Why Naming Neurons Can Help Cure Brain Disease

Summary: Researchers propose a unified classification of diverse cell types, which could shed light on how our brains are wired.

Source: Columbia University

The human brain has about 100 billion neurons, linked in intricate ways, that the Spanish neuroanatomist Ramón y Cajal compared to “the impenetrable jungles where many investigators have lost themselves.”

But to decipher how the brain works and understand how it can go awry in many diseases, it is essential to figure out how many classes of neurons it actually has and how they are connected with each other.

Now, in a paper published recently in Nature Neuroscience, a Columbia-led international group has proposed a unified nomenclature of the neurons of the cerebral cortex, the outermost layer of the brain that plays a key role in attention, perception, awareness, memory, language, and consciousness.

“A broadly agreed-upon classification is essential to archiving the hundreds of neuron types and their properties,” said Rafael Yuste, a professor in the Department of Biological Sciences at Columbia University. “If we could decipher how the cortex is built and what it does, one could scientifically understand our minds.”

How to classify neurons has been much debated since the inception of modern neuroscience. Many efforts to describe their anatomical, physiological, and molecular features have been unsuccessful due to their cellular diversity, Yuste said.

During the last two decades, however, the Human Genome Project has produced a host of molecular methods that enable identifying and phenotyping cells in great numbers.

“This molecular revolution is generating databases that are complete, accurate, and permanent–a triumvirate considered the golden standard of biology,” Yuste said.

In particular, using highly automated techniques that sequence the RNA of individual cells rapidly and cost-effectively, several groups have started to assemble datasets to classify cell types in the cortex. “The approach enables sampling tens of thousands of cells, generating what could be an essentially complete coverage of all the existing cell types in the cortex,” Yuste said.

Two years ago, during discussions at an international meeting on cortical neurons in Copenhagen, participants agreed that the time was right to finally tackle the creation of a unified classification.

A group of 74 scientists proposed the use of single-cell RNA sequencing as the skeleton for a unified classification of cortical neurons. Known as the “Copenhagen Classification,” the proposal is described in the Nature Neuroscience article.

This shows neurons
Red: inhibitory cells. Green: excitatory neurons. Image is credited to Yuste Lab, Columbia University.

“This could be a historic event, as it tackles one of the core problems in neuroscience,” Yuste said. “A unified framework is important not only for researchers and clinicians interested in understanding how the cortex works but also could inspire similar community classifications of cells.”

In fact, he added, “there are major consortia worldwide charted with classifying all the cells in the body something that could be a breakthrough for biology and medicine.”

With neuroscience rapidly transitioning to digital data, the researchers propose the classification be updated regularly using a type of algorithm often employed by the software industry for automatic data aggregation.

“It’s exciting to think that neuroscientists in the not-too-distant future could finally, through technology, break the impasse that has plagued us for centuries,” Yuste said.

About this neuroscience research article

Columbia University
Carla Cantor – Columbia University
Image Source:
The image is credited to Yuste Lab, Columbia University.

Original Research: Open access
“A community-based transcriptomics classification and nomenclature of neocortical cell types” by Rafael Yuste, Michael Hawrylycz, Nadia Aalling, Argel Aguilar-Valles, Detlev Arendt, Ruben Armananzas Arnedillo, Giorgio A. Ascoli, Concha Bielza, Vahid Bokharaie, Tobias Borgtoft Bergmann, Irina Bystron, Marco Capogna, Yoonjeung Chang, Ann Clemens, Christiaan P. J. de Kock, Javier DeFelipe, Sandra Esmeralda Dos Santos, Keagan Dunville, Dirk Feldmeyer, Richárd Fiáth, Gordon James Fishell, Angelica Foggetti, Xuefan Gao, Parviz Ghaderi, Natalia A. Goriounova, Onur Güntürkün, Kenta Hagihara, Vanessa Jane Hall, Moritz Helmstaedter, Suzana Herculano, Markus M. Hilscher, Hajime Hirase, Jens Hjerling-Leffler, Rebecca Hodge, Josh Huang, Rafiq Huda, Konstantin Khodosevich, Ole Kiehn, Henner Koch, Eric S. Kuebler, Malte Kühnemund, Pedro Larrañaga, Boudewijn Lelieveldt, Emma Louise Louth, Jan H. Lui, Huibert D. Mansvelder, Oscar Marin, Julio Martinez-Trujillo, Homeira Moradi Chameh, Alok Nath, Maiken Nedergaard, Pavel Němec, Netanel Ofer, Ulrich Gottfried Pfisterer, Samuel Pontes, William Redmond, Jean Rossier, Joshua R. Sanes, Richard Scheuermann, Esther Serrano-Saiz, Jochen F. Steiger, Peter Somogyi, Gábor Tamás, Andreas Savas Tolias, Maria Antonietta Tosches, Miguel Turrero García, Hermany Munguba Vieira, Christian Wozny, Thomas V. Wuttke, Liu Yong, Juan Yuan, Hongkui Zeng & Ed Lein. Nature Neuroscience.


A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

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