Technology Development

Technology Development: Image-Guided Robotic Patch-Clamp System Autopatcher 3D
Autopatcher
Description of the current whole-cell patch-clamp Autopatcher 3D system in slices. A) Graphic User Interface (GUI), image acquisition of the cortical slice with the yellow points indicating a sequence of coordinates for patching. B) GUI of the automatic patch-clamp interface. C) Log of the automatic patch clamp experiment, top panel represents pressure in the pipette, mmHg; bottom panel represents analog input measurement of the amplifier voltage command, V. D) Current clamp recording of the patched cell.
I aim to develop new tools in neurotechnology, which would allow us to understand how the connectivity and temporal dynamics of neural circuits lead to their function.
One of the key goals is to map functional connectivity of these neural circuits. The only method suitable for this goal is whole-cell patch clamp technology, because it allows us to measure synaptic activity of the neurons in the circuit. This method is technically challenging and requires extensive training. The methodology has not changed in 20 years. Therefore, there is an immediate need to develop automated, patch clamp techniques that would achieve high throughput, precision and repeatability.
I am developing an image-guided automated patch-clamp system (Autopatcher 3D). Using open source software, I have integrated the live image acquisition of neurons within brain slice tissue (Hamamatsu CCD camera), with micrometer precision mechanical movements of recording electrode manipulators, and a manifold of pumps and valves to achieve a completely automated patch-clamp recording.