Title: Morphmap: Systematic Morphology Mapping to Deconvolute the Cell-Cell Interaction
Problem and Rationale
Cell-cell interaction is a complex phenomenon playing a big part in various processes such as cell proliferation, migration, homeostasis etc . However, cell communication can be overlooked when studying various diseases, such as cancer. Emerging evidence suggests that tumour cell interactions with its’ surrounding cells is important for disease progression and response to the treatment [1–3]. High content screening enables research into cell interactions at a single-cell level . Image-based phenotypic profiling (e.g., Cell Painting ) can reveal cell structural changes reflecting cell proliferation and state, as well as provide mechanistic information on response to drugs . Typically, feature extraction from high throughput imaging data is achieved using open-source software like CellProfiler, ImageJ, etc . These softwares have shown promising results in extracting phenotypic variations in cells. However, they require handcrafting to each assay variation, which is time consuming and leads to bias in the results. To improve the existing feature extraction methods and make it more robust, recent studies have implemented deep neural networks that can capture phenotypic variations in cells after drug treatment [7,8]. Ljosa et al., uses CellProfiler to extract phenotypic features from breast cancer cells treated with 113 compounds . The work of Janssens et. al., and Nikita et al., apply fully unsupervised and weakly supervised neural network to cluster cells with similar phenotypes together [6,8]. All of these studies have been performed on single cell type in the presence of single compound to understand the effect of drugs on cell morphology. Therefore, it would be valuable to merge high content screening with machine learning to investigate cell-cell interactions at single-cell level. Systematic phenotypic profiling of cell changes from the innate to activated state through intracellular communication can help to build a tool that captures and predicts cell state (Fig. 1A). By layering drug induced perturbations on this dataset we will learn which pathways control these interactions (Fig. 1B).
Figure 1: Expected interaction and phenotypic changes in the cells (created with: BioRender.com)