Model-based Curvilinear Network Extraction and Tracking toward Quantitative Analysis of Biopolymer Networks

Curvilinear biopolymer networks pervade living systems. They are routinely imaged by fluorescence microscopy to gain insight into their structural, mechanical, and dynamic properties. Image analysis can facilitate understanding the mechanisms of their formation and their biological functions from a quantitative viewpoint. Due to the variability in network geometry, topology and dynamics as well as often low resolution and low signal-to-noise ratio in images, segmentation and tracking networks from these images is challenging. In this dissertation, we propose a complete framework for extracting the geometry and topology of curvilinear biopolymer networks, and also tracking their dynamics from multi-dimensional images. The proposed multiple Stretching Open Active Contours (SOACs) can identify network centerlines and junctions, and infer plausible network topology. Combined with a $k$-partite matching algorithm, temporal correspondences among all the detected filaments can be established. This work enables statistical analysis of structural parameters of biopolymer networks as well as their dynamics. Quantitative evaluation using simulated and experimental images demonstrate its effectiveness and efficiency. Moreover, a principled method of optimizing key parameters without ground truth is proposed for attaining the best extraction result for any type of images. The proposed methods are implemented into a usable open source software ``SOAX\u27\u27. Besides network extraction and tracking, SOAX provides a user-friendly cross-platform GUI for interactive visualization, manual editing and quantitative analysis. Using SOAX to analyze several types of biopolymer networks demonstrates the potential of the proposed methods to help answer key questions in cell biology and biophysics from a quantitative viewpoint

Automatic Morphological Subtyping Reveals New Roles of Caspases in Mitochondrial Dynamics

Morphological dynamics of mitochondria is associated with key cellular processes related to aging and neuronal degenerative diseases, but the lack of standard quantification of mitochondrial morphology impedes systematic investigation. This paper presents an automated system for the quantification and classification of mitochondrial morphology. We discovered six morphological subtypes of mitochondria for objective quantification of mitochondrial morphology. These six subtypes are small globules, swollen globules, straight tubules, twisted tubules, branched tubules and loops. The subtyping was derived by applying consensus clustering to a huge collection of more than 200 thousand mitochondrial images extracted from 1422 micrographs of Chinese hamster ovary (CHO) cells treated with different drugs, and was validated by evidence of functional similarity reported in the literature. Quantitative statistics of subtype compositions in cells is useful for correlating drug response and mitochondrial dynamics. Combining the quantitative results with our biochemical studies about the effects of squamocin on CHO cells reveals new roles of Caspases in the regulatory mechanisms of mitochondrial dynamics. This system is not only of value to the mitochondrial field, but also applicable to the investigation of other subcellular organelle morphology

Time-Resolved Quantification of Centrosomes by Automated Image Analysis Suggests Limiting Component to Set Centrosome Size in C. Elegans Embryos

The centrosome is a dynamic organelle found in all animal cells that serves as a microtubule organizing center during cell division. Most of the centrosome components have been identified by genetic screens over the last decade, but little is known about how these components interact with each other to form a functional centrosome. Towards a better understanding of the molecular organization of the centrosome, we investigated the mechanism that regulates the size of the centrosome in the early C. elegans embryo. For this, we monitored fluorescently labeled centrosomes in living embryos and developed a suite of image analysis algorithms to quantify the centrosomes in the resulting 3D time-lapse images. In particular, we developed a novel algorithm involving a two-stage linking process for tracking entrosomes, which is a multi-object tracking task. This fully automated analysis pipeline enabled us to acquire time-resolved data of centrosome growth in a large number of embryos and could detect subtle phenotypes that were missed by previous assays based on manual image analysis. In a first set of experiments, we quantified centrosome size over development in wild-type embryos and made three essential observations. First, centrosome volume scales proportionately with cell volume. Second, beginning at the 4-cell stage, when cells are small, centrosome size plateaus during the cell cycle. Third, the total centrosome volume the embryo gives rise to in any one cell stage is approximately constant. Based on our observations, we propose a ‘limiting component’ model in which centrosome size is limited by the amounts of maternally derived centrosome components. In a second set of experiments, we tested our hypothesis by varying cell size, centrosome number and microtubule-mediated pulling forces. We then manipulated the amounts of several centrosomal proteins and found that the conserved centriolar and pericentriolar material protein SPD-2 is one such component that determines centrosome size

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

Automatically Improving Cell Segmentation in Time-Lapse Microscopy Images Using Temporal Context From Tracking and Lineaging

