Population snapshots predict early haematopoietic and erythroid hierarchies

Nature

Volumn 555, Issue 7694, 2018, Pages 54-60

  Tusi, Betsabeh Khoramian ^a   Wolock, Samuel L ^b   Weinreb, Caleb ^b   Hwang, Yung ^a   Hidalgo, Daniel ^a   Zilionis, Rapolas ^b   Waisman, Ari ^c   Huh, Jun R ^d   Klein, Allon M ^b   Socolovsky, Merav ^a,e  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c JOHANNES GUTENBERG UNIVERSITY   (Germany)

^d BRIGHAM AND WOMEN S HOSPITAL   (United States)

^e UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTERLEUKIN 17; INTERLEUKIN 17 RECEPTOR; WNT5A PROTEIN; SIGNAL PEPTIDE; SMALL CYTOPLASMIC RNA; STEM CELL FACTOR RECEPTOR; TRANSCRIPTOME;

BIOLOGY; BLOOD; CELL; DIFFERENTIATION; FLOW CYTOMETRY; GROWTH; PROTEIN;

ANIMAL CELL; ANIMAL TISSUE; ARTICLE; BASOPHIL; BONE MARROW DERIVED MONONUCLEAR CELL; CELL DIFFERENTIATION; CELL FATE; CELL ISOLATION; CELL POPULATION; CELL STRESS; COLONY FORMING CELL; CONTROLLED STUDY; EMBRYO; ENZYME REGULATION; ERYTHROID PRECURSOR CELL; ERYTHROPOIESIS; FEMALE; FETUS; G1 PHASE CELL CYCLE CHECKPOINT; G2 PHASE CELL CYCLE CHECKPOINT; HEMATOPOIETIC STEM CELL; HUMAN; HUMAN CELL; IN VITRO STUDY; IN VIVO STUDY; MALE; MOUSE; NONHUMAN; PRIORITY JOURNAL; PROTEIN EXPRESSION; PROTEIN PHOSPHORYLATION; S PHASE CELL CYCLE CHECKPOINT; TRANSCRIPTOMICS; WESTERN BLOTTING; ANIMAL; CELL CYCLE; CELL LINEAGE; CYTOLOGY; DRUG EFFECT; ERYTHROCYTE; FLOW CYTOMETRY; GENETICS; MAST CELL; METABOLISM; PHYSIOLOGY; SINGLE CELL ANALYSIS;

ANIMALS; BASOPHILS; CELL CYCLE; CELL LINEAGE; ERYTHROCYTES; ERYTHROID PRECURSOR CELLS; ERYTHROPOIESIS; FEMALE; FLOW CYTOMETRY; INTERCELLULAR SIGNALING PEPTIDES AND PROTEINS; MAST CELLS; MICE; PROTO-ONCOGENE PROTEINS C-KIT; RNA, SMALL CYTOPLASMIC; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85042750225     PISSN: 00280836     EISSN: 14764687     Source Type: Journal    
DOI: 10.1038/nature25741     Document Type: Article

References (69)

