1932

Abstract

The recent flood of single-cell data not only boosts our knowledge of cells and cell types, but also provides new insight into development and evolution from a cellular perspective. For example, assaying the genomes of multiple cells during development reveals developmental lineage trees—the kinship lineage—whereas cellular transcriptomes inform us about the regulatory state of cells and their gradual restriction in potency—the Waddington lineage. Beyond that, the comparison of single-cell data across species allows evolutionary changes to be tracked at all stages of development from the zygote, via different kinds of stem cells, to the differentiating cells. We discuss recent insights into the evolution of stem cells and initial attempts to reconstruct the evolutionary cell type tree of the mammalian forebrain, for example, by the comparative analysis of neuron types in the mesencephalic floor. These studies illustrate the immense potential of single-cell genomics to open up a new era in developmental and evolutionary research.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-cellbio-100616-060818
2017-10-06
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/cellbio/33/1/annurev-cellbio-100616-060818.html?itemId=/content/journals/10.1146/annurev-cellbio-100616-060818&mimeType=html&fmt=ahah

Literature Cited

  1. Achim K, Arendt D. 2014. Structural evolution of cell types by step-wise assembly of cellular modules. Curr. Opin. Neurobiol. 27C:102–8 [Google Scholar]
  2. Achim K, Pettit JB, Saraiva LR, Gavriouchkina D, Larsson T. et al. 2015. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33:503–9 [Google Scholar]
  3. Ackermann C, Dorresteijn A, Fischer A. 2005. Clonal domains in postlarval Platynereis dumerilii (Annelida: Polychaeta). J. Morphol. 266:258–80 [Google Scholar]
  4. Alié A, Hayashi T, Sugimura I, Manuel M, Sugano W. et al. 2015. The ancestral gene repertoire of animal stem cells. PNAS 112:E7093–100 [Google Scholar]
  5. Altenhoff AM, Boeckmann B, Capella-Gutierrez S, Dalquen DA, DeLuca T. et al. 2016. Standardized benchmarking in the quest for orthologs. Nat. Methods 13:425–30 [Google Scholar]
  6. Anderson DT. 1973. Embryology and Phylogeny in Annelids and Arthropods Oxford/New York/Toronto/Sydney/Braunschweig: Pergamon Press [Google Scholar]
  7. Arendt D. 2008. The evolution of cell types in animals: emerging principles from molecular studies. Nat. Rev. Genet. 9:868–82 [Google Scholar]
  8. Arendt D, Musser JM, Baker CV, Bergman A, Cepko CL. et al. 2016. Evolution of sister cell types by individuation. Nat. Rev. Genet. 17:744–57 [Google Scholar]
  9. Arendt D, Nübler-Jung K. 1999. Rearranging gastrulation in the name of yolk: evolution of gastrulation in yolk-rich amniote eggs. Mech. Dev. 81:3–22 [Google Scholar]
  10. Banerji CR, Miranda-Saavedra D, Severini S, Widschwendter M, Enver T. et al. 2013. Cellular network entropy as the energy potential in Waddington's differentiation landscape. Sci. Rep. 3:3039 [Google Scholar]
  11. Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R. et al. 2016. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst 3:346–60.e4 [Google Scholar]
  12. Behjati S, Huch M, van Boxtel R, Karthaus W, Wedge DC. et al. 2014. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513:422–25 [Google Scholar]
  13. Benabid AL, Chabardes S, Mitrofanis J, Pollak P. 2009. Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson's disease. Lancet Neurol 8:67–81 [Google Scholar]
  14. Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang X. et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:1093–95 [Google Scholar]
  15. Carmona SJ, Teichmann SA, Ferreira L, Macaulay IC, Stubbington MJ. et al. 2017. Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types. Genome Res 27:3451–61 [Google Scholar]
  16. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090 [Google Scholar]
  17. Crosetto N, Bienko M, van Oudenaarden A. 2015. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16:57–66 [Google Scholar]
