Recording electrical signals from inside a neuron in the living brain can reveal a great
deal of information about that neuron’s function and how it coordinates with other
cells in the brain.
However, performing this kind of recording is extremely difficult, so only a handful
of neuroscience labs around the world do it.
To make this technique more widely available, MIT engineers have now devised a way to automate
the process, using a computer algorithm that analyzes microscope images and guides a robotic
arm to the target cell.
This technology could allow more scientists to study single neurons and learn how they
interact with other cells to enable cognition, sensory perception, and other brain functions.
Researchers could also use it to learn more about how neural circuits are affected by
brain disorders.
For more than 30 years, neuroscientists have been using a technique known as patch clamping
to record the electrical activity of cells.
This method 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.
This method usually takes a graduate student or postdoc several months to learn.
Learning to perform this on neurons in the living mammalian brain is even more difficult.
There are two types of patch clamping: a “blind” method, which is limited because researchers
cannot see where the cells are and can only record from whatever cell the pipette encounters
first, and an image-guided version that allows a specific cell to be targeted.
Five years ago, a research Team devised a way to automate the blind version of patch
clamping.
They created a computer algorithm that could guide the pipette to a cell based on measurements
of a property called electrical impedance — which reflects 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.
Once the pipette detects a cell, it can stop moving instantly, preventing it from poking
through the membrane.
A vacuum pump then 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 researchers achieved very high accuracy using this technique, but it still could not
be used to target a specific cell.
For most studies, neuroscientists have a particular cell type they would like to learn about.
- It might be a cell that is compromised in
autism or a cell that is active when a memory is stored.
"That’s the cell that you want to know about", the Researcher says.
You don’t want to patch a thousand cells until you find the one that is interesting.
To enable this kind of precise targeting, the researchers set out to automate image-guided
patch clamping.
This technique is difficult to perform manually because, although the scientist can see the
target neuron and the pipette through a microscope, he must compensate for the fact that nearby
cells will move as the pipette enters the brain.
It’s almost like trying to hit a moving target inside the brain, which is a delicate
tissue.
For machines it’s easier because they can keep track of where the cell is, they can
automatically move the focus of the microscope, and they can automatically move the pipette.
By combining several imaging processing techniques, the researchers came up with an algorithm
that guides the pipette to within about 25 microns of the target cell.
At that point, the system begins to rely on a combination of imagery and impedance, which
is more accurate at detecting contact between the pipette and the target cell than either
signal alone.
The researchers imaged the cells with two-photon microscopy, a commonly used technique that
uses a pulsed laser to send infrared light into the brain, lighting up cells that have
been engineered to express a fluorescent protein.
Using this automated approach, the researchers were able to successfully target and record
from two types of cells — a class of interneurons, which relay messages between other neurons,
and a set of excitatory neurons known as pyramidal cells.
They achieved a success rate of about 20 percent, which is comparable to the performance of
highly trained scientists performing the process manually.
This technology paves the way for in-depth studies of the behavior of specific neurons, which could shed light on
both their normal functions and how they go awry in diseases such as Alzheimer’s.