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

Systematic data analysis pipeline for quantitative morphological cell phenotyping

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

Environmentally dependent and independent control of 3D cell shape

How cancer cells determine their shape in response to three-dimensional (3D) geometric and mechanical cues is unclear. We develop an approach to quantify the 3D cell shape of over 60,000 melanoma cells in collagen hydrogels using high-throughput stage-scanning oblique plane microscopy (ssOPM). We identify stereotypic and environmentally dependent changes in shape and protrusivity depending on whether a cell is proximal to a flat and rigid surface or is embedded in a soft environment. Environmental sensitivity metrics calculated for small molecules and gene knockdowns identify interactions between the environment and cellular factors that are important for morphogenesis. We show that the Rho guanine nucleotide exchange factor (RhoGEF) TIAM2 contributes to shape determination in environmentally independent ways but that non-muscle myosin II, microtubules, and the RhoGEF FARP1 regulate shape in ways dependent on the microenvironment. Thus, changes in cancer cell shape in response to 3D geometric and mechanical cues are modulated in both an environmentally dependent and independent fashion.

Enhancing subcellular protein localization mapping analysis using Sc2promap utilizing attention mechanisms

BACKGROUND

Aberrant protein localization is a prominent feature in many human diseases and can have detrimental effects on the function of specific tissues and organs. High-throughput technologies, which continue to advance with iterations of automated equipment and the development of bioinformatics, enable the acquisition of large-scale data that are more pattern-rich, allowing for the use of a wider range of methods to extract useful patterns and knowledge from them.

METHODS

The proposed sc2promap (Spatial and Channel for SubCellular Protein Localization Mapping) model, designed to proficiently extract meaningful features from a vast repository of single-channel grayscale protein images for the purposes of protein localization analysis and clustering. Sc2promap incorporates a prediction head component enriched with supplementary protein annotations, along with the integration of a spatial-channel attention mechanism within the encoder to enables the generation of high-resolution protein localization maps that encapsulate the fundamental characteristics of cells, including elemental cellular localizations such as nuclear and non-nuclear domains.

RESULTS

Qualitative and quantitative comparisons were conducted across internal and external clustering evaluation metrics, as well as various facets of the clustering results. The study also explored different components of the model. The research outcomes conclusively indicate that, in comparison to previous methods, Sc2promap exhibits superior performance.

CONCLUSIONS

The amalgamation of the attention mechanism and prediction head components has led the model to excel in protein localization clustering and analysis tasks.

GENERAL SIGNIFICANCE

The model effectively enhances the capability to extract features and knowledge from protein fluorescence images.

Extended methods for spatial cell classification with DBSCAN-CellX

Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

MLL regulates the actin cytoskeleton and cell migration by stabilising Rho GTPases via the expression of RhoGDI1

Attainment of proper cell shape and the regulation of cell migration are essential processes in the development of an organism. The mixed lineage leukemia (MLL or KMT2A) protein, a histone 3 lysine 4 (H3K4) methyltransferase, plays a critical role in cell-fate decisions during skeletal development and haematopoiesis in higher vertebrates. Rho GTPases - RhoA, Rac1 and CDC42 - are small G proteins that regulate various key cellular processes, such as actin cytoskeleton formation, the maintenance of cell shape and cell migration. Here, we report that MLL regulates the homeostasis of these small Rho GTPases. Loss of MLL resulted in an abnormal cell shape and a disrupted actin cytoskeleton, which lead to diminished cell spreading and migration. MLL depletion affected the stability and activity of Rho GTPases in a SET domain-dependent manner, but these Rho GTPases were not direct transcriptional targets of MLL. Instead, MLL regulated the transcript levels of their chaperone protein RhoGDI1 (also known as ARHGDIA). Using MDA-MB-231, a triple-negative breast cancer cell line with high RhoGDI1 expression, we show that MLL depletion or inhibition by small molecules reduces tumour progression in nude mice. Our studies highlight the central regulatory role of MLL in Rho/Rac/CDC42 signalling pathways. This article has an associated First Person interview with the first author of the paper.

