Human post-mortem brain tissue provides a valuable resource for identifying expression-based determinants of development and subsequent dysregulation in brain disorders (Kleinman 2011). RNA sequencing (RNA-seq) generates potentially unbiased characterization of the transcriptome, and has now been performed on over 1,000 non-psychiatric human brain samples across the lifespan within our lab (LIBD) and via the BrainSpan project ( We propose to combine and re-process these data together to make them more comparable, starting from raw sequencing reads. Our goal is further interrogate the clinical relevance of developmentally dynamic regions of gene expression across brain development. Our preliminary RNAseq data demonstrates extensive developmental regulation of previously unannotated intra- and inter-genic sequence conserved across multiple brain regions in both humans and mice. Base-level analysis of these combined RNA-seq data can greatly improve existing gene annotation databases like Ensembl and UCSC, which currently lack many fetal brain-specific transcripts that we have identified and characterized in our samples. We will perform base-level resolution analyses on both the entire dataset, from first trimester of fetal life through the aged (>85 years), and then secondary analyses exploring differential expression across different brain regions, to identify dynamic expressed sequence. Identified differentially expressed regions (DERs) will be interrogated for clinical significance through enrichment analysis with regard to predefined clinical gene sets, for examples significant loci from genome-wide association studies (GWAS) for brain disorders like schizophrenia, and also by directly associating genetic risk with expression levels within identified DERs. We will further provide the genomic tools to allow researchers with interests in other genes and loci to design their own validation experiments in cell lines and/or primary brain tissue for these clinically relevant loci. A more comprehensive characterization of the human brain transcriptome, leveraging big data across two complementary datasets, will be valuable for scientists studying a wide range of developmental processes and brain disorders.

Public Health Relevance

A more comprehensive characterization of the human brain transcriptome will be valuable for scientists studying a wide range of developmental processes and brain disorders. This project will combine the two largest postmortem human brain RNA sequencing datasets, the Lieber Institute for Brain Development and the BrainSpan project, to identify novel regions of dynamic gene expression across the lifespan. These regions will then be interrogated for contributing to clinical risk of brain disorders, and may provide more complete insight into the workings of human brain development.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1)
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Arguello, Alexander
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Lieber Institute, Inc.
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Jaffe, Andrew E; Straub, Richard E; Shin, Joo Heon et al. (2018) Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat Neurosci 21:1117-1125
Zhu, Ying; Sousa, André M M; Gao, Tianliuyun et al. (2018) Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362:
Amiri, Anahita; Coppola, Gianfilippo; Scuderi, Soraya et al. (2018) Transcriptome and epigenome landscape of human cortical development modeled in organoids. Science 362:
Rhie, Suhn K; Schreiner, Shannon; Witt, Heather et al. (2018) Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation. Sci Adv 4:eaav8550
Wang, Daifeng; Liu, Shuang; Warrell, Jonathan et al. (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362:
Li, Mingfeng; Santpere, Gabriel; Imamura Kawasawa, Yuka et al. (2018) Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362:
An, Joon-Yong; Lin, Kevin; Zhu, Lingxue et al. (2018) Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science 362:
Kozlenkov, Alexey; Li, Junhao; Apontes, Pasha et al. (2018) A unique role for DNA (hydroxy)methylation in epigenetic regulation of human inhibitory neurons. Sci Adv 4:eaau6190
Jaffe, Andrew E; Tao, Ran; Norris, Alexis L et al. (2017) qSVA framework for RNA quality correction in differential expression analysis. Proc Natl Acad Sci U S A 114:7130-7135
Collado-Torres, Leonardo; Nellore, Abhinav; Kammers, Kai et al. (2017) Reproducible RNA-seq analysis using recount2. Nat Biotechnol 35:319-321

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