MIT Researchers Create Robotic System to Monitor Specific Neurons in Brain

Recording electrical signals from within a neuron in a living brain can provide a huge amount of information about that neuron’s role and how it harmonizes with other cells in the brain. However, performing this sort of recording is very difficult, therefore only a few neuroscience labs worldwide can do it.

In order to make this method more extensively available, MIT Engineers have now come up with a way to automate the process, using a computer algorithm that examines microscope images and directs a robotic arm to the target cell.

This technology could allow more Researchers to analyze single neurons and understand how they interact with other cells to enable sensory perception, cognition, and other brain functions. Researchers could also apply it to study more about how neural circuits are impacted by brain maladies.

“Knowing how neurons communicate is fundamental to basic and clinical neuroscience. Our hope is this technology will allow you to look at what’s happening inside a cell, in terms of neural computation, or in a disease state,” says Ed Boyden, an Associate Professor of Biological Engineering and Brain and Cognitive Sciences at MIT, and a member of MIT’s Media Lab and McGovern Institute for Brain Research.

Boyden is the Senior Author of the paper, which has been published in the August 30^th issue of Neuron. The paper’s Lead Author is MIT Graduate Student Ho-Jun Suk.

Precision guidance

For over three decades, Neuroscientists have been using a method known as patch clamping to record the electrical motion of cells. This technique, which requires making a miniature, hollow glass pipette to come in contact with the cell membrane of a neuron, then opening up a small pore in the membrane, which generally takes a Graduate Student or Postdoc a number of months to learn. Learning to achieve this on neurons in the living mammalian brain is a lot more challenging.

There are two types of patch clamping: a “blind” (not image-guided) technique, which is narrow because Researchers cannot see where the cells are and can just record from whatever cell the pipette bumps into first, and an image-guided version that allows a particular cell to be targeted.

Five years ago, Boyden and colleagues at MIT and Georgia Tech, including Co-author Craig Forest, developed a technique to automate the blind version of patch clamping. They prepared a computer algorithm that could direct the pipette to a cell based on measurements of a property known as electrical impedance — which reflects how hard it is for electricity to flow out of the pipette. If there are no cells present, electricity flows and impedance is low. When the tip encounters a cell, electricity cannot flow as well and impedance increases.

Once the pipette makes contact with a cell, it can stop moving promptly, stopping it from poking through the membrane. A vacuum pump is used to apply suction to form a seal with the cell’s membrane. Then, the electrode can break through the membrane to record the internal electrical activity of the cell.

The team achieved superior accuracy using this process, but it still was not able to target a specific cell. For a majority of studies, Neuroscientists have a specific cell type they would like to understand about, Boyden says.

It might be a cell that is compromised in autism, or is altered in schizophrenia, or a cell that is active when a memory is stored. That’s the cell that you want to know about. You don’t want to patch a thousand cells until you find the one that is interesting.

Ed Boyden, an Associate Professor of Biological Engineering and Brain and Cognitive Sciences at MIT, and a member of MIT’s Media Lab and McGovern Institute for Brain Research

To allow this kind of precise targeting, the team sets out to automate image-guided patch clamping. This method is tough to perform manually because, although the Researcher can view the target neuron and the pipette via a microscope, he or she has to compensate for the fact that adjacent 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,” Suk says. “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 integrating several imaging processing methods, the Researchers prepared an algorithm that directs the pipette to within approximately 25 μm of the target cell. At that point, the system starts to depend upon a mixture of imagery and impedance, which is more accurate at detecting contact between the target cell and the pipette than either signal on its own.

The Researchers imaged the cells using two-photon microscopy, a frequently used method that employs a pulsed laser to transmit infrared light into the brain, lighting up cells that have been designed to express a fluorescent protein.

Using this automated method, the Researchers successfully targeted and recorded from two types of cells — a group of interneurons, which relay messages between other neurons, and a set of excitatory neurons called pyramidal cells. They realized a success rate of almost 20%, which is comparable to the performance of well-trained Scientists doing the process manually.

Unraveling circuits

This technology opens ways for in-depth studies of the behavior of particular neurons, which could provide information on both their normal functions and how they malfunction in diseases such as schizophrenia or Alzheimer’s. For instance, the interneurons that the Researchers examined in this study have been earlier linked with Alzheimer’s. In a current study of mice, led by Li-Huei Tsai, Director of MIT’s Picower Institute for Learning and Memory, and conducted in partnership with Boyden, it was reported that stimulating a particular frequency of brain wave oscillation in interneurons in the hippocampus could assist in clearing amyloid plaques similar to those found in Alzheimer’s patients.

You really would love to know what’s happening in those cells. Are they signaling to specific downstream cells, which then contribute to the therapeutic result? The brain is a circuit, and to understand how a circuit works, you have to be able to monitor the components of the circuit while they are in action.

Ed Boyden, an Associate Professor of Biological Engineering and Brain and Cognitive Sciences at MIT, and a member of MIT’s Media Lab and McGovern Institute for Brain Research

This method could also enable studies of major questions in neuroscience, such as how each neuron interacts with the other as the brain recalls a memory or makes a decision.

Bernardo Sabatini, a Professor of Neurobiology at Harvard Medical School, says he is keen on adapting this method to use in his lab, where students devote a lot of time recording electrical activity from neurons growing in a lab dish.

“It’s silly to have amazingly intelligent students doing tedious tasks that could be done by robots,” says Sabatini, who was not involved in this study. “I would be happy to have robots do more of the experimentation so we can focus on the design and interpretation of the experiments.”

To help other labs embrace the new technology, the Researchers plan to upload the details of their method on their website, autopatcher.org.

Other Co-authors include Ingrid van Welie, Suhasa Kodandaramaiah, and Brian Allen. The research was funded by Jeremy and Joyce Wertheimer, the National Institutes of Health (including the NIH Single Cell Initiative and the NIH Director’s Pioneer Award), the HHMI-Simons Faculty Scholars Program, and the New York Stem Cell Foundation-Robertson Award.