OMEGA: a software tool for the management, analysis, and dissemination of intracellular trafficking data that incorporates motion type classification and quality control [preprint]

MOTIVATION: Particle tracking coupled with time-lapse microscopy is critical for understanding the dynamics of intracellular processes of clinical importance. Spurred on by advances in the spatiotemporal resolution of microscopy and automated computational methods, this field is increasingly amenable to multi-dimensional high-throughput data collection schemes (Snijder et al, 2012). Typically, complex particle tracking datasets generated by individual laboratories are produced with incompatible methodologies that preclude comparison to each other. There is therefore an unmet need for data management systems that facilitate data standardization, meta-analysis, and structured data dissemination. The integration of analysis, visualization, and quality control capabilities into such systems would eliminate the need for manual transfer of data to diverse downstream analysis tools. At the same time, it would lay the foundation for shared trajectory data, particle tracking, and motion analysis standards. RESULTS: Here, we present Open Microscopy Environment inteGrated Analysis (OMEGA), a cross-platform data management, analysis, and visualization system, for particle tracking data, with particular emphasis on results from viral and vesicular trafficking experiments. OMEGA provides easy to use graphical interfaces to implement integrated particle tracking and motion analysis workflows while keeping track of error propagation and data provenance. Specifically, OMEGA: 1) imports image data and metadata from data management tools such as Open Microscopy Environment Remote Objects (OMERO; Allan et al., 2012); 2) tracks intracellular particles moving across time series of image planes; 3) facilitates parameter optimization and trajectory results inspection and validation; 4) performs downstream trajectory analysis and motion type classification; 5) estimates the uncertainty associated with motion analysis; and, 6) facilitates storage and dissemination of analysis results, and analysis definition metadata, on the basis of our newly proposed Minimum Information About Particle Tracking Experiments (MIAPTE; Rigano & Strambio-De-Castillia, 2016; 2017) guidelines in combination with the OME-XML data model (Goldberg et al, 2005)

Visuelle Analyse großer Partikeldaten

Partikelsimulationen sind eine bewährte und weit verbreitete numerische Methode in der Forschung und Technik. Beispielsweise werden Partikelsimulationen zur Erforschung der Kraftstoffzerstäubung in Flugzeugturbinen eingesetzt. Auch die Entstehung des Universums wird durch die Simulation von dunkler Materiepartikeln untersucht. Die hierbei produzierten Datenmengen sind immens. So enthalten aktuelle Simulationen Billionen von Partikeln, die sich über die Zeit bewegen und miteinander interagieren. Die Visualisierung bietet ein großes Potenzial zur Exploration, Validation und Analyse wissenschaftlicher Datensätze sowie der zugrundeliegenden Modelle. Allerdings liegt der Fokus meist auf strukturierten Daten mit einer regulären Topologie. Im Gegensatz hierzu bewegen sich Partikel frei durch Raum und Zeit. Diese Betrachtungsweise ist aus der Physik als das lagrange Bezugssystem bekannt. Zwar können Partikel aus dem lagrangen in ein reguläres eulersches Bezugssystem, wie beispielsweise in ein uniformes Gitter, konvertiert werden. Dies ist bei einer großen Menge an Partikeln jedoch mit einem erheblichen Aufwand verbunden. Darüber hinaus führt diese Konversion meist zu einem Verlust der Präzision bei gleichzeitig erhöhtem Speicherverbrauch. Im Rahmen dieser Dissertation werde ich neue Visualisierungstechniken erforschen, welche speziell auf der lagrangen Sichtweise basieren. Diese ermöglichen eine effiziente und effektive visuelle Analyse großer Partikeldaten

VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table

Analyzing colony dynamics and visualizing cell diversity in spatiotemporal experiments

