Data from: Extracellular space preservation aids the connectomic analysis of neural circuits

Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

神经元环路的密集连接组图谱绘制，受限于三维电子显微镜（3D electron microscopy, EM）数据集分析所需的时间与人力投入。用于自动化图像分割的算法普遍存在较高的错误率，且需要开展大量人工纠错工作。因此，分割错误率的任何优化都将直接缩短三维EM数据的分析周期。本研究探索了在化学组织固定过程中保留细胞外间隙（extracellular space, ECS）的方法，旨在提升神经突起分割与突触接触点识别的效能。经细胞外间隙保留处理的组织，更易于通过机器学习算法完成分割，进而显著降低错误率。此外，我们观察到在保留细胞外间隙的组织样本中，电突触可被清晰识别。最后，我们证实仅需极少量透化处理，抗体即可深度渗透入此类组织，从而实现关联光学显微镜（light microscopy, LM）与电子显微镜的联合成像研究。综上，细胞外间隙保留技术可从多方面优化神经环路的连接组学分析流程。