A slew of new studies have shown that the area of the brain responsible for triggering this action – the primary motor cortex, which controls movement – has up to 116 different cell types that work together to make this happen. The 17 studies, published online in the journal Nature, are the result of five years of work by a huge consortium of researchers supported by the National Institutes of Health’s Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) initiative to identify the myriad different types of cells. in part of the brain.
This is the first step in a long-term project to generate an atlas of the whole brain to help understand how the neural networks in our head control our body and mind and how they are disrupted when problems arise. mental and physical. “If you think of the brain as an extremely complex machine, how could we understand it without first breaking it down and knowing its parts? Asked cellular neuroscientist Helen Bateup of the University of California at Berkeley, associate professor of molecular and cellular biology and co-author of the seminal article which synthesizes the results of the other articles. “The first page of any textbook on how the brain works should read: Here are all the cellular components, here is their number, here is where they are and who they connect to.”
Individual researchers have already identified dozens of cell types based on their shape, size, electrical properties, and the genes expressed in them. The new studies identify about five times as many cell types, although many are well-known cell type subtypes. For example, cells that release specific neurotransmitters, like gamma-aminobutyric acid (GABA) or glutamate, each have more than a dozen subtypes that are distinguished from each other by their gene expression and patterns. electric trigger.
While current articles deal only with the motor cortex, the BRAIN Initiative Cell Census Network (BICCN) – established in 2017 – works to map all of the different cell types in the brain, which is made up of more than 160 billion people. individual cells, both neurons and supporting cells called glia. The BRAIN initiative was launched in 2013 by then-President Barack Obama. “Once we have defined all these parts, then we can go up a level and begin to understand how these parts work together, how they form a functional circuit, how it ultimately gives rise to perceptions and behaviors and to much more complex things, ”Bateup said. noted.
Together with former UC Berkeley professor John Ngai, Bateup and his UC Berkeley colleague Dirk Hockemeyer have previously used CRISPR-Cas9 to create mice in which a specific cell type is labeled with a fluorescent marker, allowing them to follow the connections that these cells make in the brain. For the journal’s flagship article, the Berkeley team created two strains of “knock-in” reporter mice that provided new tools to illuminate connections of newly identified cell types, she said. “One of our many limitations in developing effective therapies for human brain disorders is that we simply don’t know enough which cells and connections are affected by a particular disease, and therefore cannot accurately identify what we are doing. need to target and where, ”said Ngai, who led UC Berkeley’s Brain Initiative efforts before being asked last year to lead the entire national initiative. “Detailed information on the types of cells that make up the brain and their properties will ultimately enable the development of new therapies for neurological and neuropsychiatric diseases. “
Ngai is one of 13 corresponding authors of the flagship article, which has over 250 co-authors in total. Bateup, Hockemeyer, and Ngai collaborated on an earlier study to profile all genes active in single dopamine-producing cells in the mouse midbrain, which has structures similar to those of the human brain.
This same profiling technique, which involves identifying all specific messenger RNA molecules and their levels in each cell, has been used by other researchers at BICCN to profile cells in the motor cortex. This type of analysis, using a technique called single-cell RNA sequencing, or scRNA-seq, is called transcriptomics. The scRNA-seq technique was one of nearly a dozen separate experimental methods used by the BICCN team to characterize different cell types in three different mammals: mice, marmosets, and humans. Four of them involved different ways of identifying gene expression levels and determining the chromatin architecture of the genome and the methylation status of DNA, known as the epigenome.
Other techniques included classic patch-clamp electrophysiological recordings to distinguish cells by how they trigger action potentials, categorize cells by shape, determine their connectivity, and look at where cells are located in space in. the brain. Several of them have used machine learning or artificial intelligence to distinguish cell types. “It was the most comprehensive description of these cell types, and with high resolution and different methodologies,” Hockemeyer said. “The conclusion of the article is that there is remarkable overlap and consistency in the determination of cell types with these different methods.”
A team of statisticians combined data from all of these experimental methods to determine how best to classify or group cells into different types and, presumably, different functions based on the observed differences in expression and epigenetic profiles between these cells. . While there are many statistical algorithms to analyze this data and identify clusters, the challenge was to determine which clusters were really different from each other – really different cell types – said Sandrine Dudoit, professor at UC. Berkeley and chair of the statistics department. . She and biostatistician Elizabeth Purdom, associate professor of statistics at UC Berkeley, were key members of the statistical team and co-authors of the flagship article.
“The idea is not to create a new method of clustering, but to find ways to take advantage of the strengths of different methods and combine methods and assess the stability of the results, the reproducibility of the clusters that you get. “said Dudoit. “That’s really a key message about all of these studies looking for new cell types or new cell categories: whatever algorithm you try is going to get clusters, so it’s essential to have really trust your results. Bateup noted that the number of individual cell types identified in the new study depended on the technique used and ranged from tens to 116. One finding, for example, was that humans have about twice as many different types of inhibitory neurons. as many excitatory neurons in this study. region of the brain, while mice have five times that.
“Before, we had something like 10 or 20 different cell types that had been defined, but we had no idea if the cells that we defined by their gene expression patterns were the same as those defined based on their electrophysiological properties or the same as the types of neurons defined by their morphology, “said Bateup.” The great advancement of BICCN is that we have combined many different ways of defining a cell type and integrated them to come up with a consensus taxonomy which is not only based on gene expression or physiology or morphology, but which takes all these properties into account. “said Hockemeyer.
“So now we can say that this particular cell type expresses these genes, has this morphology, has these physiological properties, and is located in this particular region of the cortex. So you have a much deeper and granular understanding of this type of cell. is and its basic properties. Dudoit warned that future studies may show the number of cell types identified in the motor cortex to be an overestimate, but current studies are a good start to assembling a cell atlas of the entire brain.
She said: “Even among biologists there are very different opinions as to what resolution you should have for these systems, if there is this very, very fine cluster structure or if you really have cell types. higher level which are more stable. “” Nonetheless, these results show the power of collaboration and mobilization of efforts between different groups. We start with a biological question, but a biologist alone could not have solved this problem. To solve a problem as difficult as this, you want a team of experts in a bunch of different disciplines who are able to communicate well and work well with each other, ”she added. (ANI)
(This story was not edited by Devdiscourse staff and is auto-generated from a syndicated feed.)