Engineers at the Mass. Institute of Technology are working to automate the process of recording electrical signals from a brain neuron, which could help researchers more precisely study individual brain cells that affect learning and functioning and cause cognitive diseases.
“Knowing how neurons communicate is fundamental to basic and clinical neuroscience,” Ed Boyden, senior author of the research paper and associate professor of biological engineering and brain and cognitive sciences, told MIT News. “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.”
The process of recording signals in cells, called patch clamping, is only done in a few labs because of its difficulty. The blind method prevents researchers from targeting a cell, which the image-guided method allows. But both take months to learn and even the image-guided method makes targeting a specific cell difficult, because the surrounding cells shift during the process.
Automating the process enables more precise cell targeting. An algorithm first guides the measuring tool, a pipette, to about 25 microns from a certain cell based on the cell’s characteristics and properties. Then using a mix of imagery and electrical impedance – a measure of how easily electricity flows through the tool – the tool comes into contact with the specific cell. By forming a seal with the cell’s membrane, creating a small hole, and inserting an electrode, a researcher or robotic device can measure the cell’s internal signals.
“It’s almost like trying to hit a moving target inside the brain, which is a delicate tissue,” lead author and MIT graduate student Ho-Jun Suk said. “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.”
A greater number of labs would be able to adopt and use this automated method because it is easier to carry out and, as a result, perform more studies on how brain neurons interact with each other and cause conditions like Alzheimer’s and schizophrenia. To help researchers implement the new approach, the MIT team published the details on autopatcher.org.
“You really would love to know what’s happening in those cells,” Boyden said. “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.”
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