Publisher Correction: Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex (Nature Neuroscience, (2018), 21, 1, (120-129), 10.1038/s41593-017-0029-5);Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex

Nature Neuroscience

Volumn 21, Issue 1, 2018, Pages 120-129

  Hrvatin, Sinisa ^a   Hochbaum, Daniel R ^a,b   Nagy, M Aurel ^a   Cicconet, Marcelo ^a   Robertson, Keiramarie ^a   Cheadle, Lucas ^a   Zilionis, Rapolas ^a,c   Ratner, Alex ^a   Borges Monroy, Rebeca ^d   Klein, Allon M ^a   Sabatini, Bernardo L ^a   Greenberg, Michael E ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

^b HARVARD UNIVERSITY   (United States)

^c VILNIUS UNIVERSITY   (Lithuania)

^d HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ANIMAL TISSUE; ARTICLE; BRAIN FUNCTION; CELL ACTIVITY; CONTROLLED STUDY; EXCITATORY POSTSYNAPTIC POTENTIAL; GENETIC TRANSCRIPTION; HIGH THROUGHPUT SEQUENCING; LIGHT EXPOSURE; MALE; MOUSE; MYELINATION; NONHUMAN; PRIORITY JOURNAL; PYRAMIDAL NERVE CELL; RNA SEQUENCE; SINGLE CELL ANALYSIS; STIMULUS RESPONSE; TRANSCRIPTOMICS; VISUAL CORTEX; ANIMAL; C57BL MOUSE; CYTOLOGY; GENE EXPRESSION REGULATION; GENE ONTOLOGY; GENETICS; GLIA; LIGHT; METABOLISM; NERVE CELL; NERVE CELL INHIBITION; NEUROVASCULAR COUPLING; NONPARAMETRIC TEST; PHOTOSTIMULATION; PHYSIOLOGY; PROCEDURES; SIGNAL TRANSDUCTION; VISUAL SYSTEM;

BASIC HELIX LOOP HELIX TRANSCRIPTION FACTOR; NERVE PROTEIN; NPAS4 PROTEIN, MOUSE; PROTEIN C FOS; TRANSCRIPTOME;

ANIMALS; BASIC HELIX-LOOP-HELIX TRANSCRIPTION FACTORS; GENE EXPRESSION REGULATION; GENE ONTOLOGY; LIGHT; MALE; MICE; MICE, INBRED C57BL; NERVE TISSUE PROTEINS; NEURAL INHIBITION; NEUROGLIA; NEURONS; NEUROVASCULAR COUPLING; PHOTIC STIMULATION; PROTO-ONCOGENE PROTEINS C-FOS; SIGNAL TRANSDUCTION; SINGLE-CELL ANALYSIS; STATISTICS, NONPARAMETRIC; TRANSCRIPTION, GENETIC; TRANSCRIPTOME; VISUAL CORTEX; VISUAL PATHWAYS;

EID: 85037674285     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/s41593-018-0112-6     Document Type: Erratum

References (72)

