IBANGS-workshop-2022

Transcript

1. 29 Applications, limitations, and future directions of spatial transcriptomics technology in the human brain Kristen Maynard, Ph.D. Leonardo Collado-Torres, Ph.D. Lieber Institute for Brain Development IBANGS Transcriptomics Workshop May 23, 2022 Keri Martinowich Stephanie C Hicks Lieber Institute Johns Hopkins @kr_maynard @lcolladotor #spatialLIBD
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 (Commercial platform 10x Genomics) 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. 3. Resources and tools for analysis of spatial transcriptomics data. 4. Using spatial transcriptomics to better understand brain disorders.
7. Study design for Visium experiments in dorsolateral prefrontal cortex (DLPFC) 7 Maynard, Collado-Torres, et al, Nat Neuro, 2021
8. Visualizing gene expression in a histological context 8 logcounts logcounts logcounts Maynard, Collado-Torres, et al, 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) PCP4 Maynard, Collado-Torres, et al, Nat Neuro, 2021
10. “Pseudo-bulking” collapses data: spot to layer level 10 Maynard, Collado-Torres, et al, Nat Neuro, 2021
11. Three statistical models to assess laminar enrichment “ANOVA” model 11 “Enrichment” model “Pairwise” model Is any layer different? Is one layer > the rest? Is layer X > layer Y? Maynard, Collado-Torres, et al, Nat Neuro, 2021
12. 12 Identification of laminar enriched genes “Enrichment” model Is one layer > the rest? Group FDR<0.05 Layer1 3033 Layer2 1562 Layer3 183 Layer4 740 Layer5 643 Layer6 379 WM 9124 Only a subset of previous layer marker genes in mouse and human showed laminar association Maynard, Collado-Torres, et al, Nat Neuro, 2021
13. 13 ISH images courtesy of Allen Human Brain Atlas: http://human.brain-map.org/ (Hawrylycz et al., 2012) Visium replicates layer-enrichment of previously identified layer marker genes L4>rest, p=1.74e-09 L6>WM, p=4.48e-19 logcounts logcounts Maynard, Collado-Torres, et al, Nat Neuro, 2021
14. Identification & validation of novel layer-enriched genes 14 L5>rest, p=4.33e-12 L6>rest, p=5.05e-12 L1>rest, p=1.47e-10 L2>rest, p=9.73e-11 ”dotdotdot” for smFISH analysis Maynard et al, Nucleic Acids Research, 2020
15. 15 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
16. http://spatial.libd.org/spatialLIBD/ Maynard, Collado-Torres, et al, Nature Neuroscience, 2021
17. Overview 17 1. Identification of layer-enriched genes in human cortex using Visium. 2. Spatial registration of single-nucleus RNA-seq data. 3. Resources and tools for analysis of spatial transcriptomics data. 4. Using spatial transcriptomics to better understand brain disorders.
18. L4 L3 L2 L1      (A) (B) (C) Maynard, Collado-Torres, et al, Nature Neuroscience, 2021 Spatial registration of sc/snRNA-seq data snRNA-seq data from Allen Institute: manual dissection of cortical layers from middle temporal gyrus (Hodge et al, Nature, 2019) Visium
19. 19 Matthew N Tran @mattntran Generation of snRNA-seq data in DLPFC n= 5,231 total nuclei n= 2 neurotypical donors n=6 broad cell classes n= 30 preliminary clusters (20 neuronal)
20. Maynard, Collado-Torres, et al, Nature Neuroscience, 2021 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 Spatial registration of snRNA-seq data in DLPFC DLPFC snRNA-seq clusters Visium Data
21. Spatial registration of sc/snRNA-seq data in Alzheimer’s Disease snRNAseq data from Mathys et al, Nature, 2019
22. 22 Matthew N Tran @mattntran https://libd.shinyapps.io/tran2021_AMY/
24. 24 Image Credit @bayraktar_lab
25. Overview 25 1. Identification of layer-enriched genes in human cortex using Visium. 2. Spatial registration of single-nucleus RNA-seq data. 3. Resources and tools for analysis of spatial transcriptomics data. 4. Using spatial transcriptomics to better understand brain disorders.