1. Fujiwara, Y., Browne, C. P., Cunniff, K., Goff, S. C. & Orkin, S. H. Arrested development of embryonic red cell precursors in mouse embryos lacking transcription factor GATA-1. Proc. Natl Acad. Sci. USA 93, 12355-12358 (1996).
2. Liu, Y. et al. Suppression of Fas-FasL coexpression by erythropoietin mediates erythroblast expansion during the erythropoietic stress response in vivo. Blood 108, 123-133 (2006).
3. Chen, K. et al. Resolving the distinct stages in erythroid differentiation based on dynamic changes in membrane protein expression during erythropoiesis. Proc. Natl Acad. Sci. USA 106, 17413-17418 (2009).
4. Hara, H. & Ogawa, M. Erythropoietic precursors in mice under erythropoietic stimulation and suppression. Exp. Hematol. 5, 141-148 (1977).
5. Gregory, C. J., McCulloch, E. A. & Till, J. E. The cellular basis for the defect in haemopoiesis in flexed-tailed mice. III. Restriction of the defect to erythropoietic progenitors capable of transient colony formation in vivo. Br. J. Haematol. 30, 401-410 (1975).
6. Pronk, C. J. et al. Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1, 428-442 (2007).
7. Flygare, J., Rayon Estrada, V., Shin, C., Gupta, S. & Lodish, H. F. HIF1α synergizes with glucocorticoids to promote BFU-E progenitor self-renewal. Blood 117, 3435-3444 (2011).
8. Li, J. et al. Isolation and transcriptome analyses of human erythroid progenitors: BFU-E and CFU-E. Blood 124, 3636-3645 (2014).
9. , Chen, J. Y., Pluvinage, J. V., Seita, J. & Weissman, I. L. Prospective isolation of human erythroid lineage-committed progenitors. Proc. Natl Acad. Sci. USA 112, 9638-9643 (2015)
10. Guo, G. et al. Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell 13, 492-505 (2013).
11. Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322-327 (2014).
12. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663-1677 (2015).
13. Busch, K. et al. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 518, 542-546 (2015).
14. Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).
15. Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20-e31 (2016).
16. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271-281 (2017).
17. Mercier, F. E. & Scadden, D. T. Not all created equal: Lineage hard-wiring in the production of blood. Cell 163, 1568-1570 (2015).
18. Kondo, M., Weissman, I. L. & Akashi, K. Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661-672 (1997).
19. Akashi, K., Traver, D., Miyamoto, T. & Weissman, I. L. A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193-197 (2000).
20. Adolfsson, J. et al. Identification of Flt3+ lympho-myeloid stem cells lacking erythro-megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121, 295-306 (2005).
21. Huang, W., Cao, X., Biase, F. H., Yu, P. & Zhong, S. Time-variant clustering model for understanding cell fate decisions. Proc. Natl Acad. Sci. USA 111, E4797-E4806 (2014).
22. Marco, E. et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. USA 111, E5643-E5650 (2014).
23. Shin, J. et al. Single-cell RNA-seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360-372 (2015).
24. Ji, Z. & Ji, H. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).
25. Welch, J. D., Hartemink, A. J. & Prins, J. F. SLICER: Inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016).
26. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845-848 (2016).
27. Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269-276 (2015).
28. Khoramian Tusi, B. & Socolovsky, M. High throughput single-cell fate potential assay of murine hematopoietic progenitors in vitro. Ex. Hematol. https://doi. org/10.1016/j.exphem.2018.01.005 (2018)
29. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015).
30. Morrison, S. J. & Weissman, I. L. The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661-673 (1994).
31. Papayannopoulou, T., Brice, M., Broudy, V. C. & Zsebo, K. M. Isolation of c-kit receptor-expressing cells from bone marrow, peripheral blood, and fetal liver: Functional properties and composite antigenic profile. Blood 78, 1403-1412 (1991).
32. Weinreb, C., Wolock, S. & Klein, A. SPRING: A kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics (2017).
33. Weinreb, C., Wolock, S., Khoramian Tusi, B., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single cell snapshots. Proc. Natl Acad. Sci. USA. http://doi.org/10.1073/pnas.1714723115 (2018).
34. Yanez, A. et al. Granulocyte-monocyte progenitors and monocyte-dendritic cell progenitors independently produce functionally distinct monocytes. Immunity 47, 890-902.e4 (2017).
35. Magwene, P. M., Lizardi, P. & Kim, J. Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19, 842-850 (2003).
36. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714-725 (2014).
37. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381-386 (2014).
38. Bresnick, E. H., Lee, H.-Y., Fujiwara, T., Johnson, K. D. & Keles, S. GATA switches as developmental drivers. J. Biol. Chem. 285, 31087-31093 (2010).
39. Li, P. et al. Regulation of bone marrow hematopoietic stem cell is involved in high-altitude erythrocytosis. Exp. Hematol. 39, 37-46 (2011).
40. Grover, A. et al. Erythropoietin guides multipotent hematopoietic progenitor cells toward an erythroid fate. J. Exp. Med. 211, 181-188 (2014).
41. Mancini, E. et al. FOG-1 and GATA-1 act sequentially to specify definitive megakaryocytic and erythroid progenitors. EMBO J. 31, 351-365 (2012).
42. Koulnis, M., Porpiglia, E., Hidalgo, D. & Socolovsky, M. in A Systems Biology Approach to Blood, Vol. 844 (eds Corey, S. J.et al.) Ch. 3, 37-58 (Springer New York, 2014).
43. Agosti, V., Karur, V., Sathyanarayana, P., Besmer, P. & Wojchowski, D. M. A KIT juxtamembrane PY567-directed pathway provides nonredundant signals for erythroid progenitor cell development and stress erythropoiesis. Exp. Hematol. 37, 159-171 (2009).
44. Koury, M. J. & Bondurant, M. C. Erythropoietin retards DNA breakdown and prevents programmed death in erythroid progenitor cells. Science 248, 378-381 (1990).
45. Yee, K., Bishop, T. R., Mather, C. & Zon, L. I. Isolation of a novel receptor tyrosine kinase cDNA expressed by developing erythroid progenitors. Blood 82, 1335-1343 (1993).
46. van den Akker, E. et al. Tyrosine kinase receptor RON functions downstream of the erythropoietin receptor to induce expansion of erythroid progenitors. Blood 103, 4457-4465 (2004).
47. Pop, R. et al. A key commitment step in erythropoiesis is synchronized with the cell cycle clock through mutual inhibition between PU.1 and S-phase progression. PLoS Biol. 8, e1000484 (2010).
48. Hwang, Y. et al. Global increase in replication fork speed during a p57KIP2-regulated erythroid cell fate switch. Sci. Adv. 3, e1700298 (2017).
49. Shearstone, J. R. et al. Global DNA demethylation during mouse erythropoiesis in vivo. Science 334, 799-802 (2011).
50. Nemeth, E. & Ganz, T. Anemia of inflammation. Hematol. Oncol. Clin. North Am. 28, 671-681 (2014).
51. Liang, R. et al. A systems approach identifies essential FOXO3 functions at key steps of terminal erythropoiesis. PLoS Genet. 11, e1005526 (2015).
52. Whitfield, M. L. et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977-2000 (2002).
53. Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protocols 12, 44-73 (2017).
54. Ester, M., Kriegel, H., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining (Eds Simoudis, E. et al.) 226-231 (AAAI, 1996).
55. Daszykowski, M., Walczak, B. & Massart, D. L. Looking for natural patterns in data: Part 1. Density-based approach. Chemomtr. Intell. Lab. Syst. 56, 83-92 (2001).
56. van der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221-3245 (2014).
57. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015).
58. Weinreb, C., Wolock, S. & Klein, A. SPRING: A kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics https://doi. org/10.1093/bioinformatics/btx792 (2017).
59. Vandin, F., Upfal, E. & Raphael, B. J. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507-522 (2011).
60. Heng, T. S. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091-1094 (2008).
61. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545-15550 (2005).
62. Lachmann, A. et al. ChEA: Transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438-2444 (2010).
63. Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54-61 (2015).
64. Santos, A., Wernersson, R. & Jensen, L. J. Cyclebase 3.0: A multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140-D1144 (2015).
65. Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308-1323.e1330 (2016).
66. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289-300 (1995).
67. Tusi, B. K. & Socolovsky, M. Novel FACS strategy for identification of early hematopoietic progenitors including BFU-e, CFU-e and erythroid-biased MPPs Protoc. Exch. http://doi.org/10.1038/protex.2018.031 (2018).
68. El Malki, K. et al. An alternative pathway of imiquimod-induced psoriasis-like skin inflammation in the absence of interleukin-17 receptor a signaling. J. Invest. Dermatol. 133, 441-451 (2013).
69. Porpiglia, E., Hidalgo, D., Koulnis, M., Tzafriri, A. R. & Socolovsky, M. Stat5 signaling specifies basal versus stress erythropoietic responses through distinct binary and graded dynamic modalities. PLoS Biol. 10, e1001383 (2012).