Image analysis platforms for exploring genetic and neuronal mechanisms regulating animal behavior

An important aim of neuroscience is to understand how gene interactions and neuronal networks regulate animal behavior. The larvae of the marine annelid Platynereis dumerilii provide a convenient system for such integrative studies. These larvae exhibit a wide range of behaviors, including phototaxis, chemotaxis and gravitaxis and at the same time exhibit relatively simple nervous system organization. Due to its small size and transparent body, the Platynereis larva is compatible with whole-body light microscopic imaging following tissue staining protocols. It is also suitable for serial electron microscopic imaging and subsequent neuronal connectome reconstruction. Despite advances in imaging techniques, automated computational tools for large data analysis are not well-established in Platynereis. In the current work, I developed image analysis software for exploring genetic and nervous system mechanisms modulating Platynereis behavior. Exploring gene expression patterns Current labeling and imaging techniques restrict the number of gene expression patterns that can be labelled and visualized in a single specimen, which hinders the study of behaviors driven by multi-molecular interactions. To address this problem, I employed image registration to generate a gene expression atlas that integrates gene expression information from multiple specimens in a common reference space. The gene expression atlas was used to investigate mechanisms regulating larval locomotion, settlement and phototaxis in Platynereis. The atlas can assist in the identification of inter-individual and inter-species variations in gene expression. To provide a representation convenient for exploring gene expression patterns, I created a model of the atlas using 3D graphics software, which enabled convenient data visualization and efficient data storage and sharing. Exploring neuronal networks regulating behavior Neuronal circuitry can be reconstructed from the images obtained from electron microscopy, which resolves very fine structures such as neuron morphology or synapses. The amount of data resulting from electron microscopy and the complexity of neuronal networks represent a significant challenge for manual analysis. To solve this problem, I developed the NeuroDetective software, which models a neuronal circuitry and analyzes the information flow within it. The software combines the advantages of 3D visualization and graph analysis software by integrating neuron morphology and spatial distribution together with synaptic connectivity. NeuroDetective allowed studying the neuronal circuitry responsible for phototaxis in Platynereis larvae, revealing the connections and the neurons important for the network functionality. NeuroDetective facilitated the establishment of a relationship between the function and the structure of the neuronal circuitry in Platynereis phototaxis. Integrating gene expression patterns with neuronal connectivity Neuronal circuitry and its associated modulating biomolecules, such as neurotransmitters and neuropeptides, are thought to be the main factors regulating animal behavior. Therefore it was important to integrate both genetic and neuronal information in order to fully understand how biomolecules in conjunction with neuronal anatomy elicit certain animal behavior. To resolve the difference in specimen preparation for gene expression versus electron microscopy preparations, I developed an image registration procedure to match the signals from these two different datasets. This method enabled the integration the spatial distribution of specific modulators into the analysis of neuronal networks, leading to an improved understanding of the genetic and neuronal mechanisms modulating behavior in Platynereis

Automatic biological object segmentation and tracking in unconstrained microscopic video conditions

Cell and small biological organism tracking research is of fundamental importance for the analysis of dynamic behaviour for assisting the development of many biomedical image related applications. With the rapid development of digitised imaging systems, the immense collections of experimental (microscopic) videos make it nearly impossible to manually analyse the obtained data. Therefore, recent research has drawn attention to building automatic tracking systems to track the movement of cells and small biological organism models using videos taken by microscopes. Although general object tracking (such as traffic cars and pedestrians) has been studied for decades, existing general object tracking systems cannot directly be applied to cell and small biological organism tracking, due to the differences in the imaging devices and conditions of the targets. This research therefore investigates the novel application of computer vision techniques to reliably, accurately and effectively track the movement of cells and small biological organisms automatically. Due to difficulties in generating video segmentation ground-truth, there is a general lack of segmentation datasets with annotated ground-truth (particularly for biomedical images). This work proposes an efficient and scalable crowdsourced approach to generate video segmentation ground-truth and develops a tracking ground-truth generation system. To illustrate the proposed approach, an annotated zebrafish larvae video segmentation dataset and three tracking datasets have been generated and made freely available online. Automatic cell tracking techniques require accurate cell image segmentation; however, current general object segmentation techniques are susceptible to errors due to the poor microscopic imaging conditions, which include low contrast typical of cell microscopic images. This work proposes a novel image pre-processing technique to enhance low greyscale image contrast for improved cell image segmentation accuracy. An adaptive, shifted bi-Gaussian mixture model is matched to the original cell image intensity histogram for greater differentiation between the cell foreground and image background, while maintaining the original intensity histogram shape. Small biological organism videos taken by microscope imaging devices under realistic experimental conditions have more complex video backgrounds than cell videos. This work first investigates single zebrafish larvae tracking using dense SIFT flow and downsampling techniques. Many existing multiple small organism tracking systems require very strict video imaging conditions, which typically result in unreliable tracking results for realistic experimental conditions. Thus, this research further investigates the adaptation of advanced segmentation techniques to improve the performance of small organism segmentation under complex imaging conditions. Finally, this work improves the multiple object association method based on the segmentation module for the proposed system, to address object misdetection and overlapping problems. This system is then evaluated on zebrafish videos, Artemia franciscana videos and Daphnia magna videos, under a wide variety of (complex) video conditions, including shadowing, labels, and background artefacts (such as water bubbles of different sizes). The tracking accuracy of the proposed system outperforms three existing tracking systems. Thus, the work in this thesis has contributions in automatic cell and biological organism tracking, where the investigation studied the region-based segmentation dataset construction generalised for biological organisms, intensity contrast enhancement for micrographs, segmentation improvement by removing imaging constraints and the final tracking accuracy enhancement

