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Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

Nature Neuroscience volume 24, pages 425–436 (2021)Cite this article

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A Publisher Correction to this article was published on 18 February 2021

This article has been updated

Abstract

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD).

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Fig. 1: Spatial transcriptomics in DLPFC using Visium.
Fig. 2: Layer-enriched gene expression in the DLPFC.
Fig. 3: Visium replicates layer enrichment of previously identified layer-marker genes.
Fig. 4: Discovery and smFISH validation of new layer-enriched genes.
Fig. 5: Spatial registration of snRNA-seq data.
Fig. 6: Layer enrichment of neurodevelopmental and neuropsychiatric gene sets.
Fig. 7: Data-driven, layer-enriched clustering in the DLPFC.

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Data availability

Processed data are publicly available from the Bioconductor package spatialLIBD60. The raw data are publicly available from the Globus endpoint ‘jhpce#HumanPilot10x’ that is also listed at http://research.libd.org/globus. The raw data provided through Globus include all the FASTQ files and raw image files. External data used in this project are detailed under snRNA-seq spatial registration as well as Clinical gene set enrichment analyses.

Code availability

The code for this project is publicly available through GitHub and archived through Zenodo. Specifically, the code is available through GitHub at https://github.com/LieberInstitute/HumanPilot (ref. 87) and https://github.com/LieberInstitute/spatialLIBD (ref. 60), both of which are described in their README.md files.

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References

  1. DeFelipe, J. & Fariñas, I. The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs. Prog. Neurobiol. 39, 563–607 (1992).

    Article  CAS  PubMed  Google Scholar 

  2. Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Narayanan, R. T., Udvary, D. & Oberlaender, M. Cell type-specific structural organization of the six layers in rat barrel cortex. Front. Neuroanat. 11, 91 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Radnikow, G. & Feldmeyer, D. Layer- and cell type-specific modulation of excitatory neuronal activity in the neocortex. Front. Neuroanat. 12, 1 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Velmeshev, D. et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science 364, 685–689 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Skene, N. G. et al. Genetic identification of brain cell types underlying schizophrenia. Nat. Genet. 50, 825–833 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sweet, R. A., Fish, K. N. & Lewis, D. A. Mapping synaptic pathology within cerebral cortical circuits in subjects with schizophrenia. Front. Hum. Neurosci. 4, 44 (2010).

    PubMed  PubMed Central  Google Scholar 

  15. Moyer, C. E., Shelton, M. A. & Sweet, R. A. Dendritic spine alterations in schizophrenia. Neurosci. Lett. 601, 46–53 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Maynard, K. R. et al. dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues. Nucleic Acids Res. https://doi.org/10.1093/nar/gkaa312 (2020).

  18. Burgess, D. J. Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Lein, E., Borm, L. E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).

    Article  CAS  PubMed  Google Scholar 

  20. Jaffe, A. E. et al. Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk. Nat. Neurosci. 23, 510–519 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Dong, X. et al. Global, integrated analysis of methylomes and transcriptomes from laser capture microdissected bronchial and alveolar cells in human lung. Epigenetics 13, 264–274 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. He, Z. et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat. Neurosci. 20, 886–895 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  CAS  Google Scholar 

  24. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Asp, M. et al. An organ-wide gene expression atlas of the developing human heart. Cell 179, 1647–1660.e19 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Maniatis, S. et al. Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis. Science 364, 89–93 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Gregory, J. M. et al. Spatial transcriptomics identifies spatially dysregulated expression of GRM3 and USP47 in amyotrophic lateral sclerosis. Neuropathol. Appl. Neurobiol. https://doi.org/10.1111/nan.12597 (2020).

  30. Hafner, A.-S., Donlin-Asp, P. G., Leitch, B., Herzog, E. & Schuman, E. M. Local protein synthesis is a ubiquitous feature of neuronal pre- and postsynaptic compartments. Science 364, eaau3644 (2019).

  31. Molyneaux, B. J., Arlotta, P., Menezes, J. R. L. & Macklis, J. D. Neuronal subtype specification in the cerebral cortex. Nat. Rev. Neurosci. 8, 427–437 (2007).

    Article  CAS  PubMed  Google Scholar 

  32. Zeng, H. et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell 149, 483–496 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Birnbaum, R., Jaffe, A. E., Hyde, T. M., Kleinman, J. E. & Weinberger, D. R. Prenatal expression patterns of genes associated with neuropsychiatric disorders. Am. J. Psychiatry 171, 758–767 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584.e23 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Collado-Torres, L. et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron 103, 203–216.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Overly, C. C., Rieff, H. I. & Hollenbeck, P. J. Organelle motility and metabolism in axons vs dendrites of cultured hippocampal neurons. J. Cell Sci. 109, 971–980 (1996).

