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Nature biotechnology
12, 2019;37 (12): 1458-1465.

Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia.

Jeffrey M Granja, Sandy Klemm, Lisa M McGinnis, Arwa S Kathiria, Anja Mezger, M Ryan Corces, Benjamin Parks, Eric Gars, Michaela Liedtke, Grace X Y Zheng, Howard Y Chang, Ravindra Majeti, William J Greenleaf
Author Information
  1. Jeffrey M Granja: Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA.
  2. Sandy Klemm: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. klemm@stanford.edu. ORCID
  3. Lisa M McGinnis: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. lisa.mcginnis@stanford.edu.
  4. Arwa S Kathiria: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  5. Anja Mezger: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
  6. M Ryan Corces: Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA.
  7. Benjamin Parks: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. ORCID
  8. Eric Gars: Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  9. Michaela Liedtke: Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
  10. Grace X Y Zheng: 10x Genomics, Pleasanton, CA, USA. ORCID
  11. Howard Y Chang: Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA. ORCID
  12. Ravindra Majeti: Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
  13. William J Greenleaf: Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA, USA. wjg@stanford.edu. ORCID
PMID: 31792411 DOI: 10.1038/s41587-019-0332-7

Abstract

Identifying the causes of human diseases requires deconvolution of abnormal molecular phenotypes spanning DNA accessibility, gene expression and protein abundance. 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. 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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Grants

  1. RM1 HG007735/NHGRI NIH HHS
  2. K99 AG059918/NIA NIH HHS
  3. U19 AI057266/NIAID NIH HHS
  4. P50 HG007735/NHGRI NIH HHS
  5. UM1 HG009436/NHGRI NIH HHS
  6. R35 CA209919/NCI NIH HHS
  7. UM1 HG009442/NHGRI NIH HHS

MeSH Term

Bone Marrow Cells
Chromatin
Cluster Analysis
Core Binding Factor Alpha 2 Subunit
Epigenesis, Genetic
Epigenomics
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Leukemia, Biphenotypic, Acute
Regulatory Sequences, Nucleic Acid
Single-Cell Analysis
Transcriptome

Chemicals

Chromatin
Core Binding Factor Alpha 2 Subunit
RUNX1 protein, human
Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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