26. bioconductor.org/packages/spatialLIBD Pardo et al, bioRxiv, 2021, https://doi.org/10.1101/2021.04.29.440149 Maynard, Collado-Torres, Nat Neuro, 2021 Brenda Pardo Abby Spangler @PardoBree @abspangler Accepted at BMC Genomics
27. 27 SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor Righelli, Weber, Crowell, et al, Bioinformatics, 2022 DOI https://doi.org/10.1093/bioinformatics/btac299 Dario Righellli Helena L Crowell @drighelli @CrowellHL Lukas M Weber @lmwebr
28. 28 Madhavi Tippani @MadhaviTippani bioRxiv, doi: https://doi.org/10.1101/2021.08.04.452489
30. 30 Zhao et al, Nature Biotechnology, 2021
31. 31 Zhao et al, Nature Biotechnology, 2021
32. Openly sharing data accelerates science: share and you will reap the benefits too! 32 Us: 346 days Them: 271 days Total sequential (fictional): 617 days Reality (preprint to BayesSpace pub): 461 days Difference saved: 156 days Preprints: 190 days
33. 33 What helps also: provide a ground truth and a path towards benchmarking • Fully unsupervised was initially very far from the ground truth • Truth has caveats and should be considered a guideline • Ultimately, the goal is not to fully reproduce the ground truth, but learn what helps and what doesn’t • Ground truth will evolve ;)
34. 34 High accessions, citations, AltMetric, … This data is way more challenging than the mouse: mouse you are looking at different brain regions
35. Unsupervised clustering across all samples 35 0.0 0.2 0.4 0.6 Graph−Based Graph−based(BC) BayesSpace BayesSpace(BC) SpaGCN Clustering Method Adjusted Rand Index You want to do this if you want cluster 1 from sample 1 to mean the same thing as cluster 1 from sample 2 Batch correction (BC) helps BayesSpace + BC was the best option we checked Abby Spangler @abspangler @Nick-Eagles (GH) Nicholas J Eagles
36. The Development Process - Making a module - New, experimental software can change dramatically (function and syntax) between versions - Promotes collaboration by allowing two researchers to share exact code and instantly run software without special set-up SpatialExperiment release 3.14 SpatialExperiment devel 3.15 module load tangram/1.0.2 module load cell2location/0.8a0 module load spagcn/1.2.0 @Nick-Eagles (GH) Nicholas J Eagles https://github.com/LieberInstitute/jhpce_mod_source https://github.com/LieberInstitute/jhpce_module_config
37. The Development Process - Regular interaction with software authors to clarify functionality and report bugs - Documentation for code and author responsiveness on GitHub can be critical in successfully applying software to our data @Nick-Eagles (GH) Nicholas J Eagles
38. Documentation + wrapper functions + tests (GitHub Actions + Bioconductor) 38 http://bioconductor.org/packages/spatialLIBD http://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/ inst/doc/TenX_data_download.html
39. Overview 39 1. Identification of layer-enriched genes in human cortex using Visium. 2. Spatial registration of single-nucleus RNA-seq data. 3. Resources and tools for analysis of spatial transcriptomics data. 4. Using spatial transcriptomics to better understand brain disorders.
40. 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
41. 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 6&='í'( 6&='í7:$6 (A) (B) DIY at http://spatial.libd.org/spatialLIBD/ Laminar-enrichment of clinical gene sets Autism Spectrum Disorder (ASD) • SFARI: Abrahams et al, Mol Autism, 2013 • ASC102: Satterstrom et al, Cell, 2020 Break up into: • ASD53: ASD dominant traits • DDID49: neurodevelopmental delay COLOR is significance (-log10[p]) NUMBER is enrichment (odds ratio) 41 Maynard, Collado-Torres, et al, Nat Neuro, 2021
42. 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 6&='í'( 6&='í7:$6 (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
43. 43 Adopted and modified from B Wang (2018) and the Brain from the Top to Bottom in McGill University Progressive neurodegeneration in Alzheimer’s disease
44. Integration of proteomic and transcriptomic data with Visium-Immunofluorescence (Visium-IF) 44 Can we define pathology-associated changes in gene expression in Alzheimer’s Disease in human brain?
