scholarly journals CUBIC-Cloud provides an integrative computational framework toward community-driven whole-mouse-brain mapping

2021 ◽  
Vol 1 (2) ◽  
pp. 100038
Author(s):  
Tomoyuki Mano ◽  
Ken Murata ◽  
Kazuhiro Kon ◽  
Chika Shimizu ◽  
Hiroaki Ono ◽  
...  
2020 ◽  
Author(s):  
Tomoyuki Mano ◽  
Ken Murata ◽  
Kazuhiro Kon ◽  
Chika Shimizu ◽  
Hiroaki Ono ◽  
...  

ABSTRACTRecent 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 whole mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analysis and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated brain-wide distribution of PV, Sst, ChAT, Th and Iba1 expressing cells. Second, Aβ plaque deposition in AD model mouse brains were quantified. Third, we reconstructed neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudo-typed Rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping.


2014 ◽  
Vol 60 ◽  
pp. 143-153 ◽  
Author(s):  
Amélie Béduer ◽  
Pierre Joris ◽  
Sébastien Mosser ◽  
Vincent Delattre ◽  
Patrick C. Fraering ◽  
...  

Neuroscience ◽  
1991 ◽  
Vol 43 (1) ◽  
pp. 21-30 ◽  
Author(s):  
E. Ban ◽  
G. Milon ◽  
N. Prudhomme ◽  
G. Fillion ◽  
F. Haour

2013 ◽  
Vol 7 ◽  
Author(s):  
Diana H. Lim ◽  
Jeffrey LeDue ◽  
Majid H. Mohajerani ◽  
Matthieu P. Vanni ◽  
Timothy H. Murphy
Keyword(s):  

2021 ◽  
Author(s):  
Stephan Preibisch ◽  
Nikos Karaiskos ◽  
Nikolaus Rajewsky

We present STIM, an imaging-based computational framework for exploring, visualizing, and processing high-throughput spatial sequencing datasets. STIM is built on the powerful ImgLib2, N5 and BigDataViewer (BDV) frameworks enabling transfer of computer vision techniques to datasets with irregular measurement-spacing and arbitrary spatial resolution, such as spatial transcriptomics data generated by multiplexed targeted hybridization or spatial sequencing technologies. We illustrate STIM's capabilities by representing, visualizing, and automatically registering publicly available spatial sequencing data from 14 serial sections of mouse brain tissue.


2020 ◽  
Author(s):  
Miguel A. Gama Sosa ◽  
Rita De Gasperi ◽  
Gissel M. Perez ◽  
Patrick R. Hof ◽  
Gregory A. Elder

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