Cell Repolarization: A Bifurcation Study of Spatio-Temporal Perturbations of Polar Cells

The intrinsic polarity of migrating cells is regulated by spatial distributions of protein activity. Those proteins (Rho-family GTPases, such as Rac and Rho) redistribute in response to stimuli, determining the cell front and back. Reaction-diffusion equations with mass conservation and positive feedback have been used to explain initial polarization of a cell. However, the sensitivity of a polar cell to a reversal stimulus has not yet been fully understood. We carry out a PDE bifurcation analysis of two polarity models to investigate routes to repolarization: (1) a single-GTPase ("wave-pinning") model and (2) a mutually antagonistic Rac-Rho model. We find distinct routes to reversal in (1) vs. (2). We show numerical simulations of full PDE solutions for the RD equations, demonstrating agreement with predictions of the bifurcation results. Finally, we show that simulations of the polarity models in deforming 1D model cells are consistent with biological experiments.

Self-supervised deep learning encodes high-resolution features of protein subcellular localization

Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself’s ability to cluster proteins into organelles and protein complexes, showing that cytoself outperforms previous self-supervised approaches. Moreover, to better understand the inner workings of our model, we dissect the emergent features from which our clustering is derived, interpret them in the context of the fluorescence images, and analyze the performance contributions of each component of our approach.

Cytoself is a self-supervised deep learning-based approach for profiling and clustering protein localization from fluorescence images. Cytoself outperforms established approaches and can accurately predict protein subcellular localization.

Interplay between mechanics and signalling in regulating cell fate

Mechanical signalling affects multiple biological processes during development and in adult organisms, including cell fate transitions, cell migration, morphogenesis and immune responses. Here, we review recent insights into the mechanisms and functions of two main routes of mechanical signalling: outside-in mechanical signalling, such as mechanosensing of substrate properties or shear stresses; and mechanical signalling regulated by the physical properties of the cell surface itself. We discuss examples of how these two classes of mechanical signalling regulate stem cell function, as well as developmental processes in vivo. We also discuss how cell surface mechanics affects intracellular signalling and, in turn, how intracellular signalling controls cell surface mechanics, generating feedback into the regulation of mechanosensing. The cooperation between mechanosensing, intracellular signalling and cell surface mechanics has a profound impact on biological processes. We discuss here our understanding of how these three elements interact to regulate stem cell fate and development.

Cellular Tango: how extracellular matrix adhesion choreographs Rac-Rho signaling and cell movement

The small GTPases Rac and Rho are known to regulate eukaryotic cell shape, promoting front protrusion (Rac) or rear retraction (Rho) of the cell edge. Such cell deformation changes the contact and adhesion of cell to the extracellular matrix (ECM), while ECM signaling through integrin receptors also affects GTPase activity. We develop and investigate a model for this three-way feedback loop in 1D and 2D spatial domains, as well as in a fully deforming 2D cell shapes with detailed adhesion-bond biophysics. The model consists of reaction-diffusion equations solved numerically with open-source software, Morpheus, and with custom-built cellular Potts model simulations. We find a variety of patterns and cell behaviors, including persistent polarity, flipped front-back cell polarity oscillations, spiral waves, and random protrusion-retraction. We show that the observed spatial patterns depend on the cell shape, and vice versa.

Mechanosensitive smooth muscle cell phenotypic plasticity emerging from a null state and the balance between Rac and Rho

Reversible differentiation of vascular smooth muscle cells (VSMCs) plays a critical role in vascular biology and disease. Changes in VSMC differentiation correlate with stiffness of the arterial extracellular matrix (ECM), but causal relationships remain unclear. We show that VSMC plasticity is mechanosensitive and that both the de-differentiated and differentiated fates are promoted by the same ECM stiffness. Differential equations developed to model this behavior predicted that a null VSMC state generates the dual fates in response to ECM stiffness. Direct measurements of cellular forces, proliferation, and contractile gene expression validated these predictions and showed that fate outcome is mediated by Rac-Rho homeostasis. Rac, through distinct effects on YAP and TAZ, is required for both fates. Rho drives the contractile state alone, so its level of activity, relative to Rac, drives phenotypic choice. Our results show how the cellular response to a single ECM stiffness generates bi-stability and VSMC plasticity.

Reversible differentiation/de-differentiation of smooth muscle cells plays a critical role in vascular biology and disease. Talwar et al. show that these differentiated and de-differentiated phenotypes emerge from a null state that is regulated by ECM stiffness and bidirectional effects of Rac on YAP and TAZ transcriptional coregulators.

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.

