Image: Boyden Laboratory
access to the inner workings of a neuron in the living brain offers a wealth of
useful information: Its patterns of electrical activity, its shape, even a
profile of which genes are turned on at a given moment. However, achieving this
entry is such a painstaking task that it is considered an art form; it is so
difficult to learn that only a small number of laboratories in the world
that could soon change: Researchers at Massachusetts Institute of Technology (MIT)
and Georgia Institute of Technology (Georgia Tech) have developed a way to
automate the process of finding and recording information from neurons in the
living brain. The researchers have shown that a robotic arm guided by a
cell-detecting computer algorithm can identify and record from neurons in the
living mouse brain with better accuracy and speed than a human experimenter.
new automated process eliminates the need for months of training and provides
long-sought information about living cells’ activities. Using this technique,
scientists could classify the thousands of different types of cells in the
brain, map how they connect to each other, and figure out how diseased cells
differ from normal cells.
project is a collaboration between the laboratories of Ed Boyden, the Benesse
Career Development associate professor of biological engineering and brain and
cognitive sciences at MIT, and Craig
professor of mechanical engineering at Georgia Tech.
team has been interdisciplinary from the beginning, and this has enabled us to
bring the principles of precision machine design to bear upon the study of the
living brain,” Forest says. His graduate
student, Suhasa Kodandaramaiah, spent the past two years as a visiting student
at MIT, and is the lead author of the study, which appears in Nature Methods.
method could be particularly useful in studying brain disorders such as
schizophrenia, Parkinson’s disease, autism, and epilepsy, Boyden says. “In all
these cases, a molecular description of a cell that is integrated with [its] electrical and circuit properties … has remained elusive,” says Boyden, who is
a member of MIT’s Media Lab and McGovern Institute for Brain Research. “If we
could really describe how diseases change molecules in specific cells within
the living brain, it might enable better drug targets to be found.”
Kodandaramaiah, Boyden, and Forest set out to
automate a 30-year-old technique known as whole-cell patch clamping, which
involves bringing a tiny hollow glass pipette in contact with the cell membrane
of a neuron, then opening up a small pore in the membrane to record the
electrical activity within the cell. This skill usually takes a graduate
student or postdoctoral researcher several months to learn.
spent about four months learning the manual patch-clamp technique, giving him
an appreciation for its difficulty. “When I got reasonably good at it, I could
sense that even though it is an art form, it can be reduced to a set of
stereotyped tasks and decisions that could be executed by a robot,” he says.
that end, Kodandaramaiah and his colleagues built a robotic arm that lowers a
glass pipette into the brain of an anesthetized mouse with micrometer accuracy.
As it moves, the pipette monitors a property called electrical impedance—a
measure of how difficult it is for electricity to flow out of the pipette. If
there are no cells around, electricity flows and impedance is low. When the tip
hits a cell, electricity can’t flow as well and impedance goes up.
pipette takes two-micrometer steps, measuring impedance 10 times per second.
Once it detects a cell, it can stop instantly, preventing it from poking
through the membrane. “This is something a robot can do that a human can’t,”
the pipette finds a cell, it applies suction to form a seal with the cell’s
membrane. Then, the electrode can break through the membrane to record the
cell’s internal electrical activity. The robotic system can detect cells with 90%
accuracy, and establish a connection with the detected cells about 40% of the
researchers also showed that their method can be used to determine the shape of
the cell by injecting a dye; they are now working on extracting a cell’s
contents to read its genetic profile.
Svoboda, a group leader at the Howard Hughes Medical Institute’s Janelia Farm
campus, says he believes the technology will be widely adopted, as it removes
the barriers that have prevented more researchers from using patch-clamp
recording. “Humans can do it as well as the machine, but it’s extremely dull
for a person. You get tired, you start to make mistakes. The robot just keeps
on going,” says Svoboda, who was not part of the research team.
of the new technology was funded primarily by the National Institutes of
Health, the National Science Foundation, and the MIT Media Lab.
New era for robotics
The researchers are now working on scaling up the number of electrodes so they
can record from multiple neurons at a time, potentially allowing them to
determine how different parts of the brain are connected.
are also working with collaborators to start classifying the thousands of types
of neurons found in the brain. This “parts list” for the brain would identify neurons
not only by their shape—which is the most common means of classification—but
also by their electrical activity and genetic profile.
you really want to know what a neuron is, you can look at the shape, and you
can look at how it fires. Then, if you pull out the genetic information, you
can really know what’s going on,” Forest says. “Now you know everything. That’s the whole picture.”
says he believes this is just the beginning of using robotics in neuroscience
to study living animals. A robot like this could potentially be used to infuse
drugs at targeted points in the brain, or to deliver gene therapy vectors. He
hopes it will also inspire neuroscientists to pursue other kinds of robotic
automation—such as in optogenetics, the use of light to perturb targeted neural
circuits and determine the causal role that neurons play in brain functions.
is one of the few areas of biology in which robots have yet to make a big
impact, Boyden says. “The genome project was done by humans and a giant set of
robots that would do all the genome sequencing. In directed evolution or in
synthetic biology, robots do a lot of the molecular biology,” he says. “In
other parts of biology, robots are essential.”