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EDITORIAL article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 |
doi: 10.3389/fnins.2025.1561852
This article is part of the Research Topic Advances in Volume Electron Microscopy for Brain Imaging: Methods, Applications, and Affordability View all 5 articles
Advances in Volume Electron Microscopy for Brain Imaging: Methods, Applications, and Affordability
Provisionally accepted- 1 Departmenrt of Neuroscience, University of Turin, Turin, Italy
- 2 Neuroscience institute Cavaleri Ottolenghi (NICO), Turin, Piedmont, Italy
- 3 Moderna Inc, Cambridge, Maryland, United States
Volume electron microscopy (vEM) has emerged as a cornerstone of modern neuroscience, providing insights into the brain's ultrastructure with nanometer precision. The combination of high spatial resolution and volumetric imaging has enabled detailed reconstructions of neural circuits, cellular morphology, and synaptic architecture, offering a deeper understanding of the structure-function relationship in the brain (Denk and Horstmann, 2004;Knott and Genoud, 2013). This special issue of Frontiers in Neuroscience showcases recent advancements in vEM, emphasizing the field's capacity to address both methodological challenges and biological questions. The contributions in this special issue reflect two primary themes: (1) advancements in vEM methodologies, including computational and material innovations, and (2) biological insights enabled by vEM, demonstrating its utility in understanding neurodevelopment, glial function, and therapeutic interventions.The technical demands of vEM, particularly for data alignment and sample preparation, remain a bottleneck for many laboratories. Innovations in these areas are essential for broadening the accessibility and impact of vEM. Watkins et al. (2023) introduce msemalign, a computational pipeline for aligning petabyte-scale datasets generated by serial section multibeam scanning electron microscopy (ssmSEM). This tool addresses the challenges of processing massive image datasets, emphasizing scalability and ease of use. Unlike existing database-driven solutions, msemalign minimizes computational overhead, making it more accessible to labs with limited resources. By aligning datasets with minimal distortion and maintaining continuity of tissue structures, this pipeline facilitates large-scale neural circuit reconstruction. Tegethoff and Briggman (2024) focus on embedding resins, a critical yet underexplored aspect of vEM sample preparation. Their study quantifies resin hardness, cutting forces, and curing uniformity, providing researchers with practical metrics for selecting optimal resins. Consistent ultrathin sectioning is crucial for achieving high-resolution volumetric imaging, and this work sets a new benchmark for reproducibility in vEM datasets.Beyond technical advancements, vEM continues to illuminate fundamental biological processes. In this issue, Calì (2024) revisits the debate on astrocytic gliotransmission, presenting ultrastructural evidence for synaptic-like microvesicles (SLMVs) in astrocytes. The potential role of regulated exocytosis in modulating synaptic activity has been a contentious topic (Bezzi et al., 2004;Savtchouk and Volterra, 2018). By leveraging vEM to visualize nanometer-scale organelles in astrocytic processes, this study provides fresh insights into the cellular mechanisms underpinning neuroglia communication.
Keywords: volume electron microscopy (vEM), resin, gliotransmission, image registration, 3D Reconstruction, ADHD, embedding, synaptic plasticitiy
Received: 16 Jan 2025; Accepted: 23 Jan 2025.
Copyright: © 2025 Calì and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Corrado Calì, Departmenrt of Neuroscience, University of Turin, Turin, Italy
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