When you clicked to read this story, a band of cells across the top of your brain sent signals down your spine and out to your hand to tell the muscles in your index finger to press down with just the right amount of pressure to activate your mouse or track pad.
A slew of new studies now shows that the area of the
brain responsible for initiating this action — the primary motor cortex, which
controls movement — has as many as 116 different types of cells that work
together to make this happen.
The 17 studies, appearing online Oct. 6 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 of different cell types in one portion of the brain. It is the first
step in a long-term project to generate an atlas of the entire brain to help
understand how the neural networks in our head control our body and mind and
how they are disrupted in cases of mental and physical problems.
“If you think of the brain as an extremely complex
machine, how could we understand it without first breaking it down and knowing
the parts?” asked cellular neuroscientist Helen Bateup, a University of California,
Berkeley, associate professor of molecular and cell biology and co-author of
the flagship paper that synthesizes the results of the other papers. “The first
page of any manual of how the brain works should read: Here are all the
cellular components, this is how many of them there are, here is where they are
located and who they connect to.”
Individual researchers have previously identified
dozens of cell types based on their shape, size, electrical properties and
which genes are expressed in them. The new studies identify about five times
more cell types, though many are subtypes of well-known cell types. For
example, cells that release specific neurotransmitters, like gamma-aminobutyric
acid (GABA) or glutamate, each have more than a dozen subtypes distinguishable
from one another by their gene expression and electrical firing patterns.
While the current papers address only the motor
cortex, the BRAIN Initiative Cell Census Network (BICCN) — created in 2017 —
endeavors to map all the different cell types throughout the brain, which
consists of more than 160 billion individual cells, both neurons and support
cells called glia. The BRAIN Initiative was launched in 2013 by then-President
Barack Obama.
“Once we have all those parts defined, we can then go
up a level and start to understand how those parts work together, how they form
a functional circuit, how that ultimately gives rise to perceptions and
behavior and much more complex things,” Bateup said.
Together with former UC Berkeley professor John Ngai,
Bateup and UC Berkeley colleague Dirk Hockemeyer have already used CRISPR-Cas9
to create mice in which a specific cell type is labeled with a fluorescent
marker, allowing them to track the connections these cells make throughout the
brain. For the flagship journal paper, the Berkeley team created two strains of
“knock-in” reporter mice that provided novel tools for illuminating the
connections of the newly identified cell types, she said.
“One of our many limitations in developing effective
therapies for human brain disorders is that we just don’t know enough about
which cells and connections are being affected by a particular disease and
therefore can’t pinpoint with precision what and where we need to target,” said
Ngai, who led UC Berkeley’s Brain Initiative efforts before being tapped last
year to direct the entire national initiative. “Detailed information about the
types of cells that make up the brain and their properties will ultimately
enable the development of new therapies for neurologic and neuropsychiatric
diseases.”
Ngai is one of 13 corresponding authors of the
flagship paper, which has more than 250 co-authors in all.
Bateup, Hockemeyer and Ngai collaborated on an earlier
study to profile all the active genes in single dopamine-producing cells in the
mouse’s midbrain, which has structures similar to human brains. This same
profiling technique, which involves identifying all the specific messenger RNA
molecules and their levels in each cell, was employed by other BICCN
researchers to profile cells in the motor cortex. This type of analysis, using
a technique called single-cell RNA sequencing, or scRNA-seq, is referred to as
transcriptomics.
The scRNA-seq technique was one of nearly a dozen
separate experimental methods used by the BICCN team to characterize the
different cell types in three different mammals: mice, marmosets and humans.
Four of these involved different ways of identifying gene expression levels and
determining the genome’s chromatin architecture and DNA methylation status, which
is called the epigenome. Other techniques included classical
electrophysiological patch clamp recordings to distinguish cells by how they
fire action potentials, categorizing cells by shape, determining their
connectivity, and looking at where the cells are spatially located within the
brain. Several of these used machine learning or artificial intelligence to
distinguish cell types.
“This was the most comprehensive description of these
cell types, and with high resolution and different methodologies,” Hockemeyer
said. “The conclusion of the paper is that there’s remarkable overlap and
consistency in determining cell types with these different methods.”
A team of statisticians combined data from all these
experimental methods to determine how best to classify or cluster cells into
different types and, presumably, different functions based on the observed
differences in expression and epigenetic profiles among these cells. While
there are many statistical algorithms for analyzing such data and identifying clusters,
the challenge was to determine which clusters were truly different from one
another — truly different cell types — said Sandrine Dudoit, a UC Berkeley
professor and chair of the Department of Statistics. She and biostatistician
Elizabeth Purdom, UC Berkeley associate professor of statistics, were key
members of the statistical team and co-authors of the flagship paper.
“The idea is not to create yet another new clustering
method, but to find ways of leveraging the strengths of different methods and
combining methods and to assess the stability of the results, the
reproducibility of the clusters you get,” Dudoit said. “That’s really a key
message about all these studies that look for novel cell types or novel
categories of cells: No matter what algorithm you try, you’ll get clusters, so
it is key to really have confidence in your results.”
Bateup noted that the number of individual cell types
identified in the new study depended on the technique used and ranged from
dozens to 116. One finding, for example, was that humans have about twice as
many different types of inhibitory neurons as excitatory neurons in this region
of the brain, while mice have five times as many.
“Before, we had something like 10 or 20 different cell
types that had been defined, but we had no idea if the cells we were defining
by their patterns of gene expression were the same ones as those defined based
on their electrophysiological properties, or the same as the neuron types
defined by their morphology,” Bateup said.
“The big advance by the BICCN is that we combined many
different ways of defining a cell type and integrated them to come up with a
consensus taxonomy that’s not just based on gene expression or on physiology or
morphology, but takes all of those properties into account,” Hockemeyer said.
“So, now we can say 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, granular
understanding of what that cell type is and its basic properties.”
Dudoit cautioned that future studies could show that
the number of cell types identified in the motor cortex is an overestimate, but
the current studies are a good start in assembling a cell atlas of the whole
brain.
“Even among biologists, there are vastly different
opinions as to how much resolution you should have for these systems, whether
there is this very, very fine clustering structure or whether you really have
higher level cell types that are more stable,” she said. “Nevertheless, these
results show the power of collaboration and pulling together efforts across
different groups. We’re starting with a biological question, but a biologist
alone could not have solved that problem. To address a big challenging problem
like that, you want a team of experts in a bunch of different disciplines that
are able to communicate well and work well with each other.”
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