Evidence map›Paper›PMID 40443890›Full record

ArticleJACS Au2025

Deep Profiling of Oocyte Aging Enabled by Simple One-Step Vial-Based Pretreatment and Single-Cell Proteomics.

Hui Zhang, Hailu Zhang, Chuanxi Huang, Qing Zeng, Chunyan Tian, Jing Yang, Fuchu He, Yun Yang

Abstract read
In one paragraph

Article in JACS Au, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
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

8 authors.

Hui ZhangDepartment of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.
Hailu ZhangState Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Chuanxi HuangInternational Academy of Phronesis Medicine (Guang Dong), Guangzhou 510000, China.
Qing ZengInternational Academy of Phronesis Medicine (Guang Dong), Guangzhou 510000, China.
Chunyan TianState Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Jing YangGuangzhou National Laboratory, Guangzhou 510005, China.
Fuchu HeDepartment of Chemistry, College of Science, Southern University of Science and Technology, Shenzhen 518055, China.
Yun YangInternational Academy of Phronesis Medicine (Guang Dong), Guangzhou 510000, China.ORCID https://orcid.org/0000-0003-0869-6498

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell proteomics is a pivotal technology for studying cellular phenotypes, offering unparalleled insights into cellular heterogeneity and dynamic functions. Technical improvement in mass spectrometry instruments and sample preparation has made proteomics profiling of single mouse oocytes or early embryos feasible in recent years. Yet, developing a simple and robust sample preparation method to enable deep proteomics profiling of single germline cells remains a significant challenge. Herein, we developed a simple one-step vial-based pretreatment (SOViP) for deep label-free single-cell proteomics of germline cells. SOViP integrates all sample preparation procedures into a single step in autosampler vials, yet it is highly efficient and high-throughput in comparison to reported multistep methods. SOViP can be finished within ∼2 h with hands-on time limited to merely a few minutes. On average, over 6500 protein groups can be quantified from a single mouse oocyte using SOViP. In total, 6983 protein groups were identified from single mouse oocytes across an entire reproductive lifespan, offering a valuable proteomics resource for oocyte aging. Unique molecular characteristics of oocytes at different ages were revealed, and a classifier consisting of ten proteins demonstrated accurate age-group classification and fertility-level prediction. Although demonstrated using mouse oocytes in this study, SOViP is adaptable to rare cell samples and other large cells including follicles and preimplantation embryo cells, among others.

Indexed as

LC-MSmachine learningoocyte agingsample preparationsingle-cell proteomics

Identifiers

PMID40443890
PMCPMC12117414

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.