spatialDLPFC Poster BDS22

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

Transcript

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
   Abstract
   Conclusion
   Acknowledgements
   Identification of Neuronal Subpopulation In Layers
   Presenter & Poster requests:
   Louise.Huuki@libd.org
   @lahuuki
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   Methods
   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
   Samples
   Number of
   Donors
   10x Visium 113,927 spots 30 10
   snRNA-seq 77,604 nuclei
   (2661,5911)
   19 10
   snRNA-seq
   Identification of Data-Driven Spatial Domains
   BayesSpace: spatially-aware unsupervised clustering Spk
   Ds
   • 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
   Sp9
   D1
   is enriched for CLDN5: vascular domain (Endothelia cells)
   Higher Resolution for Layer 1
   QR
   ToDo
   • 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
   DLPFC_Spangler2022
   k16 libd.shinyapps.io/k16_spatial
   DLPFC_Spangler2022
   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
   Sp16
   D14
   is enriched SPARC Sp16
   D2
   is enriched HTRA1
   libd.shinyapps.io/DLPFC_snRNA-
   seq_2022
   

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