Bridging from single to collective cell migration: A review of models and links to experiments

Mathematical and computational models can assist in gaining an understanding of cell behavior at many levels of organization. Here, we review models in the literature that focus on eukaryotic cell motility at 3 size scales: intracellular signaling that regulates cell shape and movement, single cell motility, and collective cell behavior from a few cells to tissues. We survey recent literature to summarize distinct computational methods (phase-field, polygonal, Cellular Potts, and spherical cells). We discuss models that bridge between levels of organization, and describe levels of detail, both biochemical and geometric, included in the models. We also highlight links between models and experiments. We find that models that span the 3 levels are still in the minority.

Cell shape regulates subcellular organelle location to control early Ca2+ signal dynamics in vascular smooth muscle cells

The shape of the cell is connected to its function; however, we do not fully understand underlying mechanisms by which global shape regulates a cell's functional capabilities. Using theory, experiments and simulation, we investigated how physiologically relevant cell shape changes affect subcellular organization, and consequently intracellular signaling, to control information flow needed for phenotypic function. Vascular smooth muscle cells going from a proliferative and motile circular shape to a contractile fusiform shape show changes in the location of the sarcoplasmic reticulum, inter-organelle distances, and differential distribution of receptors in the plasma membrane. These factors together lead to the modulation of signals transduced by the M3 muscarinic receptor/Gq/PLCβ pathway at the plasma membrane, amplifying Ca2+ dynamics in the cytoplasm, and the nucleus resulting in phenotypic changes, as determined by increased activity of myosin light chain kinase in the cytoplasm and enhanced nuclear localization of the transcription factor NFAT. Taken together, our observations show a systems level phenomenon whereby global cell shape affects subcellular organization to modulate signaling that enables phenotypic changes.

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.

Simple Rho GTPase Dynamics Generate a Complex Regulatory Landscape Associated with Cell Shape

Migratory cells exhibit a variety of morphologically distinct responses to their environments that manifest in their cell shape. Some protrude uniformly to increase substrate contacts, others are broadly contractile, some polarize to facilitate migration, and yet others exhibit mixtures of these responses. Prior studies have identified a discrete collection of shapes that the majority of cells display and demonstrated that activity levels of the cytoskeletal regulators Rac1 and RhoA GTPase regulate those shapes. Here, we use computational modeling to assess whether known GTPase dynamics can give rise to a sufficient diversity of spatial signaling states to explain the observed shapes. Results show that the combination of autoactivation and mutually antagonistic cross talk between GTPases, along with the conservative membrane binding, generates a wide array of distinct homogeneous and polarized regulatory phenotypes that arise for fixed model parameters. From a theoretical perspective, these results demonstrate that simple GTPase dynamics can generate complex multistability in which six distinct stable steady states (three homogeneous and three polarized) coexist for a fixed set of parameters, each of which naturally maps to an observed morphology. From a biological perspective, although we do not explicitly model the cytoskeleton or resulting cell morphologies, these results, along with prior literature linking GTPase activity to cell morphology, support the hypothesis that GTPase signaling dynamics can generate the broad morphological characteristics observed in many migratory cell populations. Further, the observed diversity may be the result of cells populating a complex morphological landscape generated by GTPase regulation rather than being the result of intrinsic cell-cell variation. These results demonstrate that Rho GTPases may have a central role in regulating the broad characteristics of cell shape (e.g., expansive, contractile, polarized, etc.) and that shape heterogeneity may be (at least partly) a reflection of the rich signaling dynamics regulating the cytoskeleton rather than intrinsic cell heterogeneity.

Extracellular matrix cues modulate Schwann cell morphology, proliferation, and protein expression

Peripheral nerve injuries require a complex set of signals from cells, macrophages, and the extracellular matrix (ECM) to induce regeneration across injury sites and achieve functional recovery. Schwann cells (SCs), the major glial cell in the peripheral nervous system (PNS), are critical to nerve regeneration due to their inherent capacity for altering phenotype postinjury to facilitate wound healing. The ECM plays a vital role in wound healing as well as regulating cell phenotype during tissue repair. To examine the underlying mechanisms between the ECM and SCs, this work sought to determine how specific ECM cues regulate the phenotype of SCs. To address this, SCs were cultured on polydimethylsiloxane substrates of a variable Young's modulus coated with ECM proteins. Cells were analyzed for spreading area, proliferation, cell and nuclear shape, and c-Jun expression. It was found that substrates with a stiffness of 8.67 kPa coated with laminin promoted the highest expression of c-Jun, a marker signifying a "regenerative" SC. Microcontact printed, cell adhesive areas were then utilized to precisely control the geometry and spreading of SCs and by controlling spreading area and cellular elongation; expression of c-Jun was either promoted or downregulated. These results begin to address the significant interplay between ECM cues and phenotype of SCs, while offering a potential means to enhance PNS regeneration through cellular therapies.

Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics. Here, we establish a computational framework called HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging. HACKS identifies distinct subcellular protrusion phenotypes based on machine-learning algorithms and reveals their underlying actin regulator dynamics at the leading edge. Using our method, we discover "accelerating protrusion", which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. We validate our finding by pharmacological perturbations and further identify the fine regulation of Arp2/3 and VASP recruitment associated with accelerating protrusion. Our study suggests HACKS can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation.

Computational modeling of single-cell mechanics and cytoskeletal mechanobiology

Cellular cytoskeletal mechanics plays a major role in many aspects of human health from organ development to wound healing, tissue homeostasis and cancer metastasis. We summarize the state-of-the-art techniques for mathematically modeling cellular stiffness and mechanics and the cytoskeletal components and factors that regulate them. We highlight key experiments that have assisted model parameterization and compare the advantages of different models that have been used to recapitulate these experiments. An overview of feed-forward mechanisms from signaling to cytoskeleton remodeling is provided, followed by a discussion of the rapidly growing niche of encapsulating feedback mechanisms from cytoskeletal and cell mechanics to signaling. We discuss broad areas of advancement that could accelerate research and understanding of cellular mechanobiology. A precise understanding of the molecular mechanisms that affect cell and tissue mechanics and function will underpin innovations in medical device technologies of the future. WIREs Syst Biol Med 2018, 10:e1407. doi: 10.1002/wsbm.1407 This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Cellular Models.

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.

Differential Depth Sensing Reduces Cancer Cell Proliferation via Rho-Rac-Regulated Invadopodia

Bone, which is composed of a porous matrix, is one of the principal secondary locations for cancer. However, little is known about the effect of this porous microenvironment in regulating cancer cell proliferation. Here, we examine how the depth of the pores can transduce a mechanical signal and reduce the proliferation of noncancer breast epithelial cells (MCF-10A) and malignant breast cancer cells (MDA-MB-231 and MCF-7) using micrometer-scale topographic features. Interestingly, cells extend actin-rich protrusions, such as invadopodia, to sense the depth of the matrix pore and activate actomyosin contractility to decrease MCF-10A proliferation. However, in MDA-MB-231, depth sensing inactivates Rho-Rac-regulated actomyosin contractility and phospho-ERK signaling. Inhibiting contractility on this porous matrix using blebbistatin further reduces MDA-MB-231 proliferation. Our findings support the notion of mechanically induced dormancy through depth sensing, where invadopodia-mediated depth sensing can inhibit the proliferation of noncancer and malignant breast cancer cells through differential regulation of actomyosin contractility.

A mathematical model coupling polarity signaling to cell adhesion explains diverse cell migration patterns

Protrusion and retraction of lamellipodia are common features of eukaryotic cell motility. As a cell migrates through its extracellular matrix (ECM), lamellipod growth increases cell-ECM contact area and enhances engagement of integrin receptors, locally amplifying ECM input to internal signaling cascades. In contrast, contraction of lamellipodia results in reduced integrin engagement that dampens the level of ECM-induced signaling. These changes in cell shape are both influenced by, and feed back onto ECM signaling. Motivated by experimental observations on melanoma cells lines (1205Lu and SBcl2) migrating on fibronectin (FN) coated topographic substrates (anisotropic post-density arrays), we probe this interplay between intracellular and ECM signaling. Experimentally, cells exhibited one of three lamellipodial dynamics: persistently polarized, random, or oscillatory, with competing lamellipodia oscillating out of phase (Park et al., 2017). Pharmacological treatments, changes in FN density, and substrate topography all affected the fraction of cells exhibiting these behaviours. We use these observations as constraints to test a sequence of hypotheses for how intracellular (GTPase) and ECM signaling jointly regulate lamellipodial dynamics. The models encoding these hypotheses are predicated on mutually antagonistic Rac-Rho signaling, Rac-mediated protrusion (via activation of Arp2/3 actin nucleation) and Rho-mediated contraction (via ROCK phosphorylation of myosin light chain), which are coupled to ECM signaling that is modulated by protrusion/contraction. By testing each model against experimental observations, we identify how the signaling layers interact to generate the diverse range of cell behaviors, and how various molecular perturbations and changes in ECM signaling modulate the fraction of cells exhibiting each. We identify several factors that play distinct but critical roles in generating the observed dynamic: (1) competition between lamellipodia for shared pools of Rac and Rho, (2) activation of RhoA by ECM signaling, and (3) feedback from lamellipodial growth or contraction to cell-ECM contact area and therefore to the ECM signaling level.

