Letter

Resolving early mesoderm diversification through single-cell expression profiling

  • Nature volume 535, pages 289293 (14 July 2016)
  • doi:10.1038/nature18633
  • Download Citation
Received:
Accepted:
Published:

Abstract

In mammals, specification of the three major germ layers occurs during gastrulation, when cells ingressing through the primitive streak differentiate into the precursor cells of major organ systems. However, the molecular mechanisms underlying this process remain unclear, as numbers of gastrulating cells are very limited. In the mouse embryo at embryonic day 6.5, cells located at the junction between the extra-embryonic region and the epiblast on the posterior side of the embryo undergo an epithelial-to-mesenchymal transition and ingress through the primitive streak. Subsequently, cells migrate, either surrounding the prospective ectoderm contributing to the embryo proper, or into the extra-embryonic region to form the yolk sac, umbilical cord and placenta. Fate mapping has shown that mature tissues such as blood and heart originate from specific regions of the pre-gastrula epiblast1, but the plasticity of cells within the embryo and the function of key cell-type-specific transcription factors remain unclear. Here we analyse 1,205 cells from the epiblast and nascent Flk1+ mesoderm of gastrulating mouse embryos using single-cell RNA sequencing, representing the first transcriptome-wide in vivo view of early mesoderm formation during mammalian gastrulation. Additionally, using knockout mice, we study the function of Tal1, a key haematopoietic transcription factor, and demonstrate, contrary to previous studies performed using retrospective assays2,3, that Tal1 knockout does not immediately bias precursor cells towards a cardiac fate.

  • Subscribe to Nature for full access:

    $199

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Accessions

Data deposits

ChIP-seq data are available at the NCBI Gene Expression Omnibus portal under accession number GSE74994. Processed data are also available at http://codex.stemcells.cam.ac.uk. RNAseq data are available at Array Express under accession numbers E-MTAB-4079 and E-MTAB-4026. Processed RNAseq data are also available at http://gastrulation.stemcells.cam.ac.uk/scialdone2016.

References

  1. 1.

    , & Clonal analysis of epiblast fate during germ layer formation in the mouse embryo. Development 113, 891–911 (1991)

  2. 2.

    et al. Scl represses cardiomyogenesis in prospective hemogenic endothelium and endocardium. Cell 150, 590–605 (2012)

  3. 3.

    et al. Scl binds to primed enhancers in mesoderm to regulate hematopoietic and cardiac fate divergence. EMBO J. 34, 759–777 (2015)

  4. 4.

    et al. Primitive erythropoiesis from mesodermal precursors expressing VE-cadherin, PECAM-1, Tie2, endoglin, and CD34 in the mouse embryo. Blood 108, 4018–4024 (2006)

  5. 5.

    , , , & Expression of CD41 marks the initiation of definitive hematopoiesis in the mouse embryo. Blood 101, 508–516 (2003)

  6. 6.

    , & Expression pattern of the mouse T gene and its role in mesoderm formation. Nature 343, 657–659 (1990)

  7. 7.

    & Foxa2 regulates polarity and epithelialization in the endoderm germ layer of the mouse embryo. Development 136, 1029–1038 (2009)

  8. 8.

    et al. The Slc35d3 gene, encoding an orphan nucleotide sugar transporter, regulates platelet-dense granules. Blood 109, 1533–1540 (2007)

  9. 9.

    et al. Selective expression of sense and antisense transcripts of the sushi-ichi-related retrotransposon – derived family during mouse placentogenesis. Retrovirology 12, 9 (2015)

  10. 10.

    & The allocation of epiblast cells to ectodermal and germ-line lineages is influenced by the position of the cells in the gastrulating mouse embryo. Dev. Biol. 178, 124–132 (1996)

  11. 11.

    & Gastrulation: making and shaping germ layers. Annu. Rev. Cell Dev. Biol. 28, 687–717 (2012)

  12. 12.

