Autonomous patch clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.

Patch clamping is the gold standard measurement technique for cell type characterization in vivo but it is low throughput, difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple consecutive patch clamp recordings in vivo. In practice, 40 pipettes loaded into a carousel are sequentially filled and inserted into the brain, localized to a cell, used for patch clamping, and disposed. Automated visual stimulation and electrophysiology software enables functional cell type classification of whole cell patched cells, as we show for 37 cells in the anesthetized mouse in visual cortex (V1) L5. We achieved 9% yield, with 5.3 min per attempt over hundreds of trials. The highly variable and low yield nature of in vivo patch clamp recordings will benefit from such a standardized, automated, quantitative approach, allowing development of optimal algorithms and enabling scaling required for large scale studies and integration with complementary techniques.