Dense 4D nanoscale reconstruction of living brain tissue

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue

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

A CANDLE for a deeper in-vivo insight

A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches, CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized non-local means filter, especially under low SNR conditions (PSNR < 8 dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain.We want to thank Florian Luisier for providing free plugin of his PureDenoise filter. We also want to thank Markku Makitalo for providing the code of their OVST. This study was supported by the Canadian Institutes of Health Research (CIHR, MOP-84360 to DLC and MOP-77567 to ESR) and Cda (CECR)-Gevas-OE016. MM holds a fellowship from the Deutscher Akademischer Austasch Dienst (DAAD) and a McGill Principal's Award. ESR is a tier 2 Canada Research Chair. This work has been partially supported by the Spanish Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001. This work benefited from the use of ImageJ.Coupé, P.; Munz, M.; Manjón Herrera, JV.; Ruthazer, ES.; Collins, DL. (2012). A CANDLE for a deeper in-vivo insight. Medical Image Analysis. 16(4):849-864. https://doi.org/10.1016/j.media.2012.01.002S84986416

Three-dimensional reengineering of neuronal microcircuits : The cortical column in silico

The presented thesis will describe a pipeline to reengineer three-dimensional, anatomically realistic, functional neuronal networks with subcellular resolution. The pipeline consists of five methods: 1. "NeuroCount" provides the number and three-dimensional distribution of all neuron somata in large brain regions. 2. "NeuroMorph" provides authentic neuron tracings, comprising dendrite and axon morphology. 3. "daVinci" registers the neuron morphologies to a standardized reference framework. 4. "NeuroCluster" objectively groups the standardized tracings into anatomical neuron types. 5. "NeuroNet" combines the number and distribution of neurons and neuron-types with the standardized tracings and determines the neuron-type- and position-specific number of synaptic connections for any two types of neuron. The developed methods are demonstrated by reengineering the thalamocortical lemniscal microcircuit in the somatosensory system of rats. There exists an one-to-one correspondence between the sensory information obtained by a single facial whisker and segregated areas in the thalamus and cortex. The reengineering of this pathway results in a column-shaped network model of ~15200 excitatory full-compartmental cortical neurons. This network is synaptically connected to ~285 pre-synaptic thalamic neurons. Animation of this "cortical column in silico" with measured physiological input will help to gain a mechanistic understanding of neuronal sensory information processing in the mammalian brain

CHMP2B mutants linked to frontotemporal dementia impair maturation of dendritic spines.: CHMP2B and dendritic spines

International audienceThe highly conserved ESCRT-III complex is responsible for deformation and cleavage of membranes during endosomal trafficking and other cellular activities. In humans, dominant mutations in the ESCRT-III subunit CHMP2B cause frontotemporal dementia (FTD). The decade-long process leading to this cortical degeneration is not well understood. One possibility is that, akin to other neurodegenerative diseases, the pathogenic protein affects the integrity of dendritic spines and synapses before any neuronal death. Using confocal microscopy and 3D reconstruction, we examined whether expressing the FTD-linked mutants CHMP2B(intron5) and CHMP2B(Delta10) in cultured hippocampal neurons modified the number or structure of spines. Both mutants induced a significant decrease in the proportion of large spines with mushroom morphology, without overt degeneration. Furthermore, CHMP2B(Delta10) induced a drop in frequency and amplitude of spontaneous excitatory postsynaptic currents, suggesting that the more potent synapses were lost. These effects seemed unrelated to changes in autophagy. Depletion of endogenous CHMP2B by RNAi resulted in morphological changes similar to those induced by mutant CHMP2B, consistent with dominant-negative activity of pathogenic mutants. Thus, CHMP2B is required for spine growth. Taken together, these results demonstrate that a mutant ESCRT-III subunit linked to a human neurodegenerative disease can disrupt the normal pattern of spine development

A tessellation-based colocalization analysis approach for single-molecule localization microscopy

International audienceMulticolor single-molecule localization microscopy (λSMLM) is a powerful technique to reveal the relative nanoscale organization and potential colocalization between different molecular species. While several standard analysis methods exist for pixel-based images, λSMLM still lacks such a standard. Moreover, existing methods only work on 2D data and are usually sensitive to the relative molecular organization, a very important parameter to consider in quantitative SMLM. Here, we present an efficient, parameter-free colocalization analysis method for 2D and 3D λSMLM using tessellation analysis. We demonstrate that our method allows for the efficient computation of several popular colocalization estimators directly from molecular coordinates and illustrate its capability to analyze multicolor SMLM data in a robust and efficient manner