Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
RIKEN Quantitative Biology Center, Kobe, Japan.
Sci Rep. 2019 Jan 28;9(1):788. doi: 10.1038/s41598-018-36895-y.
Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.
利用高密度微电极阵列（HD-MEAs）进行细胞外记录已经成为解析从分离细胞培养的活性网络中单神经元亚细胞细节的有力工具。为了将这项技术扩展到切片准备，我们开发了描述神经元细胞在切片中产生的细胞外信号如何被微电极阵列检测的模型。这些模型有助于分析和理解基于点电流源的体外 HD-MEA 记录场景中的电势景观。我们采用了两种建模方案，（i）一种基于镜像法（MoI）的简单解析方法，以及（ii）一种基于有限元方法（FEM）的方法。我们通过使用 HD-MEAs 对切片进行大规模、高时空分辨率的记录，对模型进行了比较和验证。然后，我们开发了一种基于模型的定位算法，并比较了 MoI 和 FEM 模型的性能。当点源在盐水中时，两种模型都提供了准确的定位结果，且系统误差可忽略不计，这种条件类似于细胞培养实验。此外，对于距 HD-MEA 表面 200 μm 以内的 z 距离，x-y-z 定位的相对随机误差仅达到 4.3%。在组织中，MoI 和 FEM 模型的系统误差都明显更高，需要进行预校准。尽管如此，FEM 值更接近组织实验结果，与 MoI 获得的 22.0 μm 相比，产生 5.2 μm 的系统平均误差。这些结果表明，当信号源距离阵列较远时，包括在 FEM 模型中的中间体积或“盐水高度”、脑切片厚度和各向异性以及参考电极的位置，会极大地影响细胞外信号和定位性能。经过预校准，组织中 z 定位的相对随机误差在 200 μm 以内时仅为 3%。然后，我们应用模型和相关的细胞外记录详细理解，实现了刺激微管的电引导导航，仅基于测量的 HD-MEA 信号，并成功靶向急性脑切片中的自发活跃神经元进行电穿孔。
