Automatic segmentation of focal adhesions from mouse embryonic fibroblasts

This work describes an automatic algorithm for the segmentation and quantification of focal adhesions from mouse embryonic fibroblasts. The main challenges solved by this algorithm are: the variability of the intensity of the focal adhesions, the detection of an outer ring, which distinguishes the cell periphery responsible for the cell migration, and the quantification of the characteristics of the focal adhesions. The algorithm detects maximal regions through gradients and uses a region-growing algorithm limited by intensity-based edges. The outer ring is calculated based on the average radial intensity from an extended centroid of the cell. Finally, traditional morphological characteristics are obtained to distinguish between two groups of cells. Two of the measurements employed showed statistical difference between two groups of cells

Biophysical techniques to study cell and matrix properties in the context of single cell migration

Single cell migration in artificial collagen gels as an in vitro model system in the context of cancer are studied. Cell and matrix mechanical properties are determined using atomic force microscopy and an advanced analysis method. Matrix pore-size is studied using a novel approach and analysis method. A novel, minimally invasive approach to determine the amount of displacement of the cell microenvironment due to force generation of single cells during migration in artificial 3D collagen gels is introduced. An automated analysis and user friendly software to analyze high-throughput cell invasion is introduced. These methods are used to study cell migration and mechanical properties of the breast cancer cell lines MDA-MB-231 and MCF-7 and the influence of cell nuclear elasticity is investigated. Using mouse embryonic fibroblasts, the role of focal adhesion kinase (FAK) during cell migration is studied using FAK deficient knock-out cell lines FAK-/- and control FAK+/+ as well as kinase-dead mutants FAKR454/R454 and control FAKWT/WT.:Abstract i Acknowledgements iii 1 Introduction 1 2 Background 5 2.1 Cancer — An ever-changing Disease 5 2.1.1 Carcinogenesis and Neoplasm 6 2.1.2 Hallmarks of Cancer 7 2.1.3 Metastasis— The malignant Progression of Cancer 7 2.1.4 Metastatic Cascade 9 2.2 The Cell— Where it begins 10 2.2.1 Actomyosin Complex 12 2.2.1.1 Actin Monomer 12 2.2.1.2 Polymerization 12 2.2.1.3 Structures 14 2.2.1.4 Actin Cortex 15 2.2.1.5 Filopodia 16 2.2.1.6 Lamellipodium 16 2.2.1.7 Invadopodium 17 2.2.1.8 Stress Fibers 17 2.2.1.9 Actin in Cancer and Metastasis 17 2.2.1.10 Myosin and Actin 18 2.2.2 Focal Adhesions 19 2.2.3 Microtubules 20 2.2.4 Intermediate Filaments 21 2.2.5 Cellular Stiffness 22 2.2.6 Nuclear Deformability 23 2.3 The Extracellular Matrix— Where it happens 24 2.3.1 Components and Structure 25 2.3.2 Collagen as a Model System 26 2.3.2.1 Collagen I Fibril Formation 27 2.3.2.2 The Rat/Bovine-Collagen-Mix Model System 28 2.4 Single Cell Migration— Why it spreads 29 3 Materials and Methods 31 3.1 Cell Culture 31 3.1.1 Cancer Cells 31 3.1.2 Mouse fibroblasts 32 3.1.3 Pharmacological treatment 34 3.2 Collagen matrices 34 3.3 Cell Elasticity 36 3.3.1 Atomic Force Microscopy 36 3.3.2 Preparation 37 3.3.3 Data Aquisition 38 3.3.4 Data Analysis 38 3.4 Matrix Stiffness 40 3.4.1 Preparation 40 3.4.2 Data Aquisition 41 3.4.3 Data Analysis 41 3.5 Invasion Assay 42 3.5.1 Preparation 42 3.5.2 Data aquisition 44 3.5.3 Data Analysis 44 3.6 Matrix Topology 48 3.6.1 Preparation 49 3.6.2 Data Acquisition 50 3.6.3 Data Analysis 51 3.6.3.1 Binarization 51 3.6.3.2 Pore-Size 53 3.6.3.3 Fiber Thickness 54 3.7 Fiber Displacement 55 3.7.1 Preparation 56 3.7.2 Data Aquisition 56 3.7.3 Data analysis 57 3.7.3.1 Fiber Displacement 59 3.7.3.2 Cell Segmentation 60 3.7.3.3 Shell Analysis 61 3.8 A toolset to understand Single Cell Migration and what influences it 62 4 Results 