    Article  CAS  PubMed  Google Scholar 

  49. Harris, J. J., Jolivet, R. & Attwell, D. Synaptic energy use and supply. Neuron 75, 762–777 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Biever, A., Donlin-Asp, P. G. & Schuman, E. M. Local translation in neuronal processes. Curr. Opin. Neurobiol. 57, 141–148 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Lipska, B. K. et al. Critical factors in gene expression in postmortem human brain: focus on studies in schizophrenia. Biol. Psychiatry 60, 650–658 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Morgan, M., Obenchain, V., Hester, J. & Pagès, H. Summarized Experiment. Bioconductor https://doi.org/10.18129/b9.bioc.summarizedexperiment (2017).

  54. Risso, D. Single cell experiment. Bioconductor https://doi.org/10.18129/b9.bioc.singlecellexperiment (2017).

  55. Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Res. 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  58. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Software 3, 861 (2018).

    Article  Google Scholar 

  59. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 1695, 1–9 (2006).

  60. Collado-Torres, L. LieberInstitute/spatialLIBD: spatialLIBD: initial bioconductor submission. Zenodo https://doi.org/10.5281/zenodo.3689719 (2020).

  61. Chang, W., Cheng, J., Allaire, J. J., Xie, Y. & McPherson, J. shiny: Web application framework for R. R version 1.4.0 (2019).

  62. Sievert, C. plotly for R. R version 4.9.2 (2018).

  63. Rajkowska, G. & Goldman-Rakic, P. S. Cytoarchitectonic definition of prefrontal areas in the normal human cortex: I. Remapping of areas 9 and 46 using quantitative criteria. Cereb. Cortex 5, 307–322 (1995).

    Article  CAS  PubMed  Google Scholar 

  64. Rajkowska, G. & Goldman-Rakic, P. S. Cytoarchitectonic definition of prefrontal areas in the normal human cortex: II. Variability in locations of areas 9 and 46 and relationship to the Talairach coordinate system. Cereb. Cortex 5, 323–337 (1995).

    Article  CAS  PubMed  Google Scholar 

  65. Lun, A. T. L. & Marioni, J. C. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics 18, 451–464 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Hu, P. et al. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-Seq. Mol. Cell 68, 1006–1015.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Amezquita, R. A. et al. Orchestrating single-cell analysis with bioconductor. Nat. Methods 17, 137–145 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Langfelder, P., Zhang, B. & with contributions from Horvath, S. dynamicTreeCut: Methods for detection of clusters in hierarchical clustering dendrograms. R version 1.63.1 (2016).

  78. Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Olmos-Serrano, J. L. et al. Down syndrome developmental brain transcriptome reveals defective oligodendrocyte differentiation and myelination. Neuron 89, 1208–1222 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Bulik-Sullivan, B. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rizzardi, L. F. et al. Neuronal brain-region-specific DNA methylation and chromatin accessibility are associated with neuropsychiatric trait heritability. Nat. Neurosci. 22, 307–316 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Lun, A. Bioc Singular. Bioconductor https://doi.org/10.18129/b9.bioc.biocsingular (2019).

  85. Melville, J. uwot: R package. R version 0.1.5 (2019).

  86. Wickham, H. ggplot2—Elegant Graphics For Data Analysis https://doi.org/10.1007/978-3-319-24277-4 (Springer, 2016).

  87. Collado-Torres, L., Weber, L. & Hicks, S. LieberInstitute/HumanPilot: archive the HumanPilot code for the preprint version of our project. Zenodo https://doi.org/10.5281/zenodo.3691916 (2020).

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Acknowledgements

We thank our colleagues whose efforts have led to the donation of postmortem tissue to advance these studies, including at the Office of the Chief Medical Examiner of the State of Maryland, Baltimore, MA and the Office of the Chief Medical Examiner of Kalamazoo County, Michigan. We also thank L. B. Bigelow and A. Deep-Soboslay for their contributions of diagnostic expertise, and D. R. Weinberger for providing constructive commentary and editing of the manuscript. Finally, we thank the families of the decedents, who donated the brain tissue used in these studies. We also thank the Accelerating Medicines Partnership–Alzheimer’s Disease (AMP-AD) Target Discovery and Preclinical Validation program and the ROSMAP study. We thank W. S. Ulrich for assistance with http://spatial.libd.org/spatialDE and the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health for hosting mirrors of our web application. We thank the Johns Hopkins University SKCCC Flow Cytometry Core and the Johns Hopkins University Transcriptomics and Deep Sequencing Core for supporting snRNA-seq experiments. This project was supported by the Lieber Institute for Brain Development. K.R.M., L.C-T., K.M. and A.E.J. were partially supported by the NIMH (grant no. U01MH122849). S.C.H. and L.M.W. were supported by the National Cancer Institute (grant no. R01CA237170). S.C.H. was also supported by the CZF2019-002443 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation, the National Human Genome Research Institute (grant no. R00HG009007).

