Abstract
Changes in tissue architecture and multicellular organisation contribute to many diseases, including cancer and cardiovascular diseases. Scratch wound assay is a commonly used tool that assesses cells’ migratory ability based on the area of a wound they cover over a certain time. However, analysis of changes in the organisational patterns formed by migrating cells following genetic or pharmacological perturbations are not well explored in these assays, in part because analysing the resulting imaging data is challenging. Here we present DeepScratch, a neural network that accurately detects the cells in scratch assays based on a heterogeneous set of markers. We demonstrate the utility of DeepScratch by analysing images of more than 232,000 lymphatic endothelial cells. In addition, we propose various topological measures of cell connectivity and local cell density (LCD) to characterise tissue remodelling during wound healing. We show that LCD-based metrics allow classification of CDH5 and CDC42 genetic perturbations that are known to affect cell migration through different biological mechanisms. Such differences cannot be captured when considering only the wound area. Taken together, single-cell detection using DeepScratch allows more detailed investigation of the roles of various genetic components in tissue topology and the biological mechanisms underlying their effects on collective cell migration.
Highlights
Cell migration plays an important role in both tissue repair and disease
Increased migratory ability in cancer cells can lead to invasion and metastasis, which are the main causes of cancer mortality [1]
Principle component analysis (PCA) We computed Local cell density (LCD) based on distance to the nearest 36th neighbour and 10th neighbour and on the area of Voronoi cells to account for different length scales
Summary
Cell migration plays an important role in both tissue repair and disease. For example, increased migratory ability in cancer cells can lead to invasion and metastasis, which are the main causes of cancer mortality [1]. Automated image analysis methods have been developed for segmenting the wound area [4,5] These approaches do not provide information on cell-level mechanisms, such as changes in proliferation rate, cell morphology and size, or tissue topology [6]. DeepScratch is the first network to detect cells from heterogeneous image data using either nuclear or membrane images Using this approach, we can extract various topological measures from scratch assays, allowing more effective characterisation of cellular mechanisms. We present here a novel pipeline, combining single-cell detection via neural networks with biologically relevant metrics for scratch assays to better characterise cellular mechanisms underlying perturbation effects on collective cell migration.
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