Abstract
The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.
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Change history
31 August 2017
In the version of this article initially published, the third and fourth paragraphs of Online Methods section “Differential stability in cortex and resting state network analysis” read as follows: The next step was to map the Allen Human Brain Atlas (AHBA) tissue samples to the HCP 52 region parcellation so that comparison could be made. Using the MNI centroid coordinate of the AHBA samples, and by manually examining each of the AHBA tissue samples using the online tools, one can assign a set of HCP space voxels to each AHBA tissue sample. As each of the 52 parcels is composed of a set of voxels, we now have potentially one-to-many map from AHBA tissue to HCP parcels. If all ABHA tissue samples belong to a common HCP parcel, we average the gene expression of that tissue in the corresponding parcel. However, some of the 52 parcels represent smaller regions of the brain and therefore there is no unique assignment of AHBA gene expression tissue samples to that region. Therefore, if a collection of AHBA tissue samples intersects more than one region, we average the gene expression values as before but fractionally weight the expression contribution to each of the interesting HCP parcels. This has the effect of allowing some assignment of expression without overweighting non-unique samples. Supplementary Table 12 gives the sample distribution by parcels as well as the uniquely assigned samples. To obtain the expression correlation matrix for a given gene (Fig. 7c, right panel), we transformed the expression values of that gene into z-scores over all the sampled brain regions (averaging sample data for those samples contained in the same parcel) and calculated the coexpression as the outer product of this z-score vector. Thus, if two regions both show high expression or low expression of the gene of interest, they will have a high positive coexpression value for that gene, whereas if they show opposite expression patterns, they will have a large negative value for that gene. After generating these matrices, we compared each of the 17,348 gene coexpression matrices to the parcellated connectome matrix by calculating the Pearson's correlation between the vectorized elements above the diagonal of the matrices (Fig. 7d). We also obtained a significance value for each gene-connectivity comparison using the randomized gene coexpression matrices. Supplementary Table 12 gives the complete distribution of tissue samples by HCP parcel for the 52 regions and the functional genetic correlations and P-values. In the current version, these paragraphs have been rewritten to unambiguously explain how each RSN parcel was mapped to the AHBA samples. The original version did not clearly delineate the approach for each of the three possible cases in which RSN parcels could overlap the AHBA samples. The new text also has an additional paragraph describing the rationale behind the two sets of P-values included in Supplementary Table 12. The error has been corrected in the HTML and PDF versions of the article.
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Acknowledgements
The authors thank the Allen Institute for Brain Science founders, Paul G. Allen and Jody Allen, for their vision, encouragement and support. Research was supported by the Allen Institute for Brain Science. We also gratefully acknowledge support from the US National Institute of Drug Abuse, grant 4R33DA027644; D. Wall of Stanford University School of Medicine; and 1U54MH091657 (NIH Blueprint for Neuroscience Research).
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Contributions
M.H., J.A.M. and V.M. performed the primary analyses, with supporting analyses by A.L.G.-B., F.C., K.A.B., P.G., Z.Y., L.S., A.-L.B. and J.S. Graphics and networks analysis were done by D.F., T.D., L.N., C.D. and J.A.M. Annotation analysis was done by A.G.J. and B.J.A. M.F.G. and D.L.D. performed resting state analysis with V.M., S.M. and D.C.V.E. Data processing and normalization were done by C.-K.L., L.N., C.D., A.B., J.P., A.S. and M.H. J.A.M., D.R.H., D.C.V.E., A.J., C.K. and E.L. wrote the manuscript.
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Integrated supplementary information
Supplementary Figure 1 Anatomic distribution of expression of potassium channels.
Hierarchical clustering of potassium channels across the brain. This gene class has higher DS than expected by chance (Mann-Whitney U, p<1.70e-12). Gene expression data are z-score normalized. A wide variety of distinct expression profiles are represented from regions representing cortex, hippocampus, striatum, thalamus, cerebellum and a variety of small marker regions including inferior olivary (IO). Structural ordering same as Fig. 1B.
Supplementary Figure 2 Anatomic organization and differential stability
Transcriptional distinctness and topographic relationships between brain regions based on multidimensional scaling (MDS) under decreasing DS gene sets (size n=1735) at 10th (A), 25th (B), 50th (C), 75th (D) percentile. Anatomic structural separation becomes less prominent for lower DS ranked genes in each of the quartiles. Legend and scales are identical for all four panels.
Supplementary Figure 3 Maximum module correlation and cross-validation analysis.