    , , & MesP1 and MesP2 are essential for the development of cardiac mesoderm. Development 127, 3215–3226 (2000)

  13. 13.

    et al. Myosin-II-mediated cell shape changes and cell intercalation contribute to primitive streak formation. Nature Cell Biol. 17, 397–408 (2015)

  14. 14.

    , & Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015)

  15. 15.

    et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nature Biotechnol. 33, 269–276 (2015)

  16. 16.

    Segmental border is defined by the key transcription factor Mesp2, by means of the suppression of Notch activity. Dev. Dyn. 236, 1450–1455 (2007)

  17. 17.

    et al. Bmp4 is required for the generation of primordial germ cells in the mouse embryo. Genes Dev. 13, 424–436 (1999)

  18. 18.

    , , , & Arrested development of embryonic red cell precursors in mouse embryos lacking transcription factor GATA-1. Proc. Natl Acad. Sci. USA 93, 12355–12358 (1996)

  19. 19.

    et al. The haemangioblast generates haematopoietic cells through a haemogenic endothelium stage. Nature 457, 892–895 (2009)

  20. 20.

    et al. Clonal analysis identifies hemogenic endothelium and not hemangioblasts as the source of the blood-endothelial common lineage in the mouse embryo. Blood 124, 2523–2532 (2014)

  21. 21.

    , , & The allocation of epiblast cells to the embryonic heart and other mesodermal lineages: the role of ingression and tissue movement during gastrulation. Development 124, 1631–1642 (1997)

  22. 22.

    et al. The T cell leukemia oncoprotein SCL/tal-1 is essential for development of all hematopoietic lineages. Cell 86, 47–57 (1996)

  23. 23.

    , & Absence of blood formation in mice lacking the T-cell leukaemia oncoprotein tal-1/SCL. Nature 373, 432–434 (1995)

  24. 24.

    , , & Direct reprogramming of murine fibroblasts to hematopoietic progenitor cells. Cell Reports 9, 1871–1884 (2014)

  25. 25.

    et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016)

  26. 26.

    & Epigenetics reloaded: the single-cell revolution. Trends Cell Biol. 24, 712–723 (2014)

  27. 27.

    et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nature Biotechnol. 33, 503–509 (2015)

  28. 28.

    , , , & Spatial reconstruction of single-cell gene expression data. Nature Biotechnol. 33, 495–502 (2015)

  29. 29.

    Dose-dependent Nodal/Smad signals pattern the early mouse embryo. Semin. Cell Dev. Biol. 32, 73–79 (2014)

  30. 30.

    & Staging of gastrulating mouse embryos by morphological landmarks in the dissecting microscope. Development 118, 1255–1266 (1993)

  31. 31.

    et al. Single site-specific integration targeting coupled with embryonic stem cell differentiation provides a high-throughput alternative to in vivo enhancer analyses. Biol. Open 2, 1229–1238 (2013)

  32. 32.

    et al. Characterization of hematopoietic progenitor cells that express the transcription factor SCL, using a lacZ “knock-in” strategy. Proc. Natl Acad. Sci. USA 95, 11897–11902 (1998)

  33. 33.

    et al. Full-length RNA-seq from single cells using Smart-seq2. Nature Protocols 9, 171–181 (2014)

  34. 34.

    , & HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015)

  35. 35.

    , & Computational and analytical challenges in single-cell transcriptomics. Nature Rev. Genet. 16, 133–145 (2015)

  36. 36.

    & Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

  37. 37.

    Barnes-Hut-SNE. Preprint at (2013)

  38. 38.

    & Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010)

  39. 39.

    et al. Accounting for technical noise in single-cell RNA-seq experiments. Nature Methods 10, 1093–1095 (2013)

  40. 40.

    et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnol. 33, 155–160 (2015)

  41. 41.

    , & edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)

  42. 42.

    , & Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008)

  43. 43.

    , & On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001)

  44. 44.

    Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

  45. 45.

    et al. destiny – diffusion maps for large-scale single-cell data in R. bioRxiv (2015)

  46. 46.

    & in Model Selection and Multimodel Inference A Practical Information-Theoretic Approach 2nd edn, Ch. 2 (Springer, 2002)

  47. 47.

    Random Forests. Mach. Learn. 45, 5–32 (2001)

  48. 48.

    & Classification and Regression by randomForest. R News 2, 18–22 (2002)

  49. 49.

    , & Derivation of mouse embryonic stem cells. Nature Protocols 1, 2082–2087 (2006)

  50. 50.

    et al. Circulation-independent differentiation pathway from extraembryonic mesoderm toward hematopoietic stem cells via hemogenic angioblasts. Cell Reports 8, 31–39 (2014)

  51. 51.

    , , , & In vitro differentiation of mouse embryonic stem cells as a model of early hematopoietic development. Methods Mol. Biol. 538, 317–334 (2009)

  52. 52.

    et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532–544 (2010)

  53. 53.

    et al. A carrier-assisted ChIP-seq method for estrogen receptor-chromatin interactions from breast cancer core needle biopsy samples. BMC Genomics 14, 232 (2013)

  54. 54.

    et al. CODEX: a next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities. Nucleic Acids Res. 43, D1117–D1123 (2015)

  55. 55.

    et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008)

  56. 56.

    & PolyaPeak: detecting transcription factor binding sites from ChIP-seq using peak shape information. PLoS ONE 9, e89694 (2014)

  57. 57.

    In Situ Hybridization (Oxford Univ. Press, 1999)

  58. 58.

    & Axis development and early asymmetry in mammals. Cell 96, 195–209 (1999)

  59. 59.

    et al. O-fucosylation of thrombospondin type 1 repeats restricts epithelial to mesenchymal transition (EMT) and maintains epiblast pluripotency during mouse gastrulation. Dev. Biol. 346, 25–38 (2010)

  60. 60.

    et al. Loss of the extraembryonic ectoderm in Elf5 mutants leads to defects in embryonic patterning. Development 132, 2299–2308 (2005)

  61. 61.

    et al. Loss of PGC-specific expression of the orphan nuclear receptor ERR-β results in reduction of germ cell number in mouse embryos. Mech. Dev. 121, 237–246 (2004)

  62. 62.

    et al. Platelet endothelial cell adhesion molecule-1 (PECAM-1/CD31): alternatively spliced, functionally distinct isoforms expressed during mammalian cardiovascular development. Development 120, 2539–2553 (1994)

  63. 63.

    , , , & Identity and fate of Tbx4-expressing cells reveal developmental cell fate decisions in the allantois, limb, and external genitalia. Dev. Dyn. 240, 2290–2300 (2011)

  64. 64.

    et al. Microarray analysis of Foxa2 mutant mouse embryos reveals novel gene expression and inductive roles for the gastrula organizer and its derivatives. BMC Genomics 9, 511 (2008)

  65. 65.

    et al. Prdm1 functions in the mesoderm of the second heart field, where it interacts genetically with Tbx1, during outflow tract morphogenesis in the mouse embryo. Hum. Mol. Genet. 23, 5087–5101 (2014)

  66. 66.

    et al. β-Catenin regulates Cripto- and Wnt3-dependent gene expression programs in mouse axis and mesoderm formation. Development 130, 6283–6294 (2003)

  67. 67.

    , & Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells. Nature Genet. 24, 372–376 (2000)

  68. 68.

    & Mml, a mouse Mix-like gene expressed in the primitive streak. Mech. Dev. 87, 189–192 (1999)

  69. 69.

    et al. Mutations in Sox18 underlie cardiovascular and hair follicle defects in ragged mice. Nature Genet. 24, 434–437 (2000)

  70. 70.

