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A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues

Author

Listed:
  • Dominik Saul

    (Mayo Clinic
    Mayo Clinic
    Georg-August-University of Goettingen)

  • Robyn Laura Kosinsky

    (Mayo Clinic)

  • Elizabeth J. Atkinson

    (Mayo Clinic)

  • Madison L. Doolittle

    (Mayo Clinic
    Mayo Clinic)

  • Xu Zhang

    (Mayo Clinic
    Mayo Clinic)

  • Nathan K. LeBrasseur

    (Mayo Clinic
    Mayo Clinic)

  • Robert J. Pignolo

    (Mayo Clinic
    Mayo Clinic
    Mayo Clinic)

  • Paul D. Robbins

    (University of Minnesota)

  • Laura J. Niedernhofer

    (University of Minnesota)

  • Yuji Ikeno

    (University of Texas Health)

  • Diana Jurk

    (Mayo Clinic
    Mayo Clinic)

  • João F. Passos

    (Mayo Clinic
    Mayo Clinic)

  • LaTonya J. Hickson

    (Mayo Clinic)

  • Ailing Xue

    (Mayo Clinic)

  • David G. Monroe

    (Mayo Clinic
    Mayo Clinic)

  • Tamara Tchkonia

    (Mayo Clinic
    Mayo Clinic)

  • James L. Kirkland

    (Mayo Clinic
    Mayo Clinic)

  • Joshua N. Farr

    (Mayo Clinic
    Mayo Clinic
    Mayo Clinic)

  • Sundeep Khosla

    (Mayo Clinic
    Mayo Clinic
    Mayo Clinic)

Abstract

Although cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype, in vivo senescent cell identification remains challenging. Here, we generate a gene set (SenMayo) and validate its enrichment in bone biopsies from two aged human cohorts. We further demonstrate reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next use SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we are able to characterize senescent cells at the single cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

Suggested Citation

  • Dominik Saul & Robyn Laura Kosinsky & Elizabeth J. Atkinson & Madison L. Doolittle & Xu Zhang & Nathan K. LeBrasseur & Robert J. Pignolo & Paul D. Robbins & Laura J. Niedernhofer & Yuji Ikeno & Diana , 2022. "A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32552-1
    DOI: 10.1038/s41467-022-32552-1
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    Cited by:

    1. Di-Yang Sun & Wen-Bin Wu & Jian-Jin Wu & Yu Shi & Jia-Jun Xu & Shen-Xi Ouyang & Chen Chi & Yi Shi & Qing-Xin Ji & Jin-Hao Miao & Jiang-Tao Fu & Jie Tong & Ping-Ping Zhang & Jia-Bao Zhang & Zhi-Yong Li, 2024. "Pro-ferroptotic signaling promotes arterial aging via vascular smooth muscle cell senescence," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    2. Han-Yi Chen & Wan-Chen Hsieh & Yu-Chieh Liu & Huei-Ying Li & Po-Yo Liu & Yu-Ting Hsu & Shao-Chun Hsu & An-Chi Luo & Wei-Chen Kuo & Yi-Jhen Huang & Gan-Guang Liou & Meng-Yun Lin & Chun-Jung Ko & Hsing-, 2024. "Mitochondrial injury induced by a Salmonella genotoxin triggers the proinflammatory senescence-associated secretory phenotype," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Madison L. Doolittle & Dominik Saul & Japneet Kaur & Jennifer L. Rowsey & Stephanie J. Vos & Kevin D. Pavelko & Joshua N. Farr & David G. Monroe & Sundeep Khosla, 2023. "Multiparametric senescent cell phenotyping reveals targets of senolytic therapy in the aged murine skeleton," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

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