Evidence map›Paper›PMID 39462118›Full record

ArticleCommunications biology2024

Profiling the transcriptomic age of single-cells in humans.

Enikő Zakar-Polyák, Attila Csordas, Róbert Pálovics, Csaba Kerepesi

Abstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Review
  3. Dilating the aging clock with light.Innovation (Cambridge (Mass.)) · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Determining the age of single cells using scMLEAge.bioRxiv : the preprint server for biology · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Human Brain Cell-Type-Specific Aging Clocks Based on Single-Nuclei Transcriptomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  20. 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

4 authors.

Enikő Zakar-PolyákInstitute for Computer Science and Control (SZTAKI), Hungarian Research Network (HUN-REN), Budapest, Hungary. zakar-polyak.eniko@sztaki.hun-ren.hu.ORCID 0000-0001-5655-4940
Attila CsordasAgeCurve Limited, Cambridge, UK.
Róbert PálovicsDepartment of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Csaba KerepesiInstitute for Computer Science and Control (SZTAKI), Hungarian Research Network (HUN-REN), Budapest, Hungary. kerepesi@sztaki.hu.ORCID 0000-0001-9541-246X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although aging clocks predicting the age of individual organisms have been extensively studied, the age of individual cells remained largely unexplored. Most recently single-cell omics clocks were developed for the mouse, however, extensive profiling the age of human cells is still lacking. To fill this gap, here we use available scRNA-seq data of 1,058,909 blood cells of 508 healthy, human donors (between 19 and 75 years), for developing single-cell transcriptomic clocks and predicting the age of human blood cells. By the application of the proposed cell-type-specific single-cell clocks, our main observations are that (i) transcriptomic age is associated with cellular senescence; (ii) the transcriptomic age of classical monocytes as well as naive B and T cells is decreased in moderate COVID-19 followed by an increase for some cell types in severe COVID-19; and (iii) the human embryo cells transcriptomically rejuvenated at the morulae and blastocyst stages. In summary, here we demonstrate that single-cell transcriptomic clocks are useful tools to investigate aging and rejuvenation at the single-cell level.

Indexed as

Cellular SenescenceCOVID-19SARS-CoV-2Single-Cell AnalysisTranscriptomeAdultAgedAgingFemaleGene Expression ProfilingHumansMaleMiddle AgedYoung Adult

Identifiers

PMID39462118
PMCPMC11513945

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Registered trials

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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.