10 research outputs found
Automated profiling of growth cone heterogeneity defines relations between morphology and motility.
No full textGrowth cones are complex, motile structures at the tip of an outgrowing neurite. They often exhibit a high density of filopodia (thin actin bundles), which complicates the unbiased quantification of their morphologies by software. Contemporary image processing methods require extensive tuning of segmentation parameters, require significant manual curation, and are often not sufficiently adaptable to capture morphology changes associated with switches in regulatory signals. To overcome these limitations, we developed Growth Cone Analyzer (GCA). GCA is designed to quantify growth cone morphodynamics from time-lapse sequences imaged both in vitro and in vivo, but is sufficiently generic that it may be applied to nonneuronal cellular structures. We demonstrate the adaptability of GCA through the analysis of growth cone morphological variation and its relation to motility in both an unperturbed system and in the context of modified Rho GTPase signaling. We find that perturbations inducing similar changes in neurite length exhibit underappreciated phenotypic nuance at the scale of the growth cone
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Learning from Cell-to-Cell Heterogeneity through Computer Vision: Fully Automated Image Analysis Identifies Relationships among Growth Cone Morphology, Rho GTPase Activation Motifs, and Neurite Elongation
No full textThe growth cone, residing at the helm of elongating neurites, is characterized by the superposition of two geometrically distinct actin networks regulated by RhoGTPase signaling patterns. This complex, dynamic structure, plays an essential role in neuronal development, integrating a variety of extrinsic environmental cues, which serve to both guide the elongating neurite along its route, and ensure the appropriate temporal coordination of many simultaneous neurite elongation events. While much progress has been made in the identification of the molecular players important for neurite elongation and guidance, much less is known about how intra-cellular signaling events coordinate in space and time to control growth cone morphology and function. High resolution imaging of growth cones expressing fluorescent biosensors provide a means to monitor fundamental signaling nodes and directly measure cellular signaling relationships in real time. However, a dearth of automated tools for the extraction of biosensor measurements remains a primary bottleneck for application of this technology to higher throughput studies. Here we remove this bottleneck by developing Growth Cone Analyzer, a computer vision software tool that fully automates the extraction of fluorescent signal in relationship to biologically relevant morphological/dynamic landmarks, from growth cones exhibiting select behaviors. We showcase the tool by focusing first upon the morphological analysis of a structurally heterogeneous set of growth cones. Using these data, we demonstrate that morphodynamic features extracted using Growth Cone Analyzer can be employed to 1) identify latent correlations between growth cone architecture and neurite elongation rate, 2) distinguish subtleties in growth cone morphology/dynamics upon perturbation of seemingly redundant regulators of the RhoGTPase signaling network, and 3) detect the timing of significant transitions in growth cone morphology as a neurite progresses along its trajectory. We then illustrate how Growth Cone Analyzer’s utility may be extended beyond morphological studies to facilitate the extraction of high-fidelity spatial profiles of RhoGTPase activation patterns measured via FRET biosensors. Hence these tools provide a vital first step towards future studies aiming to quantify the RhoGTPase signaling patterns regulating neurite elongation and determine how specific RhoGTPase regulators may cooperate to maintain these motifs.Biology, Molecular and Cellula
TACC3-ch-TOG track the growing tips of microtubules independently of clathrin and Aurora-A phosphorylation
No full textThe interaction between TACC3 (transforming acidic coiled coil protein 3) and the microtubule polymerase ch-TOG (colonic, hepatic tumor overexpressed gene) is evolutionarily conserved. Loading of TACC3–ch-TOG onto spindle microtubules requires the phosphorylation of TACC3 by Aurora-A kinase and the subsequent interaction of TACC3 with clathrin to form a microtubule binding surface. Whether there is a pool of TACC3–ch-TOG that is independent of clathrin in human cells, and what is the function of this pool, are open questions. Here, we report that TACC3 is recruited to the plus-ends of microtubules by its association with ch-TOG and that this pool is independent of phosphorylation and binding to clathrin. The plus-end binding of TACC3–ch-TOG persists in interphase and we propose that one cellular function of TACC3–ch-TOG is to modulate cell migration. We also describe the distinct subcellular pools of TACC3, ch-TOG and clathrin. TACC3 is often described as a centrosomal protein, but we show that there is no significant population of TACC3 at centrosomes. The delineation of distinct protein pools reveals a simplified view of how these proteins are organized and controlled by post-translational modification
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Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data
No full textSeamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods. © 2014 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd
Joint Hub Identification for Brain Networks by Multivariate Graph Inference
No full textRecent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer’s disease and obsessive-compulsive disorder
Exposure to Prenatal Maternal Distress and Infant White Matter Neurodevelopment
No full textThe prenatal period represents a critical time for brain growth and development. These rapid neurological advances render the fetus susceptible to various influences with life-long implications for mental health. Maternal distress signals are a dominant early life influence, contributing to birth outcomes and risk for offspring psychopathology. This prospective longitudinal study evaluated the association between prenatal maternal distress and infant white matter microstructure. Participants included a racially and socioeconomically diverse sample of 85 mother–infant dyads. Prenatal distress was assessed at 17 and 29 weeks’ gestational age (GA). Infant structural data were collected via diffusion tensor imaging (DTI) at 42–45 weeks’ postconceptional age. Findings demonstrated that higher prenatal maternal distress at 29 weeks’ GA was associated with increased fractional anisotropy, b = .283, t(64) = 2.319, p = .024, and with increased axial diffusivity, b = .254, t(64) = 2.067, p = .043, within the right anterior cingulate white matter tract. No other significant associations were found with prenatal distress exposure and tract fractional anisotropy or axial diffusivity at 29 weeks’ GA, or earlier in gestation
Association between prenatal maternal sleep quality, neonatal uncinate fasciculus white matter, and infant negative emotionality
No full textBACKGROUND: Poor prenatal maternal sleep is a pervasive, yet modifiable, health concern affecting maternal and foetal wellbeing. Experimental rodent studies demonstrate that prenatal maternal sleep deprivation affects offspring brain development and leads to adverse outcomes, including increased anxiety-like behaviour. We examined the relation between prenatal maternal sleep quality and neonatal white matter development and subsequent infant negative emotionality.
