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HBHL keynote

HBHL keynote

Keynote for the Healthy Brains Healthy Lives Research Day 2021 event https://www.mcgill.ca/hbhl/

7382f7fe30561274624635116513ca37?s=128

Leonardo Collado-Torres

June 04, 2021
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  1. 29 Studying the Topography of Spatial Gene Expression in the

    Human Brain Leonardo Collado-Torres, Ph.D., Investigator Lieber Institute for Brain Development HBHL Keynote June 7th, 2021 @lcolladotor @LieberInstitute @HBHL_Trainees Slides at https://speakerdeck.com/lcolladotor Kristen R Maynard @kr_maynard
  2. • Bioinformatics • R and Bioconductor • Reproducibility and best

    practices • Outreach and community building • Back in 2005 at @LCGUNAM: I like math and coding; biology provides the challenging problems What defines me
  3. History 2005-2009 Undergrad in Genomic Sciences 2009-2011 2011-2016 August 2016+

    Data Science Division Leader ! ! PIs: • Jeff Leek: 2012+ • Andrew Jaffe: 2013+ Ph.D. Biostatistics Staff Scientist I → II → Research Scientist → Investigator Data Science Team I PI: Andrew Jaffe 2016-2020
  4. 2008+ • BioC 2008-2011, 2014, 2017, 2019-2020 • useR!2013, 2021

    • rOpenSci unconf 2018 • RStudio::conf 2019-2021 @lcolladotor 2010+ ! @LIBDrstats 2018+ @CDSBMexico 2018+ Defunct: BmoreBiostats, Biostats Cultural Mixers Guest @RLadiesBmore #RLadiesMx Blog: http://lcolladotor.github. io 2011+ FB: 75k, Tw: 66k weekly Interests
  5. The spatial architecture of the brain is fundamentally connected to

    its function 5 chartdiagram.com slideshare.net
  6. Laminar position of a cell influences its gene expression, morphology,

    physiology, and function 6 Kwan et al., 2012, Development
  7. 7 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
  8. Visium & Single nucleus RNA-sequencing technologies 8 Single Cell Gene

    Expression Spatial Gene Expression
  9. Overview 9 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
  10. Study design for Visium experiments in dorsolateral prefrontal cortex (DLPFC)

    10 Andrew E Jaffe Keri Martinowich Stephanie C Hicks Lukas M Weber Cedric Uytingco Nikhil Rao @stephaniehicks @lmwebr @martinowk @andrewejaffe
  11. Visualizing gene expression in a histological context 11 logcounts logcounts

    logcounts Maynard, Collado-Torres, Nat Neuro, 2021
  12. 2 pairs spatial adjacent replicates x subject = 12 sections

    12 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0µm) Adjacent spatial replicates (300µm) Maynard, Collado-Torres, Nat Neuro, 2021 PCP4
  13. “Pseudo-bulking” collapses data: spot to layer level 13 Maynard, Collado-Torres,

    Nat Neuro, 2021
  14. Three statistical models to assess laminar enrichment “ANOVA” model 14

    “Enrichment” model “Pairwise” model Maynard, Collado-Torres, Nat Neuro, 2021 Is any layer different? Is one layer > the rest? Is layer X > layer Y?
  15. Identification & validation of novel layer-enriched genes 15 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
  16. 16 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
  17. Integration of proteomic and transcriptomic data with Visium-Immunofluorescence (IF) 17

    Sino Biologicals Sang Ho Kwon
  18. 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
  19. 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
  20. Identify clusters + pseudo-bulk + compute stats “ANOVA” model 20

    “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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 26 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
  27. 27 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
  28. 28 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
  29. 29 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
  30. 30 Data-driven layer-enriched clustering in the DLPFC Using spatial coordinates

    does help in some cases Maynard, Collado-Torres, Nat Neuro, 2021
  31. 31 Data-driven clustering: BayesSpace Zhao et al, Nature Biotechnology, 2021

    https://doi.org/10.1038/s41587-021-00935-2
  32. 32 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
  33. bioconductor.org/packages/spatialLIBD Pardo et al, bioRxiv, 2021 DOI 10.1101/2021.04.29.440149 Maynard, Collado-Torres,

    Nat Neuro, 2021 Brenda Pardo Abby Spangler @PardoBree @abspangler
  34. Summary: transcriptome-scale spatial gene expression in postmortem human cortex 34

    http://research.libd.org/spatialLIBD Explore the data: Maynard, Collado-Torres, Nat Neuro, 2021
  35. Acknowledgements Lieber Institute Kristen R. Maynard 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 35 @kr_maynard @lcolladotor @LieberInstitute @HBHL_Trainees
  36. 36 Interested in working with us? Let us know! https://www.stephaniehicks.com/join/

    https://www.libd.org/careers/
  37. 37 https://www.youtube.com/c/LeonardoColladoTorres/playlists

  38. 38 https://speakerdeck.com/lcolladotor/another-ds-group-type

  39. 39 https://lcolladotor.github.io/bioc_team_ds/

  40. 40 https://lcolladotor.github.io/team_surveys/

  41. 41 https://lcolladotor.github.io/team_surveys/

  42. 42 https://lcolladotor.github.io/team_surveys/

  43. 43 https://lcolladotor.github.io/bioc_team_ds/how-to-be-a-modern-scientist.html https://leanpub.com/modernscientist