Tewari Bhanu P, Sontheimer Harald
Glial Biology in Health, Disease, and Cancer Center, Fralin Biomedical Research Institute at VTC, 2 Riverside Cir., Roanoke, VA 24016, USA.
School of Neuroscience, College of Science, Virginia Tech, 300 Turner Street NW, Blacksburg, VA 24061, USA.
Bio Protoc. 2019 May 20;9(10):e3234. doi: 10.21769/BioProtoc.3234.
Perineuronal nets (PNNs) are extracellular matrix assemblies of highly negatively charged proteoglycans that wrap around fast-spiking parvalbumin (PV) expressing interneurons in the cerebral cortex. PNNs play important roles in neuronal plasticity and modulate biophysical properties of the enclosed interneurons. Various central nervous system diseases including schizophrenia, Alzheimer disease and epilepsy present with qualitative alteration in PNNs, however prior studies failed to quantitatively assess such changes at single PNN level and correlate them with functional changes in disease. We describe a method to quantify the structural integrity of PNNs using high magnification image analysis of Wisteria Floribunda Agglutinin (WFA)-labeled PNNs in combination with cell-type-specific marker such as PV and NeuN. A polyline intensity profile of WFA along the entire perimeter of cell shows alternate segments with and without WFA labeling, indicating the intact chondroitin sulfate proteoglycan (CSPG) and holes of PNN respectively. This line intensity profile defines CSPG peaks, where intact PNN is present, and CSPG valleys (holes) where the PNN is missing. The average number of peaks reflect the integrity of the lattice assembly of PNN. The average size of PNN holes can be readily computed using image analysis software. Furthermore, degradation of PNNs using a bacterial-derived enzyme, Chondroitinase ABC (ChABC), allows to experimentally manipulate PNNs brain slices during which biophysical properties can be assessed by patch-clamp recordings. We describe optimized experimental parameters to degrade PNNs in brain slices before as well as during recordings to study the possible change in function in real time. Our protocols provide effective and appropriate methods to modulate and quantify the PNN's experimental manipulations.
神经周网（PNNs）是由高度带负电荷的蛋白聚糖组成的细胞外基质集合体，包裹着大脑皮层中表达小白蛋白（PV）的快速放电中间神经元。神经周网在神经元可塑性中发挥重要作用，并调节所包裹中间神经元的生物物理特性。包括精神分裂症、阿尔茨海默病和癫痫在内的各种中枢神经系统疾病都存在神经周网的质性改变，然而先前的研究未能在单个神经周网水平上对这种变化进行定量评估，也未能将其与疾病中的功能变化相关联。我们描述了一种方法，通过对紫藤凝集素（WFA）标记的神经周网进行高倍图像分析，并结合细胞类型特异性标记物如PV和NeuN，来量化神经周网的结构完整性。沿着细胞整个周长的WFA折线强度轮廓显示有WFA标记和无WFA标记的交替段，分别指示完整的硫酸软骨素蛋白聚糖（CSPG）和神经周网的孔洞。这条线强度轮廓定义了存在完整神经周网的CSPG峰和神经周网缺失的CSPG谷（孔洞）。峰的平均数量反映了神经周网晶格组装的完整性。使用图像分析软件可以很容易地计算出神经周网孔洞的平均大小。此外，使用细菌衍生的酶软骨素酶ABC（ChABC）降解神经周网，可以在脑片中对神经周网进行实验性操作，在此期间可以通过膜片钳记录来评估生物物理特性。我们描述了在记录前以及记录过程中在脑片中降解神经周网的优化实验参数，以实时研究功能的可能变化。我们的方案提供了有效且合适的方法来调节和量化神经周网的实验操作。