    , & Expression of the hyaluronan receptor LYVE-1 is not restricted to the lymphatic vasculature; LYVE-1 is also expressed on embryonic blood vessels. Dev. Dyn. 237, 1901–1909 (2008)

  71. 71.

    , & The SCL/TAL-1 gene is expressed in progenitors of both the hematopoietic and vascular systems during embryogenesis. Blood 83, 1200–1208 (1994)

  72. 72.

    et al. Absence of yolk sac hematopoiesis from mice with a targeted disruption of the scl gene. Proc. Natl Acad. Sci. USA 92, 7075–7079 (1995)

  73. 73.

    et al. The transcriptional programme controlled by Runx1 during early embryonic blood development. Dev. Biol. 366, 404–419 (2012)

  74. 74.

    et al. Cbfa2 is required for the formation of intra-aortic hematopoietic clusters. Development 126, 2563–2575 (1999)

  75. 75.

    , & Initiation of hematopoiesis and vasculogenesis in murine yolk sac explants. Blood 86, 156–163 (1995)

  76. 76.

    et al. The erythroid phenotype of EKLF-null mice: defects in hemoglobin metabolism and membrane stability. Mol. Cell. Biol. 25, 5205–5214 (2005)

  77. 77.

    , & Erythroid Krüppel-like factor exhibits an early and sequentially localized pattern of expression during mammalian erythroid ontogeny. Dev. Dyn. 206, 248–259 (1996)

  78. 78.

    & Initiation of murine embryonic erythropoiesis: a spatial analysis. Blood 89, 1154–1164 (1997)

  79. 79.

    , & The bicoid-related homeoprotein Ptx1 defines the most anterior domain of the embryo and differentiates posterior from anterior lateral mesoderm. Development 124, 2807–2817 (1997)

  80. 80.

    , & TBX1 represses Vegfr2 gene expression and enhances the cardiac fate of VEGFR2+ cells. PLoS ONE 10, e0138525 (2015)

  81. 81.

    et al. Cre-mediated excision of Fgf8 in the Tbx1 expression domain reveals a critical role for Fgf8 in cardiovascular development in the mouse. Dev. Biol. 267, 190–202 (2004)

  82. 82.

    et al. Nodal signalling in the epiblast patterns the early mouse embryo. Nature 411, 965–969 (2001)

  83. 83.

    et al. Mouse Lefty2 and zebrafish antivin are feedback inhibitors of nodal signaling during vertebrate gastrulation. Mol. Cell 4, 287–298 (1999)

  84. 84.

    et al. Dynamic expression and essential functions of Hes7 in somite segmentation. Genes Dev. 15, 2642–2647 (2001)

  85. 85.

    , , & Mesp2 and Tbx6 cooperatively create periodic patterns coupled with the clock machinery during mouse somitogenesis. Development 135, 2555–2562 (2008)

  86. 86.

    , & Acquisition of Hox codes during gastrulation and axial elongation in the mouse embryo. Development 130, 3807–3819 (2003)

  87. 87.

    et al. The allantois and chorion, when isolated before circulation or chorio-allantoic fusion, have hematopoietic potential. Development 133, 4183–4192 (2006)

  88. 88.

    , , , & Investigation into a role for the primitive streak in development of the murine allantois. Development 131, 37–55 (2004)

  89. 89.

    , , , & Blood-borne seeding by hematopoietic and endothelial precursors from the allantois. Proc. Natl Acad. Sci. USA 95, 1641–1646 (1998)

  90. 90.

    et al. The Cdx4 mutation affects axial development and reveals an essential role of Cdx genes in the ontogenesis of the placental labyrinth in mice. Development 133, 419–428 (2006)

  91. 91.

    , & Cell adhesion events mediated by α4 integrins are essential in placental and cardiac development. Development 121, 549–560 (1995)

  92. 92.

    & Early embryonic lethality in Bmp5;Bmp7 double mutant mice suggests functional redundancy within the 60A subgroup. Development 126, 1753–1768 (1999)

  93. 93.