65 4.1 Cell Elasticity 65 4.1.1 Example Force-Distance Curves 66 4.1.2 Single Cell Elasticity 67 4.2 Matrix Stiffness 69 4.3 Invasion 71 4.4 Matrix Topology 75 4.5 Influence of Cell Nucleus on Cell Migration 79 4.5.1 Cellular Elasticity 79 4.5.2 Invasion 81 4.6 Fiber Displacement 89 4.7 Effect of FAK on Cell Invasion and Fiber Displacement 93 4.7.1 FAK Knock-Out 93 4.7.2 Kinase-dead FAK Mutant 96 5 Discussion 103 References 107Die Einzelzellmigration in künstlichen Kollagennetzwerken als ein in vitro Modellsystem im Kontext von Krebs wurde studiert. Mechanische Eigenschaften von Zellen und der verwendeten Kollagennetzwerke wurden mithilfe der Atomic Force Microscopy (AFM) und weiterentwickelten Analysemethoden bestimmt. Die Porengröße der verwendeten Kollagennetzwerke wurde mit einer neuentwickelten Auswertemethode analysiert. Eine neuartige, minimal-invasive Methode zur Bestimmung der Verformung der Mikroumgebung von Zellen während der Migration verursacht durch Kräftegenerierung der Zelle wird beschrieben. Die Analyse des Invasions-Assays wurde automatisiert und eine nutzerfreundliche Software entwickelt, mit der große Datenmengen ausgewertet werden können. Diese Methoden wurden verwendet, um mechanische Eigenschaften und Migration der humanen Brustkrebszellinien MDA-MB-231 und MCF-7 zu studieren. Die Rolle der focal adhesion kinase (FAK) wurde mithilfe von embryonalen Maus-Fibroblasten studiert. Sowohl eine FAK knock-out Zellinie FAK-/- und Kontrolle FAK+/+, als auch eine kinase-dead Mutante FAKR454/R454 und Kontrolle FAKWT/WT wurden hinsichtlich ihrer Invasion und Verformung der Mikroumgebung analysiert.:Abstract i Acknowledgements iii 1 Introduction 1 2 Background 5 2.1 Cancer — An ever-changing Disease 5 2.1.1 Carcinogenesis and Neoplasm 6 2.1.2 Hallmarks of Cancer 7 2.1.3 Metastasis— The malignant Progression of Cancer 7 2.1.4 Metastatic Cascade 9 2.2 The Cell— Where it begins 10 2.2.1 Actomyosin Complex 12 2.2.1.1 Actin Monomer 12 2.2.1.2 Polymerization 12 2.2.1.3 Structures 14 2.2.1.4 Actin Cortex 15 2.2.1.5 Filopodia 16 2.2.1.6 Lamellipodium 16 2.2.1.7 Invadopodium 17 2.2.1.8 Stress Fibers 17 2.2.1.9 Actin in Cancer and Metastasis 17 2.2.1.10 Myosin and Actin 18 2.2.2 Focal Adhesions 19 2.2.3 Microtubules 20 2.2.4 Intermediate Filaments 21 2.2.5 Cellular Stiffness 22 2.2.6 Nuclear Deformability 23 2.3 The Extracellular Matrix— Where it happens 24 2.3.1 Components and Structure 25 2.3.2 Collagen as a Model System 26 2.3.2.1 Collagen I Fibril Formation 27 2.3.2.2 The Rat/Bovine-Collagen-Mix Model System 28 2.4 Single Cell Migration— Why it spreads 29 3 Materials and Methods 31 3.1 Cell Culture 31 3.1.1 Cancer Cells 31 3.1.2 Mouse fibroblasts 32 3.1.3 Pharmacological treatment 34 3.2 Collagen matrices 34 3.3 Cell Elasticity 36 3.3.1 Atomic Force Microscopy 36 3.3.2 Preparation 37 3.3.3 Data Aquisition 38 3.3.4 Data Analysis 38 3.4 Matrix Stiffness 40 3.4.1 Preparation 40 3.4.2 Data Aquisition 41 3.4.3 Data Analysis 41 3.5 Invasion Assay 42 3.5.1 Preparation 42 3.5.2 Data aquisition 44 3.5.3 Data Analysis 44 3.6 Matrix Topology 48 3.6.1 Preparation 49 3.6.2 Data Acquisition 50 3.6.3 Data Analysis 51 3.6.3.1 Binarization 51 3.6.3.2 Pore-Size 53 3.6.3.3 Fiber Thickness 54 3.7 Fiber Displacement 55 3.7.1 Preparation 56 3.7.2 Data Aquisition 56 3.7.3 Data analysis 57 3.7.3.1 Fiber Displacement 59 3.7.3.2 Cell Segmentation 60 3.7.3.3 Shell Analysis 61 3.8 A toolset to understand Single Cell Migration and what influences it 62 4 Results 65 4.1 Cell Elasticity 65 4.1.1 Example Force-Distance Curves 66 4.1.2 Single Cell Elasticity 67 4.2 Matrix Stiffness 69 4.3 Invasion 71 4.4 Matrix Topology 75 4.5 Influence of Cell Nucleus on Cell Migration 79 4.5.1 Cellular Elasticity 79 4.5.2 Invasion 81 4.6 Fiber Displacement 89 4.7 Effect of FAK on Cell Invasion and Fiber Displacement 93 4.7.1 FAK Knock-Out 93 4.7.2 Kinase-dead FAK Mutant 96 5 Discussion 103 References 10

