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In Endocrinology ; h5-index 58.0

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 (twelve stages in the mouse). For a detailed analysis of spermatogenesis, these stages have to 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 have to be analyzed. To facilitate the analysis of spermatogenesis, we have developed a convolutional deep neural network-based approach named "STAGETOOL". STAGETOOL analyses histological images of DAPI-stained mouse testis cross-sections at ×400 magnification, and very accurately classifies tubule cross-sections into five stage classes and cells into nine categories. STAGETOOL 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 0.80-0.98. Furthermore, we show that STAGETOOL can be applied for the analysis of knockout mouse models with spermatogenic defects, as well as for automated profiling of protein expression patterns. STAGETOOL is the first fluorescent labeling-based automated method for mouse testis histological analysis that enables both stage and cell-type recognition. While STAGETOOL qualitatively parallels an experienced human histologist, it outperforms human time-wise, therefore representing a major advancement in male reproductive biology research.

Meikar Oliver, Majoral Daniel, Heikkinen Olli, Valkama Eero, Leskinen Sini, Rebane Ana, Ruusuvuori Pekka, Toppari Jorma, Mäkelä Juho-Antti, Kotaja Noora


DAPI staining, Mouse testis histology, automated analysis, deep learning, seminiferous epithelial cycle, spermatogenesis