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2023 Queen Square Institute of Neurology: Spatial DLPFC

2023 Queen Square Institute of Neurology: Spatial DLPFC

Louise Huuki-Myers

May 25, 2023
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  1. Harnessing the power of spatially resolved
    transcriptomics one step at a time
    Part 1: Spatial DLPFC
    Louise Huuki-Myers
    Staff Scientist
    1
    @lahuuki
    lahuuki.github.io
    Download these slides: speakerdeck.com/lahuuki

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  2. About LIBD
    Lieber Institute for Brain Development
    ● Non-profit Research Institute in Baltimore, MD
    ● Study the genetics of neuropsychiatric disorders 🧬
    ● 139 multidisciplinary scientists
    ● Affiliated with the Johns Hopkins Medical School
    2
    Baltimore
    Maryland 🔸

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  3. Our R/Bioconductor Powered Data Science Team
    ● Led by Leonardo Collado-Torres
    ● Computational lab specializing in:
    ○ RNA seq analysis
    ■ Bulk, single cell, spatial
    ○ Open Source software development
    ○ Knowledge sharing
    ■ Data Science Guidance Sessions
    ■ Rstat Club: Videos available
    www.youtube.com/@lcolladotor
    ● Team website
    ○ lcolladotor.github.io/
    3

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  4. New pre-print!
    4
    https://doi.org/10.1101/2023.02.15.528722 #spatialDLPFC

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  5. Background: Human DLPFC
    5
    slideshare.net

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  6. Studying Gene Expression
    in the Human Brain
    Bulk RNA-seq
    Single nucleus
    RNA-seq
    Spatial
    Transcriptomics
    6

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  7. Background Single Nucleus RNA-seq
    7
    Image Credit: 10x Genomics
    Freezing destroys cell
    membrane so we are
    limited to nucleus

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  8. Background Spatial Transcriptomics
    Image Credit: 10x Genomics
    Histology
    Neurons in GM White Matter Histological Layer
    5
    8

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  9. 9
    Study Design
    ● Neurotypical Adults
    ● High quality RNA (RIN > 7)
    ● 30 Visium samples (10 donors x 3 positions)
    ● 19 snRNA-seq samples (10 donors x 2 positions)

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  10. 10
    1. Identify data-driven spatial
    domains in visium data
    3. Apply paired spatial and
    snRNA-seq data to study cell
    type composition & cell-cell
    interactions
    2. Identify cell type
    populations in Single Nucleus
    RNA-seq data

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  11. Manual Annotation of Histological Layers
    ● Used Visium to explore spatial gene
    expression of DLPFC (n = 12)
    ● Manually annotated histological layers
    ● Found layer-enriched gene expression
    11
    Manual Layers
    Kristen Maynard Leonardo Collado-Torres

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  12. Data Driven Clustering
    ● Why unsupervised clustering?
    ○ Reduce labor
    ○ Discover novel biology
    ● BayesSpace was best method to reiterate
    histological layers
    ○ Spatially aware Bayesian clustering of visium
    data
    ○ Also tested spaGCN, Graph-Based clustering
    ● More methods have been developed since
    ○ PRECAST, GraphST, RESEPT, Starfysh
    12
    Zhao et al., 2021
    Nature
    Biotechnology
    BayesSpace

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  13. Different Resolutions of
    BayesSpace Clustering
    k = number of clusters
    ● k=2: separate white vs. grey matter
    ● k=9: best reiterated histological layers
    ● k=16: data-driven optimal k based on
    fast H+ statistic
    13
    More Clusters = More Complexity

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  14. Spatial Registration Adds Anatomical Context
    ● Validate detection of laminar structure
    ● Correlate enrichment t-statistics for top marker genes of reference
    ○ Cluster vs. manual annotation
    ● Annotate with strongly associated histological layer
    14
    Sp
    k
    D
    d
    ~L

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  15. Spatial Registration of
    Spatial Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    15

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  16. Data-driven Novel Spatial
    Domains in DLPFC
    ● k=9 resolution reveals vascular
    spatial domain Sp
    9
    D
    1
    ~L1
    ● Enriched for vasculature genes such
    as CLDN5
    ● DEGs across spatial domains can
    be explored on our web resources
    16

