BioTuring_spatialLIBD

Sliders for our BioTuring webinar on the "Topography of Spatial Gene Expression in the Human Prefrontal Cortex" by Kristen R Maynard and Leonardo Collado Torres.

Transcript

1. 29 Topography of Spatial Gene Expression in the Human Prefrontal Cortex Kristen R Maynard, Ph.D., Research Scientist Leonardo Collado-Torres, Ph.D., Investigator Lieber Institute for Brain Development BioTuringWebinar April 27, 2021 @kr_maynard @lcolladotor @LieberInstitute @BioTuring Slides at https://speakerdeck.com/lcolladotor
2. The spatial architecture of the brain is fundamentally connected to its function 2 chartdiagram.com slideshare.net
3. Laminar position of a cell influences its gene expression, morphology, physiology, and function 3 Kwan et al., 2012, Development
4. 4 Image Credit: Bo Xia, https://twitter.com/boxia7/status/1261464021322137600?s=12 Studying gene expression in human brain Bulk RNA-seq Single cell/nucleus RNA-seq Spatial transcriptomics
5. Visium & Single nucleus RNA-sequencing technologies 5 Single Cell Gene Expression Spatial Gene Expression
6. Webinar Overview 6 1. Identification of layer-enriched genes in human cortex using Visium. 2. Spatial registration of single-nucleus RNA-seq data from human cortex. 3. Layer-enriched expression of genes associated with brain disorders. Maynard, Collado-Torres, Nat Neuro, 2021
7. Study design for Visium experiments in dorsolateral prefrontal cortex (DLPFC) 7 Andrew E Jaffe Keri Martinowich Stephanie C Hicks Lukas M Weber Cedric Uytingco Nikhil Rao @stephaniehicks @lmwebr @martinowk @andrewejaffe
8. Visualizing gene expression in a histological context 8 logcounts logcounts logcounts Maynard, Collado-Torres, Nat Neuro, 2021
9. 2 pairs spatial adjacent replicates x subject = 12 sections 9 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0µm) Adjacent spatial replicates (300µm) Maynard, Collado-Torres, Nat Neuro, 2021 PCP4
10. “Pseudo-bulking” collapses data: spot to layer level 10 Maynard, Collado-Torres, Nat Neuro, 2021
11. Three statistical models to assess laminar enrichment “ANOVA” model 11 “Enrichment” model “Pairwise” model Maynard, Collado-Torres, Nat Neuro, 2021 Is any layer different? Is one layer > the rest? Is layer X > layer Y?
12. Identification & validation of novel layer-enriched genes 12 Maynard, Collado-Torres, Nat Neuro, 2021 L5>rest, p=4.33e-12 L6>rest, p=5.05e-12 L1>rest, p=1.47e-10 L2>rest, p=9.73e-11
13. 13 Segmentation of histology data identifies spots containing single cell bodies and neuropil 50um Gray matter White matter Neuron Neuropil Glial cell Mouse Brain Tissue Postmortem Human DLPFC Madhavi Tippani @MadhaviTippani Joseph L Catallini II
14. Integration of proteomic and transcriptomic data with Visium-Immunofluorescence (IF) 14 Sino Biologicals Sang Ho Kwon
15. L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Hodge et al, Nature, 2019 Maynard, Collado-Torres, Nat Neuro, 2021
16. L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Our spatial data Hodge et al, Nature, 2019 Maynard, Collado-Torres, Nat Neuro, 2021
17. Identify clusters + pseudo-bulk + compute stats “ANOVA” model 17 “Enrichment” model “Pairwise” model Is any layer different? Is one layer > the rest? Is layer X > layer Y? Maynard, Collado-Torres, Nat Neuro, 2021 Is this cluster > the rest? C1 C2 C3 C4 C5 C6 C7
18. WM L6 L5 L4 L3 L2 L1 Oli3 Oli5 Oli4 Oli0 Oli1 Ast3 Ast2 Ast0 Ast1 Mic2 Mic3 Mic0 Mic1 Opc0 Opc1 Opc2 Per End1 End2 Ex2 Ex0 Ex4 Ex6 Ex14 Ex1 Ex5 Ex7 Ex8 In0 In7 In9 In11 In2 In10 In3 In6 In1 In4 In5 In8 Ex3 Ex11 Ex12 Ex9 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 (C) Spatial registration of your sc/snRNA-seq data Interpretation guidelines: • Find strong positive correlation values (dark green) to identify cell/nuclei clusters enriched for a given layer • By row: for a given layer • By column: for a given cell/nuclei cluster Mathys et al, Nature, 2019 Maynard, Collado-Torres, Nat Neuro, 2021
19. WM Layer6 Layer5 Layer4 Layer3 Layer2 Layer1 22 (Oligo) 3 (Oligo) 23 (Oligo) 17 (Oligo) 21 (Oligo) 7 (Astro) 5 (Astro) 9 (OPC) 26 (OPC) 1 (Micro) 24 (Drop) 13 (Excit) 10 (Excit) 27 (Excit) 29 (Inhib) 14 (Inhib) 15 (Inhib) 18 (Inhib) 2 (Excit) 31 (Excit) 8 (Excit) 16 (Inhib) 28 (Inhib) 30 (Inhib) 20 (Inhib) 11 (Inhib) 25 (Inhib) 4 (Excit) 12 (Excit) 6 (Excit) 19 (Excit) −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 Matthew N Tran @mattntran Tran et al, bioRxiv, 2020 DOI 10.1101/2020.10.07.329839 Maynard, Collado-Torres, Nat Neuro, 2021
