Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Dendritic Spine Shape Analysis: A Clustering Perspective

Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

Automated Three-Dimensional Detection and Shape Classification of Dendritic Spines from Fluorescence Microscopy Images

A fundamental challenge in understanding how dendritic spine morphology controls learning and memory has been quantifying three-dimensional (3D) spine shapes with sufficient precision to distinguish morphologic types, and sufficient throughput for robust statistical analysis. The necessity to analyze large volumetric data sets accurately, efficiently, and in true 3D has been a major bottleneck in deriving reliable relationships between altered neuronal function and changes in spine morphology. We introduce a novel system for automated detection, shape analysis and classification of dendritic spines from laser scanning microscopy (LSM) images that directly addresses these limitations. The system is more accurate, and at least an order of magnitude faster, than existing technologies. By operating fully in 3D the algorithm resolves spines that are undetectable with standard two-dimensional (2D) tools. Adaptive local thresholding, voxel clustering and Rayburst Sampling generate a profile of diameter estimates used to classify spines into morphologic types, while minimizing optical smear and quantization artifacts. The technique opens new horizons on the objective evaluation of spine changes with synaptic plasticity, normal development and aging, and with neurodegenerative disorders that impair cognitive function

Computational geometry analysis of dendritic spines by structured illumination microscopy

We are currently short of methods that can extract objective parameters of dendritic spines useful for their categorization. Authors present in this study an automatic analytical pipeline for spine geometry using 3D-structured illumination microscopy, which can effectively extract many geometrical parameters of dendritic spines without bias and automatically categorize spine population based on their morphological feature

Photoactivatable genetically encoded calcium indicators for targeted neuronal imaging.

Circuit mapping requires knowledge of both structural and functional connectivity between cells. Although optical tools have been made to assess either the morphology and projections of neurons or their activity and functional connections, few probes integrate this information. We have generated a family of photoactivatable genetically encoded Ca(2+) indicators that combines attributes of high-contrast photolabeling with high-sensitivity Ca(2+) detection in a single-color protein sensor. We demonstrated in cultured neurons and in fruit fly and zebrafish larvae how single cells could be selected out of dense populations for visualization of morphology and high signal-to-noise measurements of activity, synaptic transmission and connectivity. Our design strategy is transferrable to other sensors based on circularly permutated GFP (cpGFP)

Synaptic nanomodules underlie the organization and plasticity of spine synapses.

Experience results in long-lasting changes in dendritic spine size, yet how the molecular architecture of the synapse responds to plasticity remains poorly understood. Here a combined approach of multicolor stimulated emission depletion microscopy (STED) and confocal imaging in rat and mouse demonstrates that structural plasticity is linked to the addition of unitary synaptic nanomodules to spines. Spine synapses in vivo and in vitro contain discrete and aligned subdiffraction modules of pre- and postsynaptic proteins whose number scales linearly with spine size. Live-cell time-lapse super-resolution imaging reveals that NMDA receptor-dependent increases in spine size are accompanied both by enhanced mobility of pre- and postsynaptic modules that remain aligned with each other and by a coordinated increase in the number of nanomodules. These findings suggest a simplified model for experience-dependent structural plasticity relying on an unexpectedly modular nanomolecular architecture of synaptic proteins

Automated 4D analysis of dendritic spine morphology: applications to stimulus-induced spine remodeling and pharmacological rescue in a disease model

Uncovering the mechanisms that regulate dendritic spine morphology has been limited, in part, by the lack of efficient and unbiased methods for analyzing spines. Here, we describe an automated 3D spine morphometry method and its application to spine remodeling in live neurons and spine abnormalities in a disease model. We anticipate that this approach will advance studies of synapse structure and function in brain development, plasticity, and disease

Développement d'un microscope super-résolution pour l'imagerie de l'activité neuronale

L’étude de la neurotransmission et de la plasticité synaptique à l’échelle biomoléculaire dans des cellules vivantes nécessite des outils qui permettent la visualisation et la localisation d’une grande variété de protéines synaptiques ainsi que d’autres composantes. La transparence des neurones, la taille nanométrique des structures d’intérêt et leur compacité motivent le choix des modalités d’imagerie pouvant servir à étudier ces phénomènes. La microscopie à super-résolution en fluorescence produit des images ayant une résolution de localisation de l’ordre du nanomètre d’échantillons marqués. Toutefois, cette technique ne permet d’observer que les structures ayant été marquées. C’est pourquoi nous voulons la combiner à une technique ne nécessitant aucun marquage afin d’obtenir le plus d’information possible au sujet de la structure des échantillons. L’imagerie de phase quantitative est une technique sans-marquage qui utilise l’indice de réfraction comme agent de contraste intrinsèque pour cartographier en 3D le contenu cellulaire. Le but principal de ce projet est de concevoir et construire un montage de microscopie de phase quantitative et de l’intégrer à un microscope STED existant de façon à créer un nouveau système d’imagerie multimodale. La performance de ce système sera ensuite caractérisée et sa capacité à produire des images multimodales de synapses de cellules vivantes sera évaluée. Ce projet est un premier pas vers la création d’un outil qui pourrait permettre de simultanément mesurer de façon très précise la position de structures marquées en 2D et 3D et cartographier l’indice de réfraction des cellules en 3D afin de situer les structures marquées dans leur environnement.The study of neurotransmission at the biomolecular level in live cells requires tools that allow the simultaneous visualisation and localization of a variety of neuronal proteins at their scale: the nanometric scale. In order to do so, an imaging approach offering high spatial and temporal resolution combined to low invasiveness is required. STED microscopy is an optical super-resolution fluorescence microscopy technique that produces images of labelled samples with a spatial resolution below 50 nm in living cells. However, since it is based on the detection of fluorescent molecules, labeling of the structures of interestis necessary and non-labeled structures are invisible for this type of microscope. Therefore, we want to combine it to a label-free optical microscopy technique to maximize the information that can be obtained about the global structure of the samples of interest: optical diffraction tomography (ODT). This approach uses refractive index as an intrinsic contrast agent to produce 3D maps of the cell’s internal contents.The main goal of this project is to design and build a quantitative phase imaging system and to integrate it onto an existing STED microscope to create a novel multimodal super-resolution imaging system. The performance of the microscope will then be characterized. This project is a first step towards the creation of a tool that could eventually allow simultaneous precise2D and 3D mapping of labelled structures and label-free 3D mapping of the sample’s refractive index to situate marked structures in their surroundings