1932

Abstract

Maps of the nervous system inspire experiments and theories in neuroscience. Advances in molecular biology over the past decades have revolutionized the definition of cell and tissue identity. Spatial transcriptomics has opened up a new era in neuroanatomy, where the unsupervised and unbiased exploration of the molecular signatures of tissue organization will give rise to a new generation of brain maps. We propose that the molecular classification of brain regions on the basis of their gene expression profile can circumvent subjective neuroanatomical definitions and produce common reference frameworks that can incorporate cell types, connectivity, activity, and other modalities. Here we review the technological and conceptual advances made possible by spatial transcriptomics in the context of advancing neuroanatomy and discuss how molecular neuroanatomy can redefine mapping of the nervous system.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-neuro-100520-082639
2021-07-08
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/neuro/44/1/annurev-neuro-100520-082639.html?itemId=/content/journals/10.1146/annurev-neuro-100520-082639&mimeType=html&fmt=ahah

Literature Cited

  1. Abdi H, Williams LJ. 2010. Principal component analysis. WIREs Comput. Stat. 2:433–59
    [Google Scholar]
  2. Achim K, Pettit J-B, 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. Amunts K, Mohlberg H, Bludau S, Zilles K. 2020. Julich-Brain: a 3D probabilistic atlas of the human brain's cytoarchitecture. Science 369:988–92
    [Google Scholar]
  4. Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol 11:R106
    [Google Scholar]
  5. Asp M, Bergenstråhle J, Lundeberg J. 2020. Spatially resolved transcriptomes—next generation tools for tissue exploration. BioEssays 42:1900221
    [Google Scholar]
  6. Atapour N, Majka P, Wolkowicz IH, Malamanova D, Worthy KH, Rosa MGP. 2019. Neuronal distribution across the cerebral cortex of the marmoset monkey (Callithrix jacchus). . Cereb. Cortex 29:3836–63
    [Google Scholar]
  7. Bacher R, Chu L-F, Leng N, Gasch AP, Thomson JA et al. 2017. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14:584–86
    [Google Scholar]
  8. Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH et al. 2019. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38–44
    [Google Scholar]
  9. Beul SF, Barbas H, Hilgetag CC. 2017. A predictive structural model of the primate connectome. Sci. Rep. 7:43176
    [Google Scholar]
  10. Beul SF, Grant S, Hilgetag CC. 2015. A predictive model of the cat cortical connectome based on cytoarchitecture and distance. Brain Struct. Funct. 220:3167–84
    [Google Scholar]
  11. Beyer K, Goldstein J, Ramakrishnan R, Shaft U 1999. When is “nearest neighbor” meaningful?. Database TheoryICDT’99 C Beeri, P Buneman 217–35 Berlin: Springer
    [Google Scholar]
  12. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008:P10008
    [Google Scholar]
  13. Cadwell CR, Palasantza A, Jiang X, Berens P, Deng Q et al. 2016. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34:199–203
    [Google Scholar]
  14. 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]
  15. Chen W-T, Lu A, Craessaerts K, Pavie B, Sala Frigerio C et al. 2020. Spatial transcriptomics and in situ sequencing to study Alzheimer's disease. Cell 182:976–91.e19
    [Google Scholar]
  16. Codeluppi S, Borm LE, Zeisel A, La Manno G, van Lunteren JA et al. 2018. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15:932–35
    [Google Scholar]
  17. Day WHE, Edelsbrunner H. 1984. Efficient algorithms for agglomerative hierarchical clustering methods. J. Classif. 1:7–24
    [Google Scholar]
  18. Ding S-L, Royall JJ, Sunkin SM, Ng L, Facer BAC et al. 2016. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524:3127–481
    [Google Scholar]
  19. Dries R, Zhu Q, Dong R, Eng C-HL, Li H et al. 2020. Giotto, a toolbox for integrative analysis and visualization of spatial expression data. bioRxiv 701680. https://doi.org/10.1101/701680
    [Crossref] [Google Scholar]
  20. Eng C-HL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568:235–39
    [Google Scholar]
  21. Femino AM, Fay FS, Fogarty K, Singer RH. 1998. Visualization of single RNA transcripts in situ. Science 280:585–90
    [Google Scholar]
  22. Fernández Navarro J, Lundeberg J, Ståhl PL 2019. ST viewer: a tool for analysis and visualization of spatial transcriptomics datasets. Bioinformatics 35:1058–60
    [Google Scholar]
