Inferring signalling networks from images

RNAi library screening reveals Gβ1, Casein Kinase 2 and ICAP-1 as novel regulators of LFA-1-mediated T cell polarity and migration

The αLβ2 integrin LFA-1 plays a key role in T-cell adhesion to the endothelial vasculature and migration into both secondary lymphoid organs and peripheral tissues via interactions with its target protein ICAM-1, but the pathways that regulate LFA-1-mediated T-cell polarity and migration are not fully understood. In this study we screened two RNAi libraries targeting G protein-coupled receptors (GPCR)/GPCR-associated proteins and kinases in a HuT 78 T cell line model of LFA-1-stimulated T-cell migration. Based on staining of the actin cytoskeleton, multiple parameters to measure cell morphology were used to assess the contribution of 1109 genes to LFA-1-mediated T-cell polarity and migration. These RNAi screens identified a number of both novel and previously identified genes that either increased or decreased the polarity and migratory capacity of these cells. Following multiparametric analysis, hierarchical clustering and pathway analysis, three of these genes were characterized in further detail using primary human T cells, revealing novel roles for the heterotrimeric G protein subunit Gβ1 and Casein Kinase 2 in LFA-1-mediated T-cell polarity and migration in vitro. Our studies also highlighted a new role for ICAP-1, an adaptor protein previously described to be associated with β1 integrins, in β2 integrin LFA-1-directed migration in T cells. Knockdown of ICAP-1 expression in primary T cells revealed a role in cell polarity, cell velocity and transmigration towards SDF-1 for this adaptor protein. This study therefore uncovers new roles for GPCR/GPCR-associated proteins and kinases in T-cell migration and provides potential novel targets for modulation of the T-cell immune response.

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. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.

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.

A Phenotypic High-Content Screening Assay to Identify Regulators of Membrane Protein Localization

Correct subcellular localization of proteins is a requirement for appropriate function. This is especially true in epithelial cells, which rely on the precise localization of a diverse array of epithelial polarity and cellular adhesion proteins. Loss of cell polarity and adhesion is a hallmark of cancer, and mislocalization of core polarity proteins, such as Scribble, is observed in a range of human epithelial tumors and is prognostic of poor survival. Despite this, little is known about how Scribble membrane localization is regulated. Here, we describe the development and application of a phenotypic high-content screening assay that is designed to specifically quantify membrane levels of Scribble to identify regulators of its membrane localization. A screening platform that is capable of resolving individual cells and quantifying membrane protein localization in confluent epithelial monolayers was developed by using the cytoplasm-to-cell-membrane bioapplication integrated with the Cellomics ArrayScan high-content imaging platform. Application of this method to a boutique human epithelial polarity and signaling small interfering RNA (siRNA) library resulted in highly robust coefficient-of-variance and Z' factor values. As proof of concept, we present two candidate genes whose depletion specifically reduces Scribble protein levels at the membrane. Data mining revealed that these proteins interact with components of the Scribble polarity complex, providing support for the utility of the screening approach. This method is broadly applicable to genome-wide and large-scale compound screening of membrane-bound proteins, and when coupled with pathway analysis the dataset becomes even more valuable and can provide predictive mechanistic insight.

How cells explore shape space: a quantitative statistical perspective of cellular morphogenesis

Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small number of attractor states. We propose that the topology of these landscapes is in large part determined by cell-intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell-extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes.