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Tracking single-cell evolution using clock-like chromatin accessibility loci.

Author information

Affiliations

  • 1. Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.
      Authors
    • Xiao Y 1, 2
    • Wang G 1, 2
    • Wang X 1
    (3 authors)
  • 2. Department of Biological Repositories, Human Genetic Resources Preservation Center of Hubei Province, Hubei Key Laboratory of Urological Diseases, Zhongnan Hospital of Wuhan University, Wuhan, China.
      Authors
    • Xiao Y 1, 2
    • Jin W 2, 5
    • Ju L 2
    • Wang G 1, 2
    • Yu M 2
    • Qian K 2
    (6 authors)
  • 3. Hong Kong University of Science and Technology, Hong Kong, China.
      Authors
    • Fu J 3
    (1 author)
  • 4. High Performance Computing Center, Peking-Tsinghua College of Life Sciences, Peking University, Beijing, China.
      Authors
    • Chen F 4
    (1 author)
  • 5. Euler Technology, ZGC Life Sciences Park, Beijing, China.
      Authors
    • Jin W 2, 5
    • Zhang Y 5
    (2 authors)

ORCIDs linked to this article

Show all (8)

Nature Biotechnology, 09 May 2024,
https://doi.org/10.1038/s41587-024-02241-z PMID: 38724668 

Abstract 

Single-cell chromatin accessibility sequencing (scATAC-seq) reconstructs developmental trajectory by phenotypic similarity. However, inferring the exact developmental trajectory is challenging. Previous studies showed age-associated DNA methylation (DNAm) changes in specific genomic regions, termed clock-like differential methylation loci (ClockDML). Age-associated DNAm could either result from or result in chromatin accessibility changes at ClockDML. As cells undergo mitosis, the heterogeneity of chromatin accessibility on clock-like loci is reduced, providing a measure of mitotic age. In this study, we developed a method, called EpiTrace, that counts the fraction of opened clock-like loci from scATAC-seq data to determine cell age and perform lineage tracing in various cell lineages and animal species. It shows concordance with known developmental hierarchies, correlates well with DNAm-based clocks and is complementary with mutation-based lineage tracing, RNA velocity and stemness predictions. Applying EpiTrace to scATAC-seq data reveals biological insights with clinically relevant implications, ranging from hematopoiesis, organ development, tumor biology and immunity to cortical gyrification.

Full text links 

Read article at publisher's site: https://doi.org/10.1038/s41587-024-02241-z

Read article for free, from open access legal sources, via Unpaywall: https://www.nature.com/articles/s41587-024-02241-z.pdfUnpaywall

References 

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Funding 

Funders who supported this work.

Fundamental Research Funds for the Central Universities

    National Natural Science Foundation of China

      Research Fund of Zhongnan Hospital of Wuhan University