Reconstructing neural circuits using multiresolution correlated light and electron microscopy

Correlated light and electron microscopy (CLEM) can be used to combine functional and molecular characterizations of neurons with detailed anatomical maps of their synaptic organization. Here we describe a multiresolution approach to CLEM (mrCLEM) that efficiently targets electron microscopy (EM) imaging to optically characterized cells while maintaining optimal tissue preparation for high-throughput EM reconstruction. This approach hinges on the ease with which arrays of sections collected on a solid substrate can be repeatedly imaged at different scales using scanning electron microscopy. We match this multiresolution EM imaging with multiresolution confocal mapping of the aldehyde-fixed tissue. Features visible in lower resolution EM correspond well to features visible in densely labeled optical maps of fixed tissue. Iterative feature matching, starting with gross anatomical correspondences and ending with subcellular structure, can then be used to target high-resolution EM image acquisition and annotation to cells of interest. To demonstrate this technique and range of images used to link live optical imaging to EM reconstructions, we provide a walkthrough of a mouse retinal light to EM experiment as well as some examples from mouse brain slices

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Light Sheet Microscopy and Image Analysis of Neural Development and Programmed Cell Death in C. Elegans Embryos

The positioning of neuronal cell bodies and neurites is critical for intact functioning of the nervous system. Mapping the positions of the soma and neurites in the brains of developing embryos as important central nervous system structures are being created may yield novel insight into the role of distinct cell groups in creating these structures. New developments in microscopy have made this an excellent time to study neural development in the C. elegans embryo. In the past decade, implementations of highly light efficient methods such as single plane illumination microscopy have rendered it possible to follow development of embryonic structures in 3D with excellent temporal resolution (Huisken et al., 2004) and low phototoxicity. Recent work has resulted in quantitative characterization of the outgrowth of a single neurite in the late, rapidly moving three-fold stage of the C. elegans embryo for the first time (Christensen et al., 2015). In this thesis, I first describe the construction and programming of a single plane illumination microscope (SPIM) based on a design from Hari Shroff\u27s lab (Wu et al., 2011). The microscope is developed especially for use with C. elegans embryos and permits fast image acquisition without excessive photodamage, compared to other forms of microscopy. Second, I describe the use of the SPIM microscope to image the development of a subset of sublateral neurons, the earliest known entrants to the nerve ring (Rapti et al, in preparation), into which they grow in the 1.5-fold stage. I describe an algorithm for automatically aligning developing embryos onto one another until the beginning of the rapid embryonic movements known as twitching, which begin at the start of the twofold stage. I employ my algorithm to align a group of identically imaged embryos onto one another and deduce information about the positioning of the nerve ring in an approximately uniform coordinate system. I determine that nerve rings are precisely positioned in the embryo to within about a micrometer while the cell bodies that grow into the nerve ring are positioned over a much wider distance. My work suggests that the nerve ring grows out towards the ALA neuron as an anchor, and that twitching may begin when the developing nerve ring reaches the ALA. I additionally describe observation of new phenotypes related to the cam-1 mutation, which was previously identified as a regulator of anterior-posterior placement of the nerve ring (Kennerdell et al., 2009). Third, I describe an application of the SPIM microscope for imaging the death of the tail spike cell, a complex, multi-compartment differentiated cell which dies over a period of hours during the three-fold stage, when the animal is rapidly moving in its shell, and cannot be imaged otherwise than with a rapid, light efficient microscope such as the one described here. I determined the time course and confirmed the sequence of events of wild type tail spike cell death. Additionally, I report stronger phenotypes for some known tail spike cell death genes when imaged in the embryo, suggesting that eff-1 plays a stronger role than previously known in clearance of the distal part of the tail spike cell process, and additionally that ced-5 has a strong role in clearance of the same compartment (in addition to its known role in soma clearance). In an appendix I describe work beginning on an extension of the microscope, which will hopefully see the microscope used as a tool for selectively inducing fluorescence in individual cells and following the development of those cells in time. My results demonstrate the utility of single plane illumination microscopy for study of C. elegans embryogenesis and establish fundamental facts about the variability of the C. elegans central nervous system by making direct comparisons between animals. This work contributes to our understanding of the C. elegans nervous system by establishing fundamental bounds on the range of nerve ring positioning between individuals

