Cited by 96Acute Myeloid Leukemia ResearchSingle-cell and spatial transcriptomicsCancer Genomics and Diagnostics

Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia

Open access
Beneyto-Calabuig, Sergi, Merbach, Anne Kathrin, Kniffka, Jonas-Alexander, Antes, Magdalena, Szu-Tu, Chelsea, Rohde, Christian, Waclawiczek, Alexander, Stelmach, Patrick +23 more·2023·Cell Stem Cell

Published version of a preprint

Abstract

Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.stem.2023.04.001

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Such epigenetic alterations can potentially explain the shift toward activation observed for many TF combinations in K562 cells and the co-expression of stem and progenitor genes frequently observed in leukemia.

    “Such epigenetic alterations can potentially explain the shift toward activation observed for many TF combinations in K562 cells and the co-expression of stem and progenitor genes11 frequently observed in leukemia.”

    From Design principles of cell-state-specific enhancers in hematopoiesis (2025) · cites this paper as [11]

    In “LSCs retain erythroid capacity” (page unavailable), the paper reports that leukemic C6 cells expressed HSC-associated genes in some patients and genes associated with early erythromyeloid priming. This supports the cited point that stem- and progenitor-associated expression can coexist in AML cells, but does not establish that such co-expression is frequent across leukemia generally. The citation is attached to that co-expression point, not to the proposed epigenetic explanation or the K562 result. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 48

