Relating the transcriptional and structural architecture of mouse cortical areas
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1
UKE, Institute for Computational Neuroscience, Germany
The cortical sheet of the mouse consists of a mosaic of cortical areas defined by their distinct neuronal composition and layering. The neuronal architecture and layering of the cortex is bound to both intrinsic and extrinsic factors, for instance expression of genes, patterns of afferents connections and functional activity. Numerous studies have identified genes that appear to be essential for the generation and laminar positioning of different types of neurons in the mouse cortex by using gene knock out or silencing methods. Such studies have the benefit of being causal but the number of genes that can be knocked out or silenced is limited. Moreover, knocking out certain genes may prove to be lethal and thus render the observation of the effects in the cellular organization of the adult brain impossible. Despite a plethora of existing studies, the information that gene expression paterns carry on the structural traits of the cortical areas has not yet been quantified at the whole cortex level. In other words, the extend to which gene expression patterns can predict for example the number of neurons of a cortical area has not been explicitly demonstrated.
Here we combine the Allen Brain Atlas data (detailed gene-expression mapping of thousands of genes across the brain of the C57Bl/6J mouse) with previously published data on the cellular and structural composition of the mouse cortical aras derived from the application of the isotropic fractionator. In total ~4000 genes and 6 structural traits (number of neurons, number of non-neuronal cells, density of neurons, cortical type, thickness and volume) from 38 cortical areas were examined. The two multidimensional datasets (transriptome and structural traits) were associated with partial least squares regression combined with a recursive feature elimination algorithm. This technique allows harvesting the most predictive set of genes that maximally predict the various structural traits of the cortical areas. The transcriptome-based model was tested against three benchmark models. One incorporating only the spatial relations of the cortical areas, one using the patterns of thalamic afferents and one that uses the patterns of patterns of cortical afferents. Co-expression analysis was also conducted for the set of the maximally predictive genes to investigate their potential synergistic role.
Our results indicate that the transciptional signature of an area is highly predictive of its various structural traits, explaining from ~60% up to ~90% of the variance of the structural traits from only a small set (<~2%) of the total genes. The transcriptome-based model not only performed above chance but also outperformed in a statistically significant way all the benchmark models (distance-based, thalamic afferents-based, and cortical afferents-based), thus demonstrating that simple spatial proximity or patterns of afferents from thalamic and cortical regions do not explain better the distinctive structural features of cortical areas. Our findings indicate that to a great extent the structural features of a cortical area are encoded in a very small set of co-expressed genes that might be instrumental for the development and eulamination of the cortical sheet.
Acknowledgements
This work was supported by an Alexander von Humboldt fellowship to AG and funding by the German Research Council DFG to CCH (SFB 936/A1,Z3; TRR169/A2).
Keywords:
Transcription, Genetic,
Cytoarchitecture,
Cortex,
multivariate analysis,
Allen Brain Atlas
Conference:
Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.
Presentation Type:
Poster
Topic:
Genomics and genetics
Citation:
Goulas
A and
Hilgetag
CC
(2016). Relating the transcriptional and structural architecture of mouse cortical areas.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2016.
doi: 10.3389/conf.fninf.2016.20.00028
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Received:
27 Apr 2016;
Published Online:
18 Jul 2016.
*
Correspondence:
Dr. Alexandros Goulas, UKE, Institute for Computational Neuroscience, Hamburg, Germany, alexandros.goulas@yahoo.com