(A) Maximum correlation to a module eigengene (M1-M32) is plotted for each gene in descending DS, with Lowess fit f=1/3 (www.r-project.org) shown in black. As high DS genes are used in the network construction it is expected that the vast majority of these genes (96.5%, g=8,372) will be highly correlated (ρ>0.4) to one of 32 core gene expression networks. However, a much larger class of genes (90.1%, 15,627/17,348) are well correlated with the 32 modules at ρ>0.4. (B) To measure the reproducibility of modules we performed a cross-validation study in which 6 consensus networks were constructed from all possible subsets of 5 brains, leaving a different one out in each network (e.g. network O1 omits brain 1). Using the same fully automated procedure described in the text, 33, 36, 38, 36, 39, and 36 modules were identified in networks O1-O6, respectively, consistent with, but slightly higher than, the 32 modules identified in the 6-brain network. Average module eigengene (MEs) were found in each network by independently calculating the MEs in each brain using the assigned genes and then averaging across the 5 brains. These vectors were then correlated with the corresponding patterns found in the held out brain as a consistency check. Overall, the patterns found in the held out brain largely agree with average MEs from the 5 brain networks (mean ρ=0.81, 0.86, 0.85, 0.87, 0.85, and 0.86 in O1-O6, respectively). The essential patterning of the gene set in the held out brain is preserved as 98.7% of comparisons have high correlation (ρ> 0.6).
Supplementary Figure 4 Network architecture of the canonical transcriptome.
(A) Functional interaction of canonical modules based on common GO gene ontology categories (Molecular Function, Biological Process, Cellular Component), KEGG and Reactome pathways. (Database details: https://toppgene.cchmc.org/navigation/database.jspwww.Methods). Size of colored squares between modules scales with number of common interactions. Notable common shared pathways and interactions are labeled. Neuronal processes, cell type and function, including neurogenesis and neurodevelopment are dominated by M1-M5. The dopaminergic pathway, from biosynthesis to storage and release is represented by the pair M10, M12. Other notable pathways include M19, M27, and M29 involved in mitochondrial and ribosomal function, M21 and M25 in immune response, and M30 and M32 for glial processes. The largest set of common pathways are shared by M21 and M25 centering on immune and defense response (1.92e-05, 5.85e-34) respectively and immune system development (5.98e-08, 8.60e-04). (B) The degree of the each module as a node in the graph of A. Modules having more specific neuronal or glial content have the highest degree of functionality connectivity with other modules, e.g. M4 and M25. The modules with mixed cellular content (M13-M18) including highly specific anatomic modules for pontine/arcuate nuceli, dentate gyrus, hypothalamus, and cerebellum share few functional annotations with other modules. The diameter of the graph, indicating the maximum path connecting any two modules in terms of common annotations, is 3. (C) By comparing this network with random networks of same size, neuronal modules M1-M15 are seen to substantially interact, and to a weaker extent M16-M32, with significantly fewer common interactions than by chance associations (see Supplementary Analysis). Interaction matrix indicates shared annotations that exceed or are fewer than expected by chance, scored as z-score with binomial test. See Table 1 and Supplementary Table 8 for detailed module annotation.
Supplementary Figure 5 Canonical human transcription modes M1–M8.
(Bottom row, each panel) The consensus module eigengene expression for modules M1-M8 are shown with standard error of the mean across 6 brains, with prominent structures and a representative hub gene of high correlation to the pattern listed above the bar graph. All bar graphs are on the same scale. (Top row, each panel) Anatomic visualization of each module is shown, where red represents higher expression and blue represents lower expression. Anatomical plots for all genes available through the Allen Human Brain Atlas are available as part of the resource.
Supplementary Figure 6 Canonical human transcription modes M9–M16.
(Bottom row, each panel) The consensus module eigengene expression for modules M9-M16 are shown with standard error of the mean across 6 brains, with prominent structures and a representative hub gene of high correlation to the pattern listed above the bar graph. All bar graphs except M16 are on the same scale. (Top row, each panel) Anatomic visualization of each module is shown, where red represents higher expression and blue represents lower expression. Anatomical plots for all genes available through the Allen Human Brain Atlas are available as part of the resource.
Supplementary Figure 7 Canonical human transcription modes M17–M24.
(Bottom row, each panel) The consensus module eigengene expression for modules M17-M24 are shown with standard error of the mean across 6 brains, with prominent structures and a representative hub gene of high correlation to the pattern listed above the bar graph. All bar graphs except M22 are on the same scale. (Top row, each panel) Anatomic visualization of each module is shown, where red represents higher expression and blue represents lower expression. Anatomical plots for all genes available through the Allen Human Brain Atlas are available as part of the resource.
Supplementary Figure 8 Canonical human transcription modes M25–M32.
(Bottom row, each panel) The consensus module eigengene expression for modules M25-M32 are shown with standard error of the mean across 6 brains, with prominent structures and a representative hub gene of high correlation to the pattern listed above the bar graph. All bar graphs except M26, M27, and M31 are on the same scale. (Top row, each panel) Anatomic visualization of each module is shown, where red represents higher expression and blue represents lower expression. Anatomical plots for all genes available through the Allen Human Brain Atlas are available as part of the resource.
