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HCA LA 2022

HCA LA 2022

Slides for my Human Cell Atlas - Latin America 2022 presentation.

Leonardo Collado-Torres

September 30, 2022

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  1. 29 Spatially-resolved Transcriptomics Analysis with R/Bioconductor and Beyond Leonardo Collado-Torres,

    Ph.D. Lieber Institute for Brain Development HCA LA October 6, 2022 Keri Martinowich Stephanie C Hicks Lieber Institute Johns Hopkins @lcolladotor #spatialLIBD Kristen R Maynard Lieber Institute https://speakerdeck.com/ lcolladotor/hca-la-2022
  2. The spatial architecture of the brain is fundamentally connected to

    its function 2 chartdiagram.com slideshare.net
  3. 3 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
  4. Laminar position of a cell influences its gene expression, morphology,

    physiology, and function 4 Kwan et al., 2012, Development
  5. Study design for Visium experiments in dorsolateral prefrontal cortex (DLPFC)

    6 Maynard, Collado-Torres, et al, Nat Neuro, 2021
  6. Visualizing gene expression in a histological context 7 logcounts logcounts

    logcounts Maynard, Collado-Torres, et al, Nat Neuro, 2021
  7. 2 pairs spatial adjacent replicates x subject = 12 sections

    8 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0µm) Adjacent spatial replicates (300µm) PCP4 Maynard, Collado-Torres, et al, Nat Neuro, 2021
  8. Three statistical models to assess laminar enrichment “ANOVA” model 10

    “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
  9. 11 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
  10. 12 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
  11. 15 We provided a framework for comparing clustering results vs

    the manual annotation (aka, ground truth)
  12. Openly sharing data accelerates science: share and you will reap

    the benefits too! 18 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
  13. 19 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 ;)
  14. These new methods enable us to scale up our projects

    20 Abby Spangler @abspangler @Nick-Eagles (GH) Nicholas J Eagles Louise Huuki-Myers @lahuuki Huuki-Myers, Spangler, Eagles et al. In preparation
  15. 21

  16. Software keeps evolving and as leaders in the field we

    aim to use the best methods 22 Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19, 534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2
  17. 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
  18. 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
  19. Documentation + wrapper functions + tests (GitHub Actions + Bioconductor)

    25 http://bioconductor.org/packages/spatialLIBD http://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/ inst/doc/TenX_data_download.html
  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 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 2.1 2 3.1 1.8 2.2 1.8 8.8 5 2.7 2.6 4.6 SCZD−DE SCZD−TW (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) 27 Maynard, Collado-Torres, et al, Nat Neuro, 2021
  22. 28 Adopted and modified from B Wang (2018) and the

    Brain from the Top to Bottom in McGill University Progressive neurodegeneration in Alzheimer’s disease
  23. Annotating and pseudo-bulking spots by pathology for differential expression analyses

    30 Sowmya Parthiban @sowmyapartybun (Kwon et al., in preparation)
  24. Identification of genes associated with AD pathology 31 (Kwon et

    al., in preparation) p<0.05 in targeted sequencing panel
  25. 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 32
  26. 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) 33
  27. 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/ @lcolladotor #spatialLIBD team
  28. #spatialLIBD is a supportive LIBD & JHU team 35 Check

    for your yourself at https://twitter.com/lcolladotor/status/1516587531369811971 https://lcolladotor.github.io/team_surveys/