An anatomically comprehensive atlas of the adult human brain transcriptome

By M. Hawrylycz, E. Lein, A. Guillozet‐Bongaarts, E. Shen, Lydia Ng, Jeremy A. Miller, L. N. V. D. Lagemaat, Kimberly A. Smith, Amanda J. Ebbert, Z. Riley and 57 more. 2012

3,011 citations recorded by the source corpus.

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.

Subfields: Molecular Biology, Neurology

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Citations in the corpus

Adult neurogenesis in brain repair: cellular plasticity vs. cellular replacement (2014, background): “Indeed the extensive heterogeneity of neuronal subtypes in the brain makes it difficult to drive stem cells to differentiate to specific neuronal subtypes (Hawrylycz et al., 2012), which is a major requirement for regaining the lost neurological function.”
OpenML: networked science in machine learning (2014, background): “We are ‘mapping and mining’ just about every complex phenomenon in nature, including the brain [21; 15], the ocean [18], gene sequences, genetic variants in humans [12], and gene functions [31].”
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity (2015, methodology): “For example, Spearman ’ s coefficients for day 16 and day 21 neural constructs were higher for all comparisons to eight postconception week (PCW) samples ([ ρ ] ≥ 0.82, all brain regions) than 30-y-old adult samples ([ ρ ] ≤ 0.76, all brain regions) (30, 31).”
Adult Mouse Cortical Cell Taxonomy by Single Cell Transcriptomics (2016, neutral): “2a,b, Methods: Supplementary Note 1) 19 .”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, methodology): “We mapped the whole genome transcriptional data on 6 adult human post-mortem brains (Hawrylycz et al., 2012) into the same parcellation scheme that was used to define the 308 nodes of the MSNs (Whitaker and Vértes et al., 2016; Vértes et al., 2016) (Methods).”
Basal Forebrain Cholinergic Circuits and Signaling in Cognition and Cognitive Decline (2016, neutral): “Images for A adapted from human brain atlas (Hawrylycz et al., 2012) Neuron.”
The atoms of neural computation (2014, background): “There are differences between gene expression in different areas of the cortex such that “the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes'' (Hawrylycz et al., 2012).”
A census of human RNA-binding proteins (2014, background): “58 restricted to a particular cell-type or to be temporally regulated (Hawrylycz et al., 2012; Miller et al., 2014).”
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes (2013, neutral): “Much of the transcriptional control of mammalian development is due to the diverse activity of transcription factor-bound enhancers that control cell type-specific patterns of gene expression (Bulger and Groudine, 2011; Hawrylycz et al., 2012; Maston et al., 2006).”
A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex (2015, neutral): “Fifth, extensions should include other inter-areal heterogeneities, such as in interneuron types and densities (Medalla and Barbas, 2009) and in neuromodulatory signaling (Hawrylycz et al., 2012).”