A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution.

To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.

A large-scale volumetric correlated light and electron microscopy study localizes Alzheimer's disease-related molecules in the hippocampus.

Connectomics is a nascent neuroscience field to map and analyze neuronal networks. It provides a new way to investigate abnormalities in brain tissue, including in models of Alzheimer's disease (AD). This age-related disease is associated with alterations in amyloid-ß (Aß) and phosphorylated tau (pTau). These alterations correlate with AD's clinical manifestations, but causal links remain unclear. Therefore, studying these molecular alterations within the context of the local neuronal and glial milieu may provide insight into disease mechanisms. Volume electron microscopy (vEM) is an ideal tool for performing connectomics studies at the ultrastructural level, but localizing specific biomolecules within large-volume vEM data has been challenging. Here we report a volumetric correlated light and electron microscopy (vCLEM) approach using fluorescent nanobodies as immuno-probes to localize Alzheimer's disease-related molecules in a large vEM volume. Three molecules (pTau, Aß, and a marker for activated microglia (CD11b)) were labeled without the need for detergents by three nanobody probes in a sample of the hippocampus of the 3xTg Alzheimer's disease model mouse. Confocal microscopy followed by vEM imaging of the same sample allowed for registration of the location of the molecules within the volume. This dataset revealed several ultrastructural abnormalities regarding the localizations of Aß and pTau in novel locations. For example, two pTau-positive post-synaptic spine-like protrusions innervated by axon terminals were found projecting from the axon initial segment of a pyramidal cell. Three pyramidal neurons with intracellular Aß or pTau were 3D reconstructed. Automatic synapse detection, which is necessary for connectomics analysis, revealed the changes in density and volume of synapses at different distances from an Aß plaque. This vCLEM approach is useful to uncover molecular alterations within large-scale volume electron microscopy data, opening a new connectomics pathway to study Alzheimer's disease and other types of dementia.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.

A novel method for accurate extraction of liver capsule and auxiliary diagnosis of liver cirrhosis based on high-frequency ultrasound images.

Liver cirrhosis is a common chronic progressive disease with a high mortality rate. The early diagnosis and treatment of liver cirrhosis is an important research subject in the medical field. In this paper, a novel method is proposed for the accurate extraction of the liver capsule and auxiliary diagnosis of cirrhosis based on high frequency ultrasound images. First, a self-developed method is used to extract the predictive capsule of ultrasound images, which involves the detection of liver ascites with sliding windows, image enhancement with multiscale detail and fuzzy set, structure segmentation with morphological processing, and predictive capsule detection with traversal search method. Thereafter, the real capsule is obtained by the gray difference method according to different gray values between the liver capsule region of the original ultrasound images and the set threshold. Finally, according to the analysis of smoothness, as well as the continuity and fluctuation of predictive and real capsule, four novel features called NoL, VoS, CV, and NoF are proposed for the computer auxiliary diagnosis model. This model is designed on the basis of support vector machine and k-means clustering and can classify normal liver and three liver cirrhosis stages. The experimental results reveal that the accuracy of the liver capsule extraction using this model is 95.13% and final classification accuracy of four stages can reach 92.54%, 88.46%, 89.23% and 94.55%, respectively. The results also indicate that the method proposed in this paper can achieve the classification of liver cirrhosis stages much more accurately and efficiently compared with previously utilized methods.

Spatial flocking: Control by speed, distance, noise and delay.

Fish, birds, insects and robots frequently swim or fly in groups. During their three dimensional collective motion, these agents do not stop, they avoid collisions by strong short-range repulsion, and achieve group cohesion by weak long-range attraction. In a minimal model that is isotropic, and continuous in both space and time, we demonstrate that (i) adjusting speed to a preferred value, combined with (ii) radial repulsion and an (iii) effective long-range attraction are sufficient for the stable ordering of autonomously moving agents in space. Our results imply that beyond these three rules ordering in space requires no further rules, for example, explicit velocity alignment, anisotropy of the interactions or the frequent reversal of the direction of motion, friction, elastic interactions, sticky surfaces, a viscous medium, or vertical separation that prefers interactions within horizontal layers. Noise and delays are inherent to the communication and decisions of all moving agents. Thus, next we investigate their effects on ordering in the model. First, we find that the amount of noise necessary for preventing the ordering of agents is not sufficient for destroying order. In other words, for realistic noise amplitudes the transition between order and disorder is rapid. Second, we demonstrate that ordering is more sensitive to displacements caused by delayed interactions than to uncorrelated noise (random errors). Third, we find that with changing interaction delays the ordered state disappears at roughly the same rate, whereas it emerges with different rates. In summary, we find that the model discussed here is simple enough to allow a fair understanding of the modeled phenomena, yet sufficiently detailed for the description and management of large flocks with noisy and delayed interactions. Our code is available at http://github.com/fij/floc.