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Summary: Recent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. 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. To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize, and integrate mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analyses, and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated the brain-wide distribution of five cell types. Second, we quantified Aβ plaque deposition in Alzheimer’s disease model mouse brains. Third, we reconstructed a neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudotyped rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping. Motivation: With the advancement of tissue clearing technology and 3D imaging methods, researchers are able to rapidly obtain whole mouse brain images at single-cell resolution. Because of the massive size of the resulting images, however, a high-throughput yet highly accessible image analysis method is needed to scale up 3D profiling of the brain. In addition, effective data sharing is critical to maximize the potential value of the 3D image dataset. Here, we address these challenges by designing an end-to-end image analysis platform embracing the latest cloud technology, named CUBIC-Cloud. |
