A band of brain cells at the top of your head transmitted messages to the muscles in your index finger, telling them to push down with precisely the correct amount of pressure to activate your mouse or trackpad when you clicked to read this article.
According to a spate of recent research, the primary motor cortex, which regulates movement, contains as many as 116 distinct kinds of cells working together to initiate this activity.
Researchers from the National Institutes of Health's Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative have worked to identify the many distinct kinds of brain cells for five years. Their findings were published online on Oct. 6 in the journal Nature.
It's the first stage in a long-term effort to create an atlas of the whole brain to understand better how our brain's neural networks govern our bodies and minds and how those networks are disturbed when we have mental or physical disorders.
Dozens of cell kinds have already been identified by individual researchers based on their form, size, electrical characteristics, and gene expression. Many of the novel cell types discovered in recent research are subgroups of well-known cell types.
The number of distinct cells that produce particular neurotransmitters, such as gamma-aminobutyric acid (GABA) or glutamate, extends far into the double digits.
In contrast to the present research, which focuses only on the motor cortex, the BRAIN Initiative Cell Census Network (BICCN), which was established in 2017, aims to map all the various cell types found in the brain's 160 billion neurons and glia, which serve as support cells. President Barack Obama announced the BRAIN Initiative in 2013.
It's only after identifying all of those components that we can begin to comprehend how they operate together, how they create a functional circuit, and how that eventually leads to perceptions and behavior, among other things, according to Bateup's assertions.
CRISPR-Cas9 to create mice with a fluorescent marker that can be tracked throughout their brains by former UC Berkeley professor John Ngai and colleagues from his lab in conjunction with another group at the University of California Berkley. The technique was first reported for an academic journal back in September, but it just came out this week! Together these scientists created two strains: one would label cells involved in memory formation while the other could help trace connections between brain regions associated with more closely related sensory information processing or motor control functions.
Because we do not know enough about which cells and connections are being affected by a particular disease, we are unable to pinpoint with precision where we need to target when developing effective therapies for human brain disorders, said Ngai, who led the Brain Initiative efforts at UC Berkeley before being selected last year to lead the entire national initiative. Research into the kinds of brain cells and their characteristics will help lead to novel treatments for neurologic and neuropsychiatric disorders in the future, says the author.
All the active genes in single dopamine-producing cells in the mouse midbrain's midbrain were previously profiled by Bateup, Hockemeyer, and Ngai as part of a research that had comparable structure to the human brain.
Other BICCN researchers used the same profiling method on motor cortex cells, including identifying all the particular messenger RNA molecules and their amounts in each cell. Transcriptomics is the term for this kind of study, which uses a method known as single-cell RNA sequencing.
The BICCN team employed nearly two dozen distinct experimental techniques to describe three mammal cell types: mice, marmosets, and humans. The epigenome is made up of the chromatin architecture of the genome, the degree of gene expression, and the state of DNA methylation.
Classical electrophysiological patch-clamp recordings were also used to identify cells based on how they fire action potentials. Cell shapes were classified, the connection was determined, and the location of the cells concerning the brain was investigated. When it came to identifying cell kinds, many of these researchers utilized machine learning or artificial intelligence.
The paper concludes that cell type determination with these various techniques has excellent overlap and consistency.'
A group of statisticians pooled the results of all of these experiments to figure out how to best categorize or cluster cells based on the observed variations in expression and epigenetic profiles among them into various kinds and, presumably, diverse activities. While there are numerous statistical algorithms for analyzing and identifying clusters in such data, Sandrine Dudoit, a UC Berkeley professor and chair of the Department of Statistics, explained that the challenge was to determine which groups were genuinely different from one another — genuinely different cell types. As a member of the statistical team and co-author of the landmark article, she collaborated with biostatistician Elizabeth Purdom, a UC Berkeley associate professor of statistics.
"The goal isn't to invent another clustering technique," Dudoit said, "but to figure out how to combine the strengths of various methods and evaluate the stability of the findings, as well as the repeatability of the clusters you obtain."
"That is a critical lesson about all of these studies that search for new cell kinds or unique categories of cells: regardless of the method used, clusters will occur, therefore it is critical to have complete trust in your findings."
Bateup pointed out that the number of distinct cell types discovered in the current research varied from dozens to 116, depending on the method used. According to one study, the inhibitory neurons in this part of the brain are five times more numerous in mice than in humans.
This discovery was made possible by Bateup's finding that the cells defined by their gene expression patterns were identical to the cells defined by their electrophysiological characteristics of the neuron types defined by their shape.
'The major breakthrough by the BICCN is that we've taken a lot of different methods of identifying a cell type and integrated them to come up with a consensus taxonomy that takes all of those characteristics into consideration,' said Hockemeyer.
"After all of this research, we can confidently state that a certain cell type expresses these genes, has this shape, and possesses these physiological characteristics all while being situated in a certain area of the cortex As a result, you have a far better grasp on the cell type's fundamental characteristics."
According to Dudoit, future research may reveal that the number of cell types found in the motor cortex is exaggerated. Still, the present findings are an excellent place to begin compiling brain cell atlas for the future.
When it comes to an understanding of these systems, "even among biologists there are vastly different opinions as to how much resolution you should have," she said. "Whether there is this very, very fine clustering structure or whether you really have higher-level cell types that are more stable," she explained.
"Despite this, the findings demonstrate the value of teamwork and combining efforts from many sources. Our journey begins with a scientific question that could not have been answered by a single scientist. To tackle a challenge of such magnitude, you'll need a group of specialists from a variety of fields who can communicate and collaborate effectively."
Graduate student Daniel Kramer, research technician Shona Allen from the Department of Molecular and Cell Biology, doctoral student Hector Roux de Bézieux from the School of Public Health, and postdoctoral fellow Koen Van den Berge from the Department of Statistics were also part of the UC Berkeley team. They all worked together to solve the problem.
A Helen Wills Neuroscience Institute member, Bateup works with Hockemeyer at the Innovative Genomics Institute, and the Chan Zuckerberg Biohub funds both of their research projects.