RT Journal Article
SR Electronic
T1 SMART: An open source extension of WholeBrain for iDISCO+ LSFM intact mouse brain registration and segmentation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 727529
DO 10.1101/727529
A1 Michelle Jin
A1 Joseph D. Nguyen
A1 Sophia J. Weber
A1 Carlos A. Mejias-Aponte
A1 Rajtarun Madangopal
A1 Sam A. Golden
YR 2019
UL http://biorxiv.org/content/early/2019/08/06/727529.abstract
AB Mapping immediate early gene (IEG) expression across intact brains is becoming a popular approach for identifying the brain-wide activity patterns underlying behavior. Registering whole brains to an anatomical atlas presents a technical challenge that has predominantly been tackled using automated voxel-based registration methods; however, these methods may fail when brains are damaged or only partially imaged, can be challenging to correct, and require substantial computational power. Here we present an open source package in R called SMART (semi-manual alignment to reference templates) as an extension to the WholeBrain framework for automated segmentation and semi-automated registration of experimental images to vectorized atlas plates from the Allen Brain Institute Mouse Common Coordinate Framework (CCF).The SMART package was created with the novice programmer in mind and introduces a streamlined pipeline for aligning, registering, and segmenting large LSFM volumetric datasets with the CCF across the anterior-posterior axis, using a simple ‘choice game’ and interactive user-friendly menus. SMART further provides the flexibility to register partial brains or discrete user-chosen experimental images across the CCF, making it compatible with analysis of traditionally sectioned coronal brain slices. In addition to SMART, we introduce a modified tissue clearing protocol based on the iDISCO+ procedure that is optimized for uniform Fos antibody labeling and tissue clearing across whole intact mouse brains. Here we demonstrate the utility of the SMART-WholeBrain pipeline, in conjunction with the modified iDISCO+ Fos procedure, by providing example datasets alongside a full user tutorial. Finally, we present a subset of these data online in an interactive web applet. The complete SMART package is available for download on GitHub.