Continuous Variation within Cell Types of the Nervous System

Trends in Neurosciences

Volumn 41, Issue 6, 2018, Pages 337-348

  Cembrowski, Mark S ^a   Menon, Vilas ^a,b  

^a HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^b ALLEN INSTITUTE FOR BRAIN SCIENCE   (United States)

Author keywords

cell type; continuum; gradient; RNA seq; transcriptome

Indexed keywords

RNA; TRANSCRIPTOME;

BIOLOGICAL VARIATION; BRAIN FUNCTION; CELL HETEROGENEITY; GENE EXPRESSION; NERVE CELL; NERVOUS SYSTEM; NONHUMAN; PRIORITY JOURNAL; REVIEW; RNA SEQUENCE; SIGNAL TRANSDUCTION; SPIKE; STEADY STATE; TRANSCRIPTOMICS; ANIMAL; CELL COMMUNICATION; CYTOLOGY; GLIA; HUMAN; NERVOUS SYSTEM FUNCTION; PHYSIOLOGY;

ANIMALS; CELL COMMUNICATION; HUMANS; NERVOUS SYSTEM; NERVOUS SYSTEM PHYSIOLOGICAL PHENOMENA; NEUROGLIA; NEURONS; TRANSCRIPTOME;

EID: 85044313377     PISSN: 01662236     EISSN: 1878108X     Source Type: Journal    
DOI: 10.1016/j.tins.2018.02.010     Document Type: Review

References (63)

