On Expression Patterns and Developmental Origin of Human Brain Regions

Abstract

Anatomical substructures of the human brain have characteristic cell-types, connectivity and local circuitry, which are reflected in area-specific transcriptome signatures, but the principles governing area-specific transcription and their relation to brain development are still being studied. In adult rodents, areal transcriptome patterns agree with the embryonic origin of brain regions, but the processes and genes that preserve an embryonic signature in regional expression profiles were not quantified. Furthermore, it is not clear how embryonic-origin signatures of adult-brain expression interplay with changes in expression patterns during development. Here we first quantify which genes have regional expression-patterns related to the developmental origin of brain regions, using genome-wide mRNA expression from post-mortem adult human brains. We find that almost all human genes (92%) exhibit an expression pattern that agrees with developmental brain-region ontology, but that this agreement changes at multiple phases during development. Agreement is particularly strong in neuron-specific genes, but also in genes that are not spatially correlated with neuron-specific or glia-specific markers. Surprisingly, agreement is also stronger in early-evolved genes. We further find that pairs of similar genes having high agreement to developmental region ontology tend to be more strongly correlated or anti-correlated, and that the strength of spatial correlation changes more strongly in gene pairs with stronger embryonic signatures. These results suggest that transcription regulation of most genes in the adult human brain is spatially tuned in a way that changes through life, but in agreement with development-determined brain regions.

Bridging the gap between transcriptome and connectome

“…One prominent gradient of gene expression observed in the adult brain follows the rostrocaudal axis [19,43], and is consistent with a similar transcriptional gradient observed prenatally [39,[46][47][48]. This spatial pattern may be tied to regional variations in cellular architecture, intrinsic circuitry and extrinsic connectivity.…”

A practical guide to linking brain-wide gene expression and neuroimaging data

“…Parkes et al (2017) DS Select the probe with most consistent pattern of regional variationscross the six donor brains, as quantified using a measure called Differential Stability (DS). Hawrylycz et al (2015), Kirsch and Chechik (2016) Connectivity variance/intensity Select the probe with the highest average correlation to other probes a for >2 probes; select the probe with maximum variance (connectivity variance) or maximum intensity (connectivity intensity) when only 2 probes available. Hawrylycz et al (2012Hawrylycz et al ( , 2015, Sequence mismatches Select the probe with fewest sequence mismatches, or the probe with highest standard deviation if >1 probe have the same number of mismatches b .…”

“…This unprecedented capacity to link molecular function to macroscale brain organization has given rise to the nascent field of imaging transcriptomics, which has begun to yield new insights into how regional variations in gene expression relate to functional connectivity within: canonical resting-state networks (Richiardi et al, 2015, Forest et al, 2017; fiber tract connectivity between brain regions (Goel et al, 2014); temporal and topological properties of large-scale brain functional networks (Cioli et al, 2014; the specialization of cortical and subcrotical areas (Krienen et al, 2016, Parkes et al, 2017, Anderson et al, 2018; regional maturation during embryonic and adolescent brain development (Kirsch andChechik, 2016, Whitaker et al, 2016); and pathological changes in brain disorders , Romme et al, 2017, McColgan et al, 2018, Romero-Garcia et al, 2018a. Software toolboxes to facilitate the integration of brain-wide transcriptomic and imaging data have also been developed (French and Paus, 2015, Gorgolewski et al, 2015, Rizzo et al, 2016, Rittman et al, 2017.…”

A unified computational model for cortical post-synaptic plasticity

“…3B). We prioritised the model components from hippocampal models due to the relatively small ontological differences between hippocampus and neocortex [Kirsch and Chechik, 2016]. We focused on the effects of these pathways on AMPARs due to the better description of intracellular regulation of AMPAR dynamics in comparison to that of NMDA and kainate receptors or voltage-gated ion channels.…”