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
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
"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
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
"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."
Other members of the UC Berkeley team included postdoctoral
scientists Rebecca Chance and David Stafford, graduate student Daniel
Kramer, research technician Shona Allen of the Department of Molecular
and Cell Biology, doctoral student Hector Roux de Bézieux of the School
of Public Health and postdoctoral fellow Koen Van den Berge of the
Department of Statistics. Bateup is a member of the Helen Wills
Neuroscience Institute, Hockemeyer is a member of the Innovative
Genomics Institute, and both are investigators funded by the Chan