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Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning

  • Gamete Biology
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Abstract

Purpose

Rapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on testicular strips of prepubertal animal models. The purpose of this study is to detect and count the seminiferous tubules and SSPCs in newborn mouse testis sections using a DL method.

Methods

Testicular sections of the C57BL/6-type newborn mice were obtained and enumerated. Odd-numbered sections were stained with hematoxylin and eosin (H&E), and even-numbered sections were immune labeled (IL) with SSPC specific marker, SALL4. Seminiferous tubule and SSPC datasets were created using odd-numbered sections. SALL4-labeled sections were used as positive control. The YOLO object detection model based on DL was used to detect seminiferous tubules and stem cells.

Results

Test scores of the DL model in seminiferous tubules were obtained as 0.98 mAP, 0.93 precision, 0.96 recall, and 0.94 f1-score. The SSPC test scores were obtained as 0.88 mAP, 0.80 precision, 0.93 recall, and 0.82 f1-score.

Conclusion

Seminiferous tubules and SSPCs on prepubertal testicles were detected with a high sensitivity by preventing human-induced errors. Thus, the first step was taken for a system that automates the detection and counting process of these cells in the infertility clinic.

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Code availability

https://github.com/burakkahveci/Detection-of-Spermatogonial-Stem-Cells .

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Funding

This work was financially supported by Hacettepe University Scientific Research Project Coordination Unit; the (#TYL-2019-18375) TUBİTAK (The Scientific and Technological Research Council of Turkey) 1001 program supported Burak Kahveci as a scholar (#218S421).

Author information

Authors and Affiliations

  1. Department of Bioengineering, Graduate School of Science and Engineering, Hacettepe University, Ankara, Turkey

    Burak Kahveci

  2. Department of Stem Cell Sciences, Graduate School of Health Sciences, Hacettepe University, Ankara, Turkey

    Selin Önen

  3. Department of Medical Biology, Faculty of Medicine, Atilim University, Ankara, Turkey

    Selin Önen

  4. Computer Engineering Department, Hacettepe University, Ankara, Turkey

    Fuat Akal

  5. Department of Histology and Embryology, Faculty of Medicine, Hacettepe University, Sihhiye, 06100, Ankara, Turkey

    Petek Korkusuz

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  1. Burak Kahveci
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  2. Selin Önen
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  4. Petek Korkusuz
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Contributions

Burak Kahveci and Petek Korkusuz generated the hypothesis. Sections were taken by Selin Önen and Burak Kahveci. Selin Önen guided and they both performed the histochemical and immunohistochemical staining. Burak Kahveci created the datasets. Burak Kahveci and Fuat Akal created the DL models and carried out the experiments. Petek Korkusuz and Fuat Akal edited the manuscript. All the authors read and approved the manuscript.

Corresponding author

Correspondence to Petek Korkusuz.

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Ethics approval

Animal materials used in the experiments were approved by Hacettepe University Animal Experiments Local Ethics Committee (#2018, 52338575-96).

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The authors declare no competing interests.

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Kahveci, B., Önen, S., Akal, F. et al. Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning. J Assist Reprod Genet 40, 1187–1195 (2023). https://doi.org/10.1007/s10815-023-02784-1

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