Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution (original) (raw)

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References

1. Quesenberry, P.J. & Colvin, G.A. in Williams Hematology 153–174 (McGraw-Hill, 2005).
2. Ji, H. et al. Comprehensive methylome map of lineage commitment from haematopoietic progenitors. Nature 467, 338–342 (2010).
   Article CAS PubMed PubMed Central Google Scholar
3. Lara-Astiaso, D. et al. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).
   Article CAS PubMed PubMed Central Google Scholar
4. Chen, L. et al. Transcriptional diversity during lineage commitment of human blood progenitors. Science 345, 1251033 (2014).
   Article PubMed PubMed Central Google Scholar
5. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).
   Article CAS PubMed PubMed Central Google Scholar
6. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
   Article CAS PubMed PubMed Central Google Scholar
7. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).
   Article CAS PubMed PubMed Central Google Scholar
8. Shih, A.H. et al. The role of mutations in epigenetic regulators in myeloid malignancies. Nat. Rev. Cancer 12, 599–612 (2012).
   Article CAS PubMed Google Scholar
9. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
   Article PubMed PubMed Central Google Scholar
10. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
    Article PubMed PubMed Central Google Scholar
11. Jan, M. et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci. Transl. Med. 4, 149ra118 (2012).
    Article PubMed PubMed Central Google Scholar
12. Corces-Zimmerman, M.R., Hong, W.-J., Weissman, I.L., Medeiros, B.C. & Majeti, R. Preleukemic mutations in human acute myeloid leukemia affect epigenetic regulators and persist in remission. Proc. Natl. Acad. Sci. USA 111, 2548–2553 (2014).
    Article CAS PubMed PubMed Central Google Scholar
13. Shlush, L.I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).
    Article CAS PubMed PubMed Central Google Scholar
14. Majeti, R., Park, C.Y. & Weissman, I.L. Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell 1, 635–645 (2007).
    Article CAS PubMed PubMed Central Google Scholar
15. Manz, M.G., Miyamoto, T., Akashi, K. & Weissman, I.L. Prospective isolation of human clonogenic common myeloid progenitors. Proc. Natl. Acad. Sci. USA 99, 11872–11877 (2002).
    Article CAS PubMed PubMed Central Google Scholar
16. Kohn, L.A. et al. Lymphoid priming in human bone marrow begins before expression of CD10 with upregulation of L-selectin. Nat. Immunol. 13, 963–971 (2012).
    Article CAS PubMed PubMed Central Google Scholar
17. Seita, J. & Weissman, I.L. Hematopoietic stem cell: self-renewal versus differentiation. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 640–653 (2010).
    Article CAS PubMed PubMed Central Google Scholar
18. Roadmap Epigenetics Consortium. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
19. Manning, C.D., Raghavan, P. & Schutze, H. Introduction to Information Retrieval (Cambridge University Press, 2008).
20. Cauchy, P. et al. Chronic FLT3–ITD signaling in acute myeloid leukemia is connected to a specific chromatin signature. Cell Rep. 12, 821–836 (2015).
    Article CAS PubMed PubMed Central Google Scholar
21. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime _cis_-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
    Article CAS PubMed PubMed Central Google Scholar
22. Newman, A.M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
    Article CAS PubMed PubMed Central Google Scholar
23. Buenrostro, J.D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
    Article CAS PubMed PubMed Central Google Scholar
24. Weiss, M.J. & Orkin, S.H. GATA transcription factors: key regulators of hematopoiesis. Exp. Hematol. 23, 99–107 (1995).
    CAS PubMed Google Scholar
25. Burns, C.E., Traver, D., Mayhall, E., Shepard, J.L. & Zon, L.I. Hematopoietic stem cell fate is established by the Notch–Runx pathway. Genes Dev. 19, 2331–2342 (2005).
    Article CAS PubMed PubMed Central Google Scholar
26. Nerlov, C. & Graf, T. PU.1 induces myeloid lineage commitment in multipotent hematopoietic progenitors. Genes Dev. 12, 2403–2412 (1998).
    Article CAS PubMed PubMed Central Google Scholar
27. Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).
    CAS PubMed PubMed Central Google Scholar
28. Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease. Nature 518, 365–369 (2015).
    Article CAS PubMed PubMed Central Google Scholar
29. Farh, K.K. et al. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 518, 337–343 (2015).