  1. Velten, L., Haas, S.F., Raffel, S., Blaszkiewicz, S., Islam, S., Hennig, B.P., Hirche, C., Lutz, C., Buss, E.C., Nowak, D., et al. (2017). Human haemato- poietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271-281. https://doi.org/10.1038/ncb3493. · DOI · Paper page
  2. Paul, F., Arkin, Y., Giladi, A., Jaitin, D., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015). Transcriptional heterogeneity and lineage commitment in myeloid progen- itors. Cell 163, 1663-1677. https://doi.org/10.1016/j.cell.2015.11.013. · DOI · Paper page
  3. Tusi, B.K., Wolock, S.L., Weinreb, C., Hwang, Y., Hidalgo, D., Zilionis, R., Waisman, A., Huh, J.R., Klein, A.M., and Socolovsky, M. (2018). Population snapshots predict early haematopoietic and erythroid hierar- chies. Nature 555, 54-60. https://doi.org/10.1038/nature25741. · DOI · Paper page
  4. Triana, S., Vonficht, D., Jopp-Saile, L., Raffel, S., Lutz, R., Leonce, D., Antes, M., Herna ´ndez-Malmierca, P., Ordon ˜ez-Rueda, D., Ramasz, B., et al. (2021). Single-cell proteo-genomic reference maps of the hemato- poietic system enable the purification and massive profiling of precisely defined cell states. Nat. Immunol. 22, 1577-1589. https://doi.org/10. 1038/s41590-021-01059-0. · DOI · Paper page
  5. Notta, F., Zandi, S., Takayama, N., Dobson, S., Gan, O.I., Wilson, G., Kaufmann, K.B., McLeod, J., Laurenti, E., Dunant, C.F., et al. (2016). Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116. https://doi.org/10.1126/science. aab2116. · DOI · Paper page
  6. van Egeren, D., Escabi, J., Nguyen, M., Liu, S., Reilly, C.R., Patel, S., Kamaz, B., Kalyva, M., DeAngelo, D.J., Galinsky, I., et al. (2021). Reconstructing the lineage histories and differentiation trajectories of indi- vidual cancer cells in myeloproliferative neoplasms. Cell Stem Cell 28, 514-523.e9. https://doi.org/10.1016/j.stem.2021.02.001. · DOI · Paper page
  7. Nam, A.S., Dusaj, N., Izzo, F., Murali, R., Mouhieddine, T.H., Myers, R.M., Sotelo, J., Benbarche, S., Gaiti, F., Tahri, S., et al. (2022). Single-cell multi- omics in human clonal hematopoiesis reveals that DNMT3A R882 muta- tions perturb early progenitor states through selective hypomethylation. https://doi.org/10.1101/2022.01.14.476225. · DOI
  8. Nam, A.S., Chaligne, R., and Landau, D.A. (2021). Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat. Rev. Genet. 22, 3-18. https://doi.org/10.1038/s41576-020-0265-5. · DOI
  9. Miller, T.E., Lareau, C.A., Verga, J.A., DePasquale, E.A.K., Liu, V., Ssozi, D., Sandor, K., Yin, Y., Ludwig, L.S., el Farran, C.A., et al. (2022). Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations. Nat. Biotechnol. 40, 1030- 1034. https://doi.org/10.1038/s41587-022-01210-8. · DOI
  10. Gao, R., Bai, S., Henderson, Y.C., Lin, Y., Schalck, A., Yan, Y., Kumar, T., Hu, M., Sei, E., Davis, A., et al. (2021). Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat. Biotechnol. 39, 599-608. https://doi.org/10.1038/s41587-020-00795-2. · DOI
  11. Gao, T., Soldatov, R., Sarkar, H., Kurkiewicz, A., Biederstedt, E., Loh, P.R., and Kharchenko, P.V. (2023). Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat. Biotechnol. 41, 417-426. https://doi.org/10.1038/s41587-022-01468-y. · DOI
  12. Schraivogel, D., Gschwind, A.R., Milbank, J.H., Leonce, D.R., Jakob, P., Mathur, L., Korbel, J.O., Merten, C.A., Velten, L., and Steinmetz, L.M. (2020). Targeted Perturb-seq enables genome-scale genetic screens in single cells. Nat. Methods 17, 629-635. https://doi.org/10.1038/s41592- 020-0837-5. · DOI · Paper page
  13. Gohl, D.M., Magli, A., Garbe, J., Becker, A., Johnson, D.M., Anderson, S., Auch, B., Billstein, B., Froehling, E., McDevitt, S.L., et al. (2019). Measuring sequencer size bias using REcount: a novel method for highly accurate Illumina sequencing-based quantification. Genome Biol. 20, 85. https:// doi.org/10.1186/s13059-019-1691-6. · DOI
  14. Velten, L., Story, B.A., Herna ´ndez-Malmierca, P., Raffel, S., Leonce, D.R., Milbank, J., Paulsen, M., Demir, A., Szu-Tu, C., Fro ¨mel, R., et al. (2021). Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics. Nat. Commun. 12, 1366. https://doi. org/10.1038/s41467-021-21650-1. · DOI
  15. Lareau, C.A., Ludwig, L.S., Muus, C., Gohil, S.H., Zhao, T., Chiang, Z., Pelka, K., Verboon, J.M., Luo, W., Christian, E., et al. (2021). Massively parallel sin- gle-cell mitochondrial DNA genotyping and chromatin profiling. Nat. Biotechnol. 39, 451-461. https://doi.org/10.1038/s41587-020-0645-6. · DOI
