A new study shows that a robotic arm guided by a cell-detecting computer algorithm can identify and record readings from neurons in the living brain with better accuracy and speed than a human.

Researchers at the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA) and the Georgia Institute of Technology (Georgia Tech, Atlanta, USA) developed a way to automate the process of finding and recording information from neurons in the living brain. The automated process eliminates the need for months of training, providing 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 discern how diseased cells differ from normal cells.

The new technique is a modern version of whole-cell patch clamping, which involves bringing a minuscule hollow glass pipette in contact with the cell membrane of a neuron, and then creating a small pore in the membrane to record the electrical activity within the cell; this skill usually takes several months to learn manually. To overcome this steep learning curve, the researchers built a robotic arm that lowered a glass pipette into the brain of an anesthetized mouse with micrometer accuracy. As it moves, the pipette monitors electrical impedance. If there are no cells around, electricity flows and impedance is low; when the tip hits a cell, electricity cannot flow as well, and impedance goes up.

The pipette takes two-micrometer steps, measuring impedance 10 times per second; once it detects a cell, it applies suction to form a seal with the cell's membrane, which also prevents the pipette from breaching through the membrane. The electrode can then 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 time. The method can also be used to determine the shape of the cell by injecting a dye, and in addition, the researchers are working on extracting a cell's contents to read its genetic profile.

“Our 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,” said study co-author Craig Forest, PhD, an assistant professor of mechanical engineering at Georgia Tech. “If 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. Now you know everything. That's the whole picture.”

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Massachusetts Institute of Technology
Georgia Institute of Technology