Hattab G. Analyzing colony dynamics and visualizing cell diversity in spatiotemporal experiments. Bielefeld: Universität Bielefeld; 2018.Bioimaging technologies enable the description of the life cycle of organisms at the microscopic scale, for example bacterial cells. In the particular case of time lapse imaging, the coupling of experimental setups and marker protocols results in the acquisition of biological changes in spatiotemporal experiments. Such experiments are devised to obtain a time-lapse image data, which I refer to as biomovies. Understanding how a cell behaves at every time point is crucial. In fact, this motivated all cell studies in the literature, which are single cell oriented. For the present biomovies, the task is to identify similarly fluorescing subpopulations across space and time. My interest lies in isogenic bacterial populations of *Sinorhizobium meliloti*. The biomovies’ particularity is a dynamic range of high values for a set of different properties (e.g. cell density, cell count, etc), herein, leading to a bottleneck. State of the art methods cannot address such a task, which is partly due to their inability to handle highly dense populations and their adaptability to different experimental setups. In particular, they fall short either at the segmentation step (to delineate individual cells and extract their abstraction, e.g. cell centroid) or at the tracking step (to follow identified cells in each frame). To gain insight into bacterial growth at the population level, I claim that one does not really need to know the fate of each single cell. In the context of this thesis, I present a series of pipelines and algorithms. First, preprocessing pipelines to reduce noise and enhance the object-to-background contrast. Second, an adaptive algorithm to correct spatial shift in the images (i.e. registration) and of each biomovie. Third and last, a modular algorithm that constructs coherent patch lineages by employing two adapted data abstractions, the particle and the patch, that are essential to solving the aforementioned bottleneck and are defined as follows: A particle is an intuitive geometric abstraction that results from considering whether the neighborhood around a pixel falls within a cell by checking for signal characteristics such as signal intensity, edge orientation, fluorescence signals, or texture. A patch is the aggregation of spatially contiguous particle trajectories that feature similar fluorescence patterns. The methodology that creates coherent patch lineages is automatic and modular. By integrating aspects of object recognition and spatiotemporal changes, it lays down the foundation for investigating colony growth. All of the aforementioned pipelines represent a new methodological contribution to the field of lineage analysis and colony growth. I evaluate the proposed pipelines and algorithms on simulated and biological data, respectively. In turn this enabled me to validate the algorithms, interpret changes in the colony growth and differences among conditions of an experiment. In particular, I found that in a same condition, two isogenic bacterial colonies grew differently when faced with the same stress. The methods pioneered herein provide a key step to investigating colony growth

A survey of visualisation for live cell imaging

Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualisation. Nonetheless, visualisation methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualisation developers to evaluate the advantages and disadvantages of different visualisation methods, and for visualisation researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualisation methods for live cell imaging from a visualisation research perspective for the first time. Based on recent visualisation theory, we perform a structured qualitative analysis of visualisation methods that includes characterising the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualisation; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in McLaughlin, G. A., Langdon, E. M., Crutchley, J. M., Holt, L. J., Forest, M. G., Newby, J. M., & Gladfelter, A. S. (2020). Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking. Molecular Biology of the Cell, 31(14), 1498-1511, doi:10.1091/mbc.E20-03-0210.The spatial structure and physical properties of the cytosol are not well understood. Measurements of the material state of the cytosol are challenging due to its spatial and temporal heterogeneity. Recent development of genetically encoded multimeric nanoparticles (GEMs) has opened up study of the cytosol at the length scales of multiprotein complexes (20-60 nm). We developed an image analysis pipeline for 3D imaging of GEMs in the context of large, multinucleate fungi where there is evidence of functional compartmentalization of the cytosol for both the nuclear division cycle and branching. We applied a neural network to track particles in 3D and then created quantitative visualizations of spatially varying diffusivity. Using this pipeline to analyze spatial diffusivity patterns, we found that there is substantial variability in the properties of the cytosol. We detected zones where GEMs display especially low diffusivity at hyphal tips and near some nuclei, showing that the physical state of the cytosol varies spatially within a single cell. Additionally, we observed significant cell-to-cell variability in the average diffusivity of GEMs. Thus, the physical properties of the cytosol vary substantially in time and space and can be a source of heterogeneity within individual cells and across populations.We would like to thank the 2016 Physiology course and Christina Termini at the Marine Biological Laboratory in Woods Hole, MA, Gregory Brittingham, and Marcus Roper for initial experiments and perspectives on pipeline. We thank David Adalsteinsson for help with DataTank software and many conversations about image analysis on large datasets. We thank Emmanual Levy (Weizmann Institute) for providing plasmids encoding synthetic phase separating peptides. This work was supported by Google Cloud, the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Natural Sciences and Engineering Research Council of Canada (NSERC). ASG, EML, and GAM were supported by the NSF (RoLs: 1840273), HHMI faculty scholar award and the NIH (R01GM081506). JMN was supported by the NSERC (RGPIN-2019-06435, RGPAS-2019-00014, DGECR-2019-00321) and the NSF (DMS-171474). MGF was supported by the NSF (DMS-1816630, DMS-1664645). LJH was supported by the NIH (R01GM132447)