  16. Rodriguez-Meira, A., Buck, G., Clark, S.A., Povinelli, B.J., Alcolea, V., Louka, E., McGowan, S., Hamblin, A., Sousos, N., Barkas, N., et al. (2019). Unravelling intratumoral heterogeneity through high-sensitivity sin- gle-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292-1305.e8. https://doi.org/10.1016/j.molcel.2019.01.009. · DOI · Paper page
  17. Zhang, X., Wang, X., Wang, X.Q.D., Su, J., Putluri, N., Zhou, T., Qu, Y., Jeong, M., Guzman, A., Rosas, C., et al. (2020). Dnmt3a loss and Idh2 neo- morphic mutations mutually potentiate malignant hematopoiesis. Blood 135, 845-856. https://doi.org/10.1182/blood.2019003330. · DOI
  18. Fennell, K.A., Vassiliadis, D., Lam, E.Y.N., Martelotto, L.G., Balic, J.J., Hollizeck, S., Weber, T.S., Semple, T., Wang, Q., Miles, D.C., et al. (2022). Non-genetic determinants of malignant clonal fitness at single- cell resolution. Nature 601, 125-131. https://doi.org/10.1038/s41586- 021-04206-7. · DOI
  19. Pelcovits, A., and Niroula, R. (2020). Acute myeloid leukemia: a review. R. I. Med. J. (2013) 103, 38-40.
  20. van Galen, P., Hovestadt, V., Wadsworth, M.H., Hughes, T.K., Griffin, G.K., Battaglia, S., Verga, J.A., Stephansky, J., Pastika, T.J., Lombardi Story, J., et al. (2019). Single-cell RNA-seq reveals AML hierarchies relevant to dis- ease progression and immunity. Cell 176, 1265-1281.e24. https://doi.org/ 10.1016/j.cell.2019.01.031. · DOI · Paper page
  21. Feller, N., van der Velden, V.H.J., Brooimans, R.A., Boeckx, N., Preijers, F., Kelder, A., de Greef, I., Westra, G., te Marvelde, J.G., Aerts, P., et al. (2013). Defining consensus leukemia-associated immunophenotypes for detection of minimal residual disease in acute myeloid leukemia in a multi- center setting. Blood Cancer J. 3, e129. https://doi.org/10.1038/bcj. 2013.27. · DOI
  22. Al-Mawali, A., Gillis, D., Hissaria, P., and Lewis, I. (2008). Incidence, sensitivity, and specificity of leukemia-associated phenotypes in acute myeloid leukemia using specific five-color multiparameter flow cytometry. Am. J. Clin. Pathol. 129, 934-945. https://doi.org/10.1309/ FY0UMAMM91VPMR2W. · DOI
  23. Takao, S., Morell, V., Brown, F.C., Koche, R., and Kentsis, A. (2022). Epigenetic mechanisms controlling human leukemia stem cells and ther- apy resistance. https://doi.org/10.1101/2022.09.22.509005. · DOI
  24. Zhang, Y.W., Mess, J., Aizarani, N., Mishra, P., Johnson, C., Romero- Mulero, M.C., Rettkowski, J., Scho ¨nberger, K., Obier, N., J€ acklein, K., et al. (2022). Hyaluronic Acid-GPRC5C Signalling Promotes Dormancy in Haematopoietic Stem Cells (Springer). https://doi.org/10.1038/ s41556-022-00931-x. · DOI
  25. Cabezas-Wallscheid, N., Buettner, F., Sommerkamp, P., Klimmeck, D., Ladel, L., Thalheimer, F.B., Pastor-Flores, D., Roma, L.P., Renders, S., Zeisberger, P., et al. (2017). Vitamin A-retinoic acid signaling regulates he- matopoietic stem cell dormancy. Cell 169, 807-823.e19. https://doi.org/ 10.1016/j.cell.2017.04.018. · DOI · Paper page
  26. Lehnertz, B., Chagraoui, J., MacRae, T., Tomellini, E., Corneau, S., Mayotte, N., Boivin, I., Durand, A., Gracias, D., and Sauvageau, G. (2021). HLF expression defines the human hematopoietic stem cell state. Blood 138, 2642-2654. https://doi.org/10.1182/blood.2021010745. · DOI
  27. Bonnet, D., and Dick, J.E. (1997). Human acute myeloid leukemia is orga- nized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730-737. https://doi.org/10.1038/nm0797-730. · DOI
  28. Rodriguez-Fraticelli, A.E., Weinreb, C., Wang, S.W., Migueles, R.P., Jankovic, M., Usart, M., Klein, A.M., Lowell, S., and Camargo, F.D. (2020). Single-cell lineage tracing unveils a role for TCF15 in haematopoi- esis. Nature 583, 585-589. https://doi.org/10.1038/s41586-020-2503-6. · DOI
  29. Zeng, A.G.X., Bansal, S., Jin, L., Mitchell, A., Chen, W.C., Abbas, H.A., Chan-Seng-Yue, M., Voisin, V., van Galen, P., Tierens, A., et al. (2022). A cellular hierarchy framework for understanding heterogeneity and predict- ing drug response in acute myeloid leukemia. Nat. Med. 28, 1212-1223. https://doi.org/10.1038/s41591-022-01819-x. · DOI
  30. Brett Heimlich, J., Bhat, P., Parker, A.C., Jenkins, M.T., Vlasschaert, C., Ulloa, J., Potts, C.R., Olson, S., Silver, A.J., Ahmad, A., et al. (2022). Mutated cells mediate distinct inflammatory responses in clonal hemato- poiesis. https://doi.org/10.1101/2022.12.01.518580. · DOI
  31. Krevvata, M., Shan, X., Zhou, C., dos Santos, C., Habineza Ndikuyeze, G., Secreto, A., Glover, J., Trotman, W., Brake-Silla, G., Nunez-Cruz, S., et al. (2018). Cytokines increase engraftment of human acute myeloid leukemia cells in immunocompromised mice but not engraftment of human myelo- dysplastic syndrome cells. Haematologica 103, 959-971. https://doi.org/ 10.3324/haematol.2017.183202. · DOI
