Evidence map›Paper›PMID 41671045›Full record

ArticleeLife2026

Single-cell atlas of AML reveals age-related gene regulatory networks in t(8;21) AML.

Jessica Whittle, Stefan Meyer, Georges Lacaud, Syed Murtuza-Baker, Mudassar Iqbal

Abstract read
In one paragraph

Article in eLife, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Jessica WhittleDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-1139-1671
Stefan MeyerManchester Cancer Research Centre (MCRC), Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
Georges LacaudStem Cell Biology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-5630-2417
Syed Murtuza-BakerDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
Mudassar IqbalDivision of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-5006-4331

Funding

Blood Cancer UK 15038Blood Cancer UK 23002Cancer Research UK C5759/A20971Cancer Research UK C5759/A27412Children's Cancer and Leukaemia Group 2016 09Medical Research Council MR/W007428/1Medical Research Council MR/X014088/1
6 · The paper itself

Abstract

Acute myeloid leukemia (AML) is characterized by cellular and genetic heterogeneity, which correlates with clinical course. Although single-cell RNA sequencing (scRNA-seq) reflects this diversity to some extent, the low sample numbers in individual studies limit the analytic potential when comparing specific patient groups. We performed large-scale integration of published scRNA-seq datasets to create a unique single-cell transcriptomic atlas for AML (AML scAtlas), totaling 748,679 cells, from 159 AML patients and 51 healthy donors from 20 different studies. This is the largest single-cell data resource for human AML to our knowledge, publicly available at https://cellxgene.cziscience.com/collections/071b706a-7ea7-47a4-bddf-6457725839fc. This AML scAtlas allowed investigations into 20 patients with t(8;21) AML, where we explored the clinical importance of age, given the in-utero origin of pediatric disease. We uncovered age-associated gene regulatory network (GRN) signatures, which we validated using bulk RNA sequencing data to delineate distinct groups with divergent biological characteristics. Furthermore, using an additional multiomic dataset (scRNA-seq and scATAC-seq), we validated our initial findings and created a de-noised enhancer-driven GRN reflecting the previously defined age-related signatures. Applying integrated data analysis of the AML scAtlas, we reveal age-dependent gene regulation in t(8;21) AML, potentially reflecting immature/fetal HSC origin in prenatal origin disease vs postnatal origin. Our analysis revealed that BCLAF1, which is particularly enriched in pediatric AML with t(8;21) of inferred in-utero origin, is a promising prognostic indicator. The AML scAtlas provides a powerful resource to investigate molecular mechanisms underlying different AML subtypes.

Indexed as

Gene Regulatory NetworksLeukemia, Myeloid, AcuteTranslocation, GeneticAdolescentAdultAgedAge FactorsChildChromosomes, Human, Pair 21Chromosomes, Human, Pair 8FemaleGene Expression ProfilingHumansMaleMiddle AgedSingle-Cell Analysisage-related diseaseAMLAML-ETOAtlascancer biologycomputational biologygene regulatory networkhumansingle cellsystems biology

Identifiers

PMID41671045
PMCPMC12893711

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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.