High precision vibration sectioning for 3D imaging of the whole central nervous system

Abstract

BackgroundImaging and reconstruction of the morphology of neurons within the entire central nervous system (CNS) is important for deciphering the neural circuitry and related brain functions. With combination of tissue clearing and light sheet microscopy, previous studies have imaged the mouse CNS at cellular resolution, while remaining single axons unresolvable due to the tradeoff between sample size and imaging resolution. This could be improved by sectioning the sample into thick slices and imaged with high resolution light sheet microscopy as described in our previous study. However, the achievable quality for 3D imaging of serial thick slices is often hindered by surface undulation and other artifacts introduced by sectioning and handling limitations. New methodsIn order to improve the imaging quality for mouse CNS, we develop a high-performance vibratome system for sample sectioning and handling automation. The sectioning mechanism of the system was modeled theoretically and verified experimentally. The effects of process parameters and sample properties on sectioning accuracy were studied to optimize the sectioning outcome. The resultant imaging outcome was demonstrated on mouse samples. ResultsOur theoretical model of vibratome effectively depicts the relationship between the sample surface undulation errors and the sectioning parameters. With the guidance of the theoretical model, the vibratome is able to achieve a local surface undulation error of ±0.5 µm and a surface arithmetic mean deviation (Sa) of 220 nm for 300-μm-thick tissue slices. Imaging results of mouse CNS show the continuous sectioning capability of the vibratome. Comparison with existing methodOur automatic sectioning and handling system is able to process serial thick slices for 3D imaging of the whole CNS at a single-axon resolution, superior to the commercially available vibratome devices. ConclusionOur automatic sectioning and handling system can be optimized to prepare thick sample slices with minimal surface undulation and manual manipulation in support of 3D brain mapping with high-throughput and high-accuracy.