1. Cell Biology
  2. Computational and Systems Biology
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Science Forum: The Human Cell Atlas

  1. Aviv Regev  Is a corresponding author
  2. Sarah A Teichmann  Is a corresponding author
  3. Eric S Lander  Is a corresponding author
  4. Ido Amit
  5. Christophe Benoist
  6. Ewan Birney
  7. Bernd Bodenmiller
  8. Peter Campbell
  9. Piero Carninci
  10. Menna Clatworthy
  11. Hans Clevers
  12. Bart Deplancke
  13. Ian Dunham
  14. James Eberwine
  15. Roland Eils
  16. Wolfgang Enard
  17. Andrew Farmer
  18. Lars Fugger
  19. Berthold Göttgens
  20. Nir Hacohen
  21. Muzlifah Haniffa
  22. Martin Hemberg
  23. Seung Kim
  24. Paul Klenerman
  25. Arnold Kriegstein
  26. Ed Lein
  27. Sten Linnarsson
  28. Emma Lundberg
  29. Joakim Lundeberg
  30. Partha Majumder
  31. John C Marioni
  32. Miriam Merad
  33. Musa Mhlanga
  34. Martijn Nawijn
  35. Mihai Netea
  36. Garry Nolan
  37. Dana Pe'er
  38. Anthony Phillipakis
  39. Chris P Ponting
  40. Stephen Quake
  41. Wolf Reik
  42. Orit Rozenblatt-Rosen
  43. Joshua Sanes
  44. Rahul Satija
  45. Ton N Schumacher
  46. Alex Shalek
  47. Ehud Shapiro
  48. Padmanee Sharma
  49. Jay W Shin
  50. Oliver Stegle
  51. Michael Stratton
  52. Michael J T Stubbington
  53. Fabian J Theis
  54. Matthias Uhlen
  55. Alexander van Oudenaarden
  56. Allon Wagner
  57. Fiona Watt
  58. Jonathan Weissman
  59. Barbara Wold
  60. Ramnik Xavier
  61. Nir Yosef
  62. Human Cell Atlas Meeting Participants
  1. Broad Institute of MIT and Harvard, United States
  2. Massachusetts Institute of Technology, United States
  3. Howard Hughes Medical Institute, United States
  4. Wellcome Trust Sanger Institute, Wellcome Genome Campus, United Kingdom
  5. Wellcome Genome Campus, United Kingdom
  6. University of Cambridge, United Kingdom
  7. Harvard Medical School, United States
  8. Weizmann Institute of Science, Israel
  9. University of Zürich, Switzerland
  10. RIKEN Center for Life Science Technologies, Japan
  11. Hubrecht Institute, Princess Maxima Center for Pediatric Oncology and University Medical Center Utrecht, The Netherlands
  12. Swiss Federal Institute of Technology (EPFL), Switzerland
  13. Perelman School of Medicine, University of Pennsylvania, United States
  14. German Cancer Research Center (DKFZ), Germany
  15. Heidelberg University, Germany
  16. Ludwig Maximilian University Munich, Germany
  17. Takara Bio United States, Inc., United States
  18. John Radcliffe Hospital, University of Oxford, United Kingdom
  19. Massachusetts General Hospital Cancer Center, United States
  20. Newcastle University, United Kingdom
  21. Stanford University School of Medicine, United States
  22. University of Oxford, United Kingdom
  23. John Radcliffe Hospital, United Kingdom
  24. University of California, San Francisco, United States
  25. Allen Institute for Brain Science, United States
  26. Karolinska Institutet, Sweden
  27. KTH Royal Institute of Technology, Sweden
  28. Stanford University, United States
  29. National Institute of Biomedical Genomics, India
  30. Icahn School of Medicine at Mount Sinai, United States
  31. University of Cape Town, South Africa
  32. University of Groningen, University Medical Center Groningen, The Netherlands
  33. Radboud University Medical Center, The Netherlands
  34. Sloan Kettering Institute, United States
  35. University of Edinburgh, United Kingdom
  36. Chan Zuckerberg Biohub, United States
  37. The Babraham Institute, United Kingdom
  38. Harvard University, United States
  39. New York University, United States
  40. The Netherlands Cancer Institute, The Netherlands
  41. Ragon Institute of MGH, MIT and Harvard, United States
  42. University of Texas, United States
  43. German Research Center for Environmental Health, Helmholtz Center Munich, Germany
  44. Technical University of Munich, Germany
  45. Danish Technical University, Denmark
  46. Hubrecht Institute and University Medical Center Utrecht, The Netherlands
  47. University of California, Berkeley, United States
  48. King's College London, United Kingdom
  49. California Institute of Technology, United States
  50. Massachusetts General Hospital, United States
Feature Article
Cite as: eLife 2017;6:e27041 doi: 10.7554/eLife.27041
4 figures