Supplementary Figure 9 Ontology and functional associations by module.
Counts of the number of uniquely associated ontology and functional terms by module for major categories of gene ontology, gene family, pathway, and phenotype. The detailed terms and p-values can be found in Supplementary Table 8.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–9, Supplementary Table 1 and Supplementary Analysis (PDF 4608 kb)
Supplementary Table 1: Neuroanatomical sampling overview of the Allen Human Brain Atlas and of each analysis in the manuscript.
A hierarchical ontology spanning all major architectural subdivisions was created to support the microarray sampling strategy. Each structure in this tree is designated a specific RGB color used throughout this paper. The number of samples isolated from each brain from brain region is shown in the first worksheet, along with a summary of the specific subdivisions sampled and the sample isolation method used (Macro = scalpel macrodissection, LMD = laser microdissection). The complete hierarchical ontology and fine structure sampling for each brain is provided in the second worksheet, collapsed down to a single column (complete version available at the Allen Brain Atlas data portal, or can be reconstructed using the “structure ID” and “parent structure ID” columsn). The table contains the structure ID, acronym, hemisphere, color hex triplet, and the number of samples for that structures in brains 1-6. Asterisks (*) in the “structure acronym” column indicate the 96 brain regions that were sampled sufficiently to be included in the analysis in Figure 6. Additionally, the “subregion for analysis” column specifies the portions of the ontology which were averaged together to form the 132 broad brain structures in the analyses in Figures 2–5. Asterisks (*) in this column indicate the 65 brain regions sampled across all six brains that were shown in visualizations (** note: CPLV, Pa, and CGS were present in fewer than 6 samples but were also shown in the visualizations to highlight ependymal regions in module M26). The third worksheet shows which mouse structures were matched with each human structures for comparison between species (Figure 6). Structures listed in the “Mouse counterparts” column are structure names from the ontology of the Allen Mouse Brain Atlas. A red “none” indicates that the listed human region did not have comparable structures in mouse. Note that, although cc and RaM had comparable mouse counterparts, ISH quantification was not available for these structures. (PDF 1924 kb)
Supplementary Table 2: Differential stability metrics for every gene in the analysis.
Several different metrics of differential stability are provided for all 17,348 genes included in the analysis. Genes and the corresponding probes are listed and ordered descending by Pearson correlation (this is the metric used in the manuscript). Alternative metrics include MaxDiff (the maximum occurring differential between pairs of structures), Tau (the average Kendall Tau correlation across regions for each pair of brains), Euclid (the average Euclidean distance between expression levels in pairs of brains), and AvgVar (the average across-region variability between the six brains.) (CSV 952 kb)
Supplementary Table 3: Genes with high expression but low variability are enriched for housekeeping functions.
2,236 genes have expression in the lowest quartile of variability and also have relatively low differential stability (DS<0.5). These genes are sorted descending by DS and also include their associated probes, their average log2 expression levels, and log2 standard deviations. These stable (but not differentially stable) genes are enriched for housekeeping functions such as RNA binding (p<3.26e-21), KEGG spliceosome pathway (p<6.4e-13), and mitochondrial ribosomal proteins (p<1.32e-10). (CSV 114 kb)
Supplementary Table 4: Enrichment analysis for the top 10th percentile set of genes (n=1735) ranked by differential stability.
The complete list of enrichments for the blue bars shown in Figure 2d, including significant enrichments for gene ontology categories, transcription factor binding sites, mrRNA targets, and drug targets. The first three columns indicate the category, ID, and name of the enrichment list. The next four columns show p-values from a hypergeometric test for enrichment and q-values after correction for multiple comparison, as well as the overlapping and total number of genes in the list. The final column shows all overlapping genes in each category. (CSV 2327 kb)
Supplementary Table 5: Disease enrichments for the top 10th percentile set of DS genes are primarily brain related.
The complete list of significant disease enrichments for the data shown in Figure 2f, based on 2289 gene sets from the Autworks database. Column B shows the disease tested for enrichment. The next two columns show Bonferroni corrected p-values from a hypergeometric test for enrichment, as well as the overlapping number of genes in the list. The final column shows all overlapping genes in each category. (CSV 68 kb)
Supplementary Table 6: Module assignments and eigengene correlations for each gene assigned to a module in the consensus network.
The great majority of all genes (90.1%, 15,627) are correlated with 32 modules with ME correlation > 0.4. This table includes these genes and their associated probes, their final module assignment color (column C), label (column D), and recoloring based on neuronal content (column E). The genes correlation to the corresponding ME is shown (column F) along with the DS metric (column G; reproduced from Supplementary Table 2). Finally, the initial module assignment is included (column H) to allow regeneration of the ME from the expression data. (CSV 1042 kb)
Supplementary Table 7: Per module count of marker genes for cell type and subcellular compartment.
