SMART: An open source extension of WholeBrain for iDISCO+ LSFM intact mouse brain registration and segmentation

Abstract

Biological Structure (SAG).Author contributions: MJ, RM, and SAG conceived of the pipeline and contributed intellectually to its development. MJ programmed, packaged, and created SMART and its supporting webpage. RM and CAMA developed the modified tissue clearing protocol. RM, CAMA and SAG conducted all imaging. MJ, SJW and JDN analyzed the dataset. JDN developed the Shiny applet used to display the example dataset. MJ, RM, and SAG wrote the manuscript with input from the other authors. members for their help with this project. We would like to thank Lauren E. Komer and Vadim Kashtelyan for their technical assistance. We also thank the NIDA Histology Core and staff for their support. AbstractMapping 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.

Visualizing anatomically registered data with brainrender

“…These can be loaded either directly as 3D mesh data after processing with dedicated software (e.g. A. L. Tyson et al 2020;Song et al 2020;Jin et al 2019) (Figure 3A), or as 3D volumetric data ( Figure 3E). For the latter, brainrender takes care of the conversion of voxels into a 3D mesh for rendering.…”

“…In particular, a critical step for visualizing anatomical data is the registration to a reference template (e.g., one of the atlases provided by the AtlasAPI). While this step can be challenging and time consuming, the brainglobe suite provides software to facilitate this process (e.g., brainreg and bg-space), and alternative software tools have been developed before for this purpose (e.g., Song et al 2020;Jin et al 2019). Additional information about data registration can be found in brainglobe's and brainrender's online documentation, as well as in the examples in brainrender's GitHub repository.…”

Brainrender: a python-based software for visualizing anatomically registered data

“…These can be loaded either directly as 3D mesh data after processing with dedicated software (e.g. Tyson, Rousseau, and Margrie 2020;Song et al 2020;Jin et al 2019) (Figure 3A), or as 3D volumetric data ( Figure 3E). For the latter, brainrender takes care of the conversion of voxels into a 3D mesh for rendering.…”

DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks

“…In biological research, neuroscientists often manually delineate the brain regions and nuclei in which neurons are located (Fürth et al 2018;Lein et al 2007;Lin et al 2018;Osten and Margrie 2013) with the help of a brain stereotactic reference atlas. However, mainly due to the differences in individual animals and the conditions of specific experimental settings, the correspondence of brain structures with an atlas is highly dependent on personal experience and proficiency (Fürth et al 2018;Jin et al 2019;Ni et al 2018). Moreover, the rapid development of neural circuit labeling methods and whole-brain imaging technologies (Gong et al 2016;Li et al 2010;Ragan et al 2012) have resulted in brain images becoming increasingly complicated at the mesoscopic level.…”

“…been developed for macro MRI images (Klein et al 2009). However, for mesoscopic optical images, different types of neurons exhibit different image characteristics in different brain regions and diverse labeling strategies are applied; therefore, feature-based registration methods are widely used (Fürth et al 2018;Jin et al 2019;Ni et al 2018;Ohnishi et al 2016). For instance, Ohnishi et al registered twodimensional (2D) micro-optical images to an MRI image by manually locating the feature points (Ohnishi et al 2016).…”