor interactions visualized in the heatmap. Rows visualize the significance of all cell types surrounding a cell type of interest. Columns visualize the significance of the cell type of interest surrounding other cell types. White represents a prevalence of less than 10%. (c) All interactions present in 49 breast tumor images and three matched normal tissue images are represented as a heatmap in which the cell type in the row is significantly neighbored (red) or avoided (blue) by the cell type in the column. Significance was determined by permutation test (p < 0.01). Highlighted squares indicate an example of a directional interaction: Stromal phenotype #7 significantly surrounds tumor cell type #22 (red square), but #22 is not surrounded by #7 (blue square). (d) Agglomerative clustering of all samples and cell-to-cell interactions according to the presence of significant (p < 0.01) phenotype interaction (red) or avoidance (blue). White represents interactions that are not present or not significant. (e) Force-directed cell interaction network graphs representing the interactions of PhenoGraph-defined cell phenotypes in Cluster 1 and Cluster 2 tumors. Circle color corresponds to PhenoGraph cluster. Red arrows indicate interaction and blue arrows avoidance, and intensities of the line color indicate significance. A connection is only visualized if the interaction or avoidance is significant in a least 30% of the grouped samples and the cell phenotypes are simultaneously present in at least 90% of the grouped samples.

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