Pallotto Marta, Watkins Paul V, Fubara Boma, Singer Joshua H, Briggman Kevin L
Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.
Department of Biology, University of Maryland, College Park, United States.
Elife. 2015 Dec 9;4:e08206. doi: 10.7554/eLife.08206.
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.
神经元回路的密集连接组学图谱绘制受到分析三维电子显微镜（EM）数据集所需时间和精力的限制。旨在实现图像分割自动化的算法存在大量错误率，并且需要大量人工纠错。因此，分割错误率的任何改进都将直接减少分析三维EM数据所需的时间。我们探索了在化学组织固定过程中保留细胞外空间（ECS），以提高对神经突进行分割和识别突触接触的能力。保留ECS的组织使用机器学习算法更容易分割，从而显著降低错误率。此外，我们观察到在保留ECS的组织中很容易识别出电突触。最后，我们确定抗体只需进行最小程度的通透处理就能深入渗透到保留ECS的组织中，从而实现相关光学显微镜（LM）和EM研究。我们得出结论，保留ECS有益于神经回路连接组分析的多个方面。
