155 research outputs found
Quantification of Local Morphodynamics and Local GTPase Activity by Edge Evolution Tracking
Advances in time-lapse fluorescence microscopy have enabled us to directly observe dynamic cellular phenomena. Although the techniques themselves have promoted the understanding of dynamic cellular functions, the vast number of images acquired has generated a need for automated processing tools to extract statistical information. A problem underlying the analysis of time-lapse cell images is the lack of rigorous methods to extract morphodynamic properties. Here, we propose an algorithm called edge evolution tracking (EET) to quantify the relationship between local morphological changes and local fluorescence intensities around a cell edge using time-lapse microscopy images. This algorithm enables us to trace the local edge extension and contraction by defining subdivided edges and their corresponding positions in successive frames. Thus, this algorithm enables the investigation of cross-correlations between local morphological changes and local intensity of fluorescent signals by considering the time shifts. By applying EET to fluorescence resonance energy transfer images of the Rho-family GTPases Rac1, Cdc42, and RhoA, we examined the cross-correlation between the local area difference and GTPase activity. The calculated correlations changed with time-shifts as expected, but surprisingly, the peak of the correlation coefficients appeared with a 6–8 min time shift of morphological changes and preceded the Rac1 or Cdc42 activities. Our method enables the quantification of the dynamics of local morphological change and local protein activity and statistical investigation of the relationship between them by considering time shifts in the relationship. Thus, this algorithm extends the value of time-lapse imaging data to better understand dynamics of cellular function
Computing Interpretable Representations of Cell Morphodynamics
Shape changes (morphodynamics) are one of the principal ways cells interact with their environments and perform key intrinsic behaviours like division. These dynamics arise from a myriad of complex signalling pathways that often organise with emergent simplicity to carry out critical functions including predation, collaboration and migration. A powerful method for analysis can therefore be to quantify this emergent structure, bypassing the low-level complexity. Enormous image datasets are now available to mine. However, it can be difficult to uncover interpretable representations of the global organisation of these heterogeneous dynamic processes. Here, such representations were developed for interpreting morphodynamics in two key areas: mode of action (MoA) comparison for drug discovery (developed using the economically devastating Asian soybean rust crop pathogen) and 3D migration of immune system T cells through extracellular matrices (ECMs). For MoA comparison, population development over a 2D space of shapes (morphospace) was described using two models with condition-dependent parameters: a top-down model of diffusive development over Waddington-type landscapes, and a bottom-up model of tip growth. A variety of landscapes were discovered, describing phenotype transitions during growth, and possible perturbations in the tip growth machinery that cause this variation were identified. For interpreting T cell migration, a new 3D shape descriptor that incorporates key polarisation information was developed, revealing low-dimensionality of shape, and the distinct morphodynamics of run-and-stop modes that emerge at minute timescales were mapped. Periodically oscillating morphodynamics that include retrograde deformation flows were found to underlie active translocation (run mode). Overall, it was found that highly interpretable representations could be uncovered while still leveraging the enormous discovery power of deep learning algorithms. The results show that whole-cell morphodynamics can be a convenient and powerful place to search for structure, with potentially life-saving applications in medicine and biocide discovery as well as immunotherapeutics.Open Acces
Direct multiplex imaging and optogenetics of Rho GTPases enabled by near-infrared FRET
Direct visualization and light control of several cellular processes is a challenge, owing to the spectral overlap of available genetically encoded probes. Here we report the most red-shifted monomeric near-infrared (NIR) fluorescent protein, miRFP720, and the fully NIR Forster resonance energy transfer (FRET) pair miRFP670-miRFP720, which together enabled design of biosensors compatible with CFP-YFP imaging and blue-green optogenetic tools. We developed a NIR biosensor for Rac1 GTPase and demonstrated its use in multiplexed imaging and light control of Rho GTPase signaling pathways. Specifically, we combined the Rac1 biosensor with CFP-YFP FRET biosensors for RhoA and for Rac1-GDI binding, and concurrently used the LOV-TRAP tool for upstream Rac1 activation. We directly observed and quantified antagonism between RhoA and Rac1 dependent on the RhoA-downstream effector ROCK; showed that Rac1 activity and GDI binding closely depend on the spatiotemporal coordination between these two molecules; and simultaneously observed Rac1 activity during optogenetic manipulation of Rac1.Peer reviewe
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
Persistent and polarised global actin flow is essential for directionality during cell migration
Cell migration is hypothesized to involve a cycle of behaviours beginning with leading edge extension. However, recent evidence suggests that the leading edge may be dispensable for migration, raising the question of what actually controls cell directionality. Here, we exploit the embryonic migration of Drosophila macrophages to bridge the different temporal scales of the behaviours controlling motility. This approach reveals that edge fluctuations during random motility are not persistent and are weakly correlated with motion. In contrast, flow of the actin network behind the leading edge is highly persistent. Quantification of actin flow structure during migration reveals a stable organization and asymmetry in the cell-wide flowfield that strongly correlates with cell directionality. This organization is regulated by a gradient of actin network compression and destruction, which is controlled by myosin contraction and cofilin-mediated disassembly. It is this stable actin-flow polarity, which integrates rapid fluctuations of the leading edge, that controls inherent cellular persistence
Distinct predictive performance of Rac1 and Cdc42 in cell migration.
