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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\n                    <jats:ext-link ext-link-type=\"uri\" xlink:href=\"https:\/\/cellxgene.cziscience.com\/collections\/071b706a-7ea7-47a4-bddf-6457725839fc\">https:\/\/cellxgene.cziscience.com\/collections\/071b706a-7ea7-47a4-bddf-6457725839fc<\/jats:ext-link>\n                    . 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.\n                  <\/jats:p>","DOI":"10.7554\/elife.104978","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T11:30:24Z","timestamp":1747308624000},"update-policy":"https:\/\/doi.org\/10.7554\/elife.104978","source":"Crossref","is-referenced-by-count":0,"title":["Single-cell atlas of AML reveals age-related gene regulatory networks in t(8;21) AML"],"prefix":"10.7554","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1139-1671","authenticated-orcid":true,"given":"Jessica","family":"Whittle","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/027m9bs27","id-type":"ROR","asserted-by":"publisher"}],"name":"Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, The University of Manchester","place":["Manchester, United 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