    Article CAS PubMed Google Scholar
30. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
    Article CAS PubMed PubMed Central Google Scholar
31. Döhner, H., Weisdorf, D.J. & Bloomfield, C.D. Acute myeloid leukemia. N. Engl. J. Med. 373, 1136–1152 (2015).
    Article PubMed Google Scholar
32. Bonnet, D. & Dick, J.E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737 (1997).
    Article CAS PubMed Google Scholar
33. Goardon, N. et al. Coexistence of LMPP-like and GMP-like leukemia stem cells in acute myeloid leukemia. Cancer Cell 19, 138–152 (2011).
    Article CAS PubMed Google Scholar
34. Bennett, J.M. et al. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br. J. Haematol. 33, 451–458 (1976).
    Article CAS PubMed Google Scholar
35. van't Veer, M.B. The diagnosis of acute leukemia with undifferentiated or minimally differentiated blasts. Ann. Hematol. 64, 161–165 (1992).
    Article CAS PubMed Google Scholar
36. Rangatia, J. et al. Elevated c-Jun expression in acute myeloid leukemias inhibits C/EBPα DNA binding via leucine zipper domain interaction. Oncogene 22, 4760–4764 (2003).
    Article CAS PubMed Google Scholar
37. Volk, A. et al. Co-inhibition of NF-κB and JNK is synergistic in TNF-expressing human AML. J. Exp. Med. 211, 1093–1108 (2014).
    Article CAS PubMed PubMed Central Google Scholar
38. Hartman, A.D. et al. Constitutive c-Jun N-terminal kinase activity in acute myeloid leukemia derives from Flt3 and affects survival and proliferation. Exp. Hematol. 34, 1360–1376 (2006).
    Article CAS PubMed Google Scholar
39. Magnusson, M., Brun, A.C.M., Lawrence, H.J. & Karlsson, S. Hoxa9/Hoxb3/Hoxb4 compound null mice display severe hematopoietic defects. Exp. Hematol. 35, 1421–1428 (2007).
    Article CAS PubMed Google Scholar
40. Lawrence, H.J. et al. Mice bearing a targeted interruption of the homeobox gene HOXA9 have defects in myeloid, erythroid, and lymphoid hematopoiesis. Blood 89, 1922–1930 (1997).
    CAS PubMed Google Scholar
41. Thorsteinsdottir, U. et al. Overexpression of the myeloid leukemia–associated Hoxa9 gene in bone marrow cells induces stem cell expansion. Blood 99, 121–129 (2002).
    Article CAS PubMed Google Scholar
42. González, A.J., Setty, M. & Leslie, C.S. Early enhancer establishment and regulatory locus complexity shape transcriptional programs in hematopoietic differentiation. Nat. Genet. 47, 1249–1259 (2015).
    Article PubMed PubMed Central Google Scholar
43. Whitaker, J.W., Chen, Z. & Wang, W. Predicting the human epigenome from DNA motifs. Nat. Methods 12, 265–272 (2015).
    Article CAS PubMed Google Scholar
44. Macedo, A. et al. Characterization of aberrant phenotypes in acute myeloblastic leukemia. Ann. Hematol. 70, 189–194 (1995).
    Article CAS PubMed Google Scholar
45. Tiacci, E. et al. PAX5 expression in acute leukemias: higher B-lineage specificity than CD79a and selective association with t(8;21)-acute myelogenous leukemia. Cancer Res. 64, 7399–7404 (2004).
    Article CAS PubMed Google Scholar
46. Jan, M. et al. Prospective separation of normal and leukemic stem cells based on differential expression of TIM3, a human acute myeloid leukemia stem cell marker. Proc. Natl. Acad. Sci. USA 108, 5009–5014 (2011).
    Article CAS PubMed PubMed Central Google Scholar
47. Hansen, K.D., Irizarry, R.A. & Wu, Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13, 204–216 (2012).
    Article PubMed PubMed Central Google Scholar
48. Buenrostro, J.D., Wu, B., Chang, H.Y. & Greenleaf, W.J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).
    Article Google Scholar
49. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
50. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
    Article CAS PubMed PubMed Central Google Scholar
51. Koboldt, D.C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009).
    Article CAS PubMed PubMed Central Google Scholar
52. Newman, A.M. et al. FACTERA: a practical method for the discovery of genomic rearrangements at breakpoint resolution. Bioinformatics 30, 3390–3393 (2014).
    Article CAS PubMed PubMed Central Google Scholar
53. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).
    CAS PubMed PubMed Central Google Scholar
54. Vaquerizas, J.M., Kummerfeld, S.K., Teichmann, S.A. & Luscombe, N.M. A census of human transcription factors: function, expression and evolution. Nat. Rev. Genet. 10, 252–263 (2009).
    Article CAS PubMed Google Scholar
55. Weirauch, M.T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
    Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