  18. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C. et al. 2015. A survey of human brain transcriptome diversity at the single cell level. PNAS 112:7285–90 [Google Scholar]
  19. Deng Q, Ramskold D, Reinius B, Sandberg R. 2014. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343:193–96 [Google Scholar]
  20. Filippi A, Durr K, Ryu S, Willaredt M, Holzschuh J, Driever W. 2007. Expression and function of nr4a2,. lmx1b , and pitx3 in zebrafish dopaminergic and noradrenergic neuronal development. BMC Dev. Biol. 7:135 [Google Scholar]
  21. Fu Y, Li C, Lu S, Zhou W, Tang F. et al. 2015. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. PNAS 112:11923–28 [Google Scholar]
  22. Gawad C, Koh W, Quake SR. 2016. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17:175–88 [Google Scholar]
  23. Goldstein B, Macara IG. 2007. The PAR proteins: fundamental players in animal cell polarization. Dev. Cell 13:609–22 [Google Scholar]
  24. Goolam M, Scialdone A, Graham SJ, Macaulay IC, Jedrusik A. et al. 2016. Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 165:61–74 [Google Scholar]
  25. Haghverdi L, Buttner M, Wolf FA, Buettner F, Theis FJ. 2016. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13:845–48 [Google Scholar]
  26. Hemmrich G, Khalturin K, Boehm AM, Puchert M, Anton-Erxleben F. et al. 2012. Molecular signatures of the three stem cell lineages in hydra and the emergence of stem cell function at the base of multicellularity. Mol. Biol. Evol. 29:3267–80 [Google Scholar]
  27. Herrero J, Muffato M, Beal K, Fitzgerald S, Gordon L. et al. 2016. Ensembl comparative genomics resources. Database 2016:bav096 [Google Scholar]
  28. Hirano M, Guo P, McCurley N, Schorpp M, Das S. et al. 2013. Evolutionary implications of a third lymphocyte lineage in lampreys. Nature 501:435–38 [Google Scholar]
  29. Hobert O. 2016. Terminal selectors of neuronal identity. Curr. Top. Dev. Biol. 116:455–75 [Google Scholar]
  30. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ. et al. 2016. Classification of low quality cells from single-cell RNA-seq data. Genome Biol 17:29 [Google Scholar]
  31. Jager M, Queinnec E, Le Guyader H, Manuel M. 2011. Multiple Sox genes are expressed in stem cells or in differentiating neuro-sensory cells in the hydrozoan Clytia hemisphaerica. EvoDevo 2:12 [Google Scholar]
  32. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F. et al. 2014. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–79 [Google Scholar]
  33. Junker JP, Noel ES, Guryev V, Peterson KA, Shah G. et al. 2014. Genome-wide RNA tomography in the zebrafish embryo. Cell 159:662–75 [Google Scholar]
  34. Kee N, Volakakis N, Kirkeby A, Dahl L, Storvall H. et al. 2017. Single-cell analysis reveals a close relationship between differentiating dopamine and subthalamic nucleus neuronal lineages. Cell Stem Cell 20:29–40 [Google Scholar]
  35. Khalturin K, Becker M, Rinkevich B, Bosch TC. 2003. Urochordates and the origin of natural killer cells: identification of a CD94/NKR-P1-related receptor in blood cells of Botryllus. PNAS 100:622–27 [Google Scholar]
  36. Kiontke K, Barriere A, Kolotuev I, Podbilewicz B, Sommer R. et al. 2007. Trends, stasis, and drift in the evolution of nematode vulva development. Curr. Biol. 17:1925–37 [Google Scholar]
  37. Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A. et al. 2017. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14:483–86 [Google Scholar]
  38. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A. et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–201 [Google Scholar]
  39. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. 2015. The technology and biology of single-cell RNA sequencing. Mol. Cell 58:610–20 [Google Scholar]
  40. La Manno G, Gyllborg D, Codeluppi S, Nishimura K, Salto C. et al. 2016. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167:566–80.e19 [Google Scholar]
  41. Labbé RM, Irimia M, Currie KW, Lin A, Zhu SJ. et al. 2012. A comparative transcriptomic analysis reveals conserved features of stem cell pluripotency in planarians and mammals. Stem Cells 30:1734–45 [Google Scholar]
