Evidence map›Paper›PMID 36461763›Full record

ArticleEndocrinology2022

STAGETOOL, a Novel Automated Approach for Mouse Testis Histological Analysis.

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

Abstract read
In one paragraph

Article in Endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Oliver MeikarInstitute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia.
Daniel MajoralComputational Neuroscience Lab, Institute of Computer Science, University of Tartu, 51014 Tartu, Estonia.
Olli HeikkinenInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.
Eero ValkamaInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.
Sini LeskinenInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.
Ana RebaneInstitute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia.
Pekka RuusuvuoriInstitute of Biomedicine, University of Turku, 20520 Turku, Finland.ORCID 0000-0001-9086-9591
Jorma ToppariInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.
Juho-Antti MäkeläInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.ORCID 0000-0001-8489-8724
Noora KotajaInstitute of Biomedicine, Integrative Physiology and Pharmacology Unit, University of Turku, 20520 Turku, Finland.ORCID 0000-0003-1503-9170

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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 ×400 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.

Indexed as

Seminiferous TubulesTestisAnimalsEpithelial CellsHumansMaleMiceSeminiferous EpitheliumSpermatogenesisautomated analysisDAPI stainingdeep learningmouse testis histologyseminiferous epithelial cyclespermatogenesis

Identifiers

PMID36461763
PMCPMC9780747

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.