ReviewMolecular reproduction and development2022
Single-cell RNA-sequencing of retrieved human oocytes and eggs in clinical practice and for human ovarian cell atlasing.
Review in Molecular reproduction and development, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of mRNA expression in human oocytes: understanding the molecular mechanisms underlying oocyte competence.Journal of assisted reproduction and genetics · 2023Pooled it
- Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.Human reproduction update · 2026Article
- Cross-Species Insights intoAntioxidants (Basel, Switzerland) · 2025Article
- Article
- Single-cell and spatiotemporal profile of ovulation in the mouse ovary.PLoS biology · 2025Article
- Aging hallmarks of the primate ovary revealed by spatiotemporal transcriptomics.Protein & cell · 2024Article
- The impact of epigenetic landscape on ovarian cells in infertile older women undergoing IVF procedures.Clinical epigenetics · 2023Review
- Single-cell RNA-sequencing of retrieved human oocytes and eggs in clinical practice and for human ovarian cell atlasing.Molecular reproduction and development · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
With the advancement of single-cell separation techniques and high-throughput sequencing platforms, single-cell RNA-sequencing (scRNA-seq) has emerged as a vital technology for understanding tissue and organ systems at cellular resolution. Through transcriptional analysis, it is possible to characterize unique or rare cell types, interpret their interactions, and reveal novel functional states or shifts in developmental stages. As such, this technology is uniquely suited for studying the cells within the human ovary. The ovary is a cellularly heterogeneous organ that houses follicles, the reproductive and endocrine unit that consists of an oocyte surrounded by hormone-producing support cells, as well as many other cell populations constituting stroma, vasculature, lymphatic, and immune components. Here we review studies that have utilized scRNA-seq technology to analyze cells from healthy human ovaries and discuss the single-cell isolation techniques used. We identified two overarching applications for scRNA-seq in the human ovary. The first applies this technology to investigate transcriptional differences in oocytes/eggs from patients undergoing in vitro fertilization treatments to ultimately improve clinical outcomes. The second utilizes scRNA-seq for the pursuit of creating a comprehensive single-cell atlas of the human ovary. The knowledge gained from these studies underscores the importance of scRNA-seq technologies in unlocking a new biological understanding of the human ovary.
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Registered trials
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.