  23. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F et al. 2004. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14:11–22
    [Google Scholar]
  24. Fortunato S, Barthélemy M. 2007. Resolution limit in community detection. PNAS 104:36–41
    [Google Scholar]
  25. Fürth D, Vaissière T, Tzortzi O, Xuan Y, Märtin A et al. 2018. An interactive framework for whole-brain maps at cellular resolution. Nat. Neurosci. 21:139–49
    [Google Scholar]
  26. Fuzik J, Zeisel A, Máté Z, Calvigioni D, Yanagawa Y et al. 2016. Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes. Nat. Biotechnol. 34:175–83
    [Google Scholar]
  27. Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J et al. 2016. A multi-modal parcellation of human cerebral cortex. Nature 536:171–78
    [Google Scholar]
  28. Goulas A, Uylings HBM, Hilgetag CC. 2017. Principles of ipsilateral and contralateral cortico-cortical connectivity in the mouse. Brain Struct. Funct. 222:1281–95
    [Google Scholar]
  29. Haghverdi L, Lun ATL, Morgan MD, Marioni JC. 2018. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36:421–27
    [Google Scholar]
  30. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L et al. 2012. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489:391–99
    [Google Scholar]
  31. Hicks SC, Townes FW, Teng M, Irizarry RA. 2018. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19:562–78
    [Google Scholar]
  32. Huang L, Kebschull JM, Fürth D, Musall S, Kaufman MT et al. 2020. BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals. Cell 182:177–88.e27
    [Google Scholar]
  33. Hyvarinen A. 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10:626–34
    [Google Scholar]
  34. Karaiskos N, Wahle P, Alles J, Boltengagen A, Ayoub S et al. 2017. The Drosophila embryo at single-cell transcriptome resolution. Science 358:194–99
    [Google Scholar]
  35. Ke R, Mignardi M, Pacureanu A, Svedlund J, Botling J et al. 2013. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10:857–60
    [Google Scholar]
  36. Kiselev VY, Andrews TS, Hemberg M. 2019. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20:273–82
    [Google Scholar]
  37. Kobak D, Berens P. 2019. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10:5416
    [Google Scholar]
  38. Lee JH, Daugharthy ER, Scheiman J, Kalhor R, Yang JL et al. 2014. Highly multiplexed subcellular RNA sequencing in situ. Science 343:1360–63
    [Google Scholar]
  39. Lee S-I, Batzoglou S. 2003. Application of independent component analysis to microarrays. Genome Biol 4:R76
    [Google Scholar]
  40. Lein ES, Hawrylycz MJ, Ao N, Ayres M, Bensinger A et al. 2007. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445:168–76
    [Google Scholar]
  41. Lloyd S. 1982. Least squares quantization in PCM. IEEE Trans. Inf. Theory 28:129–37
    [Google Scholar]
  42. Lun ATL, 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]
  43. Ma Y, Hof PR, Grant SC, Blackband SJ, Bennett R et al. 2005. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience 135:1203–15
    [Google Scholar]
  44. MacKenzie-Graham A, Lee E-F, Dinov ID, Bota M, Shattuck DW et al. 2004. A multimodal, multidimensional atlas of the C57BL/6J mouse brain. J. Anat. 204:93–102
    [Google Scholar]
  45. Märtin A, Calvigioni D, Tzortzi O, Fuzik J, Wärnberg E, Meletis K. 2019. A spatiomolecular map of the striatum. Cell Rep 29:4320–33.e5
    [Google Scholar]
  46. McInnes L, Healy J, Melville J. 2018. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  47. Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K et al. 2018. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362:eaau5324
    [Google Scholar]
  48. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. 2008. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5:621–28
    [Google Scholar]
  49. Ng L, Bernard A, Lau C, Overly CC, Dong H-W et al. 2009. An anatomic gene expression atlas of the adult mouse brain. Nat. Neurosci. 12:356–62
    [Google Scholar]
  50. Niedworok CJ, Brown APY, Cardoso MJ, Osten P, Ourselin S et al. 2016. aMAP is a validated pipeline for registration and segmentation of high-resolution mouse brain data. Nat. Commun. 7:11879
    [Google Scholar]
  51. Nitzan M, Karaiskos N, Friedman N, Rajewsky N. 2019. Gene expression cartography. Nature 576:132–37
    [Google Scholar]
  52. Ortiz C, Navarro JF, Jurek A, Märtin A, Lundeberg J, Meletis K. 2020. Molecular atlas of the adult mouse brain. Sci. Adv. 6:eabb3446
    [Google Scholar]
  53. Oshlack A, Wakefield MJ. 2009. Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct 4:14
    [Google Scholar]
