A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension.

The immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.

Cortical interneurons: fit for function and fit to function? Evidence from development and evolution.

Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labor, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalyzed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.

Roles of the Unsaturated Fatty Acid Docosahexaenoic Acid in the Central Nervous System: Molecular and Cellular Insights.

Fatty acids (FAs) are essential components of the central nervous system (CNS), where they exert multiple roles in health and disease. Among the FAs, docosahexaenoic acid (DHA) has been widely recognized as a key molecule for neuronal function and cell signaling. Despite its relevance, the molecular pathways underlying the beneficial effects of DHA on the cells of the CNS are still unclear. Here, we summarize and discuss the molecular mechanisms underlying the actions of DHA in neural cells with a special focus on processes of survival, morphological development, and synaptic maturation. In addition, we examine the evidence supporting a potential therapeutic role of DHA against CNS tumor diseases and tumorigenesis. The current results suggest that DHA exerts its actions on neural cells mainly through the modulation of signaling cascades involving the activation of diverse types of receptors. In addition, we found evidence connecting brain DHA and ω-3 PUFA levels with CNS diseases, such as depression, autism spectrum disorders, obesity, and neurodegenerative diseases. In the context of cancer, the existing data have shown that DHA exerts positive actions as a coadjuvant in antitumoral therapy. Although many questions in the field remain only partially resolved, we hope that future research may soon define specific pathways and receptor systems involved in the beneficial effects of DHA in cells of the CNS, opening new avenues for innovative therapeutic strategies for CNS diseases.

Feasibility of 3D Reconstruction of Neural Morphology Using Expansion Microscopy and Barcode-Guided Agglomeration.

We here introduce and study the properties, via computer simulation, of a candidate automated approach to algorithmic reconstruction of dense neural morphology, based on simulated data of the kind that would be obtained via two emerging molecular technologies-expansion microscopy (ExM) and in-situ molecular barcoding. We utilize a convolutional neural network to detect neuronal boundaries from protein-tagged plasma membrane images obtained via ExM, as well as a subsequent supervoxel-merging pipeline guided by optical readout of information-rich, cell-specific nucleic acid barcodes. We attempt to use conservative imaging and labeling parameters, with the goal of establishing a baseline case that points to the potential feasibility of optical circuit reconstruction, leaving open the possibility of higher-performance labeling technologies and algorithms. We find that, even with these conservative assumptions, an all-optical approach to dense neural morphology reconstruction may be possible via the proposed algorithmic framework. Future work should explore both the design-space of chemical labels and barcodes, as well as algorithms, to ultimately enable routine, high-performance optical circuit reconstruction.

Two-Photon STED Microscopy for Nanoscale Imaging of Neural Morphology In Vivo.

The advent of super-resolution microscopy offers to bridge the gap between electron and light microscopy. It has opened up the possibility of visualizing cellular structures and dynamic signaling events on the "mesoscale" well below the classic diffraction barrier of light microscopy (10-200 nm), while essentially retaining the advantages of fluorescence microscopy concerning multicolor labeling, detection sensitivity, signal contrast, live-cell imaging, and temporal resolution.From among the new super-resolution techniques, STED microscopy stands out as a laser-scanning imaging modality, which enables nanoscale volume-metric imaging of cellular morphology. In combination with two-photon (2P) excitation, STED microscopy facilitates the visualization of the highly complex and dynamic morphology of neurons and glia cells deep inside living brain slices and in the intact brain in vivo.Here, we present an overview of the principles and implementation of 2P-STED microscopy in vivo, providing the neurobiological context and motivation for this technique, and illustrating its capacity by showing images of dendritic spines and microglial processes obtained from living brain tissue.

Single neuron morphology in vivo with confined primed conversion.

Unraveling the structural organization of neurons can provide fundamental insights into brain function. However, visualizing neurite morphology in vivo remains difficult due to the high density and complexity of neural packing in the nervous system. Detailed analysis of neural morphology requires distinction of closely neighboring, highly intricate cellular structures such as neurites with high contrast. Green-to-red photoconvertible fluorescent proteins have become powerful tools to optically highlight molecular and cellular structures for developmental and cell biological studies. Yet, selective labeling of single cells of interest in vivo has been precluded due to inefficient photoconversion when using high intensity, pulsed, near-infrared laser sources that are commonly applied for achieving axially confined two-photon (2P) fluorescence excitation. Here we describe a novel optical mechanism, "confined primed conversion," which employs continuous dual-wave illumination to achieve confined green-to-red photoconversion of single cells in live zebrafish embryos. Confined primed conversion exhibits wide applicability and this chapter specifically elaborates on employing this imaging modality to analyze neural morphology of optically targeted single neurons in the developing zebrafish brain.

Aberrant Rho GTPases signaling and cognitive dysfunction: in vivo evidence for a compelling molecular relationship.

Rho GTPases are key intracellular signaling molecules that coordinate dynamic changes in the actin cytoskeleton, thereby stimulating a variety of processes, including morphogenesis, migration, neuronal development, cell division and adhesion. Deviations from normal Rho GTPases activation state have been proposed to disrupt cognition and synaptic plasticity. This review focuses on the functional consequences of genetic ablation of upstream and downstream Rho GTPases molecules on cognitive function and neuronal morphology and connectivity. Available information on this issue is described and compared to that gained from mice carrying mutations in the most studied Rho GTPases and from pharmacological in vivo studies in which brain Rho GTPases signaling was modulated. Results from reviewed literature provide definitive evidence of a compelling link between Rho GTPases signaling and cognitive function, thus supporting the notion that Rho GTPases and their downstream effectors may represent important therapeutic targets for disorders associated with cognitive dysfunction.

Electroacupuncture upregulated platelet derived growth factor expression in spared dorsal root ganglion of cats.

A bilateral spared dorsal root ganglion model was established in healthy adult cats by bilateral resection of L1-5 and L7-S2 dorsal root ganglia. L6 dorsal root ganglia were spared. Zusanli (ST36) and Xuanzhong (BL39) or Futu (ST32) and Sanyinjiao (SP6) were alternatively electro-stimulated on the right leg. Immunohistochemical staining of anti-serum platelet-derived growth factor demonstrated that the number of total neurons and medium-small sized platelet-derived growth factor positive neurons was significantly decreased on the 7(th) day following injury. After 7 days of acupuncture, the total number of positive and large neurons staining for platelet-derived growth factor on the acupuncture side significantly increased compared to the non-acupuncture side. After acupuncture for 14 days, the total positive and medium-small sized neurons significantly increased compared with the non-acupuncture side. Results indicate that acupuncture promoted the synthesis of platelet-derived growth factor in spared dorsal root ganglia.

Unveiling the neuromorphological space.

This article proposes the concept of neuromorphological space as the multidimensional space defined by a set of measurements of the morphology of a representative set of almost 6000 biological neurons available from the NeuroMorpho database. For the first time, we analyze such a large database in order to find the general distribution of the geometrical features. We resort to McGhee's biological shape space concept in order to formalize our analysis, allowing for comparison between the geometrically possible tree-like shapes, obtained by using a simple reference model, and real neuronal shapes. Two optimal types of projections, namely, principal component analysis and canonical analysis, are used in order to visualize the originally 20-D neuron distribution into 2-D morphological spaces. These projections allow the most important features to be identified. A data density analysis is also performed in the original 20-D feature space in order to corroborate the clustering structure. Several interesting results are reported, including the fact that real neurons occupy only a small region within the geometrically possible space and that two principal variables are enough to account for about half of the overall data variability. Most of the measurements have been found to be important in representing the morphological variability of the real neurons.