Abstract
Transcriptome analysis of human brain provides fundamental insight into development and disease, but it largely relies on existing annotation. We sequenced transcriptomes of 72 prefrontal cortex samples across six life stages and identified 50,650 differentially expression regions (DERs) associated with developmental and aging, agnostic of annotation. While many DERs annotated to non-exonic sequence (41.1%), most were similarly regulated in cytosolic mRNA extracted from independent samples. The DERs were developmentally conserved across 16 brain regions and in the developing mouse cortex, and were expressed in diverse cell and tissue types. The DERs were further enriched for active chromatin marks and clinical risk for neurodevelopmental disorders such as schizophrenia. Lastly, we demonstrate quantitatively that these DERs associate with a changing neuronal phenotype related to differentiation and maturation. These data show conserved molecular signatures of transcriptional dynamics across brain development, have potential clinical relevance and highlight the incomplete annotation of the human brain transcriptome.
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Acknowledgements
We are grateful for the vision and generosity of the Lieber and Maltz families, who made this work possible. Human brain material was acquired from the Offices of the Chief Medical Examiner of the District of Columbia and those of the Commonwealth of Virginia, Northern District, and processed and stored at the NIH Clinical Center in Bethesda, Maryland. We thank the families who donated to this research and we thank R. Straub for criticism of the data analyses. This work was supported by the Lieber Institute for Brain Development. A.E.J. was partially supported by 1R21MH102791, L.C.-T. was supported by CONACyT México (351535) and J.T.L. was supported by 1R01GM105705-01A1.
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All authors contributed to the writing of the manuscript, plus the following individual contributions: A.E.J. designed the study, performed data analyses on summarized DERs: BrainSpan, mouse, cell and tissue types, histone tail– and disease-associated enrichments, and cell composition. J.S. performed data analysis involving processing the RNA-seq data. L.C.-T. performed data analysis involving the initial global derfinder approach. J.T.L. performed data analysis involving the initial global derfinder approach. R.T. performed RNA extractions and cytosolic separations. C.L. performed RNA extractions and cytosolic separations. Y.G. created sequencing libraries and oversaw the data generation for the discovery data. Y.J. created sequencing libraries and oversaw the data generation for the validation data. B.J.M. assisted in the biological interpretation of the computational findings. T.M.H. provided brain tissue and demographic data and assisted in biological interpretation of the computational findings. J.E.K. oversaw the project, provided brain tissue and demographic data, and assisted in biological interpretation of the computational findings. D.R.W. designed the project, oversaw the project and assisted in biological interpretation of the computational findings.
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Integrated supplementary information
Supplementary Figure 1 Principal component analysis (PCA) of differentially expressed regions (DERs).
Principal components (PCs) A) 1, B), 2, and C) 3 of the normalized coverage levels across the DERs, plotted against age group.
Supplementary Figure 2 Trajectories of differentially expressed regions (DERs) across age and development, colored by the age group/range with the highest expression levels.
Six genes overlapping these DERs are shown with thicker lines and described in the text. Y-axis: mean adjusted (for sequencing depth, per million mapped reads) coverage, on the log2 scale. The majority of DERs have highest expression in fetal life.
Supplementary Figure 3 Venn diagrams depicting the overlap of differentially expressed regions (DERs).
This overlap was performed using reference data from A) Ensembl p12, B) UCSC hg19 knownGene and C) Gencode v19 datasbases. DERs must overlap at least 20bp of each feature to successfully overlap.
Supplementary Figure 4 Concordance between differential expression in total RNA compared to cytosolic RNA.
Log2 fold changes comparing fetal to adult in 12 total RNA samples (discovery, six per age group) and 6 independent samples (3 per age group) with the cytosolic fraction separated from the nuclear mRNA for (A) all DERs and (B) only DERs overlapping annotated intronic regions. ρ = Spearman correlation, κ = directionality concordance.
Supplementary Figure 5 RNA quality number (RIN) explains second principal component of BrainSpan samples across developmental DERs.
Each point is a sample colored by age (purple: prenatal and green: postnatal), where white corresponds roughly to birth.
Supplementary Figure 6 Age-associated non-exonic differentially expressed region (DER) expression patterns across multiple brain regions.
Principal component analysis (PCA) was performed on normalized coverage estimates across non-exonic DERs using all BrainSpan samples. Each point is a sample colored by age (purple: prenatal and green: postnatal), where white corresponds to birth.
Supplementary Figure 7 Developmental differentially expressed region (DER) expression patterns across multiple brain regions in latter principal components (analogous to Figure 2).
Each point is a sample colored by age (purple: prenatal and green: postnatal), where white corresponds roughly to birth.
Supplementary Figure 8 Example of “LIBD Human DLPFC Development” custom UCSC Track Hub.