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Author notes

  1. These authors contributed equally: Kristen R. Maynard, Leonardo Collado-Torres.

Authors and Affiliations

  1. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA

    Kristen R. Maynard, Leonardo Collado-Torres, Brianna K. Barry, Matthew N. Tran, Zachary Besich, Madhavi Tippani, Joel E. Kleinman, Thomas M. Hyde, Keri Martinowich & Andrew E. Jaffe

  2. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Kristen R. Maynard, Joel E. Kleinman, Thomas M. Hyde, Keri Martinowich & Andrew E. Jaffe

  3. Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA

    Leonardo Collado-Torres & Andrew E. Jaffe

  4. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Lukas M. Weber, Joseph L. Catallini II, Stephanie C. Hicks & Andrew E. Jaffe

  5. 10x Genomics, Pleasanton, CA, USA

    Cedric Uytingco, Stephen R. Williams, Jennifer Chew, Yifeng Yin & Nikhil Rao

  6. Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Brianna K. Barry, Keri Martinowich & Andrew E. Jaffe

  7. Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Matthew N. Tran, Zachary Besich & Andrew E. Jaffe

  8. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Thomas M. Hyde

  9. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Andrew E. Jaffe

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  1. Kristen R. Maynard
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Contributions

K.R.M. carried out conceptualization, methodology, validation, investigation, writing and visualization L.C.-T. dealt with methodology, software, formal analysis, data curation, writing and visualization. L.M.W. dealt with methodology, software, formal analysis, writing and visualization. C.U. dealt with methodology, investigation and resources. B.K.B. dealt with formal analysis, data curation and visualization. S.R.W. dealt with software and data curation. J.L.C. dealt with software, formal analysis and visualization. M.N.T. dealt with investigation and formal analysis. Z.B. dealt with software. M.T. dealt with formal analysis and visualization. J.C. and Y.Y. dealt with investigation. J.E.K. dealt with resources. T.M.H. dealt with methodology and resources. N.R. dealt with resources, supervision and funding acquisition. S.C.H. dealt with methodology, software, formal analysis, writing, visualization and supervision. K.M. dealt with conceptualization, methodology, writing, supervision, project administration and funding acquisition. A.E.J. dealt with conceptualization, methodology, software, formal analysis, writing, visualization, supervision, project administration and funding acquisition.

Corresponding authors

Correspondence to Keri Martinowich or Andrew E. Jaffe.

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Competing interests

C.U., S.R.W., J.C., Y.Y. and N.R. are employees of 10x Genomics. All other authors declare no conflicts of interest.

Additional information

Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 PCP4 expression, related to Fig. 1f.

Log-transformed normalized (logcounts) for PCP4 gene expression across all 12 samples arranged in rows by subject.

Extended Data Fig. 2 Layer-level dendrogram, related to Results: Gene expression in the DLPFC across cortical laminae and Fig. 2.

Dendrogram from the hierarchical clustering performed across all 76 layer-level combinations: 6 layers plus WM across 12 samples, with two layers visually absent in one sample as shown in Supplementary Fig. 5, second row. The layer-level combinations are colored by the brain subject (BR5292, Br5595, Br8100), position (0 or 300) and adjacent spatial replicate number (A or B).

Extended Data Fig. 3 Enrichment of genes expressed in synaptic terminals among neuropil spots, related to Results: Gene expression in the DLPFC across cortical laminae.

We compared DEGs from VGLUT1+ labeled synaptosomes from mouse brain from Hafner et al.30 on the x-axis versus the log2 fold change comparing spot-level expression between spots with 0 cells and spots with >0 cells. Association shown between (a) all expressed homologous genes and (b) those genes that were significant in the Hafner et al. dataset at FDR < 0.05.

Extended Data Fig. 4 Layer-level modeling strategies illustrated with MOBP, related to Results: Fig. 2.

Overview of the different modeling strategies we performed with the layer-level pseudo-bulked expression data. a, The ANOVA model, which evaluates whether the gene is variable in any of the layers (F-statistic); MOBP is the top 10th ranked of such genes. Colors represent each layer. b, The enrichment model, which tests one layer against the rest (t-statistic); MOBP is the top 36th gene for white matter against other layers. Colors show the comparison being done. c, The pairwise model where we test one layer against another (t-statistic); MOBP is the top ranked gene for WM > L3. Data from layers not used is shown in gray. 76 pseudo-bulked layers were used for computing the statistics in a–c.