    & Vasculogenesis in the day 6.5 to 9.5 mouse embryo. Blood 95, 1671–1679 (2000)

  94. 94.

    et al. ER71 acts downstream of BMP, Notch, and Wnt signaling in blood and vessel progenitor specification. Cell Stem Cell 2, 497–507 (2008)

  95. 95.

    , , & Hox genes specify vertebral types in the presomitic mesoderm. Genes Dev. 19, 2116–2121 (2005)

  96. 96.

    et al. Expression of podocalyxin separates the hematopoietic and vascular potentials of mouse embryonic stem cell-derived mesoderm. Stem Cells 32, 191–203 (2014)

  97. 97.

    Expression pattern of the Brachyury gene in whole-mount TWis/TWis mutant embryos. Development 113, 913–917 (1991)

  98. 98.

    et al. TBX3 directs cell-fate decision toward mesendoderm. Stem Cell Rep. 1, 248–265 (2013)

  99. 99.

    , , , & HNF3β and Lim1 interact in the visceral endoderm to regulate primitive streak formation and anterior-posterior polarity in the mouse embryo. Development 126, 4499–4511 (1999)

  100. 100.

    et al. MesP1 is expressed in the heart precursor cells and required for the formation of a single heart tube. Development 126, 3437–3447 (1999)

  101. 101.

    , , & Mechanisms of developmental control of transcription in the murine α- and β-globin loci. Genes Dev. 13, 112–124 (1999)

  102. 102.

    , , & Yolk sac-derived primitive erythroblasts enucleate during mammalian embryogenesis. Blood 104, 19–25 (2004)

  103. 103.

    et al. A global role for EKLF in definitive and primitive erythropoiesis. Blood 107, 3359–3370 (2006)

  104. 104.

    et al. Single-lineage transcriptome analysis reveals key regulatory pathways in primitive erythroid progenitors in the mouse embryo. Blood 117, 4924–4934 (2011)

  105. 105.

    , , & Gene set control analysis predicts hematopoietic control mechanisms from genome-wide transcription factor binding data. Exp. Hematol. 41, 354–366.e14 (2013)

  106. 106.

    et al. Dynamic gene regulatory networks drive hematopoietic specification and differentiation. Dev. Cell 36, 572–587 (2016)

Download references

Acknowledgements

We thank M. de Bruijn, A. Martinez-Arias, J. Nichols and C. Mulas for discussion, the Cambridge Institute for Medical Research Flow Cytometry facility for their expertise in single-cell index sorting, and S. Lorenz from the Sanger Single Cell Genomics Core for supervising purification of Tal1−/− sequencing libraries. ChIP-seq reads were processed by R. Hannah. Research in the authors’ laboratories is supported by the Medical Research Council, Cancer Research UK, the Biotechnology and Biological Sciences Research Council, Bloodwise, the Leukemia and Lymphoma Society, and the Sanger-EBI Single Cell Centre, and by core support grants from the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust - MRC Cambridge Stem Cell Institute and by core funding from Cancer Research UK and the European Molecular Biology Laboratory. Y.T. was supported by a fellowship from the Japan Society for the Promotion of Science. W.J. is a Wellcome Trust Clinical Research Fellow. A.S. is supported by the Sanger-EBI Single Cell Centre. This work was funded as part of Wellcome Trust Strategic Award 105031/D/14/Z ‘Tracing early mammalian lineage decisions by single-cell genomics’ awarded to W. Reik, S. Teichmann, J. Nichols, B. Simons, T. Voet, S. Srinivas, L. Vallier, B. Göttgens and J. Marioni.

Author information

Author notes

    • Yosuke Tanaka

    Present address: Division of Cellular Therapy, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.

    • Antonio Scialdone
    • , Yosuke Tanaka
    • , Wajid Jawaid
    •  & Victoria Moignard

    These authors contributed equally to this work.

