NOVEL STAIN SEPARATION METHOD FOR AUTOMATIC STEREOLOGY OF IMMUNOSTAINED TISSUE SECTIONS
NOVEL STAIN SEPARATION METHOD FOR AUTOMATIC STEREOLOGY OF IMMUNOSTAINED TISSUE SECTIONS
复制标题
DOI：
10.1093/geroni/igz038.958
复制
发表时间：
2019-11-08
影响因子：
7
通讯作者：
Mouton PR
中科院分区：
文献类型：
--
作者：
Dave P;Goldgof D;Hall LO;Alahmari S;Mouton PR
许多脑老化和神经退行性疾病（如阿尔茨海默病和帕金森病）的研究需要快速计数组织切片上的高信号：噪声（S：N）染色的脑细胞（如神经元和神经胶质细胞（小胶质细胞））。为了提高这项工作的吞吐量效率，我们结合了深度学习（DL）神经网络和计算机体视学（DL-体视学），与时间密集型手动计数相比，自动细胞计数的误差较低（<10%）。然而，到目前为止，这种方法仅限于具有神经元（NeuN）或小胶质细胞（Iba-1）的单一高S：N免疫染色的切片。本研究将这种方法扩展到联合收割机免疫染色与复染结合的方案，例如，甲酚紫（CV）。在我们的方法中，一种称为稀疏非负矩阵分解（SNMF）的染色分离技术将双染色的彩色图像转换为仅显示主要免疫染色剂的单一灰度图像。验证测试使用来自tau蛋白病的转基因小鼠模型（Tg 4510小鼠）和对照的新皮质的用CV复染免疫染色的神经元或小胶质细胞的切片的半和自动基于体视学的计数来进行。与手动细胞计数（地面实况）相比，使用主染色灰度图像的细胞计数结果显示，对于神经元和小胶质细胞，半自动方法的平均错误率分别为16.78%和28.47%，而全自动DL体视学方法的平均错误率分别为8.51%和9.36%。这项工作表明，通过SNMF进行的染色分离可以支持复染组织切片上基于DL体视学的神经元和小胶质细胞计数的高通量、全自动化。
Many studies of brain aging and neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases require rapid counts of high signal: noise (S:N) stained brain cells such as neurons and neuroglial (microglia cells) on tissue sections. To increase throughput efficiency of this work, we have combined deep learned (DL) neural networks and computerized stereology (DL-stereology) for automatic cell counts with low error (<10%) compared to time-intensive manual counts. To date, however, this approach has been limited to sections with a single high S:N immunostain for neurons (NeuN) or microglial cells (Iba-1). The present study expands this approach to protocols that combine immunostains with counterstains, e.g., cresyl violet (CV). In our method, a stain separation technique called Sparse Non-negative Matrix Factorization (SNMF) converts a dual-stained color image to a single gray image showing only the principal immunostain. Validation testing was done using semi- and automatic stereology-based counts of sections immunostained for neurons or microglia with CV counterstaining from the neocortex of a transgenic mouse model of tauopathy (Tg4510 mouse) and controls. Cell count results with principal stain gray images show an average error rate of 16.78% and 28.47% for the semi-automatic approach and 8.51% and 9.36% for the fully-automatic DL-stereology approach for neurons and microglia, respectively, as compared to manual cell counts (ground truth). This work indicates that stain separation by SNMF can support high throughput, fully automatic DL-stereology based counts of neurons and microglia on counterstained tissue sections.