Zhang Jiahao, Vaidya Rohit, Chung Hee Jung, Selvin Paul R
Dept. of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
bioRxiv. 2024 Jul 18:2024.07.15.603616. doi: 10.1101/2024.07.15.603616.
Dendritic spines are the main sites for synaptic communication in neurons, and alterations in their density, size, and shapes occur in many brain disorders. Current spine segmentation methods perform poorly in conditions with low signal-to-noise and resolution, particularly in the widefield images of thick (10 μm) brain slices. Here, we combined two open-source machine-learning models to achieve automatic 3D spine segmentation in widefield diffraction-limited fluorescence images of neurons in thick brain slices. We validated the performance by comparison with manually segmented super-resolution images of spines reconstructed from direct stochastic optical reconstruction microscopy (dSTORM). Lastly, we show an application of our approach by combining spine segmentation from diffraction-limited images with dSTORM of synaptic protein PSD-95 in the same field-of-view. This allowed us to automatically analyze and quantify the nanoscale distribution of PSD-95 inside the spine. Importantly, we found the numbers, but not the average sizes, of synaptic nanomodules and nanodomains increase with spine size.
树突棘是神经元中突触通讯的主要部位，其密度、大小和形状的改变在许多脑部疾病中都会出现。当前的棘突分割方法在低信噪比和分辨率的情况下表现不佳，尤其是在厚（10μm）脑片的宽场图像中。在这里，我们结合了两个开源机器学习模型，以在厚脑片神经元的宽场衍射极限荧光图像中实现自动3D棘突分割。我们通过与从直接随机光学重建显微镜（dSTORM）重建的棘突手动分割超分辨率图像进行比较来验证性能。最后，我们通过在同一视野中将衍射极限图像的棘突分割与突触蛋白PSD-95的dSTORM相结合，展示了我们方法的应用。这使我们能够自动分析和量化棘突内PSD-95的纳米级分布。重要的是，我们发现突触纳米模块和纳米域的数量而非平均大小随棘突大小增加。
