Serial block-face scanning electron microscopy (SBFSEM) enables the reconstruction of three-dimensional (3D) tissue nanostructures at high resolution. This technique combines automated block-face imaging with serial sectioning within a scanning electron microscope (SEM), allowing for the visualization of heavy-metal-stained tissue at nanoscale resolution. Backscattering contrast is used to enhance image quality, and low-vacuum conditions prevent charging of the uncoated block face. The method provides sufficient resolution to trace even the thinnest axons and identify small organelles such as synaptic vesicles. Stacks of several hundred sections, 50–70 nm thick, have been obtained with lateral position jitter typically under 10 nm. This approach enables the acquisition of electron-microscope-level 3D datasets necessary for the complete reconstruction of neuronal circuits. The technique addresses the limitations of conventional methods in capturing 3D tissue structures at the necessary resolution. While light microscopy is insufficient for resolving densely packed cellular processes and estimating neuronal geometry, electron microscopy offers the required spatial resolution. However, traditional transmission electron microscopy (TEM) is limited in its ability to image large volumes, while scanning electron microscopy (SEM) is typically used for surface imaging. SBFSEM overcomes these limitations by allowing continuous imaging of the block face while serial sections are cut, enabling the reconstruction of 3D data over hundreds of micrometers. The method involves the use of a custom-designed microtome that allows for precise and automated sectioning within the SEM chamber. This ensures stable alignment of successive images, which is crucial for 3D reconstruction. The technique has been successfully applied to various tissues, including mammalian muscle, cortex, cerebellum, retina, and brain, demonstrating its versatility. The resolution achieved is sufficient to identify synaptic contacts and other cellular structures, making it a powerful tool for studying neural circuits. The study highlights the potential of SBFSEM for automated 3D reconstruction of biological structures, particularly in neuroscience. It provides a means to obtain high-resolution data without the need for manual handling of sections, reducing the risk of misalignment and improving data consistency. The method also allows for the analysis of large volumes, making it suitable for studying complex neural networks. The results demonstrate that SBFSEM can provide the necessary data for the complete reconstruction of local neural circuits, offering significant advantages over conventional techniques.Serial block-face scanning electron microscopy (SBFSEM) enables the reconstruction of three-dimensional (3D) tissue nanostructures at high resolution. This technique combines automated block-face imaging with serial sectioning within a scanning electron microscope (SEM), allowing for the visualization of heavy-metal-stained tissue at nanoscale resolution. Backscattering contrast is used to enhance image quality, and low-vacuum conditions prevent charging of the uncoated block face. The method provides sufficient resolution to trace even the thinnest axons and identify small organelles such as synaptic vesicles. Stacks of several hundred sections, 50–70 nm thick, have been obtained with lateral position jitter typically under 10 nm. This approach enables the acquisition of electron-microscope-level 3D datasets necessary for the complete reconstruction of neuronal circuits. The technique addresses the limitations of conventional methods in capturing 3D tissue structures at the necessary resolution. While light microscopy is insufficient for resolving densely packed cellular processes and estimating neuronal geometry, electron microscopy offers the required spatial resolution. However, traditional transmission electron microscopy (TEM) is limited in its ability to image large volumes, while scanning electron microscopy (SEM) is typically used for surface imaging. SBFSEM overcomes these limitations by allowing continuous imaging of the block face while serial sections are cut, enabling the reconstruction of 3D data over hundreds of micrometers. The method involves the use of a custom-designed microtome that allows for precise and automated sectioning within the SEM chamber. This ensures stable alignment of successive images, which is crucial for 3D reconstruction. The technique has been successfully applied to various tissues, including mammalian muscle, cortex, cerebellum, retina, and brain, demonstrating its versatility. The resolution achieved is sufficient to identify synaptic contacts and other cellular structures, making it a powerful tool for studying neural circuits. The study highlights the potential of SBFSEM for automated 3D reconstruction of biological structures, particularly in neuroscience. It provides a means to obtain high-resolution data without the need for manual handling of sections, reducing the risk of misalignment and improving data consistency. The method also allows for the analysis of large volumes, making it suitable for studying complex neural networks. The results demonstrate that SBFSEM can provide the necessary data for the complete reconstruction of local neural circuits, offering significant advantages over conventional techniques.