  54. Paxinos G, Franklin KBJ. 2004. The Mouse Brain in Stereotaxic Coordinates. Houston, TX: Gulf Prof. Publ.
  55. Polański K, Young MD, Miao Z, Meyer KB, Teichmann SA, Park J-E. 2020. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36:964–65
    [Google Scholar]
  56. Renier N, Adams EL, Kirst C, Wu Z, Azevedo R et al. 2016. Mapping of brain activity by automated volume analysis of immediate early genes. Cell 165:1789–802
    [Google Scholar]
  57. Robinson MD, Oshlack A. 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25
    [Google Scholar]
  58. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E et al. 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363:1463–67
    [Google Scholar]
  59. Sandberg R. 2014. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11:22–24
    [Google Scholar]
  60. Sandberg R, Yasuda R, Pankratz DG, Carter TA, Rio JAD et al. 2000. Regional and strain-specific gene expression mapping in the adult mouse brain. PNAS 97:11038–43
    [Google Scholar]
  61. 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]
  62. Saunders A, Macosko EZ, Wysoker A, Goldman M, Krienen FM et al. 2018. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174:1015–30.e16
    [Google Scholar]
  63. Shimogori T, Abe A, Go Y, Hashikawa T, Kishi N et al. 2018. Digital gene atlas of neonate common marmoset brain. Neurosci. Res. 128:1–13
    [Google Scholar]
  64. Singer RH, Ward DC. 1982. Actin gene expression visualized in chicken muscle tissue culture by using in situ hybridization with a biotinated nucleotide analog. PNAS 79:7331–35
    [Google Scholar]
  65. Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF et al. 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82
    [Google Scholar]
  66. Stegle O, Teichmann SA, Marioni JC. 2015. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16:133–45
    [Google Scholar]
  67. Stickels RR, Murray E, Kumar P, Li J, Marshall JL et al. 2020. Sensitive spatial genome wide expression profiling at cellular resolution. bioRxiv 2020.03.12.989806. https://doi.org/10.1101/2020.03.12.989806
    [Crossref] [Google Scholar]
  68. Strell C, Hilscher MM, Laxman N, Svedlund J, Wu C et al. 2019. Placing RNA in context and space—methods for spatially resolved transcriptomics. FEBS J 286:1468–81
    [Google Scholar]
  69. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888–902.e21
    [Google Scholar]
  70. Swanson LW. 2018. Brain maps 4.0—Structure of the rat brain: an open access atlas with global nervous system nomenclature ontology and flatmaps. J. Comp. Neurol. 526:935–43
    [Google Scholar]
  71. Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS et al. 2020. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21:12
    [Google Scholar]
  72. Tung P-Y, Blischak JD, Hsiao CJ, Knowles DA, Burnett JE et al. 2017. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7:39921
    [Google Scholar]
  73. Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC. 2017. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14:565–71
    [Google Scholar]
  74. van der Maaten L, Hinton G. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  75. Van Essen DC, Glasser MF. 2018. Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans. Neuron 99:640–63
    [Google Scholar]
  76. Vickovic S, Eraslan G, Salmén F, Klughammer J, Stenbeck L et al. 2019. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16:987–90
    [Google Scholar]
  77. Wagner GP, Kin K, Lynch VJ 2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:281–85
    [Google Scholar]
  78. Waltman L, van Eck NJ. 2013. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86:471
    [Google Scholar]
  79. Wang F, Flanagan J, Su N, Wang LC, Bui S et al. 2012. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14:22–29
    [Google Scholar]
  80. Wang Q, Ding S-L, Li Y, Royall J, Feng D et al. 2020. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181:936–53.e20
    [Google Scholar]
  81. Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
    [Google Scholar]
  82. Wattenberg M, Viégas F, Johnson I 2016. How to use t-SNE effectively. DistillOct. 13 http://doi.org/10.23915/distill.00002
    [Crossref] [Google Scholar]
  83. Wolf FA, Angerer P, Theis FJ. 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15
    [Google Scholar]
  84. Zeisel A, Hochgerner H, Lönnerberg P, Johnsson A, Memic F et al. 2018. Molecular architecture of the mouse nervous system. Cell 174:999–1014.e22
    [Google Scholar]
/content/journals/10.1146/annurev-neuro-100520-082639
Loading
/content/journals/10.1146/annurev-neuro-100520-082639
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