45. 45 Visium-IF AD Study Design (Inferior Temporal Cortex) Sang Ho Kwon @sanghokwon17 (Kwon et al., in preparation)
46. 46 Case AgeDeath Race RIN Braak CERAD Neurotypical Br3874 73 EUR/CAUC 7.2 B2 C0 AD #1 Br3854 65 EUR/CAUC 7.0 B3 C3 AD #2 Br3873 88 EUR/CAUC 7.2 B3 C3 AD #3 Br3880 90 EUR/CAUC 7.1 B3 C3 Study design Whole genome + Targeted sequencing
47. 10X Genomics Targeted Gene Expression 47 1.Target-specific enrichment 2. Lower sequencing cost (~90%)
48. Human Neuroscience Panel 48 Layer Gene Ensemble ID L1 RELN ENSG00000189056 L2 & L3 CALB1 ENSG00000104327 L4 PVALB ENSG00000100362 L5 HTR2C ENSG00000147246 L6 NR4A2 ENSG00000153234 WM NKX6-2 ENSG00000148826 Maynard et al, Nature Neuroscience, 2021 http://spatial.libd.org/spatialLIBD *Layer-specific/associated genes *AD-associated genes
49. Visium * ~5k spots in honeycomb * gene expression per spot * tissue (H&E staining) Immunofluorescence (IF) * multi-channel (6) images * identifies morphological features of interest * large: might be broken in tiles Channel 1 * triangle feature Channel 2 * cloud feature Channel 6 * xyz feature Tissue (bright field image) Visium spot Channel 1 feature Channel 2 feature + Visium-IF raw data: 2 types
50. Spot ID # Triangle # Cloud % triangle % cloud spot0001 0 12 0 17 spot0002 4 0 27 0 Merge Visium & IF IF Spot ID Gene 1 Gene 2 Gene X In Tissue # cells spot0001 0 12 39 true 3 spot0002 4 0 27 false 0 Visium downstream * QC * analyses
51. 51 Registering pathology maps with gene expression spots Madhavi Tippani @MadhaviTippani (Kwon et al., in preparation) Prop IF/Spot VistoSeg now supports Visium-IF
52. Annotating and pseudo-bulking spots by pathology for differential expression analyses 52 Sowmya Parthiban @sowmyapartybun (Kwon et al., in preparation)
53. 53 0.0 2.5 5.0 7.5 10.0 V10A27106_A1_Br387402%3 0 1 2 3 4 5 V10T31036_A1_Br387402%3 0 1 2 3 V10A27004_A1_Br387402%3 0 50 100 150 0 10 20 30 V10A27106_A1_Br3874 SNAP25 0 10 20 30 10T31036_A1_Br3874 SNAP25 10A27004_A1_Br3874 SNAP25 1 2 V10A27106_A1_Br3874 1 2 V10T31036_A1_Br3874 1 2 V10A27004_A1_Br3874
54. 54 1 2 V10A27106_B1_Br3854 1 2 V10A27106_C1_Br3873 1 2 V10A27106_D1_Br3880 1 2 V10T31036_B1_Br3854 1 2 V10T31036_C1_Br3873 1 2 V10T31036_D1_Br3880 1 2 V10A27004_D1_Br3880
55. 55 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10A27106_B1_Br3854 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10A27106_C1_Br3873 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10A27106_D1_Br3880 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10T31036_B1_Br3854 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10T31036_C1_Br3873 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10T31036_D1_Br3880 none Ab+ next_Ab+ pT+ next_pT+ both next_both V10A27004_D1_Br3880
56. Pathology is less common in the white matter 56 640 155 49 6 55 17 709 15 438 43 156 309 7 709 104 581 11 1193 49 2 3 1 12 277 631 73 73 57 283 1776 21 666 68 148 3 301 20 376 46 30 10 44 14 1757 37 831 67 132 4 137 19 1448 102 279 21 883 73 4 11 6 219 524 15 36 17 141 2450 54 712 136 117 1 121 31 205 899 45 39 35 141 2112 22 556 101 173 1 216 13 S1_B1_3854 S1_C1_3873 S1_D1_3880 S2_B1_3854 S2_C1_3873 S2_D1_3880 S3_D1_3880 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0.5 1.0 1.5 2.0 2.5 0% 25% 50% 75% 100% Percentage
57. Gray matter only pseudo-bulk analysis 57 P1 P7 Pathology Pathology Groups
