New Cell Subtypes Classified in Mouse Brain

Summary: A new process for classifying neurons in the brains of mice has been outlined by researchers from Cold Spring Harbor Laboratory. The platform pairs microscopy with genetic labeling at single neuron resolution, allowing researchers to reconstruct the brain, by providing information on every neuronal type’s location.

Source: Cold Spring Harbor Laboratory

It’s been estimated that the human brain contains roughly 100 billion neurons, together completing countless tasks through countless connections. So how do we make sense of the roles each of these neurons play? As part of the United States BRAIN Initiative, scientists from Cold Spring Harbor Laboratory (CSHL) have outlined a way to classify neurons based not only on how they look but on with which other neurons they are capable of communicating.

So, why is this necessary? Think of it this way: if you were mapping a city, you’d want a sensible system for identifying streets, buildings, landmarks and understanding the distinctions between them. In a similar way, in mapping a brain, it’s logical to name and distinguish between neuronal types in order to understand how they relate to each other.

CSHL Professor Z. Josh Huang, a project leader at the Center for the Mouse Brain Cell Atlas, explains that to classify a neuron as a specific neuronal type, “what is needed is a comprehensive image of a whole cell, and then to label the cells that are like it–to quantify those and to register them in the whole brain to compare. This has never been achieved, but technology is finally catching up.”

Now, in the latest issue of Cell Reports, Huang and colleagues from the US and China describe how they’ve created a process for classifying neurons in a mouse brain in a suitably comprehensive manner.

According to the team, the new platform embraces four major aspects of brain mapping:

First, the team identifies and labels neurons of the same shape by discerning key genetic clues that are unique to the cell type. Importantly, because this genetic information describes how a neuron’s synapses are structured, it can also indicate with which other cells a neuron is capable of communicating.

Second, through cutting-edge technology, scientists are able to image the entire brain. This “wide angle” look at a neuronal type’s home regions provides an exceptionally important context for the third aspect: reconstruction.

By pairing imagery and genetic labeling at the single-neuron resolution with an “atlas” of the whole brain, the team can painstakingly “reconstruct” a brain, providing data on each and every neuronal type’s location.

Lastly, with this information about shape, connectivity, and location in hand, computational biologists can crunch the numbers, determining what is relevant and unique to any one cell type, and what is not.

AAC cells in the mouse cortex

Pictured is an axo-axonic cell (AAC) found in the cortex of a mouse brain. Huang and his colleagues discovered that there are multiple subtypes of AACs. Each is distinguished by the arrangement of important connections the chandelier-shaped cells form with neighboring neurons. The image is credited to Wang et al. Cell Reports 2019.

As a proof-of-concept, Huang and CSHL computational neuroscientist Partha Mitra looked specifically at axo-axonic cells (AACs) found in the cortex. Despite already being remarkably specific in location, shape, and purpose, the team determined that there are actually a wide variety of AAC subtypes.

According to Huang, the BRAIN Initiative will continue to refine their process until neuron classification is not only easy but second nature in the quest to accurately map an entire brain.

Funding: National Natural Science Foundation of China, Director Fund of WNLO, CSHL Robertson Neuroscience Fund, Burroughs Wellcome Fund Collaborative Research Travel Grant, Crick-Clay Professorship, NIH/National Research Service funded this study.

About this neuroscience research article

Cold Spring Harbor Laboratory
Media Contacts:
Sara Roncero-Menendez – Cold Spring Harbor Laboratory
Image Source:
The image is credited to Wang et al. Cell Reports 2019.

Original Research:
“Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells”
Xiaojun Wang,Jason Tucciarone,Siqi Jiang,Fangfang Yin,Bor-Shuen Wang,Dingkang Wang,Yao Jia,Xueyan Jia,Yuxin Li,Tao Yang,Zhengchao Xu,Masood A. Akram,Yusu Wang,Shaoqun Zeng,Giorgio A. Ascoli,Partha Mitra,Hui Gong,Qingming Luo,Z. Josh Huang. Cell Reports VOLUME 26, ISSUE 11, P3145-3159.E5, MARCH 12, 2019. doi:10.1016/j.celrep.2019.02.040


Genetic Single Neuron Anatomy Reveals Fine Granularity of Cortical Axo-Axonic Cells

• An integrated platform that resolves, registers, and quantifies single-neuron morphology
• The pipeline facilitates high-resolution, scalable single neuron anatomy in mouse brain
• Cortical axo-axonic interneurons consist of multiple morphological subtypes
• AAC subtypes differ in laminar position and dendritic and axonal arborization patterns

Parsing diverse nerve cells into biological types is necessary for understanding neural circuit organization. Morphology is an intuitive criterion for neuronal classification and a proxy of connectivity, but morphological diversity and variability often preclude resolving the granularity of neuron types. Combining genetic labeling with high-resolution, large-volume light microscopy, we established a single neuron anatomy platform that resolves, registers, and quantifies complete neuron morphologies in the mouse brain. We discovered that cortical axo-axonic cells (AACs), a cardinal GABAergic interneuron type that controls pyramidal neuron (PyN) spiking at axon initial segments, consist of multiple subtypes distinguished by highly laminar-specific soma position and dendritic and axonal arborization patterns. Whereas the laminar arrangements of AAC dendrites reflect differential recruitment by input streams, the laminar distribution and local geometry of AAC axons enable differential innervation of PyN ensembles. This platform will facilitate genetically targeted, high-resolution, and scalable single neuron anatomy in the mouse brain.

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