McCreedy Dylan A, Margul Daniel J, Seidlits Stephanie K, Antane Jennifer T, Thomas Ryan J, Sissman Gillian M, Boehler Ryan M, Smith Dominique R, Goldsmith Sam W, Kukushliev Todor V, Lamano Jonathan B, Vedia Bansi H, He Ting, Shea Lonnie D
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
J Neurosci Methods. 2016 Apr 1;263:15-22. doi: 10.1016/j.jneumeth.2016.01.021. Epub 2016 Jan 25.
Spinal cord injury (SCI) is a debilitating event with multiple mechanisms of degeneration leading to life-long paralysis. Biomaterial strategies, including bridges that span the injury and provide a pathway to reconnect severed regions of the spinal cord, can promote partial restoration of motor function following SCI. Axon growth through the bridge is essential to characterizing regeneration, as recovery can occur via other mechanisms such as plasticity. Quantitative analysis of axons by manual counting of histological sections can be slow, which can limit the number of bridge designs evaluated. In this study, we report a semi-automated process to resolve axon numbers in histological sections, which allows for efficient analysis of large data sets.
Axon numbers were estimated in SCI cross-sections from animals implanted with poly(lactide co-glycolide) (PLG) bridges with multiple channels for guiding axons. Immunofluorescence images of histological sections were filtered using a Hessian-based approach prior to threshold detection to improve the signal-to-noise ratio and filter out background staining associated with PLG polymer.
Semi-automated counting successfully recapitulated average axon densities and myelination in a blinded PLG bridge implantation study.
Axon counts obtained with the semi-automated technique correlated well with manual axon counts from blinded independent observers across sections with a wide range of total axons.
This semi-automated detection of Hessian-filtered axons provides an accurate and significantly faster alternative to manual counting of axons for quantitative analysis of regeneration following SCI.
脊髓损伤（SCI）是一种使人衰弱的病症，其具有多种导致终身瘫痪的退化机制。生物材料策略，包括跨越损伤部位并提供脊髓切断区域重新连接通路的桥梁，可以促进脊髓损伤后运动功能的部分恢复。轴突通过桥梁生长对于表征再生至关重要，因为恢复可通过可塑性等其他机制发生。通过手动计数组织学切片对轴突进行定量分析可能会很缓慢，这可能会限制所评估的桥梁设计数量。在本研究中，我们报告了一种半自动化方法来解析组织学切片中的轴突数量，从而能够高效分析大型数据集。
在植入具有多个引导轴突通道的聚（丙交酯 - 乙交酯）（PLG）桥梁的动物的脊髓损伤横截面中估计轴突数量。在阈值检测之前，使用基于黑塞矩阵的方法对组织学切片的免疫荧光图像进行滤波，以提高信噪比并滤除与PLG聚合物相关的背景染色。
在一项盲法PLG桥梁植入研究中，半自动化计数成功再现了平均轴突密度和髓鞘形成情况。
在具有广泛总轴突数的切片中，通过半自动化技术获得的轴突计数与来自盲法独立观察者的手动轴突计数高度相关。
这种对经黑塞矩阵滤波的轴突进行半自动化检测，为脊髓损伤后再生的定量分析提供了一种准确且明显更快的替代手动计数轴突的方法。
