A group of researchers has developed a 3D atlas of developing mouse brains, enriching our understanding of mammalian brain development. This atlas acts as a shared reference and anatomical guide for researchers to explore brain maturation and investigate neurodevelopmental disorders.
Researchers from Penn State College of Medicine, along with partners from five other institutions, have crafted a detailed 3D atlas of growing mice brains using cutting-edge imaging and microscopy methods. This innovative atlas provides a dynamic, comprehensive view of the entire mammalian brain during both the embryonic and early post-natal phases. It serves as a unified reference and anatomical framework that will aid researchers in their study of brain development and neurodevelopmental disorders.
They released their findings today (Oct 21) in Nature Communications.
“Atlas maps are essential for building knowledge, yet a high-resolution 3D atlas of developing brains has been lacking,” stated Yongsoo Kim, an associate professor of neural and behavioral sciences at Penn State College of Medicine and the lead author of the study. “We’re creating high-resolution maps to understand normal brain growth and the impacts of brain disorders.”
Just as geographical atlases compile maps to give a detailed view of Earth’s features, including borders, landscapes, and infrastructure, brain atlases are crucial for understanding the brain’s structure. They enable researchers to visualize the brain’s organization and comprehend its functionality, as well as how different regions and neurons interconnect. Previously, scientists had to rely on 2D histological images, which make it difficult to interpret anatomical areas in three dimensions and recognize any changes that occur, according to Kim.
Recent developments in whole brain imaging technology have allowed researchers to inspect the entire brain in high resolution, resulting in extensive 3D datasets. To interpret this data, Kim explained that scientists have created 3D reference atlases for the adult mouse brain, a model for mammalian brains. These atlases provide a common anatomical foundation that facilitates the overlay of various datasets and enables comparative research. However, there was no similar resource for the developing mouse brain, which experiences rapid changes in shape and size during the embryonic and post-natal phases.
“Without a 3D map of the developing brain, we cannot synchronize data from new 3D studies into a standardized spatial framework or analyze it consistently,” Kim noted. In essence, the absence of a 3D map impedes progress in neuroscience research.
The research team established a multimodal 3D common coordinate system for the mouse brain at seven developmental stages — four during the embryonic period and three in the immediate postnatal phase. Using MRI, they captured images reflecting the brain’s overall shape and structure. Furthermore, they utilized light sheet fluorescence microscopy, which allows for the visualization of the entire brain at a single-cell level. These high-resolution images were aligned with MRI templates to form the 3D atlas. The team included samples from both male and female mice.
To showcase how the atlas can be utilized to analyze various datasets and monitor the emergence of specific cell types in the developing brain, the team focused on GABAergic neurons. These nerve cells are vital for communication within the brain and have been linked to schizophrenia, autism, and other neurological conditions.
While research has been conducted on GABAergic neurons in the brain’s outer layer, known as the cortex, little information exists on how these cells develop throughout the entire brain during its maturation, the researchers explained. Gaining insight into how these cell clusters form under normal circumstances may be crucial for identifying what goes wrong in abnormal developments.
To foster collaboration and propel neuroscience research forward, the team created an interactive web-based version of the atlas that is accessible to the public free of charge. Their goal is to significantly reduce technical barriers for researchers globally to utilize this valuable resource.
“This provides a framework that can integrate various types of data—genomic, neuroimaging, microscopy, and more—into a cohesive data structure. It will propel the next phase of brain research powered by machine learning and artificial intelligence,” Kim concluded.