We propose a new computation-based approach for elucidating how signaling molecules are decoded in cell migration. In this approach, we performed FRET time-lapse imaging of Rac1 and Cdc42, members of Rho GTPases which are responsible for cell motility, and quantitatively identified the response functions that describe the conversion from the molecular activities to the morphological changes. Based on the identified response functions, we clarified the profiles of how the morphology spatiotemporally changes in response to local and transient activation of Rac1 and Cdc42, and found that Rac1 and Cdc42 activation triggers laterally propagating membrane protrusion. The response functions were also endowed with property of differentiator, which is beneficial for maintaining sensitivity under adaptation to the mean level of input. Using the response function, we could predict the morphological change from molecular activity, and its predictive performance provides a new quantitative measure of how much the Rho GTPases participate in the cell migration. Interestingly, we discovered distinct predictive performance of Rac1 and Cdc42 depending on the migration modes, indicating that Rac1 and Cdc42 contribute to persistent and random migration, respectively. Thus, our proposed predictive approach enabled us to uncover the hidden information processing rules of Rho GTPases in the cell migration
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Leveraging Microtechnology to Study Multicellular Microvascular Systems and Macromolecular Interaction
Biological systems are large-scale, complex systems comprised of many hierarchical subsystems interacting physico-chemically in a dynamic and coordinated fashion. The complex interactions of subsystems (in micro-scale) lead to the formation of emergent properties (in macro-scale); these are properties that are not visible if individual subsystems are studied. The inherent high-throughput characteristics of microfabrication technology (microtechnology) along with its ability to manipulate biological species at the micro-scale makes it an ideal tool to elucidate the mechanisms leading to the formation of emergent properties at the macro-scale.
In this dissertation, by combining microtechnologies with advanced computational algorithms, we demonstrate system-level analysis of biological systems in development and disease. The abundance of high quality molecular and genetic data along with the drastic increase in computational power resulted in considerable progress in genomics, epigenomics and proteomics, but not for the so-called cellomics as we define it here: high-throughput study of single-cell phenotype and heterotypic cell-cell interaction via micromanipulation and bioinformatics analysis. Lack of high-throughput robust experimental tools is the major roadblock to cellomics. Using microtechnologies, in the context of developmental biology we studied vascular tissue morphogenesis (vasculogenesis). Formation of microvessels is of critical significance in development and for vascularizing newly engineered tissues in regenerative medicine.
First, we sought to map the heterogeneous morphodynamic behavior of individual clonal cells in the process of capillary-like structure (CLS) formation (Chapter 2 and 3). Then we looked into deciphering the role of extracellular matrix (ECM) mediated mechanical signals in deriving the process of CLS formation (Chapter 4). In the second half of this thesis, we demonstrated the capabilities of microtechnologies and advanced computational algorithms in tackling the challenging problems in disease: global health diagnostics and cancer drug screening.
First, we studied the performance of microfluidic-based diagnostic as a large-scale complex system under real-world constraints (Chapter 5). Then, we present the development of two microfluidic-based platforms to study the heterotypic interaction of cells in both a biomimetic in vitro and a realistic in vivo setting. We developed an implantable construct carrying a densely-packed heterogeneous panel of tumor cells. This platform could ultimately be used to test anti-cancer drug efficacy against a large number of genotypes in an in vivo setting (Appendices A and B).
Together, these methods provide a powerful suite of tools for high-throughput analysis of biological species at the micro-scale and could potentially unlock the mysteries behind the emergent properties observed at the macro-scale
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
A Dynamical Systems Modelling Framework for Breast Cancer Cell Motility and Morphology Analysis
Cancer is a worldwide disease and, in the UK, breast cancer is the most common. Compared to healthy cells, cancer cells migrate abnormally, associated with alterations in cell motility and morphology. The development of biomedical imaging techniques result in the production of large amounts of data. The analysis of such large data, the variety of cancer cell shapes and the potential links between cell motility and morphology present a challenge for cell migration study: how to analyse cell motility and morphology simultaneously.
This thesis proposes a computational framework to address integrated cancer cell migration analysis. Firstly, automated tracking of cell boundaries is undertaken by a DWNA kinematic model of cell boundaries, described by B-spline active contours. The tracked cell states intrinsically links cell morphology to motility features. As a result, cell centroid and boundary dynamics are successfully tracked, followed by quantitative motility analysis.
A module to quantitatively analyse cell morphology is proposed after tracking. Cell shapes are described by a 2D descriptor. Accordingly, cell morphodynamics are modelled as a hidden Markov process, along with three shape states: round, elongated and teardrop. In order to explore the potential interactions between cell shapes and motility, cell centroid motility characteristics are associated to the identified shape states. When the analysis was applied to breast cancer control cells, the identified shape states showed distinct motility characteristics.
Finally, the proposed framework is adapted to the comparison of MDA-MB-231 cell behaviours with regulating migration-associated proteins: i) Blebbistatin and Y-27632, which are chemical inhibitors of two different proteins working on the same pathway, showed identical, but different degrees of effects on the motility and morphology characteristics of MDA-MB-231 cells. ii) The absence of FA-associated genes, including FAK, RhoE and beta-PIX, respectively showed distinct effects on cell migrations
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