Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Single Cell and Spatial Transcriptomics in the Postmortem Brain

Single Cell and Spatial Transcriptomics in the Postmortem Brain

Details the "spatialDLPFC" project to build a large paired single cell and spatial gene expression reference dataset in Human DLPFC. Discusses the application of unsupervised clustering to identify of novel spatial domains, identification of spatially resolved cell type populations via snRNA-seq , and utility for this data spatially inform study of disease.

Presented at the FDA Single Cell Omics Symposium, February 23 2023.

Based on work from this pre-print:
https://www.biorxiv.org/content/10.1101/2023.02.15.528722v1

Louise Huuki-Myers

February 23, 2023
Tweet

More Decks by Louise Huuki-Myers

Other Decks in Science

Transcript

  1. Single Cell and Spatial Transcriptomics in the
    Postmortem Brain
    Louise Huuki-Myers
    Staff Scientist
    1
    @lahuuki
    lahuuki.github.io
    Download these slides: speakerdeck.com/lahuuki

    View Slide

  2. Background: Human DLPFC
    2
    slideshare.net

    View Slide

  3. Studying Gene Expression
    in the Human Brain
    Bulk RNA-seq
    Single nucleus
    RNA-seq
    Spatial
    Transcriptomics
    3

    View Slide

  4. Background Single Nucleus RNA-seq
    4
    Image Credit: 10x Genomics
    Freezing destroys cell
    membrane so we are
    limited to nucleus

    View Slide

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

    View Slide

  6. 6
    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)

    View Slide

  7. 7
    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 Slide

  8. 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
    8
    Manual Layers
    Kristen Maynard Leonardo Collado-Torres

    View Slide

  9. 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
    9
    Zhao et al. 2021, Nature
    Biotechnology
    BayesSpace

    View Slide

  10. 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
    10
    More Clusters = More Complexity

    View Slide

  11. 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
    11
    Sp
    k
    D
    d
    ~L

    View Slide

  12. Spatial Registration of Spatial
    Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    12

    View Slide

  13. 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
    13

    View Slide

  14. 14
    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 Slide

  15. ● 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
    15
    Single Nucleus RNA-seq
    Endothelial/Mural

    View Slide

  16. Identify Layer Associated Neuron Populations
    16
    ● Apply Spatial Registration with
    manual layers
    ● 13 layer-level cell types
    ○ Assign Excitatory Neurons histological
    layers
    ○ Pool other cell type groups

    View Slide

  17. Layer-level Cell Type Marker genes
    17

    View Slide

  18. Spatial
    Registration with
    Data-driven
    spatial domains
    18

    View Slide

  19. 19
    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 Slide

  20. Spot Deconvolution Predicts Cell Composition of Spots
    20

    View Slide

  21. Spot Deconvolution of DLPFC Visium Data
    21

    View Slide

  22. Spatially Map Disease Ligand Receptor Interactions
    22
    ● 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

    View Slide

  23. Add Spatial Information to Clinical Gene Datasets
    23
    Velmeshev et al. 2021, Science

    View Slide

  24. Summary
    ● Large paired single cell 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
    24

    View Slide

  25. 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
    25

    View Slide

  26. Samui Browser to Explore High-Resolution Images
    ● https://samuibrowser.com/
    ● True scale visium spots & full
    resolution histological images
    26

    View Slide

  27. Now in pre-print!
    27
    https://doi.org/10.1101/2023.02.15.528722

    View Slide

  28. 28
    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?

    View Slide

  29. 29

    View Slide