Affiliations

  1. EMBL-European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK

    • Antonio Scialdone
    •  & John C. Marioni
  2. Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK

    • Antonio Scialdone
    • , Iain C. Macaulay
    •  & John C. Marioni
  3. Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK

    • Yosuke Tanaka
    • , Wajid Jawaid
    • , Victoria Moignard
    • , Nicola K. Wilson
    •  & Berthold Göttgens
  4. Wellcome Trust - Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    • Yosuke Tanaka
    • , Wajid Jawaid
    • , Victoria Moignard
    • , Nicola K. Wilson
    •  & Berthold Göttgens
  5. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK

    • John C. Marioni

Authors

  1. Search for Antonio Scialdone in:

  2. Search for Yosuke Tanaka in:

  3. Search for Wajid Jawaid in:

  4. Search for Victoria Moignard in:

  5. Search for Nicola K. Wilson in:

  6. Search for Iain C. Macaulay in:

  7. Search for John C. Marioni in:

  8. Search for Berthold Göttgens in:

Contributions

A.S. and W.J. processed and analysed single-cell RNA sequencing (RNA-seq) data. A.S. and V.M. generated figures. Y.T. and W.J. performed embryo dissection. N.K.W., V.M. and I.C.M. performed single-cell RNA-seq experiments. Y.T. performed flow cytometry, ESC differentiation and in situ hybridization. V.M. performed ChIP-seq assays. A.S., W.J., Y.T., V.M., B.G. and J.C.M. interpreted results and wrote the paper. B.G. and J.C.M. supervised and conceived the study.

Corresponding authors

Correspondence to John C. Marioni or Berthold Göttgens.

Reviewer Information Nature thanks A.-K. Hadjantonakis, P. Robson and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data

Supplementary information

Excel files

  1. 1.

    Supplementary Table 1

    Genes correlated with T in E6.5 epiblast cells. Correlation was calculated using Spearman rank correlation. All genes significantly correlated at FDR <0.1 are listed.

  2. 2.

    Supplementary Table 2

    Gene ontology terms for genes dynamically expressed across pseudospace and pseudotime in Figures 2 and 4. Significant GO terms associated with the gene clusters in Figure 3c and Figure 4c (both p-value <10-4). In both cases, no significant GO terms were associated with the smallest cluster of transient expressed genes, likely due to the low numbers of genes in these clusters.

  3. 3.

    Supplementary Table 3

    Full list of marker genes associated with clusters in Figures 1 and Extended Data Fig. 2.

  4. 4.

    Supplementary Table 4

    List of genes differentially expressed along the pseudo-space trajectory shown in Figure 3. The three clusters of genes differentially expressed along the pseudo-space trajectory are listed (see Figures 3 and Extended Data Fig. 5) with their ΔAIC scores (see Methods).

  5. 5.

    Supplementary Table 5

    List of genes differentially expressed along the pseudo-time trajectory shown in Figure 4. The three clusters of genes differentially expressed along the pseudo-time trajectory are listed (see Figures 4 and Extended Data Fig. 8), along with their ΔAIC scores (see Methods).

  6. 6.

    Supplementary Table 6

    Lists of genes bound by Gata in ChIP-seq experiment, and differentially expressed along the pseudo-time trajectory shown in Figure 4 and bound by Gata1.

  7. 7.

    Supplementary Table 7

    List of 319 genes up-regulated in endothelium (red, cluster 6) and blood cells (brown, cluster 8) when compared to nascent mesoderm population (blue, cluster 4) in Figure 5d.

  8. 8.

    Supplementary Table 8

    List of genes differentially expressed between Tal1-/- endothelium and WT endothelium in Figure 5e. Genes up-regulated in Tal1-/- mice correspond to fold-changes greater than 1.

  9. 9.

    Supplementary Table 9

    Genes up- and down-regulated by Tal1. Gene sets used in Figure 5e, taken from Org et al., 20153 Supplementary Tables 1c and 1d.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.