Recent advancement in the challenges to connectomics

Cross-Species Framework for Emotional Well-Being and Brain Aging: Lessons From Behavioral Neuroscience

Importance

Emotional well-being (EWB) is an emerging therapeutic target for managing and preventing symptoms associated with Alzheimer disease and related dementias (ADRD). However, more research is needed to establish causal inferences between brain changes, EWB, and behavioral changes observed in typical aging and ADRD.

Observations

This article presents a framework for using a cross-species behavioral neuroscience approach to study EWB and brain aging, adopting a well-established biobehavioral model that highlights the reciprocal roles of brain changes, EWB, and ADRD symptoms. First, the challenges and opportunities in this field are reviewed. Then, a practical solution to improve comparability between animal and human studies is proposed.

Conclusions and Relevance

The goal is to draw comprehensive parallels and distinctions that could enhance the understanding of the mechanisms linking brain aging, EWB, and ADRD symptomatic disturbances across different species.

Volume electron microscopy for genetically and molecularly defined neural circuits

The brain networks responsible for adaptive behavioral changes are based on the physical connections between neurons. Light and electron microscopy have long been used to study neural projections and the physical connections between neurons. Volume electron microscopy has recently expanded its scale of analysis due to methodological advances, resulting in complete wiring maps of neurites in a large volume of brain tissues and even entire nervous systems in a growing number of species. However, structural approaches frequently suffer from inherent limitations in which elements in images are identified solely by morphological criteria. Recently, an increasing number of tools and technologies have been developed to characterize cells and cellular components in the context of molecules and gene expression. These advancements include newly developed probes for visualization in electron microscopic images as well as correlative integration methods for the same elements across multiple microscopic modalities. Such approaches advance our understanding of interactions between specific neurons and circuits and may help to elucidate novel aspects of the basal ganglia network involving dopamine neurons. These advancements are expected to reveal mechanisms for processing adaptive changes in specific neural circuits that modulate brain functions.

Brain Ultrastructure: Putting the Pieces Together

Unraveling the fine structure of the brain is important to provide a better understanding of its normal and abnormal functioning. Application of high-resolution electron microscopic techniques gives us an unprecedented opportunity to discern details of the brain parenchyma at nanoscale resolution, although identifying different cell types and their unique features in two-dimensional, or three-dimensional images, remains a challenge even to experts in the field. This article provides insights into how to identify the different cell types in the central nervous system, based on nuclear and cytoplasmic features, amongst other unique characteristics. From the basic distinction between neurons and their supporting cells, the glia, to differences in their subcellular compartments, organelles and their interactions, ultrastructural analyses can provide unique insights into the changes in brain function during aging and disease conditions, such as stroke, neurodegeneration, infection and trauma. Brain parenchyma is composed of a dense mixture of neuronal and glial cell bodies, together with their intertwined processes. Intracellular components that vary between cells, and can become altered with aging or disease, relate to the cytoplasmic and nucleoplasmic density, nuclear heterochromatin pattern, mitochondria, endoplasmic reticulum and Golgi complex, lysosomes, neurosecretory vesicles, and cytoskeletal elements (actin, intermediate filaments, and microtubules). Applying immunolabeling techniques to visualize membrane-bound or intracellular proteins in neurons and glial cells gives an even better appreciation of the subtle differences unique to these cells across contexts of health and disease. Together, our observations reveal how simple ultrastructural features can be used to identify specific changes in cell types, their health status, and functional relationships in the brain.

Brain Ultrastructure: Putting the Pieces Together

Unraveling the fine structure of the brain is important to provide a better understanding of its normal and abnormal functioning. Application of high-resolution electron microscopic techniques gives us an unprecedented opportunity to discern details of the brain parenchyma at nanoscale resolution, although identifying different cell types and their unique features in two-dimensional, or three-dimensional images, remains a challenge even to experts in the field. This article provides insights into how to identify the different cell types in the central nervous system, based on nuclear and cytoplasmic features, amongst other unique characteristics. From the basic distinction between neurons and their supporting cells, the glia, to differences in their subcellular compartments, organelles and their interactions, ultrastructural analyses can provide unique insights into the changes in brain function during aging and disease conditions, such as stroke, neurodegeneration, infection and trauma. Brain parenchyma is composed of a dense mixture of neuronal and glial cell bodies, together with their intertwined processes. Intracellular components that vary between cells, and can become altered with aging or disease, relate to the cytoplasmic and nucleoplasmic density, nuclear heterochromatin pattern, mitochondria, endoplasmic reticulum and Golgi complex, lysosomes, neurosecretory vesicles, and cytoskeletal elements (actin, intermediate filaments, and microtubules). Applying immunolabeling techniques to visualize membrane-bound or intracellular proteins in neurons and glial cells gives an even better appreciation of the subtle differences unique to these cells across contexts of health and disease. Together, our observations reveal how simple ultrastructural features can be used to identify specific changes in cell types, their health status, and functional relationships in the brain.