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Correlated Multimodal Imaging in Life Sciences:Expanding the Biomedical Horizon

International audienceThe frontiers of bioimaging are currently being pushed toward the integration and correlation of several modalities to tackle biomedical research questions holistically and across multiple scales. Correlated Multimodal Imaging (CMI) gathers information about exactly the same specimen with two or more complementary modalities that-in combination-create a composite and complementary view of the sample (including insights into structure, function, dynamics and molecular composition). CMI allows to describe biomedical processes within their overall spatio-temporal context and gain a mechanistic understanding of cells, tissues, diseases or organisms by untangling their molecular mechanisms within their native environment. The two best-established CMI implementations for small animals and model organisms are hardware-fused platforms in preclinical imaging (Hybrid Imaging) and Correlated Light and Electron Microscopy (CLEM) in biological imaging. Although the merits of Preclinical Hybrid Imaging (PHI) and CLEM are well-established, both approaches would benefit from standardization of protocols, ontologies and data handling, and the development of optimized and advanced implementations. Specifically, CMI pipelines that aim at bridging preclinical and biological imaging beyond CLEM and PHI are rare but bear great potential to substantially advance both bioimaging and biomedical research. CMI faces three mai

Automated Correlative Light and Electron Microscopy using FIB-SEM as a tool to screen for ultrastructural phenotypes

In Correlative Light and Electron Microscopy (CLEM), two imaging modalities are combined to take advantage of the localization capabilities of light microscopy (LM) to guide the capture of high-resolution details in the electron microscope (EM). However, traditional approaches have proven to be very laborious, thus yielding a too low throughput for quantitative or exploratory studies of populations. Recently, in the electron microscopy field, FIB-SEM (Focused Ion Beam -Scanning Electron Microscope) tomography has emerged as a flexible method that enables semi-automated 3D volume acquisitions. During my thesis, I developed CLEMSite, a tool that takes advantage of the semi-automation and scanning capabilities of the FIB-SEM to automatically acquire volumes of adherent cultured cells. CLEMSite is a combination of computer vision and machine learning applications with a library for controlling the microscope ( product from a collaboration with Carl Zeiss GmbH and Fibics Inc.). Thanks to this, the microscope was able to automatically track, find and acquire cell regions previously identified in the light microscope. More specifically, two main modules were implemented. First, a correlation module was designed to detect and record reference points from a grid pattern present on the culture substrate in both modalities (LM and EM). Second, I designed a module that retrieves the regions of interest in the FIB-SEM and that drives the acquisition of image stacks between different targets in an unattended fashion. The automated CLEM approach is demonstrated on a project where 3D EM volumes are examined upon multiple siRNA treatments for knocking down genes involved in the morphogenesis of the Golgi apparatus. Additionally, the power of CLEM approaches using FIB-SEM is demonstrated with the detailed structural analysis of two events: the breakage of the nuclear envelope within constricted cells and an intriguing catastrophic DNA Damage Response in binucleated cells. Our results demonstrate that executing high throughput volume acquisition in electron microscopy is possible and that EM can provide incredible insights to guide new biological discoveries

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