We thank C. Mazumdar and A. Raj for assistance with RNA-seq, A. Newman for expert assistance with CIBERSORT, and our laboratory members for discussion. We thank the Stanford Hematology Division Tissue Bank and the patients for donating their samples. M.R.C. acknowledges NIH training grant R25CA180993 and NIH F31 Predoctoral fellowship F31CA180659. J.D.B. acknowledges National Science Foundation Graduate Research Fellowships and NIH training grant T32HG000044 for support. M.P.S. acknowledges the NIH and NHGRI for funding through 5U54HG00455805. Research was also supported by the NIH (P50HG007735 to H.Y.C., W.J.G., and M.P.S.), UH2AR067676 (H.Y.C.), the Stanford Cancer Center (H.Y.C.), the Howard Hughes Medical Institute (H.Y.C. and J.K.P.), the Stinehart-Reed Foundation (R.M.), the Ludwig Institute (R.M.), and the NIH (R01CA18805 to R.M.). R.M. is a New York Stem Cell Foundation Robertson Investigator.

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Author notes

1. M Ryan Corces and Jason D Buenrostro: These authors contributed equally to this work.
2. Jason D Buenrostro, Ravindra Majeti and Howard Y Chang: These authors jointly directed this work.

Authors and Affiliations

1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California, USA
   M Ryan Corces, Julie L Koenig & Ravindra Majeti
2. Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
   M Ryan Corces, Julie L Koenig & Ravindra Majeti
3. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, California, USA
   M Ryan Corces, Jason D Buenrostro, Michael P Snyder, William J Greenleaf & Howard Y Chang
4. Department of Genetics, Stanford University, Stanford, California, USA
   Jason D Buenrostro, Beijing Wu, Peyton G Greenside, Michael P Snyder, Jonathan K Pritchard, Anshul Kundaje & William J Greenleaf
5. Broad Institute of MIT and Harvard, Harvard University, Cambridge, Massachusetts, USA
   Jason D Buenrostro
6. Program in Biomedical Informatics, Stanford University School of Medicine, Stanford, California, USA
   Peyton G Greenside
7. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
   Steven M Chan
8. Department of Biology, Stanford University, Stanford, California, USA
   Jonathan K Pritchard
9. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA
   Jonathan K Pritchard
10. Department of Computer Science, Stanford University, Stanford, California, USA
    Anshul Kundaje

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1. M Ryan Corces
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2. Jason D Buenrostro
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3. Beijing Wu
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10. William J Greenleaf
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11. Ravindra Majeti
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12. Howard Y Chang
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Contributions

M.R.C., J.D.B., R.M., and H.Y.C. conceived the project. M.R.C. performed all cell sorting, RNA-seq, and CIBERSORT analysis, AML cell culture experiments, and mouse experiments. J.D.B. performed all ATAC-seq data analysis and regulatory network analysis, and oversaw all ATAC-seq library generation and protocol optimization performed by B.W. M.R.C. and J.L.K. performed DNA genotyping for patients with AML. J.D.B., P.G.G., and A.K. performed GWAS correlation analyses. W.J.G., M.P.S., and J.K.P. assisted with sequencing and study design. S.M.C. collected patient follow-up data and performed all survival analyses. M.R.C., J.D.B., R.M., and H.Y.C. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence toRavindra Majeti or Howard Y Chang.

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Competing interests

Stanford University has filed a provisional patent application on the methods described, and J.D.B., H.Y.C., and W.J.G. are named as inventors. H.Y.C. and W.J.G. are founders of Epinomics.

Integrated supplementary information

Supplementary Figure 1 Validation of Fast-ATAC protocol and comparison to the original ATAC-seq protocol.

