Volume electron microscopy (vEM) allows visualizing the cellular ultrastructure at the nanometer level in three dimensions. This approach has been widely applied in cell biology research to unveil cellular morphologies, as well as large-scale brain mapping (i.e., Connectomics) to resolve neuronal connectivity and circuit organization.
Conventionally, vEM encompasses an imaging pipeline that generates 3D image data from biological samples and a computational pipeline that processes and analyzes the acquired datasets. The imaging pipeline relies on the use of either scanning or transmission electron microscopy (SEM or TEM) combined with a serial sectioning strategy. The computational pipeline involves automated image registration, segmentation, and pattern recognition using start-of-the-art machine learning methods and data-driven analytics for targeted exploration.
One practical challenge for many laboratories to utilize the modern vEM approach is their lack of expertise or experience in procedures involved in either pipeline or both. Additionally, as increasingly large volumes of tissue samples and the resulting big data emerge, new technical challenges have arisen, ranging from tissue preparation for large samples to big data inferences. Furthermore, developing techniques that are open-access, well-documented, and computationally affordable has also been a growing need in the vEM community.
This Research Topic aims to enhance our knowledge on the latest advances in the field of vEM, presenting novel techniques adopted to tackle specific biological questions in neuroscience research such as large-scale brain mapping in different model organisms to unveil neural circuits’ structure and function, or highly detailed cellular ultrastructural morphologies to reveal organelles distribution under specific conditions; methods and technological advances (e.g., correlative light and vEM, tissue processing for large samples, novel methods for image processing and visualization); novel datasets to inform and advance our understanding on brain’s functional architecture, and new software or applications with detailed documentation or tutorials.
We particularly welcome the submission of all types of articles on the following sub-topics:
• Sample preparation methods for vEM (particularly for processing large tissue blocks)
• Imaging techniques based on vEM
• Automated image processing for vEM datasets, including pre-processing, stitching and alignment, segmentation, pattern recognition
• Ground-truth labeling and proofreading
• Data analysis and visualization for (big) vEM datasets
• Fully proofread datasets
• Open-access experimental and computational tools, tutorials, documentation
Dr. Corrado Calì is the president and founder of the startup Intarvides. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Volume electron microscopy (vEM) allows visualizing the cellular ultrastructure at the nanometer level in three dimensions. This approach has been widely applied in cell biology research to unveil cellular morphologies, as well as large-scale brain mapping (i.e., Connectomics) to resolve neuronal connectivity and circuit organization.
Conventionally, vEM encompasses an imaging pipeline that generates 3D image data from biological samples and a computational pipeline that processes and analyzes the acquired datasets. The imaging pipeline relies on the use of either scanning or transmission electron microscopy (SEM or TEM) combined with a serial sectioning strategy. The computational pipeline involves automated image registration, segmentation, and pattern recognition using start-of-the-art machine learning methods and data-driven analytics for targeted exploration.
One practical challenge for many laboratories to utilize the modern vEM approach is their lack of expertise or experience in procedures involved in either pipeline or both. Additionally, as increasingly large volumes of tissue samples and the resulting big data emerge, new technical challenges have arisen, ranging from tissue preparation for large samples to big data inferences. Furthermore, developing techniques that are open-access, well-documented, and computationally affordable has also been a growing need in the vEM community.
This Research Topic aims to enhance our knowledge on the latest advances in the field of vEM, presenting novel techniques adopted to tackle specific biological questions in neuroscience research such as large-scale brain mapping in different model organisms to unveil neural circuits’ structure and function, or highly detailed cellular ultrastructural morphologies to reveal organelles distribution under specific conditions; methods and technological advances (e.g., correlative light and vEM, tissue processing for large samples, novel methods for image processing and visualization); novel datasets to inform and advance our understanding on brain’s functional architecture, and new software or applications with detailed documentation or tutorials.
We particularly welcome the submission of all types of articles on the following sub-topics:
• Sample preparation methods for vEM (particularly for processing large tissue blocks)
• Imaging techniques based on vEM
• Automated image processing for vEM datasets, including pre-processing, stitching and alignment, segmentation, pattern recognition
• Ground-truth labeling and proofreading
• Data analysis and visualization for (big) vEM datasets
• Fully proofread datasets
• Open-access experimental and computational tools, tutorials, documentation
Dr. Corrado Calì is the president and founder of the startup Intarvides. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