  42. Lambert JD. 2010. Developmental patterns in spiralian embryos. Curr. Biol. 20:R72–77 [Google Scholar]
  43. Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L. 2014. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11:360–61 [Google Scholar]
  44. Lun AT, Bach K, Marioni JC. 2016. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17:75 [Google Scholar]
  45. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K. et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–14 [Google Scholar]
  46. Martinez-Jimenez CP, Eling N, Chen HC, Vallejos CA, Kolodziejczyk AA. et al. 2017. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355:1433–36 [Google Scholar]
  47. Muraro MJ, Dharmadhikari G, Grun D, Groen N, Dielen T. et al. 2016. A single-cell transcriptome atlas of the human pancreas. Cell Syst 3:385–94.e3 [Google Scholar]
  48. Nestorowa S, Hamey FK, Pijuan Sala B, Diamanti E, Shepherd M. et al. 2016. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128:e20–31 [Google Scholar]
  49. Onal P, Grun D, Adamidi C, Rybak A, Solana J. et al. 2012. Gene expression of pluripotency determinants is conserved between mammalian and planarian stem cells. EMBO J 31:2755–69 [Google Scholar]
  50. Paul F, Arkin Y, Giladi A, Jaitin DA, Kenigsberg E. et al. 2015. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163:1663–77 [Google Scholar]
  51. Prakash N, Puelles E, Freude K, Trumbach D, Omodei D. et al. 2009. Nkx6-1 controls the identity and fate of red nucleus and oculomotor neurons in the mouse midbrain. Development 136:2545–55 [Google Scholar]
  52. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33:495–502 [Google Scholar]
  53. Scialdone A, Tanaka Y, Jawaid W, Moignard V, Wilson NK. et al. 2016. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535:289–93 [Google Scholar]
  54. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM. et al. 2016. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34:637–45 [Google Scholar]
  55. Shapiro E, Biezuner T, Linnarsson S. 2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14:618–30 [Google Scholar]
  56. Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H. et al. 2014. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11:817–20 [Google Scholar]
  57. Solana J, Kao D, Mihaylova Y, Jaber-Hijazi F, Malla S. et al. 2012. Defining the molecular profile of planarian pluripotent stem cells using a combinatorial RNAseq, RNA interference and irradiation approach. Genome Biol 13:R19 [Google Scholar]
  58. Stegle O, Teichmann SA, Marioni JC. 2015. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16:133–45 [Google Scholar]
  59. Swanson LW. 2000. Cerebral hemisphere regulation of motivated behavior. Brain Res 886:113–64 [Google Scholar]
  60. Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T. et al. 2016. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19:335–46 [Google Scholar]
  61. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S. et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32:381–86 [Google Scholar]
  62. Vergara H, Bertucci PY, Hantz P, Tosches MA, Achim K. et al. 2017. Whole-organism cellular gene-expression atlas reveals conserved cell types in the ventral nerve cord of Platynereis dumerilii. PNAS 114:235878–85 [Google Scholar]
  63. Waddington CH. 1957. The Strategy of the Genes: A Discussion of Some Aspects of Theoretical Biology London: Allen & Unwin [Google Scholar]
  64. Yamamoto K, Vernier P. 2011. The evolution of dopamine systems in chordates. Front. Neuroanat. 5:21 [Google Scholar]
  65. Yates A, Akanni W, Amode MR, Barrell D, Billis K. et al. 2016. Ensembl 2016. Nucleic Acids Res 44:D710–16 [Google Scholar]
  66. Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G. et al. 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347:1138–42 [Google Scholar]
/content/journals/10.1146/annurev-cellbio-100616-060818
Loading
/content/journals/10.1146/annurev-cellbio-100616-060818
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error