Ao Nianfei, Zang Min, Lu Yue, Jiao Yiping, Lu Haoda, Cai Chengfei, Wang Xiangxue, Li Xin, Xie Minge, Zhao Tingting, Xu Jun, Xu Eugene Yujun
Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.
State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Andrology. 2025 Sep;13(6):1556-1574. doi: 10.1111/andr.13773. Epub 2024 Oct 7.
Histological analysis of the testicular sections is paramount in infertility research but tedious and often requires months of training and practice.
Establish an expeditious histopathological analysis of mutant mice testicular sections stained with commonly available hematoxylin and eosin (H&E) via enhanced deep learning model MATERIALS AND METHODS: Automated segmentation and cellular composition analysis on the testes of six mouse reproductive mutants of key reproductive gene family, DAZ and PUMILIO gene family via H&E-stained mouse testicular sections.
We improved the deep learning model with human interaction to achieve better pixel accuracy and reduced annotation time for histologists; revealed distinctive cell composition features consistent with previously published phenotypes for four mutants and novel spermatogenic defects in two newly generated mutants; established a fast spermatogenic defect detection protocol for quantitative and qualitative assessment of testicular defects within 2.5-3 h, requiring as few as 8 H&E-stained testis sections; uncovered novel defects in AcDKO and a meiotic arrest defect in HDBKO, supporting the synergistic interaction of Sertoli Pum1 and Pum2 as well as redundant meiotic function of Dazl and Boule.
Our testicular compositional analysis not only could reveal spermatogenic defects from staged seminiferous tubules but also from unstaged seminiferous tubule sections.
Our SCSD-Net model offers a rapid protocol for detecting reproductive defects from H&E-stained testicular sections in as few as 3 h, providing both quantitative and qualitative assessments of spermatogenic defects. Our analysis uncovered evidence supporting the synergistic interaction of Sertoli PUM1 and PUM2 in maintaining average testis size, and redundant roles of DAZ family proteins DAZL and BOULE in meiosis.
睾丸切片的组织学分析在不育症研究中至关重要，但过程繁琐，通常需要数月的培训和实践。
通过增强深度学习模型，对用常用苏木精和伊红（H&E）染色的突变小鼠睾丸切片进行快速组织病理学分析。
通过H&E染色的小鼠睾丸切片，对关键生殖基因家族DAZ和PUMILIO基因家族的六个小鼠生殖突变体的睾丸进行自动分割和细胞组成分析。
我们通过人机交互改进了深度学习模型，以实现更高的像素精度并减少组织病理学家的注释时间；揭示了四个突变体与先前发表的表型一致的独特细胞组成特征以及两个新生成突变体中的新精子发生缺陷；建立了一个快速精子发生缺陷检测方案，可在2.5至3小时内对睾丸缺陷进行定量和定性评估，所需的H&E染色睾丸切片最少为8张；发现了AcDKO中的新缺陷和HDBKO中的减数分裂阻滞缺陷，支持了支持细胞Pum1和Pum2的协同相互作用以及Dazl和Boule的冗余减数分裂功能。
我们的睾丸组成分析不仅可以从分期的生精小管中揭示精子发生缺陷，还可以从未分期的生精小管切片中揭示。
我们的SCSD-Net模型提供了一种快速方案，可在短短3小时内从H&E染色的睾丸切片中检测生殖缺陷，对精子发生缺陷进行定量和定性评估。我们的分析发现了支持支持细胞PUM1和PUM2在维持平均睾丸大小方面协同相互作用以及DAZ家族蛋白DAZL和BOULE在减数分裂中冗余作用的证据。