58. 58 Whole genome Targeted sequencing −20 −10 0 10 20 −20 0 20 40 60 80 runPCA 01 (42%) runPCA 02 (10%) path_groups none Ab+ next_Ab+ pT+ next_pT+ both next_both −20 −10 0 10 20 −20 0 20 40 60 80 runPCA 01 (42%) runPCA 02 (10%) sample_id V10A27004_D1_Br3880 V10A27106_B1_Br3854 V10A27106_C1_Br3873 V10A27106_D1_Br3880 V10T31036_B1_Br3854 V10T31036_C1_Br3873 V10T31036_D1_Br3880 −20 0 20 40 −25 0 25 50 runPCA 01 (33%) runPCA 02 (17%) path_groups none Ab+ next_Ab+ pT+ next_pT+ both next_both −20 0 20 40 −25 0 25 50 runPCA 01 (33%) runPCA 02 (17%) sample_id V10A27004_D1_Br3880 V10A27106_B1_Br3854 V10A27106_C1_Br3873 V10A27106_D1_Br3880 V10T31036_B1_Br3854 V10T31036_C1_Br3873 V10T31036_D1_Br3880
59. Identification of genes associated with AD pathology 59 (Kwon et al., in preparation) p<0.05 in targeted sequencing panel
60. Working with Visium • It’s very powerful • Open source friendly • 6.5 mm2 too restrictive? Opportunity for creativity • Visium and Visium-IF have required the development of software • It’s fun to work on something where there are no answers on Google =) but also a challenge 60
61. 61 @HeenaDivecha Heena R Divecha
62. 62 B1 A1 D1 C1 C1 B1 A1 D1 @CerceoPage Stephanie C Page
63. Summary 63 • Identification of layer-enriched genes in human dorsolateral prefrontal cortex using Visium technology. • Spatial registration of single-nucleus RNA-seq data to determine enrichment of cell populations in specific cortical layers. • Single nucleus and spatial transcriptomics approaches can be used to better understand molecular associations with brain disorders, including neurodevelopmental and neurodegenerative disorders. • Development of tools and resources to analyze spatial transcriptomics data.
64. Future Directions • Integration of proteomic and transcriptomic data • Visium-IF AD proof-of-concept • Integration of snRNA-seq and Visium data • Visium + snRNA-seq on LC • Increasing resolution (# spots) and area (array size) • Visium HD • Leveraging rich histology/imaging data • Clustering (SpaGCN), spot deconvolution, etc. • Building educational resources • Completing Orchestrating Spatially Resolved Transcriptomics Analysis with Bioconductor (OSTA) 64
65. #spatialLIBD is a supportive LIBD & JHU team 65 Check for your yourself at https://twitter.com/lcolladotor/status/1516587531369811971 https://lcolladotor.github.io/team_surveys/
66. We are hiring! https://www.libd.org/careers/ 66
67. Acknowledgements Lieber Institute Sang Ho-Kwon MadhaviTippani Abby Spanger Brenda Pardo Joseph L. Catallini II Matthew N. Tran Vijay Sadashivaiah Heena Divecha Kelsey Montgomery Nick Eagles Josh Stolz Louise Huuki Rahul Bharadwaj Stephanie Page Leonardo Collado-Torres Keri Martinowich Andrew Jaffe Joel E. Kleinman Thomas M. Hyde Daniel R. Weinberger JHU Biostatistics Dept Stephanie Hicks Lukas Weber Sowmya Parthiban 10x Genomics Courtney Anderson Cedric Uytingco Stephen R. Williams Charles Bruce Jennifer Chew YifengYin Nikhil Rao Michelle Mak Guixia Yu Julianna Avalos-Gracia JHU Oncology Tissue Services (Kristen Lecksell) JHU SKCCC Flow Core (Jessica Gucwa) JHU Transcriptomics & Deep Sequencing Core (Linda Orzolek) JHU Tumor Microenvironment Core (Liz Engle) We are hiring! https://www.libd.org/careers/ @kr_maynard @lcolladotor #spatialLIBD team
68. 68 https://twitter.com/bayraktar_lab/status/1481719801789939712