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

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
   

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2. Background: Human DLPFC
   2
   slideshare.net
   

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

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

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

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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)
   

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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
   

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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
   

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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
   

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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
    

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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
    

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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
    

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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
    

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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
    

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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
    

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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
    

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17. Layer-level Cell Type Marker genes
    17
    

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18. Spatial
    Registration with
    Data-driven
    spatial domains
    18
    

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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
    

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20. Spot Deconvolution Predicts Cell Composition of Spots
    20
    

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21. Spot Deconvolution of DLPFC Visium Data
    21
    

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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
    

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

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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
    

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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
    

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

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

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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?
    

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