Staging and cell detection of seminiferous tubules using neural networks

Abstract

Spermatogenesis is a complex differentiation process that takes place in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium enables a synchronous progress of germ cells at certain steps of differentiation on the spermatogenic pathway. This can be observed in testis cross-sections where seminiferous tubules can be classified into distinct stages of constant cellular composition (12 stages in the mouse). For a detailed analysis of spermatogenesis, these stages must be individually observed from testis cross-sections. However, the recognition of stages requires special training and expertise. Furthermore, the manual scoring is laborious considering the high number of tubule cross-sections that must be analysed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach which analyses histological images of 4′,6- diamidine-2′-phenylindole dihydrochloride (DAPI)-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into 5 stage classes and cells into 9 categories. Classification accuracy for stage classes of seminiferous tubules of a whole-testis cross-section is 99.1%. For cellular level analysis, the F1 score for nine seminiferous epithelial cell types ranges from 0.80 to 0.98. Furthermore, we show that our tool can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. Our tool is the first fluorescent labelling–based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While our approach qualitatively parallels an experienced human histologist, it outperforms humans timewise, therefore representing a major advancement in male reproductive biology research.