Decoding mTOR signalling heterogeneity in the tumour microenvironment using multiplexed imaging and graph convolutional networks.

Evaluating the contribution of the tumour microenvironment (TME) in tumour progression has proven a complex challenge due to the intricate interactions within the TME. Multiplexed imaging is an emerging technology that allows concurrent assessment of multiple of these components simultaneously. Here we utilise a highly multiplexed dataset of 61 markers across 746 colorectal tumours to investigate how complex mTOR signalling in different tissue compartments influences patient prognosis.

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution.

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data.

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome.

ShapoGraphy: A User-Friendly Web Application for Creating Bespoke and Intuitive Visualisation of Biomedical Data.

Effective visualisation of quantitative microscopy data is crucial for interpreting and discovering new patterns from complex bioimage data. Existing visualisation approaches, such as bar charts, scatter plots and heat maps, do not accommodate the complexity of visual information present in microscopy data. Here we develop ShapoGraphy, a first of its kind method accompanied by an interactive web-based application for creating customisable quantitative pictorial representations to facilitate the understanding and analysis of image datasets (www.

A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples.

Multiplexed immunofluorescence provides an un-precedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.

Efficient Bayesian inference for mechanistic modelling with high-throughput data.

Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive.

High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells.

Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations.

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.

Morphological landscape of endothelial cell networks reveals a functional role of glutamate receptors in angiogenesis.

Angiogenesis plays a key role in several diseases including cancer, ischemic vascular disease, and Alzheimer's disease. Chemical genetic screening of endothelial tube formation provides a robust approach for identifying signalling components that impact microvascular network morphology as well as endothelial cell biology. However, the analysis of the resulting imaging datasets has been limited to a few phenotypic features such as the total tube length or the number of branching points.

HighVia-A Flexible Live-Cell High-Content Screening Pipeline to Assess Cellular Toxicity.

High-content screening to monitor disease-modifying phenotypes upon small-molecule addition has become an essential component of many drug and target discovery platforms. One of the most common phenotypic approaches, especially in the field of oncology research, is the assessment of cell viability. However, frequently used viability readouts employing metabolic proxy assays based on homogeneous colorimetric/fluorescent reagents are one-dimensional, provide limited information, and can in many cases yield conflicting or difficult-to-interpret results, leading to misinterpretation of data and wasted resources.

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.

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.

Analysis of live cell images: Methods, tools and opportunities.

Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets.

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.

Visualizing quantitative microscopy data: History and challenges.

Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner.

Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis.

Background: The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.

Methods And Findings: We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology.

Cell shape and the microenvironment regulate nuclear translocation of NF-κB in breast epithelial and tumor cells.

Although a great deal is known about the signaling events that promote nuclear translocation of NF-κB, how cellular biophysics and the microenvironment might regulate the dynamics of this pathway is poorly understood. In this study, we used high-content image analysis and Bayesian network modeling to ask whether cell shape and context features influence NF-κB activation using the inherent variability present in unperturbed populations of breast tumor and non-tumor cell lines. Cell–cell contact, cell and nuclear area, and protrusiveness all contributed to variability in NF-κB localization in the absence and presence of TNFα.

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.

Cross-talk between Rho and Rac GTPases drives deterministic exploration of cellular shape space and morphological heterogeneity.

One goal of cell biology is to understand how cells adopt different shapes in response to varying environmental and cellular conditions. Achieving a comprehensive understanding of the relationship between cell shape and environment requires a systems-level understanding of the signalling networks that respond to external cues and regulate the cytoskeleton. Classical biochemical and genetic approaches have identified thousands of individual components that contribute to cell shape, but it remains difficult to predict how cell shape is generated by the activity of these components using bottom-up approaches because of the complex nature of their interactions in space and time.

A screen for morphological complexity identifies regulators of switch-like transitions between discrete cell shapes.

The way in which cells adopt different morphologies is not fully understood. Cell shape could be a continuous variable or restricted to a set of discrete forms. We developed quantitative methods to describe cell shape and show that Drosophila haemocytes in culture are a heterogeneous mixture of five discrete morphologies.

Differential RNAi screening provides insights into the rewiring of signalling networks during oxidative stress.

Reactive Oxygen Species (ROS) are a natural by-product of cellular growth and proliferation, and are required for fundamental processes such as protein-folding and signal transduction. However, ROS accumulation, and the onset of oxidative stress, can negatively impact cellular and genomic integrity. Signalling networks have evolved to respond to oxidative stress by engaging diverse enzymatic and non-enzymatic antioxidant mechanisms to restore redox homeostasis.