Mechanisms of Cell Polarization

Cell polarization is a key step in the migration, development, and organization of eukaryotic cells, both at the single cell and multicellular level. Research on the mechanisms that give rise to polarization of a given cell, and organization of polarity within a tissue has led to new understanding across cellular and developmental biology. In this review, we describe some of the history of theoretical and experimental aspects of the field, as well as some interesting questions and challenges for the future.

RNAi screens for Rho GTPase regulators of cell shape and YAP/TAZ localisation in triple negative breast cancer

In order to metastasise, triple negative breast cancer (TNBC) must make dynamic changes in cell shape. The shape of all eukaryotic cells is regulated by Rho Guanine Nucleotide Exchange Factors (RhoGEFs), which activate Rho-family GTPases in response to mechanical and informational cues. In contrast, Rho GTPase-activating proteins (RhoGAPs) inhibit Rho GTPases. However, which RhoGEFs and RhoGAPS couple TNBC cell shape to changes in their environment is very poorly understood. Moreover, whether the activity of particular RhoGEFs and RhoGAPs become dysregulated as cells evolve the ability to metastasise is not clear. Towards the ultimate goal of identifying RhoGEFs and RhoGAPs that are essential for TNBC metastasis, we performed an RNAi screen to isolate RhoGEFs and RhoGAPs that contribute to the morphogenesis of the highly metastatic TNBC cell line LM2, and its less-metastatic parental cell line MDA-MB-231. For ~6 million cells from each cell line, we measured 127 different features following the depletion of 142 genes. Using a linear classifier scheme we also describe the morphological heterogeneity of each gene-depleted population.

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.

Multiparametric Analysis of Cell Shape Demonstrates that β-PIX Directly Couples YAP Activation to Extracellular Matrix Adhesion

Mechanical signals from the extracellular matrix (ECM) and cellular geometry regulate the nuclear translocation of transcriptional regulators such as Yes-associated protein (YAP). Elucidating how physical signals control the activity of mechanosensitive proteins poses a technical challenge, because perturbations that affect cell shape may also affect protein localization indirectly. Here, we present an approach that mitigates confounding effects of cell-shape changes, allowing us to identify direct regulators of YAP localization. This method uses single-cell image analysis and statistical models that exploit the naturally occurring heterogeneity of cellular populations. Through systematic depletion of all human kinases, Rho family GTPases, GEFs, and GTPase activating proteins (GAPs), together with targeted chemical perturbations, we found that β-PIX, a Rac1/Ccd42 GEF, and PAK2, a Rac1/Cdc42 effector, drive both YAP activation and cell-ECM adhesion turnover during cell spreading. Our observations suggest that coupling YAP to adhesion dynamics acts as a mechano-timer, allowing cells to rapidly tune gene expression in response to physical signals.

Rac and Rho compete to cooperate

Antagonism between RhoA and Rac1 pathways creates cell heterogeneity that aids epidermal morphogenesis.

Rac1/RhoA antagonism defines cell-to-cell heterogeneity during epidermal morphogenesis in nematodes

Antagonism between the GTPases Rac1 and RhoA controls different morphogenetic processes during embryonic development. Martin et al. use quantitative imaging analyses to demonstrate that cell-autonomous antagonism between the RhoA- and Rac1-like pathways defines cell-to-cell heterogeneity during epidermal morphogenesis in Caenorhabditis elegans.

