Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar ex...
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Published in | bioRxiv |
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Main Authors | , , , , , , , , , , , , , , , , , |
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Cold Spring Harbor
Cold Spring Harbor Laboratory Press
28.02.2020
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Abstract | We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD) Footnotes * http://research.libd.org/spatialLIBD |
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AbstractList | We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex (DLPFC). We identified extensive layer-enriched expression signatures, and refined associations to previous laminar markers. We overlaid our laminar expression signatures onto large-scale single nuclei RNA sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially-defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions where morphological architecture is not as well-defined as cortical laminae. We lastly created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD) Footnotes * http://research.libd.org/spatialLIBD |
Author | Yin, Yifeng Hicks, Stephanie C Uytingco, Cedric Chew, Jennifer Hyde, Thomas M Tippani, Madhavi Martinowich, Keri Weber, Lukas M Williams, Stephen R Collado-Torres, Leonardo Maynard, Kristen E Barry, Brianna K Tran, Matthew N Jaffe, Andrew E Catallini, Joseph L Besich, Zachary Kleinman, Joel E Rao, Nikhil |
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Title | Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex |
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