Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Open accessAbstract
Identifying the causes of human diseases requires deconvolution of abnormal molecular phenotypes spanning DNA accessibility, gene expression and protein abundance 1-3. We present a single-cell framework that integrates highly multiplexed protein quantification, transcriptome profiling and analysis of chromatin accessibility. Using this approach, we establish a normal epigenetic baseline for healthy blood development, which we then use to deconvolve aberrant molecular features within blood from patients with mixed-phenotype acute leukemia 4,5. Despite widespread epigenetic heterogeneity within the patient cohort, we observe common malignant signatures across patients as well as patient-specific regulatory features that are shared across phenotypic compartments of individual patients. Integrative analysis of transcriptomic and chromatin-accessibility maps identified 91,601 putative peak-to-gene linkages and transcription factors that regulate leukemia-specific genes, such as RUNX1-linked regulatory elements proximal to the marker gene CD69. These results demonstrate how integrative, multiomic analysis of single cells within the framework of normal development can reveal both distinct and shared molecular mechanisms of disease from patient samples.
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In total, 100 erythroid genes and 135 myeloid genes were identified from the single-cell dataset.
“In total, 100 erythroid genes and 135 myeloid genes were identified from this single-cell dataset (Supplementary Table 3) and were used to compute the scores presented in Extended Data Fig. 5g–j.”
From Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution (2023) · cites this paper as [24]
The paper’s hematopoiesis discussion (page unavailable) identifies erythroid and myeloid lineages and examples of lineage-associated genes. Searches for “100 erythroid,” “135 myeloid,” and the claimed gene counts did not find a passage reporting those totals or the gene sets used for the cited scores. (LLM, conf=0.72)
Experimental — accuracy not yet measuredSingle-cell multi-omics integration of DNA accessibility, gene expression and protein abundance in mixed-phenotype acute leukaemia models determined that RUNX1 is a potential oncogene associated with poor survival.
“Another study applied single-cell multi-omics integration of DNA accessibility, gene expression and protein abundance to mixed-phenotype acute leukaemia models, determining that the gene RUNX1, encoding a transcription factor, is a potential oncogene in mixed-phenotype acute leukaemia associated with poor survival163.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [163]
Peak-to-gene link analysis linking differentially accessible genomic regions to known leukaemia genes showed that specific peak-to-gene links were enriched in immunity regulation.
“Additionally, peak-to-gene link analysis was performed by linking differentially accessible genomic regions, depicted as peaks, to known leukaemia genes, showing that specific peak-to-gene links were enriched in immunity regulation.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [163]
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- Authors
- Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., Chang, H. Y., Majeti, R., & Greenleaf, W. J.
- Affiliations
- Stanford Medicine (US)
- Karolinska Institutet (SE)
- 10X Genomics (United States) (US)
- Howard Hughes Medical Institute (US)
- Chan Zuckerberg Biohub San Francisco (US)
- Corresponding author
- Klemm, Sandy
- McGinnis, Lisa M.
- Greenleaf, William J.
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2019-12
- Volume
- 37
- Issue
- 12
- Pages
- 1458-1465
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC7258684
- OpenAlex
- W2991023348
- Cited by
- 498
- References
- 54
- Citations by year
- 2026: 61 · 2025: 88 · 2024: 76 · 2023: 74 · 2022: 79 · 2021: 64 · 2020: 52 · 2019: 1
- FWCI
- 16.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- American Society of Hematology Scholars
- Ludwig Cancer Research
- Chan Zuckerberg Investigator
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Keywords
- mixed phenotype acute leukemia, single-cell multi-omics, chromatin accessibility, transcription factor binding sites, RUNX1, CD69, epigenetic heterogeneity, normal hematopoiesis, protein expression profiling, gene expression profiling
- MeSH terms
- Bone Marrow Cells, Chromatin, Humans, Regulatory Sequences, Nucleic Acid, Leukemia, Biphenotypic, Acute, Gene Expression Regulation, Neoplastic, Cluster Analysis, Gene Expression Profiling, Epigenesis, Genetic, Core Binding Factor Alpha 2 Subunit, Epigenomics, Single-Cell Analysis, Transcriptome
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.
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