The antagonism between the GTPases Rac1 and RhoA controls cell-to-cell heterogeneity in isogenic populations of cells in vitro and epithelial morphogenesis in vivo. Its involvement in the regulation of cell-to-cell heterogeneity during epidermal morphogenesis has, however, never been addressed. We used a quantitative cell imaging approach to characterize epidermal morphogenesis at a single-cell level during early elongation of Caenorhabditis elegans embryos. This study reveals that a Rac1-like pathway, involving the Rac/Cdc42 guanine-exchange factor β-PIX/PIX-1 and effector PAK1/PAK-1, and a RhoA-like pathway, involving ROCK/LET-502, control the remodeling of apical junctions and the formation of basolateral protrusions in distinct subsets of hypodermal cells. In these contexts, protrusions adopt lamellipodia or an amoeboid morphology. We propose that lamella formation may reduce tension building at cell–cell junctions during morphogenesis. Cell-autonomous antagonism between these pathways enables cells to switch between Rac1- and RhoA-like morphogenetic programs. This study identifies the first case of cell-to-cell heterogeneity controlled by Rac1/RhoA antagonism during epidermal morphogenesis.

Regulation of pulmonary endothelial barrier function by kinases

The pulmonary endothelium is the target of continuous physiological and pathological stimuli that affect its crucial barrier function. The regulation, defense, and repair of endothelial barrier function require complex biochemical processes. This review examines the role of endothelial phosphorylating enzymes, kinases, a class with profound, interdigitating influences on endothelial permeability and lung function.

RhoA GTPase inhibition organizes contraction during epithelial morphogenesis

Mason et al. show that RhoA activity is regulated in space and time by a GEF/GAP module that tunes cell behavior and is required for proper tissue folding and shape during Drosophila morphogenesis.

During morphogenesis, contraction of the actomyosin cytoskeleton within individual cells drives cell shape changes that fold tissues. Coordination of cytoskeletal contractility is mediated by regulating RhoA GTPase activity. Guanine nucleotide exchange factors (GEFs) activate and GTPase-activating proteins (GAPs) inhibit RhoA activity. Most studies of tissue folding, including apical constriction, have focused on how RhoA is activated by GEFs to promote cell contractility, with little investigation as to how GAPs may be important. Here, we identify a critical role for a RhoA GAP, Cumberland GAP (C-GAP), which coordinates with a RhoA GEF, RhoGEF2, to organize spatiotemporal contractility during Drosophila melanogaster apical constriction. C-GAP spatially restricts RhoA pathway activity to a central position in the apical cortex. RhoGEF2 pulses precede myosin, and C-GAP is required for pulsation, suggesting that contractile pulses result from RhoA activity cycling. Finally, C-GAP expression level influences the transition from reversible to irreversible cell shape change, which defines the onset of tissue shape change. Our data demonstrate that RhoA activity cycling and modulating the ratio of RhoGEF2 to C-GAP are required for tissue folding.

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. Higher levels of nuclear NF-κB were associated with mesenchymal-like versus epithelial-like morphologies, and RhoA-ROCK-myosin II signaling was critical for mediating shape-based differences in NF-κB localization and oscillations. Thus, mechanical factors such as cell shape and the microenvironment can influence NF-κB signaling and may in part explain how different phenotypic outcomes can arise from the same chemical cues.

Building cell models and simulations from microscope images

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

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. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Joint modeling of cell and nuclear shape variation

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.

Apolar and polar transitions drive the conversion between amoeboid and mesenchymal shapes in melanoma cells

Melanoma cells can adopt two functionally distinct forms, amoeboid and mesenchymal, which facilitates their ability to invade and colonize diverse environments during the metastatic process. Using quantitative imaging of single living tumor cells invading three-dimensional collagen matrices, in tandem with unsupervised computational analysis, we found that melanoma cells can switch between amoeboid and mesenchymal forms via two different routes in shape space--an apolar and polar route. We show that whereas particular Rho-family GTPases are required for the morphogenesis of amoeboid and mesenchymal forms, others are required for transitions via the apolar or polar route and not amoeboid or mesenchymal morphogenesis per se. Altering the transition rates between particular routes by depleting Rho-family GTPases can change the morphological heterogeneity of cell populations. The apolar and polar routes may have evolved in order to facilitate conversion between amoeboid and mesenchymal forms, as cells are either searching for, or attracted to, particular migratory cues, respectively.

