In this paper, we present a computational framework for single-cell-resolution whole-mouse-brain analysis, named CUBIC-Cloud.
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Fourth, these software tools should offer superior accessibility and usability to the end users, without requiring specialized expertise in programming or powerful computer resources. Third, because of the complexity and the large size of the whole-brain data sets, the framework should be equipped with a toolkit to visualize and quantify the data to assist intuitive understanding. Second, the framework should be constructed around the research community, which allows researchers to submit the data, as well as to browse and search the brains in the previous studies. We think that CUBIC-Atlas would play a central role in addressing this challenge. First, the reference brain (equivalent to the template sequence), to which all brain data are aligned, is necessary. Based on these considerations, we suggest that it is now possible to construct the community-supported mouse brain data repository, borrowing collaborative ideas from genome sciences.īy referring to the previous image analysis pipelines for tissue clearing samples, we postulated that the following elements should be considered in composing a framework for whole mouse brain mapping. However, a common platform has been yet to appear that offers the opportunity for the community to submit and share new data, embracing the tissue clearing and rapid brain scanning techniques.
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In neuroscience, several large-scale mouse brain datasets have been constructed, such as Allen Mouse Brain Atlas and Brain Architecture Project, primarily using serial sectioning tomography methods. Such distributed collaboration prompted a rapidly growing coverage of various organisms and individuals, pioneering the data-driven discoveries of gene functions and new therapeutics. The emergence of the database to browse and search genomes (such as UCSC Genome Browser ) played a critical role in integrating data collected in numerous sites across the globe. In genome science, the importance of the data repository cannot be understated. In this regard, the current technological stage can be thought of as parallel to the dawn of genome sequencing technology in early 2000s. These scientific advancements encourage us to conceive a future where whole-brain mapping projects, which conventionally required institution-scale resources and efforts, can be carried out by individual laboratories, or even by a single researcher. Built on top of these technological advancements, we recently reported the construction of CUBIC-Atlas, a 3D mouse brain atlas with single cell resolution, where all of the cells in the brain (amounting to approximately 0.1 billion) were digitally analyzed and recorded. Combined with light-sheet fluorescence microscopy (LSFM) and genetical, viral and immunohistochemical labelling techniques, tissue clearing now enables high-speed volumetric imaging of mammalian (most prominently mouse) brains at cellular resolution. Recent advancements in tissue clearing technology have brought new breakthroughs in this landscape. In particular, comprehensive approaches to identifying the properties of every single cells in situ within this complex system would be pivotal. It would also be true for studying highly-evolved mammalian brains, where a complex system of intricately connected cells gives rise to intelligent behaviors. Massive and collective observation of complex systems (often referred to as omics approaches) is the driving force of modern biology. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping. Last, we show brain-wide connectivity mapping by pseudo-typed Rabies virus. Third, we reconstructed neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Second, Aβ plaque deposition in AD model mouse brains were quantified. First, we investigated brain-wide distribution of PV, Sst, ChAT, Th and Iba1 expressing cells. We demonstrate the generality of CUBIC-Cloud by a variety of applications. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analysis and publish the results.
To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize and integrate whole mouse brain data. With the expansion of this experimental technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze and integrate whole-brain mapping datasets. Recent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution.
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