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  17. 17
    1. Identify data-driven spatial
    domains in visium data
    3. Apply paired spatial and
    snRNA-seq data to study cell
    type composition & cell-cell
    interactions
    2. Identify cell type
    populations in Single Nucleus
    RNA-seq data

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  18. ● Nucleus (not single cell) as we
    are working with frozen tissue
    ● n = 19, from 10 donors
    ● 56,447 high quality nuclei
    ● 29 clusters, 7 broad cell types
    18
    Single Nucleus RNA-seq
    Endothelial/Mural

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  19. Identify Layer Associated Neuron Populations
    19
    ● Apply Spatial Registration with
    manual layers
    ● 13 layer-level cell types
    ○ Assign Excitatory Neurons
    histological layers
    ○ Pool other cell type groups

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  20. Layer-level Cell Type Marker genes
    20

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  21. Spatial
    Registration with
    Data-driven
    spatial domains
    21

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  22. 22
    1. Identify data-driven spatial
    domains in visium data
    3. Apply paired spatial and
    snRNA-seq data to study cell
    type composition & cell-cell
    interactions
    2. Identify cell type
    populations in Single Nucleus
    RNA-seq data

    View full-size slide

  23. Spot Deconvolution Predicts Cell Composition of
    Spots
    23

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  24. Spot Deconvolution of DLPFC Visium Data
    24

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  25. Spatially Map Disease Ligand Receptor Interactions
    25
    ● Identified ligand-receptor in
    snRNA-seq data & Schizophrenia
    risk L-R analysis
    ● Cell-cell communication analysis
    ● Spatial co-localization of
    EFNA5-EPHA5 in Sp
    9
    D
    7
    ~L6

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  26. Add Spatial Information to Clinical Gene Datasets
    26
    Velmeshev et al. 2021, Science

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  27. Summary
    ● Large paired single nucleus and spatial gene expression
    reference dataset in Human DLPFC
    ● Unsupervised clustering enabled identification of novel
    spatial domains
    ● Spatially resolved cell type populations in snRNA-seq
    ● Utility for informing spatially informed study of disease
    27

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  28. Interactive Web Resources
    ● research.libd.org/spatialDLPFC
    ● Shiny app for spatial data
    ○ Spatial domains, DE analysis,
    spatial gene expression
    ● iSEE app for snRNA-seq data
    ○ Gene expression and reduced
    dimensions
    28

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  29. Samui Browser to Explore High-Resolution Images
    ● https://samuibrowser.com/
    ● True scale visium spots & full
    resolution histological images
    29

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  30. spatialLIBD Bioconductor Package
    ● research.libd.org/spatialLIBD/
    ● Access this dataset
    ● Software tools:
    ○ Spatial registration
    ○ Visualization of spatial data
    ○ Run local version of the web application
    30

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  31. Now in pre-print!
    31
    https://doi.org/10.1101/2023.02.15.528722 #spatialDLPFC

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  32. 32
    Acknowledgements
    Lieber Institute
    Abby Spangler
    Nick Eagles
    Kelsey D. Montgomery
    Sang Ho Kwon
    Heena R. Divecha
    Madhavi Tippani
    Chaichontat Sriworarat
    Annie B. Nguyen
    Matthew N. Tran
    Arta Seyedian
    Thomas M. Hyde
    Joel E. Kleinman
    Stephanie C. Page
    Keri Martinowich
    Leonardo Collado-Torres
    Kristen R. Maynard
    JHU Biostatistics Dept
    Boyi Guo
    Stephanie C. Hicks
    JHU Biomed Engineering
    Prashanthi Ravichandran
    Alexis Battle
    University College London
    Genetics and Genomic
    Medicine
    Melissa Grant-Peters
    Mina Ryten
    PsychENCODE Consortium
    Get in touch!
    lahuuki.github.io
    @lahuuki
    Download these slides:
    speakerdeck.com/lahuuki
    Thank you! 🧠
    Any Questions?

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