Evidence map›Paper›PMID 38044578›Full record

ArticleAging cell2024

Single-worm quantitative proteomics reveals aging heterogeneity in isogenic Caenorhabditis elegans.

Tian-Yi Zhu, Shang-Tong Li, Dan-Dan Liu, Xiajun Zhang, Lianqi Zhou, Rong Zhou, Bing Yang

Abstract read
In one paragraph

Article in Aging cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Single-tissue proteomics inProceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  10. Article
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Tian-Yi ZhuZhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China.
Shang-Tong LiGlbizzia Biosciences Co., Ltd, Beijing, China.
Dan-Dan LiuZhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China.
Xiajun ZhangZhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China.
Lianqi ZhouZhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China.
Rong ZhouInstitute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
Bing YangZhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China.ORCID 0000-0001-6739-2932

Funding

National Key Research and Development Program of China 2022YFF0608402National Natural Science Foundation of China 22074132National Natural Science Foundation of China 31972541National Natural Science Foundation of China 91953103National Natural Science Foundation of China C-0023Open Project Program of Jiangsu Provincial Key Laboratory of Poultry Genetics and BreedingOpen Project Program of the State Key Laboratory of Proteomics, China SKLPO201806the outstanding youth fund of Zhejiang Province LR20B050001
6 · The paper itself

Abstract

The heterogeneity of aging has been investigated at cellular and organic levels in the mouse model and human, but the exploration of aging heterogeneity at whole-organism level is lacking. C. elegans is an ideal model organism for studying this question as they are self-fertilized and cultured in the same chamber. Despite the tremendous progress made in single-cell proteomic analysis, there is few single-worm proteomics studies about aging. Here, we apply single-worm quantitative mass spectrometry to quantify the heterogenous proteomic changes during aging across individuals, a total of 3524 proteins from 157 C. eleagns individuals were quantified. A reconstructed C. elegans aging trajectory and proteomic landscape of fast-aging individuals were used to analyze the heterogeneity of C. elegans aging. We characterized inter-individual proteomic variation during aging and revealed contributing factors that distinguish fast-aging individuals from their siblings.

Indexed as

Caenorhabditis elegansCaenorhabditis elegans ProteinsAgingAnimalsDisease Models, AnimalHumansMiceProteomicsCaenorhabditis elegans Proteinsagingmass spectrometrysingle worm proteomics

Identifiers

PMID38044578
PMCPMC10928571

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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.