Single-cell, spatially resolved ‘omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational multiplex image cytometry analysis toolbox (miCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of miCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.
A key behavior observed during morphogenesis, wound healing, and cancer invasion is that of collective and coordinated cellular motion. Hence, understanding the different aspects of such coordinated migration is fundamental for describing and treating cancer and other pathological defects. In general, individual cells exert forces on their environment in order to move, and collective motion is coordinated by cell–cell adhesion‐based forces. However, this notion ignores other mechanisms that encourage cellular movement, such as pressure differences. Here, using model tumors, it is found that increased pressure drove coordinated cellular motion independent of cell–cell adhesion by triggering cell swelling in a soft extracellular matrix (ECM). In the resulting phenotype, a rapid burst‐like stream of cervical cancer cells emerged from 3D aggregates embedded in soft collagen matrices (0.5 mg mL−1). This fluid‐like pushing mechanism, recorded within 8 h after embedding, shows high cell velocities and super‐diffusive motion. Because the swelling in this model system critically depends on integrin‐mediated cell–ECM adhesions and cellular contractility, the swelling is likely triggered by unsustained mechanotransduction, providing new evidence that pressure‐driven effects must be considered to more completely understand the mechanical forces involved in cell and tissue movement as well as invasion.
2Single-cell, spatially resolved 'omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed a computational multiplexed image cytometry analysis toolbox (miCAT) to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. miCAT will be useful in all areas of tissue-based research. We highlight the unique abilities of miCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues. imaged with low-plex fluorescence microscopy or basic tissue histology and are not geared to the analysis of highly multiplexed measurements [14][15][16] . On the other hand, tools that have been peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/109207 doi: bioRxiv preprint first posted online Feb. 17, 2017; 3 developed to perform analyses of non-imaging, highly-multiplexed single cell data (e.g., suspension based mass cytometry) do not exploit spatial information (Supplementary Fig. 1) 17,18 .In order to provide a complete picture of a tissue ecosystem, define molecular and algorithms for the comprehensive study of cell-to-cell interactions and the social networks of cells within complex tissues (Fig. 1). This provides unprecedented capabilities for investigators from biology, biomedicine, and pathology to investigate tissue changes during health, disease, and treatment.In miCAT, all single cell information, including spatial features (Fig. 1a), is linked to the corresponding multiplexed image enabling visualization of images and single-cell analysis in parallel (Fig. 1b, c). miCAT uses images and a corresponding segmentation mask to extract single-cell data including abundances of all measured markers for a cell and area of interest, as The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/109207 doi: bioRxiv preprint first posted online Feb. 17, 2017; 4 algorithm to identify all direct cell-cell neighboring interactions (Fig. 1d) and determine the significant interactions and unique cell environments across entire datasets and within specific cohorts (Fig. 1d, e).To combine image-based spatial information and high-dimensional cytometry data, the miCAT GUI is divided in two parallel sections for paired image and cytometry analysis (Supplementary File). In the image visualization section of miCAT, high-dimensional images (Fig. 2a) as well as cell masks, single-cell marker quantification, and cell identification labels can be visualized. In the analysis section of miCAT, image-derived marker quantification and spatial features of single-cell data are extracted for each image (Fig. 2a), combined (Fig. 2b), and visualized using multi-dimensional reduction tools such as t-SNE 17 maps (Fig. 2b), scatter plots, histograms, box plots, or other visualizations (Sup...
Collective migration of cells is a key behaviour observed during morphogenesis, wound healing and cancer cell invasion. Hence, understanding the different aspects of collective migration is at the core of further progress in describing and treating cancer and other pathological defects. The standard dogma in cell migration is that cells exert forces on the environment to move and cell-cell adhesion-based forces provide the coordination for collective migration. Here, we report a new collective migration mechanism that is independent of pulling forces on the extra-cellular matrix (ECM), as it is driven by the pressure difference generated inside model tumours. We observe a striking collective migration phenotype, where a rapid burst-like stream of HeLa cervical cancer cells emerges from the 3D aggregate embedded in matrices with low collagen concentration (0.5 mg ml−1). This invasion-like behaviour is recorded within 8 hours post embedding (hpe), and is characterised by high cell velocity and super-diffusive collective motion. We show that cellular swelling, triggered by the soft matrix, leads to a rise in intrinsic pressure, which eventually drives an invasion-like phenotype of HeLa cancer aggregates. These dynamic observations provide new evidence that pressure-driven effects need to be considered for a complete description of the mechanical forces involved in collective migration and invasion.
In article number 2104808 by Swetha Raghuraman, Timo and co-workers, a novel migration phenomenon is presented wherein cancer cells burst out from tumor aggregates as an act of pressure release into the surrounding in-homogeneous soft extra-cellular matrix (ECM) made up of collagen. The pressure within tumor aggregates manifests due to cellular swelling, leading to a super-diffusive coordinated expulsion of cells within 12 hours, directed towards regions providing least mechanical resistance
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