(a) ATAC-seq insert size distribution for three biological replicates of HSCs. (b,c) Enrichment of signal at annotated transcription start sites (TSSs) from Fast-ATAC data compared to DNase-seq (b) and previously published ATAC-seq (c) data using the original ATAC-seq protocol6. (d,e) The fraction of reads mapping to mitochondrial DNA (d) and the number of unique fragments in peaks per cell used in the assay (e), comparing the original ATAC-seq protocol and the Fast-ATAC protocol. Error bars in e, s.e.m. (f) Accessible chromatin landscape surrounding a constitutively accessible region of the genome. Profiles represent the union of all technical and biological replicates for each cell type. Genomic coordinates: chr. 19: 36,102,236–36,277,236. The _y_-axis scales range from 0–10 in normalized arbitrary units. (g,h) Reproducibility, measured by Pearson correlation of normalized counts per peak (Online Methods), for each cell type (g) and comparing technical and biological replicates (h). Error bars, 1 s.d.

Supplementary Figure 2 Sorting strategy for HSPCs.

Representative examples of sorting strategies and post-sort analysis for the seven CD34+ HSPC populations isolated in this study. Post-sort analyses represent a minimum of 100 events. Events displayed represent single-cell events for lineage-negative, live cells.

Supplementary Figure 3 Sorting strategy for differentiated lymphoid cells.

Representative examples of sorting strategies and post-sort analysis for the differentiated lymphoid cells isolated in this study. Post-sort analyses represent a minimum of 1,000 events. Events displayed represent single-cell events for lineage-negative, live cells.

Supplementary Figure 4 Sorting strategy for differentiated monocytes and erythroblasts.

Representative examples of sorting strategies and post-sort analysis for the differentiated erythroid (left) and myeloid (right) cells isolated in this study. Post-sort analyses represent a minimum of 1,000 events. Events displayed represent single-cell events from lineage-negative, live cells.

Supplementary Figure 5 Data processing pipelines.

(a,b) Data processing pipeline for ATAC-seq (a) and RNA-seq (b) data (see the Online Methods for a full description).

Supplementary Figure 6 Validation of RNA-seq and ATAC-seq using GO term enrichment analysis and motif enrichment analysis.

(a,b) GO term analyses from unique gene expression (a) and accessible peaks (b) from normal hematopoietic cells. (c) Enrichment of developmentally relevant motifs in accessible peaks using the HOMER21 motif enrichment tool.

Supplementary Figure 7 PCA and t-SNE analysis of RNA-seq and ATAC-seq data from primary human hematopoietic cells.

(a,b) PCA of RNA-seq (a) and ATAC-seq (b) data using all genes and peaks, respectively. (c,d) t-SNE analysis of RNA-seq (c) and ATAC-seq (d) data using all genes and peaks, respectively.

Supplementary Figure 8 Enhancer cytometry with CIBERSORT provides robust classification of most human hematopoietic cell types.

(a–c) Leave-one-out cross-validation of CIBERSORT, comparing the predicted fraction to the synthesized (ground truth) fraction in MPPs (a), CLPs (b), and monocytes (c). For these in silico validations, one replicate of each normal cell type was excluded from use in generating the signature matrix using CIBERSORT. Random synthetic mixtures of these ‘left-out’ samples were then used to assess the ability of CIBERSORT to correctly predict cellular composition. (d) Overall classification performance of deconvolving synthesized mixtures for each hematopoietic cell type shown in Figure 3d. r squared value is derived from correlation of the CIBERSORT-predicted fraction with the synthesized ground truth fraction of 100 random permutations. (e,f) Correlation of deviation between MPPs and HSCs (e) and MPPs and CMPs (f) derived from Figure 3d. Higher correlation of observed deviations implies that the two cell types are more frequently misclassified for one another using CIBERSORT. (g) Heat map representation of all pairwise correlations for deviation as shown in e and f. Heat map color represents the _r_2 value from the correlation of deviation between two cell types (Online Methods). (h) Enhancer cytometry performed without manual removal of peaks corresponding to chromosome X, TSS peaks, and cancer-specific peaks shows a lower correlation with ground truth data (_r_2 = 0.91 as compared to _r_2 = 0.95 with manual curation of peaks). (i) Enhancer cytometry of DNase I hypersensitivity data using a signature matrix derived from our ATAC-seq data.

Supplementary Figure 9 Trans regulators of hematopoiesis.

