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spatialDLPFC Poster BDS22

spatialDLPFC Poster BDS22

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

Louise Huuki-Myers

November 07, 2022

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  1. Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the
    human dorsolateral prefrontal cortex
    Louise A. Huuki-Myers1, Abby B. Spangler1, Nicholas J. Eagles1, Kelsey D. Montgomery1, Sang Ho Kwon1,2, Heena R. Divecha1, Madhavi Tippani1, Thomas M.
    Hyde1, Stephanie C. Hicks3, Stephanie C. Page1, Keri Martinowich1, Leonardo Collado-Torres1,4, Kristen R. Maynard1,5
    1. Lieber Institute for Brain Development, 2. Department of Neuroscience Johns Hopkins School of Medicine, 3. Department of Biostatistics Johns Hopkins Bloomberg School of Public Health, 4.
    Department of Psychiatry and Behavioral Sciences JHSOM, 5. Center for Computational Biology Johns Hopkins University
    Identification of Neuronal Subpopulation In Layers
    Presenter & Poster requests:
    [email protected]
    this Poster:
    Access this Dataset
    DE Genes genes in Spatial Domains
    Maynard et al., Nat Neurosci, 2021, 10.1038/s41593-020-00787-0
    Zhao et al., Nat BioTech, 2021, 10.1038/s41587-021-00935-2
    Emani et al, 2022, (syn30106435) 10.7303/syn4921369
    Assay n Number of
    Number of
    10x Visium 113,927 spots 30 10
    snRNA-seq 77,604 nuclei
    19 10
    Identification of Data-Driven Spatial Domains
    BayesSpace: spatially-aware unsupervised clustering Spk
    • K = 2: white matter vs. grey matter
    • K = 9: classic histological layers
    • K = 16: laminar with 2+ domains per histological layer
    Spatial Registration of PsychENCODE Datasets
    To facilitate ongoing efforts to spatially map and annotate cell
    types in the human cortex, we generated a datahub for the
    scientific community to explore these integrated spatial and
    single cell datasets. This contribution represents a landmark
    reference resource for registering cortical cell types identified
    from snRNA-seq to spatial domains, and assessing enrichment
    of clinical gene sets from large-scale association studies, snRNA-
    seq studies and bulk RNA-seq studies within a spatial context.
    The molecular organization of the human neocortex has been
    extensively studied in the context of its classic histological layers.
    However, emerging spatial transcriptomic technologies, such as the
    10x Genomics Visium spatial gene expression platform, enables
    unbiased identification of transcriptionally-defined spatial domains
    that reflect features of existing brain architecture.
    Using spatially-aware unsupervised clustering across all samples,
    we generated a comprehensive, data-driven molecular
    neuroanatomical atlas. We defined unique gene expression
    signatures for unsupervised spatial domains at different
    resolutions: one broad resolution (k = 9), which most closely
    aligned with classic histological layers, and finer unsupervised
    resolutions (k = 16, 28), which identified novel spatial patterns. We
    identified 29 fine resolution cell type clusters that we reduced to
    12 layer-level associated cell types. We added cellular resolution to
    our molecular atlas by performing cell type spot deconvolution
    using the snRNA-seq data as reference. Using publicly available
    neuropsychiatric disease-associated data, we identified gene sets
    enriched across the spatial domains.
    Single Nucleus RNA-seq
    is enriched for CLDN5: vascular domain (Endothelia cells)
    Higher Resolution for Layer 1
    • Spatially registered nuclei on to classic layers reduces
    dataset to 12 layer associated cell types
    • Spatial registration on finer resolution novel domains may
    reveal additional information about cell type clusters
    • Spatially registered multiple DLPFC snRNA-seq datasets
    shows spatial specificity of some cell types
    Louise Huuki-Myers Kelsey D. Montgomery Sang Ho Kwon
    Stephanie C. Page
    Stephanie C. Hicks Kristen R. Maynard
    Leonardo Collado-Torres
    Abby Spangler Nicholas J. Eagles
    Keri Martinowich
    Heena R. Divecha Madhavi Tippani
    Thomas M. Hyde
    k09 libd.shinyapps.io/k9_spatial
    k16 libd.shinyapps.io/k16_spatial
    Spatial Data Shiny App snRNA-seq on iSEE App
    Spot Deconvolution Benchmarking
    Check out poster 73
    by Nick Eagles
    Spatial Registration of Clinical Data
    Pilot Data
    • Improved reproducibility
    with k=9 domains &
    expanded dataset
    k = number of domains
    s = domain number
    Identified 29 fine resolution clusters across 70k nuclei
    is enriched SPARC Sp16
    is enriched HTRA1

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