Implications of the Hybrid Epithelial/Mesenchymal Phenotype in Metastasis

Transitions between epithelial and mesenchymal phenotypes – the epithelial to ­mesenchymal transition (EMT) and its reverse the mesenchymal to epithelial transition (MET) – are hallmarks of cancer metastasis. While transitioning between the epithelial and mesenchymal phenotypes, cells can also attain a hybrid epithelial/mesenchymal (E/M) (i.e., partial or intermediate EMT) phenotype. Cells in this phenotype have mixed epithelial (e.g., adhesion) and mesenchymal (e.g., migration) properties, thereby allowing them to move collectively as clusters. If these clusters reach the bloodstream intact, they can give rise to clusters of circulating tumor cells (CTCs), as have often been seen experimentally. Here, we review the operating principles of the core regulatory network for EMT/MET that acts as a “three-way” switch giving rise to three distinct phenotypes – E, M and hybrid E/M – and present a theoretical framework that can elucidate the role of many other players in regulating epithelial plasticity. Furthermore, we highlight recent studies on partial EMT and its association with drug resistance and tumor-initiating potential; and discuss how cell–cell communication between cells in a partial EMT phenotype can enable the formation of clusters of CTCs. These clusters can be more apoptosis-resistant and have more tumor-initiating potential than singly moving CTCs with a wholly mesenchymal (complete EMT) phenotype. Also, more such clusters can be formed under inflammatory conditions that are often generated by various therapies. Finally, we discuss the multiple advantages that the partial EMT or hybrid E/M phenotype have as compared to a complete EMT phenotype and argue that these collectively migrating cells are the primary “bad actors” of metastasis.

Fibroblastic Transformation of Corneal Keratocytes by Rac Inhibition is Modulated by Extracellular Matrix Structure and Stiffness

The goal of this study was to investigate how alterations in extracellular matrix (ECM) biophysical properties modulate corneal keratocyte phenotypes in response to specific wound healing cytokines and Rho GTPases. Rabbit corneal keratocytes were plated within standard collagen matrices (2.5 mg/mL) or compressed collagen matrices (~100 mg/mL) and cultured in serum-free media, PDGF BB, IGF, FGF2 or TGFβ1, with or without the Rac1 inhibitor NSC23766 and/or the Rho kinase inhibitor Y-27632. After 1 to 4 days, cells were labeled for F-actin and imaged using confocal microscopy. Keratocytes within standard collagen matrices (which are highly compliant) maintained a dendritic phenotype following culture in serum-free media, PDGF, IGF and FGF, but developed stress fibers in TGFβ1. Keratocytes within compressed collagen (which has high stiffness and low porosity) maintained a dendritic phenotype following culture in serum-free media, PDGF and IGF, but developed stress fibers in both FGF and TGFβ1. The Rac inhibitor had no significant impact on growth factor responses in compliant matrices. Within compressed collagen matrices however, the Rac inhibitor induced fibroblastic transformation in serum-free media, PDGF and IGF. Fibroblast and myofibroblast transformation was blocked by Rho kinase inhibition. Overall, keratocyte growth factor responses appear to be regulated by both the interplay between Rho and Rac signaling, and the structural and mechanical properties of the ECM.

The three-way switch operation of Rac1/RhoA GTPase-based circuit controlling amoeboid-hybrid-mesenchymal transition

Metastatic carcinoma cells exhibit at least two different phenotypes of motility and invasion - amoeboid and mesenchymal. This plasticity poses a major clinical challenge for treating metastasis, while its underlying mechanisms remain enigmatic. Transitions between these phenotypes are mediated by the Rac1/RhoA circuit that responds to external signals such as HGF/SF via c-MET pathway. Using detailed modeling of GTPase-based regulation to study the Rac1/RhoA circuit's dynamics, we found that it can operate as a three-way switch. We propose to associate the circuit's three possible states to the amoeboid, mesenchymal and amoeboid/mesenchymal hybrid phenotype. In particular, we investigated the range of existence of, and the transition between, the three states (phenotypes) in response to Grb2 and Gab1 - two downstream adaptors of c-MET. The results help to explain the regulation of metastatic cells by c-MET pathway and hence can contribute to the assessment of possible clinical interventions.

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.

User-friendly tools for quantifying the dynamics of cellular morphology and intracellular protein clusters

Understanding the heterogeneous dynamics of cellular processes requires not only tools to visualize molecular behavior but also versatile approaches to extract and analyze the information contained in live-cell movies of many cells. Automated identification and tracking of cellular features enable thorough and consistent comparative analyses in a high-throughput manner. Here, we present tools for two challenging problems in computational image analysis: (1) classification of motion for cells with complex shapes and dynamics and (2) segmentation of clustered cells and quantification of intracellular protein distributions based on a single fluorescence channel. We describe these methods and user-friendly software(1) (MATLAB applications with graphical user interfaces) so these tools can be readily applied without an extensive knowledge of computational techniques.