The number of genes showing at least 1.5-fold enrichment for astrocytes (column C), neurons (column D), and oligodendrocytes (column E; Ref. #23) is unevenly distributed across modules. Modules are labeled based on the percent of neuron-related genes (100 * column D / column B) in each module (see Fig. 3C). The number of genes in each module associated with discrete cellular subcompartments was also determined (columns F-O; Foster, LJ, de Hoog, CL, Zhang, Y, Zhang, Y, Xie, X, Mootha, VK, Mann, M. 2006. A Mammalian Organelle Map by Protein Correlation Profiling. Cell 125-1: 187-199); however, these data were not used in the manuscript. (CSV 1 kb)
Supplementary Table 8: Complete module enrichments based on ToppGene lists.
The complete list of enrichments for the heatmap shown in Figure 3e. We used the ToppGene portal to identify significant enrichments in gene ontology, pathways, cytoband, disease association, transcription factor binding sites, micro RNAs, drug targets, and protein-protein interactions. The first three columns indicate the category, ID, and name of the enrichment list. The first three columns indicate the category, ID, and name of the enrichment list. The next four columns show p-values from a hypergeometric test for enrichment and q-values after correction for multiple comparison, as well as the overlapping and total number of genes in the list. Column H shows the module being tested. The final column shows all overlapping genes in each category. All enrichments shown have an FDR corrected q-value < 0.05. (CSV 3868 kb)
Supplementary Table 9: Enrichment analysis for the 302 genes with high DS that are not assigned to any module.
The complete list of enrichments for the heatmap shown in Figure 3e. The “Singular Genes” tab lists the genes and their differential stability metric, while the “Annotation” tab lists the enrichments. We used the ToppGene portal to identify significant enrichments in gene ontology, pathways, transcription factor binding sites, and other categories. The first four columns indicate the category, ID, and name of the enrichment list, as well as the source of the category. The first three columns indicate the category, ID, and name of the enrichment list. The next four columns show p-values from a hypergeometric test for enrichment and q-values after correction for multiple comparison, as well as the overlapping and total number of genes in the list. The final column shows all overlapping genes in each category. All enrichments shown have a B&H FDR corrected q-value < 0.01. (XLSX 91 kb)
Supplementary Table 10: Conserved or non-conserved expression patterning between mouse and human.
All 2,651 genes with reliable expression patterns in both mouse and human data sets (Methods), as well as their corresponding human probes are shown. Column C indicates whether the pattern agrees between species (correlated to correct module eigengene in mouse with ρ > 0.4), or disagrees between species (correlated to the correct module eigengene in mouse with ρ < 0.4, but highly correlated to a different module with ρ > 0.8). All remaining genes, which cannot definitively be definitively listed as agreeing or strongly disagreeing between species, are listed as ambiguous (or uncorrelated). The final column lists the original module assignment in human. Note that unassigned genes which are correlated to any module with ρ > 0.8 in mouse are listed as disagreeing between species. (XLSX 101 kb)
Supplementary Table 11: Local differential stability metrics for 20 brain regions.
Local differential stability metrics provided for all 17,348 genes included in the analysis. Genes are listed and ordered descending by Pearson correlation (metric used in the manuscript). The remaining columns list differential stability calculated using only the subregions of a particular brain regions. Note that for cortical and cerebellar regions, these metrics include the full set of subregions and not the data averaged by lobe. Figure 7 uses the DS values for cerebral cortex that are listed in column E. (CSV 2306 kb)
Supplementary Table 12: Parcel assignment from AHBA to Human Connectome functional imaging parcellation.
For each of 52 parcels of the functional connectome from the number of samples from each of the 6 AHBA brains is shown followed by the average number over 6 brains. As each AHBA parcel may belong to multiple regions, the next six columns give the number of unique AHBA parcels assigned to each functional connectome parcel. The final two columns give the size in voxels of corresponding functional connectome parcel and the percentage of total cortex voxels of that parcel. Analyses in the main manuscript are presented in for all AHBA parcels and in the Supplementary methods using unique parcels. (XLSX 3044 kb)
Supplementary Data Set 1
Zip file containing code and input required to reproduce figures. (ZIP 25557 kb)
Supplementary Data Set 2
Zip file containing truncated and summarized gene expression data which is used along with Supplementary Data Set 1. (ZIP 32118 kb)
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Hawrylycz, M., Miller, J., Menon, V. et al. Canonical genetic signatures of the adult human brain. Nat Neurosci 18, 1832–1844 (2015). https://doi.org/10.1038/nn.4171
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DOI: https://doi.org/10.1038/nn.4171
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