We have described the generation and analysis of the transcriptome and corresponding genotyping dataset covering multiple regions of the developing and adult brain. This dataset (available at www.humanbraintranscriptome.org
), along with an accompanying study50
, provides opportunities for a variety of further investigations and comparisons with other transcriptome-related datasets of both healthy and diseased states.
Our analysis revealed several important aspects of the human brain transcriptome and expands current knowledge of the transcriptional events in human neurodevelopment. Consistent with many previous studies in other species, we found that gene expression and exon usage have complex, dynamically regulated patterns across time and space and are far more prominent than differences between sexes, ethnicities, or individuals despite their underlying genomic differences.
We confirmed and expanded on previous findings of sexual dimorphism in gene expression and exon usage, including several disease-related genes. These findings offer possible transcriptional mechanisms underlying sex differences in the incidence, prevalence, and severity of many disorders. We also demonstrated how the dataset can be used to profile trajectories of genes associated with specific neurobiological categories or disorders, many of which would not likely be evident in the transcriptomes of commonly studied model systems. Coupled with analysis of co-expressed genes in the dataset, these provide information about when and where these genes are expressed in the brain, which can help infer their function. Our data can enhance genome-wide association and linkage studies by narrowing the focus to the candidate genes that are specifically expressed during development or restricted to a specific region known to be preferentially affected.
We show associations between specific SNPs and expression levels in different regions of the developing human brain, indicating that genetic variation contributes to inter-individual transcriptome variability across regions and development. While the current number of specimens in our dataset restricted our power to detect many of the possible eQTLs, we have identified a set of cis-eQTLs, including many not previously reported, that may provide insights into expression-regulatory elements operating in the brain.
Although these findings highlight the complexity of gene expression and exon usage in the human brain, there are several potential limitations in our data that warrant discussion. Foremost, we used stringent criteria in order to faithfully characterize general transcriptional patterns and minimize false positives, rather than to capture all the changes that may occur. Also, we analyzed dissected tissue samples that contain multiple cell types, thus diluting the genetic contribution and dynamic range of genes expressed in a more cell type specific manner. Current limitations prevent us from using cell type specific approaches in systematically analyzing the spatiotemporal transcriptome. Furthermore, the number of brains and regions analyzed so far is not sufficient to fully investigate the whole magnitude of transcriptional changes or the full range of eQTLs. Application of sequencing technology will allow even more in-depth analysis of the transcriptome and discovery of novel and low-expressing transcripts and their associated spatiotemporal patterns. Finally, while specific patterns of expression are often linked to equally specialized biological processes, it is important to keep in mind that the relationship between mRNA and protein levels is not always linear or translated into apparent phenotypic differences. As these concerns are addressed in the future with the addition of samples to our dataset and the generation of new datasets from human and nonhuman brains throughout development, it will be possible to uncover further insights into the transcriptional foundations of human brain development and evolution.