This Track Hub displays the regions representing the significant DERs, the F-statistic indicating differential expression at a particular base, and the mean age group normalized coverage levels across the genome.
Supplementary Figure 9 Estimated proportions of cell types within postmortem brain samples.
We utilized DNA methylation data to calculate the relative proportion of each cell type based on publicly available cellular populations, consisting of A) NeuN+ and B) NeuN- cells from primary tissue, and C) cell line data from ES-derived NPCs. We can correlate these composition estimates to the expression levels within identified differentially expressed regions (DERs) and the corresponding –log10(p-values) are shown in Panel D.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–9 (PDF 3881 kb)
Supplementary Table 1: Demographic/phenotype information for 36 DLPFC discovery samples.
BrNum/RNum: ID columns, BrainCloud: whether the sample was included in the lifespan series in Colantuoni et al 2011; Age: age at death; Sex: M(ale) or F(emale); Race: AA=African American, CAUC = Caucasian, AS=Asian, HISP=Hispanic; ageGroup: used for differential expression analysis, RIN: RNA integrity number, a measure of RNA quality; totalMapped: total number of reads mapping to autosomes and sex chromosomes (excluding mitochondrial) using TopHat2; Cohort: Discovery or validation data; Accession: SRA accession numbers for BAM files of discovery cohort. (XLSX 14 kb)
Supplementary Table 2: List of significant differentially expressed regions (DERs).
Coordinates are relative to the hg19 genome build. Width in base pairs; value: average f-statistic in region; area: sum of f-statistic across the region (used to rank regions); fwer: family wise error rate, the proportion of null permutations with a larger area; meanCoverage: average coverage/number of reads across all samples across the region; nearestGene: nearest RefSeq gene symbol; annotation: RefSeq ID; description: relation of DER to gene; distToTSS: distance in base pairs to transcriptional start site; subregion: where DER overlaps gene; exonnumber: the closest exon to the DER; annoStrand: strand of the RefSeq gene; geneL: gene length in base pairs; codingL: coding length in base pairs; Fetal_adjMeans: mean adjusted coverage of fetal (-1,0] samples, Infant_adjMeans: mean adjusted coverage of infant (0,1] samples, Child_adjMeans: mean adjusted coverage of child (1-10] samples, Teen_adjMeans: mean adjusted coverage of teen (10-20] samples; Adult_adjMeans: mean adjusted coverage of adult (20-50] samples; 50plus_adjMeans: mean adjusted coverage of 50+ (50,100] samples. (XLSX 24071 kb)
Supplementary Table 3: Gene ontology (GO) results.
A) all differentially expressed regions (DERs), B) top 1000 DERs, C) DERs with highest fetal expression, D) DERs with highest infant expression, E) DERs with highest child expression, F) DERs with highest teen expression, G) DERs with highest adult expression, H) DERs with highest 50+ expression. For each sheet, GOBPID: gene ontology ID; Pvalue: unadjusted p-value from hypergeometric test; OddsRatio: corresponding odds ratio of enrichment; Size: number of genes in set; Term: GO category description (XLSX 173 kb)
Supplementary Table 4: Phenotype data for nuclear and cytosolic separation independent validation.
BrNum/RNum: ID columns; Zone: Nuclear or cytosol fraction; Age: age at death; Sex: M(ale) or F(emale); Race: AA=African American, CAUC = Caucasian; RIN: RNA integrity number, a measure of RNA quality; ugTotal: total yield of RNA; Yield(ug/mg): normalized yield of RNA; Group: fetal or adult age group; totalMapped: total number of reads mapping to autosomes and sex chromosomes (excluding mitochondrial) using TopHat2. (XLSX 8 kb)
Supplementary Table 5: Odds ratios for overlap between DERs and fetal brain histone marks, stratified by DER annotation.
Odds ratios are for a bin size of 1kb. (XLSX 8 kb)
Supplementary Table 6: List of CpGs, and their mean DNA methylation levels, used in the cell type proportion calculations.
This file can be used to estimate these relative cell types in other datasets using the estimateCellCounts function in the minfi package. Row names correspond to identifiers on the Illumina 450k microarray. (XLSX 21 kb)
Supplementary Data 1: RPKM counts from cell and tissue type analysis.
Includes the RPKM matrix (rpkmMatrix_n121_combinedPheno.txt), annotation (geneAnnotation_n121_combinedPheno.txt) and phenotype data (phenotypeDat_n121_combinedPheno.txt) (ZIP 32380 kb)
Supplementary Data 2: R code.
See corresponding 00_README file for descriptions. (ZIP 40 kb)
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Jaffe, A., Shin, J., Collado-Torres, L. et al. Developmental regulation of human cortex transcription and its clinical relevance at single base resolution. Nat Neurosci 18, 154–161 (2015). https://doi.org/10.1038/nn.3898
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DOI: https://doi.org/10.1038/nn.3898
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