Figures

A hierarchical view of human anatomy.

A graphical depiction of the anatomical hierarchy from organs (such as the gut), to tissues (such as the epithelium in the crypt in the small intestine), to their constituent cells (such as epithelial, immune, stromal and neural cells).

https://doi.org/10.7554/eLife.27041.002
Anatomy: cell types and tissue structure.

The first three plots show single cells (dots) embedded in low-dimensional space based on similarities between their RNA-expression profiles (A, C) or protein-expression profiles (B), using either t-stochastic neighborhood embedding (A,B) or circular projection (C) for dimensionality reduction and embedding. (A) Bi-polar neurons from the mouse retina. (B) Human bone marrow immune cells. (C) Immune cells from the mouse spleen. (D) Histology. Projection of single-cell data onto tissue structures: image shows the mapping of individual cells onto locations in the marine annelid brain, based on the correspondence (color bar) between their single-cell expression profiles and independent FISH assays for a set of landmark transcripts.

© 2016 Elsevier Inc. Figure 2A reprinted from Shekhar et al., 2016 with permission.

© 2015 Elsevier Inc. Figure 2B reprinted from Levine et al., 2015 with permission.

© 2014 AAAS. Figure 2C reprinted from Jaitin et al., 2014 with permission.

© 2015 Macmillan Publishers Limited. Figure 2D adapted from Achim et al., 2015 with permission.

https://doi.org/10.7554/eLife.27041.005
Developmental trajectories.

Each plot shows single cells (dots; colored by trajectory assignment, sampled time point, or developmental stage) embedded in low-dimensional space based on their RNA (A-C) or protein (D) profiles, using different methods for dimensionality reduction and embedding: Gaussian process patent variable model (A); t-stochastic neighborhood embedding (B, D); diffusion maps (C). Computational methods then identify trajectories of pseudo-temporal progression in each case. (A) Myoblast differentiation in vitro. (B) Neurogenesis in the mouse brain dentate gyrus. (C) Embryonic stem cell differentiation in vitro. (D) Early hematopoiesis.

© 2017 AAAS. Figure 3A reprinted from Lönnberg et al., 2017 with permission.

© 2016 AAAS. Figure 3B reprinted from Habib et al., 2016a with permission.

© 2016 Macmillan Publishers Limited. Figure 3C adapted from Haghverdi et al., 2016 with permission.

© 2016 Macmillan Publishers Limited. Figure 3D adapted from Setty et al., 2016 with permission.

https://doi.org/10.7554/eLife.27041.006
Physiology.

Each plot shows single cells (dots) embedded in low-dimensional space on the basis of their RNA profile, based on predefined gene signatures (A) or PCA (B, C), highlighting distinct dynamic processes. (A) The cell cycle in mouse hematopoietic stem and progenitor cells; adapted under terms of CC BY 4.0 from Scialdone et al. (2015). (B) Response to lipopolysaccharide (LPS) in mouse immune dendritic cells. (C) Variation in the extent of pathogenicity in mouse Th17 cells.

© 2014 Macmillan Publishers Limited. Figure 4B adapted from Shalek et al., 2014 with permission.

© 2015 Elsevier Inc. Figure 4C reprinted from Gaublomme et al., 2015 with permission.

https://doi.org/10.7554/eLife.27041.007

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