  32. Jahn, K., Kuipers, J., and Beerenwinkel, N. (2016). Tree inference for sin- gle-cell data. Genome Biol. 17, 86. https://doi.org/10.1186/s13059-016- 0936-x. · DOI
  33. Malikic, S., Mehrabadi, F.R., Ciccolella, S., Rahman, M.K., Ricketts, C., Haghshenas, E., Seidman, D., Hach, F., Hajirasouliha, I., and Sahinalp, S.C. (2019). PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data. Genome Res. 29, 1860-1877. https://doi.org/10.1101/ gr.234435.118. · DOI
  34. Zafar, H., Navin, N., Chen, K., and Nakhleh, L. (2019). SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Res. 29, 1847-1859. https://doi.org/10.1101/gr.243121.118. · DOI
  35. McCarthy, D.J., Rostom, R., Huang, Y., Kunz, D.J., Danecek, P., Bonder, M.J., Hagai, T., Lyu, R.; HipSci Consortium, and Wang, W., et al. (2020). Cardelino: computational integration of somatic clonal substructure and single-cell transcriptomes. Nat. Methods 17, 414-421. https://doi.org/ 10.1038/s41592-020-0766-3. · DOI
  36. Miles, L.A., Bowman, R.L., Merlinsky, T.R., Csete, I.S., Ooi, A.T., Durruthy- Durruthy, R., Bowman, M., Famulare, C., Patel, M.A., Mendez, P., et al. (2020). Single-cell mutation analysis of clonal evolution in myeloid malig- nancies. Nature 587, 477-482. https://doi.org/10.1038/s41586-020- 2864-x. · DOI
  37. Hennig, B.P., Velten, L., Racke, I., Tu, C.S., Thoms, M., Rybin, V., Besir, H., Remans, K., and Steinmetz, L.M. (2018). Large-scale low-cost NGS library preparation using a robust Tn5 purification and tagmentation protocol. G3 (Bethesda) 8, 79-89. https://doi.org/10.1534/g3.117.300257. · DOI
  38. Gong, H., Holcomb, I., Ooi, A., Wang, X., Majonis, D., Unger, M.A., and Ramakrishnan, R. (2016). Simple method to prepare oligonucleotide-con- jugated antibodies and its application in multiplex protein detection in sin- gle cells. Bioconjug. Chem. 27, 217-225. https://doi.org/10.1021/acs.bio- conjchem.5b00613. · DOI
  39. Paczulla, A.M., Rothfelder, K., Raffel, S., Konantz, M., Steinbacher, J., Wang, H., Tandler, C., Mbarga, M., Schaefer, T., Falcone, M., et al. (2019). Absence of NKG2D ligands defines leukaemia stem cells and me- diates their immune evasion. Nature 572, 254-259. https://doi.org/10. 1038/s41586-019-1410-1. · DOI · Paper page
  40. Wolock, S.L., Lopez, R., and Klein, A.M. (2019). Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281-291.e9. https://doi.org/10.1016/j.cels.2018.11.005. · DOI · Paper page
  41. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. (2015). Highly parallel genome-wide expression profiling of individual cells using nanoli- ter droplets. Cell 161, 1202-1214. https://doi.org/10.1016/j.cell.2015. 05.002. · DOI · Paper page
  42. Mo ¨lder, F., Jablonski, K.P., Letcher, B., Hall, M.B., Tomkins-Tinch, C.H., Sochat, V., Forster, J., Lee, S., Twardziok, S.O., Kanitz, A., et al. (2021). Sustainable data analysis with Snakemake. F1000Res 10, 33. https:// doi.org/10.12688/f1000research.29032.2. · DOI · Paper page
  43. Kiselev, V.Y., Yiu, A., and Hemberg, M. (2018). scmap: projection of single- cell RNA-seq data across data sets. Nat. Methods 15, 359-362. https:// doi.org/10.1038/nmeth.4644. · DOI
  44. Hie, B., Bryson, B., and Berger, B. (2019). Efficient integration of heteroge- neous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685-691. https://doi.org/10.1038/s41587-019-0113-3. · DOI
  45. Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W.M., Hao, Y., Stoeckius, M., Smibert, P., and Satija, R. (2019). Comprehensive integration of single-cell data. Cell 177, 1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031. · DOI · Paper page
  46. Bauer, M., Vaxevanis, C., Bethmann, D., Massa, C., Pazaitis, N., Wickenhauser, C., and Seliger, B. (2020). Multiplex immunohistochemistry as a novel tool for the topographic assessment of the bone marrow stem cell niche. Methods Enzymol. 635, 67-79. https://doi.org/10.1016/bs.mie. 2019.05.055. · DOI
  47. Bingham, E., Chen, J.P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N.D. (2018). Pyro: deep universal probabilistic programming. https://doi.org/ 10.48550/arXiv.1810.09538. · DOI
  48. Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A.K., Slichter, C.K., Miller, H.W., McElrath, M.J., Prlic, M., et al. (2015). MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278. https://doi.org/10.1186/s13059-015-0844-5. · DOI · Paper page