Transgenic models for investigating the nervous system: Currently available neurofluorescent reporters and potential neuronal markers

Recombinant DNA technologies have enabled the development of transgenic animal models for use in studying a myriad of diseases and biological states. By placing fluorescent reporters under the direct regulation of the promoter region of specific marker proteins, these models can localize and characterize very specific cell types. One important application of transgenic species is the study of the cytoarchitecture of the nervous system. Neurofluorescent reporters can be used to study the structural patterns of nerves in the central or peripheral nervous system in vivo, as well as phenomena involving embryologic or adult neurogenesis, injury, degeneration, and recovery. Furthermore, crucial molecular factors can also be screened via the transgenic approach, which may eventually play a major role in the development of therapeutic strategies against diseases like Alzheimer's or Parkinson's. This review describes currently available reporters and their uses in the literature as well as potential neural markers that can be leveraged to create additional, robust transgenic models for future studies.

Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

Mammalian germ cell migration during development, growth, and homeostasis

BACKGROUND

Germ cells represent one of the typical cell types that moves over a long period of time and large distance within the animal body. To continue its life cycle, germ cells must migrate to spatially distinct locations for proper development. Defects in such migration processes can result in infertility. Thus, for more than a century, the principles of germ cell migration have been a focus of interest in the field of reproductive biology.

METHODS

Based on published reports (mainly from rodents), investigations of germ cell migration before releasing from the body, including primordial germ cells (PGCs), gonocytes, spermatogonia, and immature spermatozoon, were summarized.

MAIN FINDINGS

Germ cells migrate with various patterns, with each migration step regulated by distinct mechanisms. During development, PGCs actively and passively migrate from the extraembryonic region toward genital ridges through the hindgut epithelium. After sex determination, male germline cells migrate heterogeneously in a developmental stage-dependent manner within the testis.

CONCLUSION

During migration, there are multiple gates that disallow germ cells from re-entering the proper developmental pathway after wandering off the original migration path. The presence of gates may ensure the robustness of germ cell development during development, growth, and homeostasis.

Methodological Improvements With Conductive Materials for Volume Imaging of Neural Circuits by Electron Microscopy

Recent advancements in electron microscope volume imaging, such as serial imaging using scanning electron microscopy (SEM), have facilitated the acquisition of three-dimensional ultrastructural information of biological samples. These advancements help build a comprehensive understanding of the functional structures in entire organelles, cells, organs and organisms, including large-scale wiring maps of neural circuitry in various species. Advanced volume imaging of biological specimens has often been limited by artifacts and insufficient contrast, which are partly caused by problems in staining, serial sectioning and electron beam irradiation. To address these issues, methods of sample preparation have been modified and improved in order to achieve better resolution and higher signal-to-noise ratios (SNRs) in large tissue volumes. These improvements include the development of new embedding media for electron microscope imaging that have desirable physical properties such as less deformation in the electron beam and higher stability for sectioning. The optimization of embedding media involves multiple resins and filler materials including biological tissues, metallic particles and conductive carbon black. These materials alter the physical properties of the embedding media, such as conductivity, which reduces specimen charge, ameliorates damage to sections, reduces image deformation and results in better ultrastructural data. These improvements and further studies to improve electron microscope volume imaging methods provide options for better scale, quality and throughput in the three-dimensional ultrastructural analyses of biological samples. These efforts will enable a deeper understanding of neuronal circuitry and the structural foundation of basic and higher brain functions.

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.

Mesoscale imaging with cryo-light and X-rays: Larger than molecular machines, smaller than a cell

In the context of cell biology, the term mesoscale describes length scales ranging from that of an individual cell, down to the size of the molecular machines. In this spatial regime, small building blocks self-organise to form large, functional structures. A comprehensive set of rules governing mesoscale self-organisation has not been established, making the prediction of many cell behaviours difficult, if not impossible. Our knowledge of mesoscale biology comes from experimental data, in particular, imaging. Here, we explore the application of soft X-ray tomography (SXT) to imaging the mesoscale, and describe the structural insights this technology can generate. We also discuss how SXT imaging is complemented by the addition of correlative fluorescence data measured from the same cell. This combination of two discrete imaging modalities produces a 3D view of the cell that blends high-resolution structural information with precise molecular localisation data.