Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics.

The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein-protein interaction data to systematically describe a "shape-gene network" that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis

Data-analysis strategies for image-based cell profiling

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.Peer reviewe

Identification of clinically predictive metagenes that encode components of a network coupling cell shape to transcription by image-omics

The associations between clinical phenotypes (tumor grade, survival) and cell phenotypes, such as shape, signaling activity, and gene expression, are the basis for cancer pathology, but the mechanisms explaining these relationships are not always clear. The generation of large data sets containing information regarding cell phenotypes and clinical data provides an opportunity to describe these mechanisms. Here, we develop an image-omics approach to integrate quantitative cell imaging data, gene expression, and protein–protein interaction data to systematically describe a “shape-gene network” that couples specific aspects of breast cancer cell shape to signaling and transcriptional events. The actions of this network converge on NF-κB, and support the idea that NF-κB is responsive to mechanical stimuli. By integrating RNAi screening data, we identify components of the shape-gene network that regulate NF-κB in response to cell shape changes. This network was also used to generate metagene models that predict NF-κB activity and aspects of morphology such as cell area, elongation, and protrusiveness. Critically, these metagenes also have predictive value regarding tumor grade and patient outcomes. Taken together, these data strongly suggest that changes in cell shape, driven by gene expression and/or mechanical forces, can promote breast cancer progression by modulating NF-κB activation. Our findings highlight the importance of integrating phenotypic data at the molecular level (signaling and gene expression) with those at the cellular and tissue levels to better understand breast cancer oncogenesis

Visualizing cellular imaging data using PhenoPlot

Visualization is essential for data interpretation, hypothesis formulation and communication of results. However, there is a paucity of visualization methods for image-derived data sets generated by high-content analysis in which complex cellular phenotypes are described as high-dimensional vectors of features. Here we present a visualization tool, PhenoPlot, which represents quantitative high-content imaging data as easily interpretable glyphs, and we illustrate how PhenoPlot can be used to improve the exploration and interpretation of complex breast cancer cell phenotypes

DeepScratch: single-cell based topological metrics of scratch wound assays

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

KCML: a machine‐learning framework for inference of multi‐scale gene functions from genetic perturbation screens

Characterising context‐dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large‐scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge‐ and Context‐driven Machine Learning (KCML), a framework that systematically predicts multiple context‐specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale‐crossing and context‐dependent gene functions. KCML is highly generalisable and applicable to various large‐scale genetic perturbation screens

Identification of molecular targets for the targeted treatment of gastric cancer using dasatinib

Gastric cancer (GC) remains the third leading cause of cancer-related death despite several improvements in targeted therapy. There is therefore an urgent need to investigate new treatment strategies, including the identification of novel biomarkers for patient stratification. In this study, we evaluated the effect of FDA-approved kinase inhibitors on GC. Through a combination of cell growth, migration and invasion assays, we identified dasatinib as an efficient inhibitor of GC proliferation. Mass-spectrometry-based selectivity profiling and subsequent knockdown experiments identified members of the SRC family of kinases including SRC, FRK, LYN and YES, as well as other kinases such as DDR1, ABL2, SIK2, RIPK2, EPHA2, and EPHB2 as dasatinib targets. The expression levels of the identified kinases were investigated on RNA and protein level in 200 classified tumor samples from patients, who had undergone gastrectomy, but had received no treatment. Levels of FRK, DDR1 and SRC expression on both mRNA and protein level were significantly higher in metastatic patient samples regardless of the tumor stage, while expression levels of SIK2 correlated with tumor size. Collectively, our data suggest dasatinib for treatment of GC based on its unique property, inhibiting a small number of key kinases (SRC, FRK, DDR1 and SIK2), highly expressed in GC patients