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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.

References

Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304 (2018). [PMID: 5957518]
Corces, M. R et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018). [PMID: 30361341]
Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015). [PMID: 25693567]
Weinberg, O. K. & Arber, D. A. Mixed-phenotype acute leukemia: historical overview and a new definition. Leukemia 24, 1844–1851 (2010). [PMID: 20844566]
Arber, D. A. et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 127, 2391–2405 (2016). [PMID: 27069254]
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). [PMID: 28759029]
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019). [PMID: 31375813]
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). [PMID: 28091601]
Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018). [PMID: 29539636]
Cusanovich, D. A. et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174, 1309–1324 (2018). [PMID: 30078704]
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018). [PMID: 29608179]
McInnes, L., Healy, J. & Melville, J. UMAP: Uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://arxiv.org/abs/1802.03426 (2018).
Janeway, C. J., Travers, P., Walport, M. & Shlomchik, M. J. Immunobiology 5th edn (Garland Science, 2001).
Pliner, H. A. et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol. Cell 71, 858–871 (2018). [PMID: 30078726]
Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017). [PMID: 28825706]
Alexander, T. B. et al. The genetic basis and cell of origin of mixed phenotype acute leukaemia. Nature 562, 373–379 (2018). [PMID: 6195459]
Takahashi, K. et al. Integrative genomic analysis of adult mixed phenotype acute leukemia delineates lineage associated molecular subtypes. Nat. Commun. 9, 2670 (2018). [PMID: 29991687]
Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016). [PMID: 27526324]
van Galen, P. et al. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281 (2019). [PMID: 30827681]
Satpathy, A. T. et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat. Med. 24, 580–590 (2018). [PMID: 29686426]
Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018). [PMID: 30194434]
Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548 (2018). [PMID: 29706549]
Li, B. et al. Census of immune cells. HCA https://data.humancellatlas.org/explore/projects/cc95ff89-2e68-4a08-a234-480eca21ce79 (2018).
Mitchell, K. et al. IL1RAP potentiates multiple oncogenic signaling pathways in AML. J. Exp. Med. 215, 1709–1727 (2018). [PMID: 29773641]
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012). [DOI: 10.1089/omi.2011.0118]
Lim, S. & Kaldis, P. Cdks, cyclins and CKIs: roles beyond cell cycle regulation. Development 140, 3079–3093 (2013). [PMID: 23861057]
Wolach, O. & Stone, R. M. How I treat mixed-phenotype acute leukemia. Blood 125, 2477–2485 (2015). [PMID: 25605373]
Zheng, C. et al. What is the optimal treatment for biphenotypic acute leukemia? Haematologica 94, 1778–1780 (2009). [PMID: 19996120]
Osato, M. et al. Biallelic and heterozygous point mutations in the runt domain of the AML1/PEBP2αB gene associated with myeloblastic leukemias. Blood 93, 1817–1824 (1999). [PMID: 10068652]
Haferlach, T. et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 28, 241–247 (2014). [PMID: 24220272]
Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012). [PMID: 22237106]
Della Gatta, G. et al. Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL. Nat. Med. 18, 436–440 (2012). [PMID: 22366949]
Wang, X et al. Breast tumors educate the proteome of stromal tissue in an individualized but coordinated manner. Sci. Signal. 10, eaam8065 (2017). [PMID: 28790197]
Sanda, T. et al. Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia. Cancer Cell 22, 209–221 (2012). [PMID: 22897851]
Ben-Ami, O. et al. Addiction of t(8;21) and inv(16) acute myeloid leukemia to native RUNX1. Cell Rep. 4, 1131–1143 (2013). [PMID: 24055056]
Wilkinson, A. C. et al. RUNX1 is a key target in t(4;11) leukemias that contributes to gene activation through an AF4–MLL complex interaction. Cell Rep. 3, 116–127 (2013). [PMID: 23352661]
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019). [PMID: 31178122]
Mumbach, M. R. et al. Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements. Nat. Genet. 49, 1602–1612 (2017). [PMID: 28945252]
Martín, P. et al. CD69 association with Jak3/Stat5 proteins regulates Th17 cell differentiation. Mol. Cell. Biol. 30, 4877–4889 (2010). [PMID: 20696842]
Shiow, L. R. et al. CD69 acts downstream of interferon-α/β to inhibit S1P1 and lymphocyte egress from lymphoid organs. Nature 440, 540–544 (2006). [PMID: 16525420]
Egawa, T., Tillman, R. E., Naoe, Y., Taniuchi, I. & Littman, D. R. The role of the Runx transcription factors in thymocyte differentiation and in homeostasis of naive T cells. J. Exp. Med. 204, 1945–1957 (2007). [PMID: 17646406]
Laguna, T. et al. New insights on the transcriptional regulation of CD69 gene through a potent enhancer located in the conserved non-coding sequence 2. Mol. Immunol. 66, 171–179 (2015). [PMID: 25801305]
Simeonov, D. R. et al. Discovery of stimulation-responsive immune enhancers with CRISPR activation. Nature 549, 111–115 (2017). [PMID: 28854172]
Feld, C. et al. Combined cistrome and transcriptome analysis of SKI in AML cells identifies SKI as a co-repressor for RUNX1. Nucleic Acids Res. 46, 3412–3428 (2018). [PMID: 29471413]
Cancer Genome Atlas Research Network Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). [PMID: 19910308]
Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018). [PMID: 29481549]

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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