1. Ramón y Cajal, S. (1911) Histologie du Système Nerveux de l'Homme & des Vertébrés, A. Maloine (in French).
2. O'Keefe, J., Nadel, L., The Hippocampus as a Cognitive Map. 1978, Oxford University Press.
3. Hubel, D.H., Wiesel, T.N., Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160 (1962), 106–154.
4. Lein, E.S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445 (2007), 168–176.
5. Sugino, K., et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nat. Neurosci. 9 (2006), 99–107.
6. Ascoli, G.A., et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9 (2008), 557–568.
7. Hawrylycz, M.J., et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489 (2012), 391–399.
8. Cahoy, J.D., et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28 (2008), 264–278.
9. Bernard, A., et al. Transcriptional architecture of the primate neocortex. Neuron 73 (2012), 1083–1099.
10. Zeisel, A., et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347 (2015), 1138–1142.
11. Macosko, E.Z., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
12. Tasic, B., et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19 (2016), 335–346.
13. Habib, N., et al. Div-seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353 (2016), 925–928.
14. Zhang, Y., et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34 (2014), 11929–11947.
15. Pollen, A.A., et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32 (2014), 1053–1058.
16. Usoskin, D., et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18 (2015), 145–153.
17. Gokce, O., et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16 (2016), 1126–1137.
18. Li, C.L., et al. Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity. Cell Res. 26 (2016), 83–102.
19. La Manno, G., et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167 (2016), 566–580 e19.
20. Marques, S., et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352 (2016), 1326–1329.
21. Lake, B.B., et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352 (2016), 1586–1590.
22. Shekhar, K., et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166 (2016), 1308–1323 e30.
23. Mortazavi, A., et al. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5 (2008), 621–628.
24. Chen, H., et al. Genome-wide gene expression profiling of nucleus accumbens neurons projecting to ventral pallidum using both microarray and transcriptome sequencing. Front. Neurosci., 5, 2011, 98.
25. Cloonan, N., et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5 (2008), 613–619.
26. Brenner, S., et al. Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays. Nat. Biotechnol. 18 (2000), 630–634.
27. Cembrowski, M.S. et al. Dissociable structural and functional hippocampal outputs via distinct subiculum cell classes. Cell (in press).
28. Chevee, M., et al. Variation in activity state, axonal projection, and position define the transcriptional identity of individual neocortical projection neurons. Cell Rep. 22 (2018), 441–455.
29. Shin, J., et al. Decoding neural transcriptomes and epigenomes via high-throughput sequencing. Nat. Neurosci. 17 (2014), 1463–1475.
30. Okaty, B.W., et al. Cell type-specific transcriptomics in the brain. J. Neurosci. 31 (2011), 6939–6943.
31. Lein, E.S., et al. Transcriptomic perspectives on neocortical structure, development, evolution, and disease. Annu. Rev. Neurosci. 40 (2017), 629–652.
32. Zeng, H., Sanes, J.R., Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18 (2017), 530–546.
33. Soltesz, I., Diversity in the Neuronal Machine: Order and Variability in Interneuronal Microcircuits. 2006, Oxford University Press.
34. Tasic, B., et al. Single-cell transcriptomic characterization of vertebrate brain composition, development, and function. Çelik, A., Wernet, M.F., (eds.) Decoding Neural Circuit Structure and Function: Cellular Dissection Using Genetic Model Organisms, 2017, Springer, 437–468.
35. Liu, S., Trapnell, C., Single-cell transcriptome sequencing: recent advances and remaining challenges. F1000Research, 5, 2016, 182.
36. Waddington, C.H., The Strategy of the Genes; A Discussion of Some Aspects of Theoretical Biology. 1957, Allen & Unwin.
37. Cembrowski, M.S., et al. Spatial gene-expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons. Neuron 89 (2016), 351–368.
38. Cembrowski, M.S., et al. Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons. Elife, 5, 2016, e14997.
39. Chen, R., et al. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Rep. 18 (2017), 3227–3241.
40. Ramsden, H.L., et al. Laminar and dorsoventral molecular organization of the medial entorhinal cortex revealed by large-scale anatomical analysis of gene expression. PLoS Comput. Biol., 11, 2015, e1004032.
41. Romanov, R.A., et al. Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes. Nat. Neurosci. 20 (2017), 176–188.
42. Campbell, J.N., et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20 (2017), 484–496.
43. Malik, R., et al. Mapping the electrophysiological and morphological properties of CA1 pyramidal neurons along the longitudinal hippocampal axis. Hippocampus 26 (2016), 341–361.
44. Milior, G., et al. Electrophysiological properties of CA1 pyramidal neurons along the longitudinal axis of the mouse hippocampus. Sci. Rep., 6, 2016, 38242.
45. Kishi, T., et al. Topographical projection from the hippocampal formation to the amygdala: a combined anterograde and retrograde tracing study in the rat. J. Comp. Neurol. 496 (2006), 349–368.
46. Tamamaki, N., Nojyo, Y., Preservation of topography in the connections between the subiculum, field CA1, and the entorhinal cortex in rats. J. Comp. Neurol. 353 (1995), 379–390.
47. Amaral, D.G., et al. Organization of CA1 projections to the subiculum: a PHA-L analysis in the rat. Hippocampus 1 (1991), 415–435.
48. Maurer, A.P., et al. Self-motion and the origin of differential spatial scaling along the septo-temporal axis of the hippocampus. Hippocampus 15 (2005), 841–852.
49. Tripathy, S.J., et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol., 13, 2017, e1005814.
50. Henry, F.E., et al. Cell type-specific transcriptomics of hypothalamic energy-sensing neuron responses to weight-loss. Elife, 4, 2015, e09800.
51. Bardy, C., et al. Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology. Mol. Psychiatry 21 (2016), 1573–1588.
52. Metz, A.E., et al. R-type calcium channels contribute to afterdepolarization and bursting in hippocampal CA1 pyramidal neurons. J. Neurosci. 25 (2005), 5763–5773.
53. Shin, J., et al. Single-cell RNA-seq with Waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17 (2015), 360–372.
54. Hrvatin, S., et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21 (2018), 120–129.
55. Llorens-Bobadilla, E., et al. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell 17 (2015), 329–340.
56. Wood, S.H., et al. Whole transcriptome sequencing of the aging rat brain reveals dynamic RNA changes in the dark matter of the genome. Age (Dordr.) 35 (2013), 763–776.
57. Hargis, K.E., Blalock, E.M., Transcriptional signatures of brain aging and Alzheimer's disease: what are our rodent models telling us?. Behav. Brain Res. 322:Pt B (2017), 311–328.
58. Lacar, B., et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun., 7, 2016, 11022.
59. Dehorter, N., et al. Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch. Science 349 (2015), 1216–1220.
60. Dueck, H., et al. Variation is function: are single cell differences functionally important? Testing the hypothesis that single cell variation is required for aggregate function. Bioessays 38 (2016), 172–180.
61. Tripathy, S.J., et al. Intermediate intrinsic diversity enhances neural population coding. Proc. Natl. Acad. Sci. U. S. A. 110 (2013), 8248–8253.
62. Goaillard, J.M., et al. Functional consequences of animal-to-animal variation in circuit parameters. Nat. Neurosci. 12 (2009), 1424–1430.
63. Thompson, C.L., et al. Genomic anatomy of the hippocampus. Neuron 60 (2008), 1010–1021.