Extended Data Fig. 5 Known marker genes compared to the best gene, related to Results: Identifying novel layer-enriched genes in human cortex.

Using the optimal models (Method Details: Known marker genes optimal modeling) for each known marker gene we compared the marker genes against the best gene for that given model. Results are visualized using the -log10 p-values for the marker gene (y-axis) against the best gene for that model (x-axis). Points are colored by the -log10 rank percentile of that gene in such a way that the top ranked gene is -log10(1/22,331) and colored in yellow.

Extended Data Fig. 6 Replication of Visium layer-enriched genes by Allen Brain Atlas in situ hybridization (ISH) data, Related to Fig. 3.

a–f, Left panels: Boxplots of log-transformed normalized expression (logcounts) for genes CUX2 (a, L2 > L6, p = 3.75e-19), ADCYAP1 (b, L3>rest, p = 3.57e-08), RORB (c, L4 > rest, p = 2.91e-07), PCP4 (d, L5 > rest, p = 1.81e-19), NTNG2 (E, L6>rest, p = 5.22e-13), and MBP (f, WM>rest, p = 1.71e-20). Middle panels: Spotplots of log-transformed normalized expression (logcounts) for sample 151673 for CUX2 (a), ADCYAP1 (b), RORB (c), PCP4 (d), NTNG2 (e), and MBP (f). Right panels: in situ hybridization (ISH) images from DLPFC (a, c, d, e, f) or frontal cortex (b) of adult human brain from Allen Brain Institute’s Human Brain Atlas: http://human.brain-map.org/33. Scale bar for Allen Brain Atlas ISH images = 1.6 mm. 76 pseudo-bulked layers were used for computing the statistics in a-f.

Extended Data Fig. 7 smFISH validation of L1- and L5-enriched genes, related to Fig. 4.

a,b, Left panels: Boxplots of log-transformed normalized expression (logcounts) for previously identified L1 and L5 marker genes RELN (a, L1>rest, p = 7.94e-15,) and BCL11B (b, L5>L3, p = 4.44e-02), respectively. Right panels: Spotplots of log-transformed normalized expression (logcounts) for sample 151673 for genes RELN (a) and BCL11B (b). Corresponding boxplots and spotplots for Visium-identified genes AQP4 and TRABD2A in Fig. 4. c, Multiplex single molecule fluorescent in situ hybridization (smFISH) in a cortical strip of DLPFC. Maximum intensity confocal projections depicting expression of DAPI (nuclei), RELN (L1), AQP4 (L1), BCL11B (L5), TRABD2A (L5) and lipofuscin autofluorescence. Merged image without lipofuscin autofluorescence. Scale bar = 500 μm. 76 pseudo-bulked layers were used for computing the statistics in a, b.

Extended Data Fig. 8 Spatial registration of snRNA-seq data, related to Fig. 5.

Heatmaps of Pearson correlation values evaluating the relationship between our Visium-derived layer-enriched statistics (y-axis) for 700 genes and a, Data from DLPFC from two donors, with data-driven cluster numbers and broad cell classes on the x-axis. b, Data from Velmeshev et al. with data-driven clusters provided in their processed data.

Extended Data Fig. 9 ‘Unsupervised’ clustering results for sample 151673, related to Fig. 7.

Visualization of clustering results for ‘unsupervised’ methods (Supplementary Table 10) for sample 151673. Each panel displays clustering results from one clustering method. Rows display methods either without (top row) or with (bottom row) spatial coordinates included as additional features for clustering. A complete description of the different combinations of methodologies implemented in the clustering methods is provided in Supplementary Table 10.

Extended Data Fig. 10 ‘Semi-supervised’ and ‘markers’ clustering results for sample 151673, related to Fig. 7.

Visualization of clustering results for ‘semi-supervised’ and known ‘markers’ gene set-based methods (Supplementary Table 10) for sample 151673. Each panel displays clustering results from one clustering method. Rows display methods either without (top row) or with (bottom row) spatial coordinates included as additional features for clustering. A complete description of the different combinations of methodologies implemented in the clustering methods is provided in Supplementary Table 10.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Supplementary Tables 1–10 (titles and legends).

Reporting Summary

Supplementary Tables 1–10.

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Maynard, K.R., Collado-Torres, L., Weber, L.M. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci 24, 425–436 (2021). https://doi.org/10.1038/s41593-020-00787-0

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