1. Hensch, T. K. Critical period plasticity in local cortical circuits. Nat. Rev. Neurosci. 6, 877-888 (2005)
2. Wiesel, T. N. &Hubel, D. H. Single-cell responses in striate cortex of kittens deprived of vision in one eye. J. Neurophysiol. 26, 1003-1017 (1963)
3. Zucker, R. S. &Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355-405 (2002)
4. West, A. E. &Greenberg, M. E. Neuronal activity-regulated gene transcription in synapse development and cognitive function. Cold Spring Harb. Perspect. Biol. 3, a005744 (2011)
5. Bading, H. Nuclear calcium signalling in the regulation of brain function. Nat. Rev. Neurosci. 14, 593-608 (2013)
6. MacManus, J. P. et al. Translation-state analysis of gene expression in mouse brain after focal ischemia. J. Cereb. Blood Flow Metab. 24, 657-667 (2004)
7. Ginty, D. D. et al. Regulation of CREB phosphorylation in the suprachiasmatic nucleus by light and a circadian clock. Science 260, 238-241 (1993)
8. Campbell, J. N. et al. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20, 484-496 (2017)
9. Renier, N. et al. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165, 1789-1802 (2016)
10. Ben-David, E. &Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012)
11. Thomas, G. M. &Huganir, R. L. MAPK cascade signalling and synaptic plasticity. Nat. Rev. Neurosci. 5, 173-183 (2004)
12. Shepherd, J. D. &Bear, M. F. New views of Arc, a master regulator of synaptic plasticity. Nat. Neurosci. 14, 279-284 (2011)
13. Mardinly, A. R. et al. Sensory experience regulates cortical inhibition by inducing IGF1 in VIP neurons. Nature 531, 371-375 (2016)
14. Harward, S. C. et al. Autocrine BDNF-TrkB signalling within a single dendritic spine. Nature 538, 99-103 (2016)
15. Lin, Y. et al. Activity-dependent regulation of inhibitory synapse development by Npas4. Nature 455, 1198-1204 (2008)
16. Majdan, M. &Shatz, C. J. Effects of visual experience on activity-dependent gene regulation in cortex. Nat. Neurosci. 9, 650-659 (2006)
17. Spiegel, I. et al. Npas4 regulates excitatory-inhibitory balance within neural circuits through cell-type-specific gene programs. Cell 157, 1216-1229 (2014)
18. Lacoste, B. et al. Sensory-related neural activity regulates the structure of vascular networks in the cerebral cortex. Neuron 83, 1117-1130 (2014)
19. Gibson, E. M. et al. Neuronal activity promotes oligodendrogenesis and adaptive myelination in the mammalian brain. Science 344, 1252304 (2014)
20. Wang, X. et al. Astrocytic Ca2+ signaling evoked by sensory stimulation in vivo. Nat. Neurosci. 9, 816-823 (2006)
21. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015)
22. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016)
23. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335-346 (2016)
24. Wu, G.-Y., Deisseroth, K. &Tsien, R. W. Activity-dependent CREB phosphorylation: convergence of a fast, sensitive calmodulin kinase pathway and a slow, less sensitive mitogen-activated protein kinase pathway. Proc. Natl. Acad. Sci. USA 98, 2808-2813 (2001)
25. Harris, K. D. &Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170-181 (2015)
26. Chacon, P. J. et al. Cerebellin 4, a synaptic protein, enhances inhibitory activity and resistance of neurons to amyloid-B toxicity. Neurobiol. Aging 36, 1057-1071 (2015)
27. Hattox, A. M. &Nelson, S. B. Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. J. Neurophysiol. 98, 3330-3340 (2007)
28. Schulz, T. W. et al. Actin/alpha-actinin-dependent transport of AMPA receptors in dendritic spines: role of the PDZ-LIM protein RIL. J. Neurosci. 24, 8584-8594 (2004)
29. Niell, C. M. &Stryker, M. P. Highly selective receptive fields in mouse visual cortex. J. Neurosci. 28, 7520-7536 (2008)
30. Kock, C. P. J., Bruno, R. M., Spors, H. &Sakmann, B. Layer-and cell-typespecific suprathreshold stimulus representation in rat primary somatosensory cortex. J. Physiol. (Lond) 581, 139-154 (2007)
31. Nassi, J. J. &Callaway, E. M. Parallel processing strategies of the primate visual system. Nat. Rev. Neurosci. 10, 360-372 (2009)
32. Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015)
33. Kratzer, S. et al. Activation of CRH receptor type 1 expressed on glutamatergic neurons increases excitability of CA1 pyramidal neurons by the modulation of voltage-gated ion channels. Front. Cell. Neurosci. 7, 91 (2013)
34. Li, K., Nakajima, M., Ibanez-Tallon, I. &Heintz, N. A cortical circuit for sexually dimorphic oxytocin-dependent anxiety behaviors. Cell 167, 60-72. e11 (2016)
