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

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.

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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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).

Author information

Affiliations

Authors

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.

Ethics declarations

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.

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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 ac.

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.

af, 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.

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Supplementary Information

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

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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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