CDC/ATSDR R User Group 2021

2021-01-28 presentation on #spatialLIBD and #recount3 for the CDC/ATSDR R User Group

Transcript

1. 29 Expanding the resolution of gene expression analyses: spatially (spatialLIBD) and in numbers (recount3) Leonardo Collado-Torres, Ph.D., Investigator Lieber Institute for Brain Development CDC/ATSDR R User Group 2021-01-28 @lcolladotor @LieberInstitute #recount3 #spatialLIBD
2. 2 https://doi.org/10.1016/j.biopsych.2020.06.005
3. The spatial architecture of the brain is fundamentally connected to its function 3 chartdiagram.com slideshare.net
4. Laminar position of a cell influences its gene expression, morphology, physiology, and function 4 Kwan et al., 2012, Development
5. Single nucleus RNA-sequencing & Visium technologies 5 Single Cell Gene Expression Spatial Gene Expression
6. 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, et al, bioRxiv, 2020 @kr_maynard
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, et al, bioRxiv, 2020
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, et al, bioRxiv, 2020 PCP4
10. “Pseudo-bulking” collapses data: spot to layer level 10 Maynard, Collado-Torres, et al, bioRxiv, 2020
11. Three statistical models to assess laminar enrichment “ANOVA” model 11 “Enrichment” model “Pairwise” model Maynard, Collado-Torres, et al, bioRxiv, 2020 Is any layer different? Is one layer > the rest? Is layer X > layer Y?
12. 12 ISH images courtesy of Allen Human Brain Atlas: http://human.brain-map.org/ (Hawrylycz et al., 2012) Maynard, Collado-Torres, et al, bioRxiv, 2020 Visium replicates layer-enrichment of previously identified layer marker genes L4>rest, p=1.74e-09 L6>WM, p=4.48e-19 logcounts logcounts
13. Identification & validation of novel layer-enriched genes 13 Maynard, Collado-Torres, et al, bioRxiv, 2020 L5>rest, p=4.33e-12 L6>rest, p=5.05e-12 L1>rest, p=1.47e-10 L2>rest, p=9.73e-11
14. L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Hodge et al, Nature, 2019
15. L4 L3 L2 L1 0.0 0.2 0.4 0.6 0.8 (A) (B) (C) Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data Your sc/snRNA-seq data Our spatial data Hodge et al, Nature, 2019
16. 16 Maynard, Collado-Torres, et al, bioRxiv, 2020 12 15 Matthew N Tran Brianna K Barry @mattntran Identify clusters in your sc/snRNA-seq data - Pre-process your sc/snRNA-seq data - Identify cell/nuclei clusters - Find data-driven marker genes and/or combine with known marker genes - Label clusters
17. 17 Maynard, Collado-Torres, et al, bioRxiv, 2020 # columns for us: 12 * 7 = 84 (76) “Pseudo-bulk” our spatial transcriptomics data
18. 18 Maynard, Collado-Torres, et al, bioRxiv, 2020 Your sc/snRNA-seq: cell or nuclei clusters * subjects or other analysis variables “Pseudo-bulk” your sc/snRNA-seq data
19. Three statistical models to assess laminar enrichment “ANOVA” model 19 “Enrichment” model “Pairwise” model Is any layer different? Is one layer > the rest? Is layer X > layer Y? Maynard, Collado-Torres, et al, bioRxiv, 2020
20. 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) Maynard, Collado-Torres, et al, bioRxiv, 2020 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
21. Maynard, Collado-Torres, et al, bioRxiv, 2020 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 Brianna K Barry @mattntran 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
22. http://spatial.libd.org/spatialLIBD/ Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data: DIY
23. 23 Maynard, Collado-Torres, et al, bioRxiv, 2020 Cluster1 Cluster2 Cluster3 ENSG00000104419 3 -2 0.3 ENSG0000018400 7 1 0.67 4 … … … … Full example table https://github.com/LieberInstitute/spatialLIBD/blob/master/data-raw/tstats_Human_DLPFC_snRNAseq_Nguyen_topLayer.csv Save your “enrichment” t- statistics for your sc/snRNA-seq clusters Spatial registration of your sc/snRNA-seq data: DIY
24. 24 Maynard, Collado-Torres, et al, bioRxiv, 2020 Spatial registration of your sc/snRNA-seq data: DIY spatial.libd.org/spatialLIBD/ Cluster1 Cluster2 Cluster3 ENSG00000104419 3 -2 0.3 ENSG00000184007 1 0.67 4 … … … …
25. 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
26. 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/ Maynard, Collado-Torres, et al, bioRxiv, 2020 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
27. 27 Stephanie C Hicks Lukas M Weber @stephaniehicks @lmwebr Maynard, Collado-Torres, et al, bioRxiv, 2020 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?
28. 28 Maynard, Collado-Torres, et al, bioRxiv, 2020 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!
29. 29 Maynard, Collado-Torres, et al, bioRxiv, 2020 Data-driven layer-enriched clustering in the DLPFC SpatialDE by Svensson et al, Nature Methods, 2018 “ANOVA” model F-statistics SpatialDE statistic
30. 30 Maynard, Collado-Torres, et al, bioRxiv, 2020 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
31. 31 Maynard, Collado-Torres, et al, bioRxiv, 2020 Data-driven layer-enriched clustering in the DLPFC Using spatial coordinates does help in some cases
32. http://spatial.libd.org/spatialLIBD/ Maynard, Collado-Torres, et al, bioRxiv, 2020 Explore our spatial data (or adapt for yours) + perform spatial registration & gene enrichment analyses
33. Summary: transcriptome-scale spatial gene expression in postmortem human cortex 33 http://research.libd.org/spatialLIBD Explore the data: Maynard, Collado-Torres, et al, bioRxiv, 2020
34. SRA
35. GTEx TCGA slide adapted from Shannon Ellis
36. jx 1 jx 2 jx 3 jx 4 jx 5 jx 6 Coverage Reads Gene Isoform 1 Isoform 2 Potential isoform 3 exon 1 exon 2 exon 3 exon 4 Expressed region 1: potential exon 5 doi.org/10.12688/f1000research.12223.1 Collado-Torres et al, F1000Research, 2017
37. slide adapted from Jeff Leek
38. https://jhubiostatistics.shinyapps.io/recount/ Nellore, Collado-Torres, et al, Nature Biotechnology, 2017
39. recount3: over 700,000 human and mouse RNA-seq samples 39 http://research.libd.org/recount3-docs/ Wilks et al, 2021
41. 41 https://bioconductor.org/ packages/recount3
42. 42 Wilks et al, 2021 Variation: mostly by tissue with some more variable (blood) than others (brain)
43. 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 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 43 @kr_maynard @lcolladotor #spatialLIBD Interested in working with us? Let us know!
44. expression data for ~700,000 human samples (multiple) positions available This project involves the Hansen, Langmead, Leek and Battle labs at JHU & the Nellore lab at OHSU & the Collado-Torres lab at LIBD Contact: • Kasper D. Hansen www.hansenlab.org • Ben Langmead www.langmead-lab.org/ • Leonardo Collado-Torres lcolladotor.github.io/ • Abhinav Nellore nellore.bio/ • Alexis Battle battlelab.jhu.edu/ • Jeff Leek jtleek.com/ • Andrew Jaffe aejaffe.com/ @chrisnwilks #recount3