Over the past decade biologists and microscopists have produced truly amazing movies, showing in wonderful detail the dynamics of living cells and subcellular structures. Access to this degree of detail in living cells is a key aspect of current biological research. This wealth of data and potential discovery is constrained by a lack of software tools. The standard approach to biological image analysis begins with segmentation to identify individual cells, tracking to maintain cellular identities over time, and lineaging to identify parent-daughter relationships. This thesis presents new algorithms for improving the segmentation, tracking and lineaging of live cell time-lapse microscopy images. A new ''segmentation from lineage'' algorithm feeds lineage or other high-level behavioral information back into segmentation algorithms along with temporal context provided by the multitemporal association tracker to create a powerful iterative learning algorithm that significantly improves segmentation and tracking results. A tree inference algorithm is used to improve automated lineage generation by integrating known cellular behavior constraints as well as fluorescent signals if available. The ''learn from edits'' technique uses tracking information to propagate user corrections to automatically correct further tracking mistakes. Finally, the new pixel replication algorithm is used for accurately partitioning touching cells using elliptical shape models. These algorithms are integrated into the LEVER lineage editing and validation software, providing user interfaces for automated segmentation, tracking and lineaging, as well as the ability to easily correct the automated results. These algorithms, integrated into LEVER, have identified key behavioral differences in embryonic and adult neural stem cells. Edit-based and functional validation techniques are used to evaluate and compare the new algorithms with current state of the art segmentation and tracking approaches. All the software as well as the image data and analysis results are released under a new open source/open data model built around Gitlab and the new CloneView interactive web tool.Ph.D., Electrical Engineering -- Drexel University, 201

Studies on the function of PRG2/PLPPR3 in neuron morphogenesis

Neuron development follows a multifaceted sequence of cell migration, polarisation, neurite elongation, branching, tiling, and pruning. The implementation of this sequence differs between neuronal cell types and even in individual neurons between sub-compartments such as dendrites and axons. Membrane proteins are at a prime position in neurons to couple extrinsic morphogenetic signals with their intrinsic responses to orchestrate this defined morphological progression. The Phospholipid phosphatase-related / Plasticity-related gene (PLPPR/PRG)-family comprises five neuron-enriched and developmentally regulated membrane proteins with functions in cellular morphogenesis. At the start of this project, no publication had characterised the function of PLPPR3/PRG2 during neuron development. The presented work describes PLPPR3 as an axon-enriched protein localising to the plasma membrane and internal membrane compartments of neurons. Mutagenesis studies in cell lines establish the plasma membrane localisation of PLPPR3 as a regulator of its function to increase filopodia density (Chapter 2). Furthermore, the generation of a Plppr3-/- mouse line using CRISPR/Cas9 genome editing techniques (Chapter 3) enabled characterising endogenous phenotypes of PLPPR3 in neurons. In primary neuronal cultures, PLPPR3 was found to specifically control branch formation in a pathway with the phosphatase PTEN, without altering the overall growth capacity of neurons (Chapter 4). Loss of PLPPR3 specifically reduced branches forming from filopodia without affecting the stability of branches. This precise characterisation of PLPPR3 function unravelled the existence of parallel, independent programs for branching morphogenesis that are utilised and implemented differentially in developing axons and dendrites (Chapter 5). Furthermore, this thesis establishes multiple tools to study PLPPR3, the membrane lipid phosphatidylinositol-trisphosphate, and neuron morphogenesis by providing molecular tools, protocols, and semi-automated and automated image analysis pipelines (Appendix Chapter 7) and discusses experiments to test, refine and extend models of PLPPR3 function (Chapter 6). In summary, this thesis generated and utilised several tools and a Plppr3-/- mouse model to characterise PLPPR3 as a specific regulator of neuron branching morphogenesis. This precise characterisation refined and expanded the understanding of axon-specific branching morphogenesis.Nervenzellen entwickeln ihre komplexe Morphologie durch das Zusammenwirken diverser molekularer Entwicklungs-Programme der Zellkörper-Migration, der Polarisierung und der Morphogenese durch Wachstum, Verzweigung, Stabilisierung und Koordinierung ihrer Neuriten. Dabei unterscheidet sich die exakte Implementierung zwischen Nervenzell-Typen und selbst innerhalb einzelner Zellen zwischen Axonen und Dendriten. Diese unterschiedliche Morphogenese wird dabei speziell durch Membranproteine stark beeinflusst, die durch ihre Präsenz an der Plasmamembran Zell-extrinsische Signale mit den Zell-intrinsischen Morphogeneseprogrammen verbinden und beeinflussen. Die Familie der Phospholipid phosphatase-related / Plasticity-related gene (PLPPR/PRG) Proteine umfasst fünf Nervenzell-spezifische Membranproteine mit Effekten auf die Morphologie von Zellen. Zu Beginn dieses Projektes hatte noch keine Studie die Funktion des Familienmitglieds PLPPR3/PRG2 in Nervenzellen untersucht. Diese Dissertation beschreibt die Lokalisation von PLPPR3 an der Plasmamembran und in Zell-internen Membranstrukturen von Nervenzellen. Experimente in Zellkultur zeigen eine erhöhte Filopodien-Dichte nach Überexpression von PLPPR3, Mutagenese-Studien deuten eine strikte Kontrolle der Plasmamembran-Lokalisation an (Kapitel 2). Die Generierung einer Plppr3 Knockout Mauslinie mittels CRISPR/Cas9 Genom-Modifizierung (Kapitel 3) erlaubte eine Charakterisierung der endogenen Funktion von PLPPR3 in Nervenzellen. In Primärzellkultur von Nervenzellen des murinen Hippocampus zeigte sich, dass PLPPR3 im Zusammenspiel mit der Phosphatase PTEN spezifisch die Verzweigung von Nervenzellen kontrolliert, ohne deren Wachstumspotential global zu verändern (Kapitel 4). Dadurch kann PLPPR3 als ein Schalter zwischen Verzweigung und Verlängerung eines Nervenzell-Fortsatzes agieren. Der Verlust von PLPPR3 verursachte reduzierte spezifisch die Anzahl an Verzweigungen, die aus Filopodien entstanden, ohne dabei die Stabilität dieser Verzweigungen zu beeinflussen. Die präzise Charakterisierung dieser Funktion von PLPPR3 deckte auf, dass Verzweigungen von Nervenzell-Fortsätzen durch voneinander unabhängige Entwicklungsprogramme ausgebildet und stabilisiert werden können (Kapitel 5). Diese Programme werden von Axonen und Dendriten in unterschiedlicher Weise eingesetzt. Zusätzlich etabliert diese Arbeit sowohl diverse molekulare Werkzeuge und Visualisierungs-Protokolle zur Analyse von PLPPR3 und dem Membranlipid Phosphatidylinositol-Trisphosphat, als auch automatisierte Quantifizierungssoftware zur Studie der Nervenzellmorphologie (Appendix-Kapitel 7). Abschließend entwickelt und verfeinert die Dissertation mögliche Modelle zur PLPPR3-Funktion und zeigt experimentelle Strategien auf, um diese Modelle besser charakterisieren zu können (Kapitel 6). Zusammenfassend wurden in dieser Promotionsarbeit diverse Experimental- und Analyse-Strategien und eine Plppr3-/- Mauslinie entwickelt und genutzt, um PLPPR3 als einen spezifischen Regulator der Nervenzell-Morphogenese zu etablieren. Diese präzise Charakterisierung des PLPPR3 Phänotyps erlaubte zusätzlich eine Verfeinerung und Erweiterung der Erkenntnisse zur Axon-spezifischen Entwicklung von Verzweigungen