METHODS: Participants included 116 mother-infant (53% female) dyads. Prenatal sleep quality was prospectively assessed three times during gestation (16, 29, and 35 gestational weeks) using the Pittsburgh Sleep Quality Index. Neonatal white matter, as indexed by fractional anisotropy (FA), was assessed via diffusion weighted magnetic resonance imaging. Negative emotionality was measured via behavioural observation and maternal report when the infant was 6-months of age.
FINDINGS: More prenatal sleep problems across pregnancy were associated with higher neonatal FA in the uncinate fasciculus (left: b = 0.20, p = .004; right: b = 0.15, p = .027). Higher neonatal uncinate FA was linked to infant negative emotionality, and uncinate FA partially mediated the association between prenatal maternal sleep and behavioural observation of infant negative emotionality.
INTERPRETATION: Findings highlight prenatal sleep as an environmental signal that affects the developing neonatal brain and later infant negative emotionality.
FUNDING: National Institutes of Health (R01MH109662, R01HL155744, P50HD103573, K12AR084226, F32 Training fellowships MH125572, HL165844, MH106440, and diversity supplement R01HL155744-01S1)
Association between prenatal maternal sleep quality, neonatal uncinate fasciculus white matter, and infant negative emotionalityResearch in context
No full textSummary: Background: Poor prenatal maternal sleep is a pervasive, yet modifiable, health concern affecting maternal and foetal wellbeing. Experimental rodent studies demonstrate that prenatal maternal sleep deprivation affects offspring brain development and leads to adverse outcomes, including increased anxiety-like behaviour. We examined the relation between prenatal maternal sleep quality and neonatal white matter development and subsequent infant negative emotionality. Methods: Participants included 116 mother-infant (53% female) dyads. Prenatal sleep quality was prospectively assessed three times during gestation (16, 29, and 35 gestational weeks) using the Pittsburgh Sleep Quality Index. Neonatal white matter, as indexed by fractional anisotropy (FA), was assessed via diffusion weighted magnetic resonance imaging. Negative emotionality was measured via behavioural observation and maternal report when the infant was 6-months of age. Findings: More prenatal sleep problems across pregnancy were associated with higher neonatal FA in the uncinate fasciculus (left: b = 0.20, p = .004; right: b = 0.15, p = .027). Higher neonatal uncinate FA was linked to infant negative emotionality, and uncinate FA partially mediated the association between prenatal maternal sleep and behavioural observation of infant negative emotionality. Interpretation: Findings highlight prenatal sleep as an environmental signal that affects the developing neonatal brain and later infant negative emotionality. Funding: National Institutes of Health (R01MH109662, R01HL155744, P50HD103573, K12AR084226, F32 Training fellowships MH125572, HL165844, MH106440, and diversity supplement R01HL155744-01S1)
Anatomy of a simple acyl intermediate in enzyme catalysis: combined biophysical and modeling studies on ornithine acetyl transferase.
No full textAcyl-enzyme complexes are intermediates in reactions catalyzed by many hydrolases and related enzymes which employ nucleophilic catalysis. However, most of the reported structural data on acyl-enzyme complexes has been acquired under noncatalytic conditions. Recent IR analyses have indicated that some acyl-enzyme complexes may be more flexible than most crystallographic analyses have implied. OAT2 is a member of the N-terminal nucleophile (Ntn) hydrolase enzyme superfamily and catalyzes the reversible transfer of an acetyl group between the alpha-amino groups of ornithine and glutamate in a mechanism proposed to involve an acyl-enzyme complex. We have carried out biophysical analyses on ornithine acetyl transferase (OAT2), both in solution and in the crystalline state. Mass spectrometric studies identified Thr-181 as the residue acetylated during OAT2 catalysis; (13)C NMR analyses implied the presence of an acyl-enzyme complex in solution. Crystallization of OAT2 in the presence of N-alpha-acetyl-L-glutamate led to a structure in which Thr-181 was acetylated; the carbonyl oxygen of the acyl-enzyme complex was located in an oxyanion hole and positioned to hydrogen bond with the backbone amide NH of Gly-112 and the alcohol of Thr-111. While the crystallographic analyses revealed only one structure, IR spectroscopy demonstrated the presence of two distinct acyl-enzyme complex structures with carbonyl stretching frequencies at 1691 and 1701 cm(-1). Modeling studies implied two possible acyl-enzyme complex structures, one of which correlates with that observed in the crystal structure and with the 1691 cm(-1) IR absorption. The second acyl-enzyme complex structure, which has only a single oxyanion hole hydrogen bond, is proposed to give rise to the 1701 cm(-1) IR absorption. The two acyl-enzyme complex structures can interconvert by movement of the Thr-111 side-chain alcohol hydrogen away from the oxyanion hole to hydrogen bond with the backbone carbonyl of the acylated residue, Thr-181. Overall, the results reveal that acyl-enzyme complex structures may be more dynamic than previously thought and support the use of a comprehensive biophysical and modeling approach in studying such intermediates
Computer vision profiling of neurite outgrowth mordphodynamics reveals spatio-temporal modularity of Rho GTPase signaling
No full textNeurite 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
