A 3D-reconstituted fluorescent image of a nuclear-stained marmoset hemisphere taken using the CUBIC method

In collaboration with several Japanese institutes, a team of scientists at the RIKEN Quantitative Biology Center, Saitama, Japan, have uncovered an easy and fast way to achieve whole brain imaging for 3D analysis of gene expression profiles and neural circuits at the systems level.

One technology needed to help understand how neural activity in the brain is translated into consciousness and other complex brain activities involves whole-brain imaging at single-cell resolution. This would usually involve preparing a transparent sample to minimize light scattering and then imaging neurons tagged with fluorescent probes at different slices to produce a 3D representation. However, limitations in current methods prevent comprehensive study of the relationship.

A new high-throughput method, CUBIC (Clear, Unobstructed Brain Imaging Cocktails and Computational Analysis), they say, offers unprecedented rapid whole-brain imaging at single cell resolution, along with a simple protocol to make the brain sample transparent using amino-alcohols.

In combination with light sheet fluorescence microscopy, CUBIC was tested for rapid imaging of a number of mammalian systems, including mouse and primate, showing its scalability for brains of different size. Additionally, it was used to acquire new spatial-temporal details of gene expression patterns in the hypothalamic circadian rhythm center. Moreover, by combining images taken from opposite directions, CUBIC enables whole brain imaging and direct comparison of brains in different environmental conditions.

CUBIC overcame several obstacles compared with previous methods, including the clearing and transparency protocol. In addition, CUBIC is compatible with many fluorescent probes because of low quenching, which allows for probes with longer wavelengths and reduces concern for scattering when whole brain imaging while at the same time inviting multi-color imaging. Finally, it is highly reproducible and scalable.