We’re all in this together: open-access data is the future of neuroscience
Identification of different cell types. (Image credit: Hongkui Zeng, Allen Institute)
The human brain, considered the most complex thing in our known universe, contains nearly 100 billion neurons. Neurons, the main type of nerve cell in the brain, are essential to basically everything we do. To crack away at such complexity, scientists have been asking the following questions: How do we begin to understand what neurons are doing? What makes neurons similar and different from one another? And finally, with efforts to treat diseases of the brain, which category of neurons should we be paying particular attention to?
In 2017, the National Institutes of Health (NIH), launched an effort to classify neurons, calling it, the BRAIN Initiative Cell Census Network (BICCN). One of the goals of this multimillion-dollar project was to categorize millions of neurons across human, monkey, and mouse brains to create a comprehensive map of one, tiny area in the brain. The hope was that this map could determine conserved cell types based on key features, such as where these cells are found, what they do, and how they do it. With considerable investments in time and money, as well as extensive multi-lab collaboration efforts, a map of this tiny region was made. The jury’s still out on how this single region may (or may not) generalize to the rest of the brain, nevertheless, a series of 17 scientific articles were recently published in Nature, showcasing the fruits of BICCN’s painstaking efforts and glimpse into the future of neuroscience.
This enormous quest to classify neurons turned out to be well worth the effort. After profiling millions of cells based their shape, connections to other cells, communication styles, and genes they express, BICCN has released open-access data sets and fancy analytical tools never shared before. Classifying neurons isn’t new, however, open access to data and completely new tool kits at this scale are. Additionally, new mouse strains are now available, allowing researchers to investigate key cell types with a greater level of specificity. Access to these data and tools boosts the already expensive and sluggish pace of scientific discovery and understanding, and scientists can now capitalize on this vital undertaking.
In terms of understanding disease, classification of different cell types is important as cells are often impacted differently by disease. For mysterious reasons, diseases can injure specific cells; damage to a subset of cells means that if researchers can rescue a vulnerable yet smaller population then the rest of the brain can be spared. Neuroscientists commonly classify neurons into cells that excite other cells: excitatory neurons, and cells that inhibit other cells: interneurons. Interneurons are the most diverse and conserved type of cell in the mammalian brain, underlining their pivotal role in neurological diseases. Take for example, a type of epilepsy disorder called Dravet syndrome. In Dravet, mostly interneurons are vulnerable to dysfunction, leading to severe epilepsy and death. Studying different mouse models that genetically target interneurons has led to a deeper understanding of this disorder at the molecular, cellular, and behavioral levels. These advanced models have led to the development of novel ways to treat Dravet and not just the disease symptoms.
Scientists’ ability to determine specific cell types associated with disease will continue to lead to the development of reliable disease models and promising treatment strategies. Efforts that aim to share data and ideas are expensive and time consuming, however, are perhaps promising steps closer to understanding the most complicated thing in our universe.
Arena Manning is a PhD student in the Graduate Program in Neuroscience at UW. By
analyzing brain activity and imaging the brain, she studies the role that different
interneurons (a type of neuron) play in mediating epilepsy.