ANALYSES OF THE EFFECTS OF DISRUPTING TALIN-PROTEIN KINASE A INTERACTION ON FOCAL ADHESION MORPHOLOGY AND DYNAMICS

Focal adhesions (FAs) are specialized structures that link the extracellular matrix (ECM) to actin cytoskeleton, playing essential roles in regulating cell migration and adhesion. Talin is a key protein resided in FAs and serves as a link between integrins and actomyosin II cytoskeleton. Recently, talin has been identified as an A-kinase anchoring protein (AKAP) that binds to protein kinase A (PKA). However, the effects of talin-PKA interaction on FA morphology and dynamics are not fully understood. In this study, a computational tool using FIJI was established to analyze FAs in rat embryo fibroblast cells (REF52s). The effects of disrupted talin-PKA interaction on the number, area, circularity, and aspect ratio of FA were investigated by comparing the point mutation AV1806DD in mouse talin 1 (mTLN1) to wildtype (WT), E1770A mutant, and the double mutant E1770A/AV1806DD. The FA analysis tool enabled accurate quantification of adhesion morphologic properties with raw images and provided insights into the role of PKA in regulating FA formation and turnover through talin interaction. The results of the study demonstrate that disrupted talin-PKA interaction alters FA morphology in REF52s, specifically resulting in a decrease in the total number of FA per cell and a more elongated shape compared to the WT (p\u3c0.05). Although the biochemical mechanism underlying the phenotypes remains unknown, it is suggested that talin phosphorylation by PKA is likely involved, leading to alterations in talin’s interaction with integrins and/or other proteins. These findings suggest an important role for talin-PKA interaction in FA morphology and dynamics

Lamellipodin tunes cell migration by stabilizing protrusions and promoting adhesion formation

Efficient migration on adhesive surfaces involves the protrusion of lamellipodial actin networks and their subsequent stabilization by nascent adhesions. The actin-binding protein lamellipodin (Lpd) is thought to play a critical role in lamellipodium protrusion, by delivering Ena/VASP proteins onto the growing plus ends of actin filaments and by interacting with the WAVE regulatory complex, an activator of the Arp2/3 complex, at the leading edge. Using B16-F1 melanoma cell lines, we demonstrate that genetic ablation of Lpd compromises protrusion efficiency and coincident cell migration without altering essential parameters of lamellipodia, including their maximal rate of forward advancement and actin polymerization. We also confirmed lamellipodia and migration phenotypes with CRISPR/Cas9-mediated Lpd knockout Rat2 fibroblasts, excluding cell type-specific effects. Moreover, computer-aided analysis of cell-edge morphodynamics on B16-F1 cell lamellipodia revealed that loss of Lpd correlates with reduced temporal protrusion maintenance as a prerequisite of nascent adhesion formation. We conclude that Lpd optimizes protrusion and nascent adhesion formation by counteracting frequent, chaotic retraction and membrane ruffling.This article has an associated First Person interview with the first author of the paper

Homocysteine and Cardiac Neural Crest Cell Cytoskeletal Proteins in the Chick Embryo

Elevated serum homocysteine (Hcys) is correlated with cardiovascular disease and with embryonic malformations related to neural crest cells (NCCs). We predicted Hcys may alter the balance of actin networks, stress fibers and focal adhesions, altering migration. We cultured neural tube explants in control and Hcys-treated medium and visualized actin, α-actinin, vinculin, filamin, and LIM3 protein in NCCs migrating outward. In Hcys, phalloidin-stained actin in stress fibers was brighter, and vinculin was more abundant in focal adhesions and lamellipodia. α-actinin and LIM3 were also enhanced around nuclei and in focal adhesions, and α-actinin also in filopodia. Filamin was unchanged. Hcys caused more spreading and migration of NCCs, but not more cell-cell adhesions. The findings support our hypothesis that Hcys adjusts NCCs for greater adhesion and migration. Its effect on LIM3 suggests it may modulate signaling that adjusts the cytoskeleton for enhanced migration, leading to mistimed and defective development of target tissues

Computer vision profiling of neurite outgrowth mordphodynamics reveals spatio-temporal modularity of Rho GTPase signaling