(a) Summary of motif usage across hematopoiesis. The values shown represent normalized relative deviation scores. Relative deviation scores for representative transcription factor motifs are shown in Figure 4b,c. Scores are normalized by the maximum and minimum (range) across the 13 cell types assayed, such that the values shown range from 0 to 1. The range for the raw scores is shown above each column. Raw values can be found in Supplementary Table 3. (b) Unsupervised hierarchical clustering of hematopoiesis transcription factor motifs (n = 46; rows) with _in vitro_–derived motifs (CIS-BP) (n = 806; columns). Values shown represent motif similarity using Pearson correlation (Online Methods). Boxes highlight clustered CIS-BP motifs. (c,d) Examples of individual motif PWMs from clustered motifs as in b for the GATA (c) and MEIS1 (d) hematopoiesis transcription factor motifs. The top row represents a hematopoietic TF motif and subsequent rows show similar CIS-BP motifs from the cluster analysis. (e) Histogram of all correlation values in b. A correlation cutoff of 0.8 was used to assign potential regulators to motifs. Putative hematopoietic regulators are highlighted (n = 255). (f) Correlation of motif deviation scores to gene expression changes across hematopoietic cell types for two developmentally important transcription factors, GATA1 and PAX5. Each point represents a cell type comparison (for example, HSC versus MPP). Error bars, error in the gene expression measurement, as estimated by DESeq2. (g,h) Summary list of putative transcription factor positive (g) and negative (h) regulators of hematopoiesis motifs. Motifs are listed on the left, and the names of the genes predicted to regulate that motif are listed on the right. Values represent correlation coefficients (Pearson) comparing changes in motif deviation scores to the expression of putative regulators, as shown in f and Figure 4e, only the most correlated gene is shown here; a full list of correlation coefficients and P values can be found in Supplementary Table 4.

Supplementary Figure 10 GWAS enrichments across hematopoiesis.

(a) Representative example of GWAS enrichment across tissues. Fold enrichment (see the Online Methods for description of the calculation) is calculated from increasingly stringent GWAS SNP associations within different tissues. Line color represents tissue as shown in b. (b) Hierarchical clustering of row-normalized, rank fold enrichment values (Online Methods), for all GWAS collected (n = 235) across diverse tissues. The colored dendrogram and color bar below denote GWAS enriched in blood cells. (c) Summary of GWAS deviations across hematopoiesis. Scores are normalized by the maximum and minimum (range) across the 13 cell types assayed, such that the values shown range from 0 to 1. The range for the raw scores is shown above each column. Raw values can be found in Supplementary Table 3.

Supplementary Figure 11 Characterization of leukemogenic evolution in the patient cohort.

(a) Representative sorting strategy for HSCs (green), LSCs (yellow), and blasts (orange) from a single patient with AML (SU353). HSCs are characterized immunophenotypically as CD34+CD38−CD99−TIM3−. LSCs are characterized immunophenotypically as CD34+CD38−CD99+TIM3+. Blasts are characterized as CD99+TIM3+CD45midSSChigh. Depending on the sample, blasts can be CD34+ or CD34−. (b) Preleukemic HSC purification strategies for all patients with AML analyzed by ATAC-seq and RNA-seq. Healthy bone marrow after CD34 magnetic bead enrichment is included as a normal control. (c) Mutation frequencies observed in the full patient cohort (Fig. 5a; n = 39). This cohort is biased toward samples harboring internal tandem duplications in FLT3. This lesion leads to a higher white blood cell count and, therefore, more cells to work with, making these samples more attractive. (d) Variant allele frequencies in HSCs isolated from patients with AML (n = 39) as determined by targeted amplicon sequencing to depth >500 reads. *P < 0.05, **P < 0.01, _χ_2 test. (e,f) Principal-component analysis of RNA-seq data (e) and ATAC-seq data (f) showing the spread of AML cell types across the myelopoietic continuum. The position of each normal hematopoietic cell type is shown using cartoon cells. These normal hematopoietic cell type positions represent the average for all biological and technical replicates.

Supplementary Figure 12 Validation of enhancer cytometry in AML cell lines and primary cells by single-cell ATAC-seq.

