Abstract
Imaging entire mouse brains at submicron resolution has historically been a challenging undertaking and largely confined to the province of dedicated atlasing initiatives. This has limited systematic investigations into important areas of neuroscience, such as neural circuits, brain mapping and neurodegeneration. In this article, we describe in detail Serial Two-Photon (STP) tomography, a robust, reliable method for imaging entire brains with histological detail. We provide examples of how the basic methodology can be extended to other imaging modalities, such as Optical Coherence Tomography (OCT), in order to provide unique contrast mechanisms. Furthermore, we provide a survey of the research that STP tomography has enabled in the field of neuroscience, provide examples of how this technology enables quantitative whole brain studies, and discuss the current limitations of STP tomography-based approaches.
Highlights
Over the last two decades there have been dramatic improvements in the fields of microscopy, tissue labeling, and computational image analysis. Each of these fields address an important aspect of quantitative biological imaging: optical microscopy methods visualize tissues and map the spatial-temporal relationship between components too small to be seen by the naked eye; tissue labeling techniques establish contrast and biochemical specificity of tissue components; computeraided image analyses quantitatively explore relationships between the various components and makes it possible to handle the vast amounts of data that is generated (Klunk et al, 2002; Dean and Palmer, 2014; Piccinini and Shagrir, 2014; Jordan and Mitchell, 2015; Feng et al, 2015)
Serial Two-Photon (STP) tomography is based upon the basic methodology of alternating 3D imaging and physical sectioning and while our approach utilizes two photon microscopy (TPM), this technique is compatible with a variety of 3D imaging modalities
The basic methodology can be expanded to include additional imaging and biochemical modalities leading to rich multi-component datasets
Summary
Over the last two decades there have been dramatic improvements in the fields of microscopy, tissue labeling, and computational image analysis. For studies attempting to construct a 3D representation of the original tissue, the individually imaged sections must be aligned and digitally reassembled As this method requires that tissue samples be sectioned prior to imaging, irreducible distortions are introduced into the tissue sections that preclude accurate full 3D reconstructions of the tissue. Scattered or fluorescent light from layers below the block surface will distort the surface image, thereby reducing effective axial resolution and contrast (Krishnamurthi et al, 2010) To address these limitations, we have developed a robust alternative that allows for a high degree of automation while maintaining sub-micron resolution and compatibility with current histological preparations, fluorescent proteins and dyes. We describe the key features of STP tomography and provide examples of research currently utilizing the method
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