2023 Queen Square Institute of Neurology: Spatial DLPFC

Paired talk with: https://speakerdeck.com/lcolladotor/2023-queen-square-institute-of-neurology

Transcript

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
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 🔸
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
4. New pre-print! 4 https://doi.org/10.1101/2023.02.15.528722 #spatialDLPFC
5. Background: Human DLPFC 5 slideshare.net
6. Studying Gene Expression in the Human Brain Bulk RNA-seq Single nucleus RNA-seq Spatial Transcriptomics 6
7. Background Single Nucleus RNA-seq 7 Image Credit: 10x Genomics Freezing destroys cell membrane so we are limited to nucleus
8. Background Spatial Transcriptomics Image Credit: 10x Genomics Histology Neurons in GM White Matter Histological Layer 5 8
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)
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
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
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
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
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
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
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
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
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
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
20. Layer-level Cell Type Marker genes 20
21. Spatial Registration with Data-driven spatial domains 21
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
23. Spot Deconvolution Predicts Cell Composition of Spots 23
24. Spot Deconvolution of DLPFC Visium Data 24
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
26. Add Spatial Information to Clinical Gene Datasets 26 Velmeshev et al. 2021, Science
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
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
29. Samui Browser to Explore High-Resolution Images • https://samuibrowser.com/ • True scale visium spots & full resolution histological images 29
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
31. Now in pre-print! 31 https://doi.org/10.1101/2023.02.15.528722 #spatialDLPFC
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?