Cited by

Sorted by citations

Authors
Beneyto-Calabuig, S., Merbach, A. K., Kniffka, J.-A., Antes, M., Szu-Tu, C., Rohde, C., Waclawiczek, A., Stelmach, P., Gräßle, S., Pervan, P., Janssen, M., Landry, J. J. M., Benes, V., Jauch, A., Brough, M., Bauer, M., Besenbeck, B., Felden, J., Bäumer, S., Hundemer, M., Sauer, T., Pabst, C., Wickenhauser, C., Angenendt, L., Schliemann, C., Trumpp, A., Haas, S., Scherer, M., Raffel, S., Müller-Tidow, C., & Velten, L.
Affiliations
  • Heidelberg University (DE)
  • University Hospital Heidelberg (DE)
  • German Cancer Research Center (DE)
  • Max Delbrück Center (DE)
  • Barcelona Institute of Science and Technology (ES)
  • European Molecular Biology Laboratory (DE)
  • University Hospital in Halle (DE)
  • University Hospital Münster (DE)
  • ETH Zurich (CH)
  • Universitat Pompeu Fabra (ES)
Corresponding author
Velten, Lars
Venue
Cell Stem Cell
Publisher
Elsevier BV
Published
2023-05
Volume
30
Issue
5
Pages
706-721.e8
ISSN
  • 1875-9777 (ISSN-L)
  • 1934-5909
OpenAlex
W4366824744
Cited by
96
References
11
Citations by year
2026: 22 · 2025: 33 · 2024: 31 · 2023: 8 · 2022: 2
FWCI
19.67
Type
journal article
Language
en
Open access
Locations
  • Cell stem cell (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (acceptedVersion, cc-by)
  • MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion, cc-by)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection
Funders
  • Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (INST 35/1503-1 FUGG)
  • Ministerio de Ciencia e Innovación
  • José Carreras Leukämie-Stiftung
  • Deutsches Krebsforschungszentrum
  • Deutsche Forschungsgemeinschaft (INST 35/1314-1 FUGG, MU1328/18-1, MU1328/21-1, MU1328/23-1)
  • Bundesministerium für Bildung und Forschung (031L0212A)
  • Centre de Regulació Genòmica
  • Agencia Estatal de Investigación (PID2019-108082GA-I00, PRE2020-093229)
  • Universitat Pompeu Fabra
  • University of the East
  • Emerson Collective (643577)
  • Department of Medicine, Georgetown University
  • Deutsche Krebshilfe (70113908)
  • European Molecular Biology Laboratory
  • European Regional Development Fund
  • Ministerio de Ciencia, Innovación y Universidades
  • Bundesministerium fur Bildung und Forschung Dienststelle Berlin
Topics
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Keywords
acute myeloid leukemia, leukemia stem cells, single-cell RNA sequencing, cell differentiation, myeloid progenitor cells, erythroid differentiation, chemotherapy response, monocyte differentiation, preleukemic cells, cell surface markers, interpatient variability
MeSH terms
Multiomics, Cell Differentiation, Humans, Neoplastic Stem Cells, Leukemia, Myeloid, Acute

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review