期刊
ENDOCRINOLOGY
卷 164, 期 2, 页码 -出版社
ENDOCRINE SOC
DOI: 10.1210/endocr/bqac202
关键词
mouse testis histology; spermatogenesis; seminiferous epithelial cycle; automated analysis; deep learning; DAPI staining
资金
- European Regional Development Fund
- programme Mobilitas Pluss MOBTP62
- Centre of Excellence for Genomics and Translational Medicine) [2014-2020.4.01.15-0012]
- Estonian Research Council
- Estonian Centre of Excellence in IT (EXCITE) [PRG1259, PRG1604]
- European Union's Horizon 2020 research and innovation programme project TRUST-AI [TK148]
- Sigrid Juselius Foundation [952060]
- Novo Nordisk Foundation
- Jalmari and Rauha Ahokas Foundation
- Academy of Finland
Spermatogenesis is a complex differentiation process that occurs in the seminiferous tubules. A specific organization of spermatogenic cells within the seminiferous epithelium allows for synchronous progression of germ cells at certain stages of differentiation. To facilitate the analysis of spermatogenesis, researchers have developed a convolutional deep neural network-based approach called STAGETOOL. STAGETOOL accurately classifies histological images of DAPI-stained mouse testis cross-sections into different stage classes and cell categories. It has high classification accuracy and can be applied for analyzing spermatogenic defects in knockout mouse models and profiling protein expression patterns. STAGETOOL represents a major advancement in male reproductive biology research.
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 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 4 ',6-diamidine-2 '-phenylindole dihydrochloride (DAPI)-stained mouse testis cross-sections at x400 magnification, and very accurately classifies tubule cross-sections into 5 stage classes and cells into 9 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 9 seminiferous epithelial cell types ranges from 0.80 to 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 humans time-wise, therefore representing a major advancement in male reproductive biology research.
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