20. Gandal et al, Science, 2018 SFARI GENE; 2.0 by Abrahams et al, Mol Autism, 2013 Jaffe et al, Nature Neuroscience, 2020 - Curated lists - GWAS/TWAS hits - Differential expression - … Layer-enriched gene expression profiling
21. 0 2 4 6 8 10 12 WM L6 L5 L4 L3 L2 L1 SFAR I ASC 102 ASD 53 D D ID 49 D E.U p D E.D ow n 2.7 2.1 2.7 4 3.6 4.9 4.5 2.5 5 2.8 5 6.4 2.8 ASD 0 2 4 6 8 10 12 WM L6 L5 L4 L3 L2 L1 PE.U p PE.D ow n BS2.U p BS2.D ow n BS2.U p BS2.D ow n PE.U p PE.D ow n 2.1 2 3.1 1.8 2.2 1.8 8.8 5 2.7 2.6 4.6 SCZD−DE SCZD−TWAS (A) (B) DIY at http://spatial.libd.org/spatialLIBD/ Layer-enriched gene expression profiling Autism Spectrum Disorder • SFARI: Abrahams et al, Mol Autism, 2013 • ASC102: Satterstrom et al, Cell, 2020 Break up into: • ASD53: ASD dominant traits • DDID49: neurodevelopmental delay Maynard, Collado-Torres, Nat Neuro, 2021
22. 0 2 4 6 8 10 12 WM L6 L5 L4 L3 L2 L1 SFAR I ASC 102 ASD 53 D D ID 49 D E.U p D E.D ow n 2.7 2.1 2.7 4 3.6 4.9 4.5 2.5 5 2.8 5 6.4 2.8 ASD 0 2 4 6 8 10 12 WM L6 L5 L4 L3 L2 L1 PE.U p PE.D ow n BS2.U p BS2.D ow n BS2.U p BS2.D ow n PE.U p PE.D ow n 2.1 2 3.1 1.8 2.2 1.8 8.8 5 2.7 2.6 4.6 SCZD−DE SCZD−TWAS (A) (B) DIY at http://spatial.libd.org/spatialLIBD/ Layer-enriched gene expression profiling Gandal et al, Science, 2018 Collado-Torres et al, Neuron, 2019 Maynard, Collado-Torres, Nat Neuro, 2021
23. 23 Stephanie C Hicks Lukas M Weber @stephaniehicks @lmwebr Data-driven layer-enriched clustering in the DLPFC Spatially-varying genes Highly-variable genes Spot-level clustering Manual layer annotation using spatialLIBD • Which samples to use? • All samples? • Sample by sample then merge? • Use image-derived information? Maynard, Collado-Torres, Nat Neuro, 2021
24. 24 Data-driven layer-enriched clustering in the DLPFC SpatialDE by Svensson et al, Nature Methods, 2018 Are the spatial patterns relevant? Remember to inspect your data! Maynard, Collado-Torres, Nat Neuro, 2021
25. 25 Data-driven layer-enriched clustering in the DLPFC SpatialDE by Svensson et al, Nature Methods, 2018 “ANOVA” model F-statistics SpatialDE statistic Maynard, Collado-Torres, Nat Neuro, 2021
26. 26 Use known marker genes only Use layer- enriched genes (scenario where you have more datasets) Only use the data Requires >=1 expert Benefits from known marker genes (if expressed) & prior knowledge Maynard, Collado-Torres, Nat Neuro, 2021
27. 27 Data-driven layer-enriched clustering in the DLPFC Using spatial coordinates does help in some cases Maynard, Collado-Torres, Nat Neuro, 2021
28. 28 Data-driven clustering: BayesSpace Zhao et al, bioRxiv, 2020 https://www.biorxiv.org/content/10.1101/2020.09.04.283812v1
29. 29 SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor Righelli, Weber, Crowell, et al, bioRxiv, 2021 DOI 10.1101/2021.01.27.428431 Dario Righellli Helena L Crowell @drighelli @CrowellHL Lukas M Weber @lmwebr
30. bioconductor.org/packages/spatialLIBD Pardo et al, bioRxiv, 2021 (next week!) Maynard, Collado-Torres, Nat Neuro, 2021 Brenda Pardo Abby Spangler @PardoBree @abspangler
31. Summary: transcriptome-scale spatial gene expression in postmortem human cortex 31 http://research.libd.org/spatialLIBD Explore the data: Maynard, Collado-Torres, Nat Neuro, 2021
32. Acknowledgements Lieber Institute Keri Martinowich Andrew E. Jaffe Brianna K. Barry Joseph L. Catallini II Matthew N. Tran Zachary Besich Madhavi Tippani Joel E. Kleinman Thomas M. Hyde Daniel R. Weinberger Sang Ho Kwon Brenda Pardo Abby Spangler JHU Biostatics Dept JHU Oncology Tissue Services (Kristen Lecksell) Stephanie C. Hicks JHU SKCCC Flow Core (Jessica Gucwa) Lukas M. Weber JHU Transcriptomics & Deep Sequencing Core (Linda Orzolek) 10x Genomics Cedric Uytingco Stephen R. Williams Jennifer Chew Yifeng Yin Nikhil Rao 32 @kr_maynard @lcolladotor @LieberInstitute @BioTuring
33. 33 twitter.com/lcolladotor/status/1385708584453935106?s=20 Interested in working with us? Let us know! twitter.com/stephaniehicks/status/1371444821173268482?s=20 https://www.libd.org/careers/