Suk Ho-Jun, van Welie Ingrid, Kodandaramaiah Suhasa B, Allen Brian, Forest Craig R, Boyden Edward S
Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuron. 2017 Aug 30;95(5):1037-1047.e11. doi: 10.1016/j.neuron.2017.08.011.
Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits.
靶向膜片钳记录是一种用于表征完整神经回路中视觉识别细胞的强大方法，但操作起来需要技巧。我们之前开发了一种算法，可在体内实现“盲”膜片钳操作自动化，但视觉引导的、靶向性体内膜片钳操作的完全自动化尚未得到证实，目前可用的方法需要人工干预来补偿膜片吸管接近目标神经元时细胞的移动。在此，我们提出一种闭环实时成像策略，该策略通过在接近目标时跟踪细胞位置并调整吸管运动来自动补偿细胞移动。我们展示了我们的系统在连续双光子成像和实时分析下，无需人工干预即可自适应地对活体小鼠皮层中多种类型的荧光团表达神经元进行膜片钳操作的能力，其成功率与熟练的人类实验者相当。我们的“图像膜片钳”机器人易于实现，将有助于对完整神经回路中已识别细胞类型进行可扩展的表征。
