The whole-cell patch-clamp method is a gold standard for single-cell analysis of electrical activity, cellular morphology, and gene expression. Prior to our discovery that patch-clamp pipettes could be cleaned and reused, experimental throughput and automation were limited by the need to replace pipettes manually after each experiment. This article presents an optimized protocol for pipette cleaning, which enables it to be performed quickly (< 30 s), resulting in a high yield of whole-cell recording success rate (> 90%) for over 100 reuses of a single pipette. For most patch-clamp experiments (< 30 whole-cell recordings per day), this method enables a single pipette to be used for an entire day of experiments.In addition, we describe easily implementable hardware and software as well as troubleshooting tips to help other labs implement this method in their own experiments. Pipette cleaning enables patch-clamp experiments to be performed with higher throughput, whether manually or in an automated fashion, by eliminating the tedious and skillful task of replacing pipettes. From our experience with numerous electrophysiology laboratories, pipette cleaning can be integrated into existing patch-clamp setups in approximately one day using the hardware and software described in this article.
A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts. We also compared networks trained on unaltered and enhanced images, achieving up to 77% and 72% mean average precision, respectively. This novel, deep learning-based method accomplishes automated neuronal detection in brain slice at 18 frames per second with a small data set of 1138 annotated neurons, rapid training time, and high precision. Lastly, we verified the health of the identified neurons with a patch clamp experiment where the average access resistance was 29.25 M$$\Omega$$
Ω
(n = 9). The addition of this technology during live-cell imaging for patch clamp experiments can not only improve manual patch clamping by reducing the neuroscience expertise required to select healthy cells, but also help achieve full automation of patch clamping by nominating cells without human assistance.
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