Neurite outgrowth is essential to build the neuronal processes that produce axons and dendrites that connect the adult brain. In cultured cells, the neurite outgrowth process is highly dynamic, and consists of a series of repetitive morphogenetic sub-processes (MSPs), such as neurite initiation, elongation, branching, growth cone motility and collapse (da Silva and Dotti 2002). Neurons also actively migrate, which might in part reflect neuronal migration during brain development. Each of the different MSPs inherent to neurite outgrowth and cell migration is likely to be regulated by precise spatio-temporal signaling networks that control cytoskeletal dynamics, trafficking and adhesion events. These MSPs can occur on a range of time and length scales. For example, microtubule bundling in the neurite shaft can be maintained during hours, while growth cone filopodia dynamically explore their surrounding on time scales of seconds and length scales of single microns. This implies that a correct understanding of these processes will require analysis with an adequate spatio-temporal resolution. The Rho family of GTPases are signaling switches that regulate a wide variety of cellular processes, such as actin and adhesion dynamics, gene transcription, and neuronal differentiation (Boguski and McCormick 1993). Rho GTPases are activated by guanine nucleotide exchange factors (GEFs), and are switched off by GTPase activating proteins (GAPs). Upon activation, Rho GTPases can associate with effectors to initiate a downstream response. Current models propose that Rac1 and Cdc42 regulate neurite extension, while RhoA controls growth cone collapse and neurite retraction (da Silva and Dotti 2002). However, until now the effects of Rho GTPases on neurite outgrowth have mostly been assessed using protein mutants in steady-state experiments, most often at late differentiation stages, which do not provide any insight about the different MSPs during neurite outgrowth. However, our proteomic analysis of biochemically-purified neurites from N1E-115 neuronal-like cells (Pertz et al. 2008), has suggested the existence of an unexpectedly complex 220 proteins signaling network consisting of multiple GEFs, GAPs, Rho GTPases, effectors and additional interactors. This is inconsistent with the simplistic view that classical experiments have provided before. In order to gain insight into the complexity of this Rho GTPase signaling network, we performed a siRNA screen that targets each of these 220 proteins individually. We hypothesized that specific spatio-temporal Rho GTPase signaling networks control different MSPs occurring during neurite outgrowth, and therefore designed an integrated approach to capture the whole morphodynamic continuum of this process. Perturbations of candidates that lead to a similar phenotype might be part of a given spatio-temporal signaling network. This approach consisted of: 1) A high content microscopy platform that allowed us to produce 8000 timelapse movies of 660 siRNA perturbations; 2) A custom built, computer vision approach that allowed us to automatically segment and track neurite and soma morphodynamics in the timelapse movies (collaboration with the group of Pascal Fua, EPFL, Lausanne); 3) A sophisticated statistical analysis pipeline that allowed the extraction of morphological and morphodynamic signatures (MDSs) relevant to each siRNA perturbation (collaboration with the group of Francois Fleuret, IDIAP). Analysis of our dataset revealed that each siRNA perturbation led to a quantifiable phenotype, emphasizing the quality of our proteomic dataset. Hierarchical clustering of the MDSs revealed the existence of 24 phenoclusters that provide information about neurite length, branching, number of neurites, soma migration speed, and a panel of additional morphological and morphodynamic features that are more difficult to grasp using visual inspection. This complex phenotypic space can more easily be understood when classified according to the first 4 features. Our screen then suggests the existence of 4 major morphodynamic phenotypes that define distinct stages of the neurite outgrowth process. These consist of phenotypes with short neurites, multiple short neurites, long neurites, and long and branched neurites. Further subdivision using the other features provides more information, with cell migration features being very often affected. This implies a high overlap between the signaling machinery that regulates the neurite outgrowth and cell migration processes. The high phenotypical redundancy (24 clusters for 220 candidate genes) provides only limited information to deduce unambiguous signaling networks regulating distinct MSPs. Further knowledge acquired from other approaches we used to study Rho GTPase signaling (FRET biosensors, and other live cell imaging techniques), made us realize that some morphodynamic phenotypes can only be understood when growth cone dynamics are inspected at a much higher resolution. For this purpose, we decided to further investigate a defined subset of genes using high resolution live cell imaging and a custom built growth cone segmentation and tracking pipeline for accurate quantification (collaboration with the group of Gaudenz Danuser, Harvard Medical School, Boston). These results shed light into how distinct cytoskeletal networks enabling growth cone advance can globally impact the neurite outgrowth process. A clear understanding of spatio-temporal Rho GTPase signaling will therefore require multi-scale approaches. Our results provide the first insight into the complexity of spatio-temporal Rho GTPase signaling during neurite outgrowth. The technologies we devised and our initial results, pave the way for a systems biology understanding of these complex signaling systems

Heading in the right direction : guiding cellular alignment by substrate anisotropy

Energie en entropie sturen cellen in de zelfde richtin