Computational Image Analysis For Axonal Transport, Phenotypic Profiling, And Digital Pathology

Recent advances in fluorescent probes, microscopy, and imaging platforms have revolutionized biology and medicine, generating multi-dimensional image datasets at unprecedented scales. Traditional, low-throughput methods of image analysis are inadequate to handle the increased “volume, velocity, and variety” that characterize the realm of big data. Thus, biomedical imaging requires a new set of tools, which include advanced computer vision and machine learning algorithms. In this work, we develop computational image analysis solutions to biological questions at the level of single-molecules, cells, and tissues. At the molecular level, we dissect the regulation of dynein-dynactin transport initiation using in vitro reconstitution, single-particle tracking, super-resolution microscopy, live-cell imaging in neurons, and computational modeling. We show that at least two mechanisms regulate dynein transport initiation neurons: (1) cytoplasmic linker proteins, which are regulated by phosphorylation, increase the capture radius around the microtubule, thus reducing the time cargo spends in a diffusive search; and (2) a spatial gradient of tyrosinated alpha-tubulin enriched in the distal axon increases the affinity of dynein-dynactin for microtubules. Together, these mechanisms support a multi-modal recruitment model where interacting layers of regulation provide efficient, robust, and spatiotemporal control of transport initiation. At the cellular level, we develop and train deep residual convolutional neural networks on a large and diverse set of cellular microscopy images. Then, we apply networks trained for one task as deep feature extractors for unsupervised phenotypic profiling in a different task. We show that neural networks trained on one dataset encode robust image phenotypes that are sufficient to cluster subcellular structures by type and separate drug compounds by the mechanism of action, without additional training, supporting the strength and flexibility of this approach. Future applications include phenotypic profiling in image-based screens, where clustering genetic or drug treatments by image phenotypes may reveal novel relationships among genetic or pharmacologic pathways. Finally, at the tissue level, we apply deep learning pipelines in digital pathology to segment cardiac tissue and classify clinical heart failure using whole-slide images of cardiac histopathology. Together, these results demonstrate the power and promise of computational image analysis, computer vision, and deep learning in biological image analysis

Automatic extraction of actin networks in plants

This is the author accepted manuscript. The final version is available on open access from Public Library of Science via the DOI in this recordData Availability: The authors confirm that all data underlying the findings are fully available without restriction. The complete code for this paper is available on a GitHub repository at https://github.com/JordanHembrow5/DRAGoN.The actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.Biotechnology and Biological Sciences Research Council (BBSRC)Wellcome TrustMedical Research Council (MRC