35. Roy, C. S. &Sherrington, C. S. On the regulation of the blood-supply of the brain. J. Physiol. 11, 1587-15817 (1890)
36. Attwell, D. et al. Glial and neuronal control of brain blood flow. Nature 468, 232-243 (2010)
37. Gurnik, S. et al. Angiopoietin-2-induced blood-brain barrier compromise and increased stroke size are rescued by VE-PTP-dependent restoration of Tie2 signaling. Acta Neuropathol. 131, 753-773 (2016)
38. Augustin, H. G., Koh, G. Y., Thurston, G. &Alitalo, K. Control of vascular morphogenesis and homeostasis through the angiopoietin-Tie system. Nat. Rev. Mol. Cell Biol. 10, 165-177 (2009)
39. Weinl, C. et al. Endothelial SRF/MRTF ablation causes vascular disease phenotypes in murine retinae. J. Clin. Invest. 123, 2193-2206 (2013)
40. Zhou, Z. et al. Cerebral cavernous malformations arise from endothelial gain of MEKK3-KLF2/4 signalling. Nature 532, 122-126 (2016)
41. Hines, J. H., Ravanelli, A. M., Schwindt, R., Scott, E. K. &Appel, B. Neuronal activity biases axon selection for myelination in vivo. Nat. Neurosci. 18, 683-689 (2015)
42. Miyata, S., Hattori, T., Shimizu, S., Ito, A. &Tohyama, M. Disturbance of oligodendrocyte function plays a key role in the pathogenesis of schizophrenia and major depressive disorder. BioMed Res. Int. 2015, 492367 (2015)
43. Ramilowski, J. A. et al. A draft network of ligand-receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015)
44. Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925-928 (2016)
45. Marques, S. et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326-1329 (2016)
46. Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16, 1126-1137 (2016)
47. Su, Y. et al. Neuronal activity modifies the chromatin accessibility landscape in the adult brain. Nat. Neurosci. 20, 476-483 (2017)
48. Tu Z. &Bai X. Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1744-1757 (2010)
49. Richmond, D., Kainmueller, D., Glocker, B., Rother, C. &Myers, G. Uncertainty-driven forest predictors for vertebra localization and segmentation. in International Conference on Medical Image Computing and Computer-Assisted Intervention 653-660 (Springer, Munich, 2015)
50. Aguet, F., Antonescu, C. N., Mettlen, M., Schmid, S. L. &Danuser, G. Advances in analysis of low signal-to-noise images link dynamin and AP2 to the functions of an endocytic checkpoint. Dev. Cell 26, 279-291 (2013)
51. Cicconet, M., Hochbaum, D. R., Richmond, D. &Sabatini, B. L. Bots for software-assisted analysis of image-based transcriptomics. Preprint at https:// www.biorxiv.org/content/early/2017/08/03/172296 (2017)
52. Fan, J. Notes on Poisson distribution-based minimum error thresholding. Pattern Recognit. Lett. 19, 425-431 (1998)
53. Bolger, A. M., Lohse, M. &Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114-2120 (2014)
54. Trapnell, C., Pachter, L. &Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105-1111 (2009)
55. Langmead, B., Trapnell, C., Pop, M. &Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)
56. Liao, Y., Smyth, G. K. &Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923-930 (2014)
57. Robinson, M. D., McCarthy, D. J. &Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 (2010)
58. McCarthy, D. J., Chen, Y. &Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288-4297 (2012)
59. Law, C. W., Chen, Y., Shi, W. &Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014)
60. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015)
61. Zilionis, R. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12, 44-73 (2017)
62. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163-166 (2014)
63. van der Maaten, L. &Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605 (2008)
64. Rodriguez, A. &Laio, A. Clustering by fast search and find of density peaks. Science 344, 1492-1496 (2014)
65. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. &Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495-502 (2015)
66. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015)
67. Straub, A. C. et al. Endothelial cell expression of haemoglobin A regulates nitric oxide signalling. Nature 491, 473-477 (2012)
68. 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)
69. Huang, W., Sherman, B. T. &Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44-57 (2009)
70. Huang, W., Sherman, B. T. &Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1-13 (2009)
71. Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308-1323. e30 (2016)
72. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell Syst. 3, 346-360 (2016)