Accurate follicle enumeration in adult mouse ovaries

Amy L. Winship, Urooza C. Sarma, Lauren R. Alesi, Karla J. Hutt

Research output: Contribution to journalArticleOtherpeer-review

2 Citations (Scopus)

Abstract

Sexually reproducing female mammals are born with their entire lifetime supply of oocytes. Immature, quiescent oocytes are found within primordial follicles, the storage unit of the female germline. They are non-renewable, thus their number at birth and subsequent rate of loss largely dictates the female fertile lifespan. Accurate quantification of primordial follicle numbers in women and animals is essential for determining the impact of medicines and toxicants on the ovarian reserve. It is also necessary for evaluating the need for, and success of, existing and emerging fertility preservation techniques. Currently, no methods exist to accurately measure the number of primordial follicles comprising the ovarian reserve in women. Furthermore, obtaining ovarian tissue from large animals or endangered species for experimentation is often not feasible. Thus, mice have become an essential model for such studies, and the ability to evaluate primordial follicle numbers in whole mouse ovaries is a critical tool. However, reports of absolute follicle numbers in mouse ovaries in the literature are highly variable, making it difficult to compare and/or replicate data. This is due to a number of factors including strain, age, treatment groups, as well as technical differences in the methods of counting employed. In this article, we provide a stepby- step instructional guide for preparing histological sections and counting primordial follicles in mouse ovaries using two different methods: [1] stereology, which relies on the fractionator/optical dissector technique; and [2] the direct count technique. Some of the key advantages and drawbacks of each method will be highlighted so that reproducibility can be improved in the field and to enable researchers to select the most appropriate method for their studies.

Original languageEnglish
Article numbere61782
Number of pages15
JournalJournal of Visualized Experiments
Volume2020
Issue number164
DOIs
Publication statusPublished - 16 Oct 2020

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