(a) Enhancer cytometry of ATAC-seq data as shown in Figure 6a but ordered by patient instead of by AML cell type. (b) Projection of downsampled bulk hematopoiesis data onto a 2D PCA. Bulk ATAC-seq data were downsampled to give 1,000 fragments, approximately the lower bound of fragments obtained by scATAC-seq. Each dot represents an individual permutation of downsampled bulk ATAC-seq data from HSCs (green), GMPs (yellow), and monocytes (orange). Principal components were derived from bulk ATAC-seq data for normal hematopoietic cells. (c) Projection of downsampled bulk hematopoiesis data onto a one-dimensional myelopoietic differentiation trajectory. (d,e) scATAC-seq data derived from FACS-purified LMPPs (n = 94) (d) and monocytes (n = 88) (e) projected onto a 2D PCA plot. Principal components were derived from bulk ATAC-seq data for normal hematopoietic cells. (f) Enhancer cytometry of bulk ATAC-seq data derived from various blood cell lines demonstrates mixed regulatory contribution from various normal hematopoietic cell types. (g) Projection of scATAC-seq data derived from healthy monocytes, LMPPs, and leukemic cell types from patients SU070 and SU353 onto a one-dimensional myeloid differentiation trajectory. (h) Projection of scATAC-seq data derived from HL60 cells (n = 90) onto a one-dimensional myeloid differentiation trajectory. (i,j) Projection of scATAC-seq data derived from single cells of the common cell lines K562 (n = 227 total) and TF1 (n = 56) (i) and GM12878 (n = 75) (j) onto a one-dimensional erythroid (i) and lymphoid (k) differentiation trajectory. AU, arbitrary units.

Supplementary Figure 13 Analysis of the synthetic normal analog comparisons used for AML cell types.

(a) Pearson correlations derived from comparison of all peaks for AML cell types with the closest normal analog (color) and the enhancer cytometry–derived synthetic normal (gray). (b) Total significant peaks observed after comparison of AML cell types to synthetic normal analogs. Significant peaks were defined as those with a log2 (fold change) value greater than 3 or less than –3 for comparison of AML with the closest normal or synthetic normal analog. (c) Comparison of AML cell types to synthetic normal analogs as shown in Figure 6i except reordered by patient instead of AML cell type. For each sample, the closest normal is shown with the color of the bar. The percentage of the total significant peaks that were removed by comparison to synthetic normal analogs is plotted for each sample. (d) Global epigenetic variance of AML cell types after subtraction of signal corresponding to synthetic normal analogs. Values are normalized to the maximum variance observed in Figure 5d to allow for comparison across plots.

Supplementary Figure 14 Validation of regulatory network analysis in AML cell types

(a) Mean Euclidean distance of centroids, derived by k-means clustering, of differentially accessible peaks in AML samples compared to their synthetic normal, for increasing K from 1 to 20. Mean distances across centroids stabilize after a K of 7.

(b) Expression of JUN in various normal hematopoietic cell types, pHSCs, and blasts. *P <0.05, two-tailed t-test

(c-e) The effect of JNK/ERK inhibition by (a) JNK-IN-8, (b) SP600125, and (c) SCH772984 was determined by IC50 of sorted primary AML blast cells in comparison to CD34+ HSPCs derived from umbilical cord blood. Viability was determined by flow cytometric assessment of Annexin V and DAPI.

(f) Strategy for in vitro differentiation of HSPCs down the myeloid and erythroid lineages. HSPCs are grown in defined culture media for 6 days and then analyzed for cell surface markers of stemness or differentiation. Immature cells at day 6 express CD34 and have not yet upregulated CD33.

(g) Quantitative reverse-transcriptase PCR validation of HOXA9 knockdown via shRNA. Knockdown performed in THP1 cells for 72 hours and validated with two separate primer sets.

(h,i) Fold change in the percent of (h) CD15+ granulocytes or (i) CD71+GPA+ erythroblasts between cord blood-derived CD34+ HSPCs infected with lentiviruses with non-targeting or HOXA9 shRNAs after 6 days of differentiation down the (h) myeloid or (i) erythroid lineage. ***P <0.001, ****P <0.0001 by two-tailed t-test

(j) Fold change in the percent of CD34+ HSPCs after 6 days of culture in stemness retention media (see methods) between cord blood-derived CD34+ HSPCs infected with lentiviruses with non-targeting or HOXA9 shRNAs.

(k) Burden (% of cells with mutation) of mutations in DNMT3A, TET2, IDH1/2, or other genes when detected in pre-leukemic HSC. *P < 0.05, **P < 0.01 by two-tailed t-test

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Corces, M., Buenrostro, J., Wu, B. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution.Nat Genet 48, 1193–1203 (2016). https://doi.org/10.1038/ng.3646

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• Received: 16 November 2015
• Accepted: 18 July 2016
• Published: 15 August 2016
• Issue Date: October 2016
• DOI: https://doi.org/10.1038/ng.3646