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.

中文翻译：

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闭环实时成像可实现全自动的针对细胞的膜片钳神经记录。

有针对性的膜片钳记录是表征完整神经回路中视觉识别的细胞的有力方法，但它需要技巧来执行。我们之前开发了一种算法，可以在体内实现“盲”贴片的自动化，但是尚未证明视觉引导的靶向体内贴片的完全自动化，目前可用的方法需要人工干预以补偿贴片移液器接近目标神经元时的细胞运动。 。在这里，我们提出了一种闭环实时成像策略，该策略可以通过跟踪细胞位置并在接近目标时调整移液器运动来自动补偿细胞运动。我们展示了我们的系统在连续双光子成像和实时分析下的自适应修补能力，在没有人为干预的情况下，在活的小鼠皮质中表达荧光团的多种神经元类型，其产量可与熟练的人类实验人员相提并论。我们的“图像修补”机器人易于实现，将有助于在完整的神经回路中对已鉴定的细胞类型进行可扩展的表征。