R/Medicine 2022

Talk for R/Medicine 2022 https://events.linuxfoundation.org/r-medicine/program/schedule/

Transcript

1. 29
   Spatially-resolved Transcriptomics Analysis
   with R/Bioconductor and Beyond
   Leonardo Collado-Torres, Ph.D.
   Lieber Institute for Brain Development
   R/Medicine
   August 25, 2022
   Keri Martinowich Stephanie C Hicks
   Lieber Institute Johns Hopkins
   @lcolladotor
   #spatialLIBD
   Kristen R Maynard
   Lieber Institute
   https://speakerdeck.com/
   lcolladotor/medicine-2022
   

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2. The spatial architecture of the brain is
   fundamentally connected to its function
   2
   chartdiagram.com slideshare.net
   

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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
   

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4. Laminar position of a cell influences its gene
   expression, morphology, physiology, and function
   4
   Kwan et al., 2012, Development
   

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5. Visium & Single nucleus RNA-sequencing technologies
   (Commercial platform 10x Genomics)
   5
   Single Cell Gene Expression
   Spatial Gene Expression
   

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6. Study design for Visium experiments in
   dorsolateral prefrontal cortex (DLPFC)
   6
   Maynard, Collado-Torres, et al, Nat Neuro, 2021
   

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

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8. 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
   

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9. “Pseudo-bulking” collapses data: spot to layer level
   9
   Maynard, Collado-Torres, et al, Nat Neuro, 2021
   

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10. 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
    

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11. 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
    

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12. bioconductor.org/packages/spatialLIBD
    Pardo et al, BMC Genomics, 2022,
    https://doi.org/10.1186/s12864-022-08601-w
    Maynard, Collado-Torres, Nat Neuro, 2021
    Brenda Pardo Abby Spangler
    @PardoBree @abspangler
    

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13. 13
    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
    

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14. 14
    Madhavi Tippani
    @MadhaviTippani
    bioRxiv, doi: https://doi.org/10.1101/2021.08.04.452489
    

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15. 15
    We provided a framework for
    comparing clustering results vs the
    manual annotation (aka, ground
    truth)
    

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16. 16 Zhao et al, Nature Biotechnology, 2021
    

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17. 17 Zhao et al, Nature Biotechnology, 2021
    

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18. 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
    

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19. 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 ;)
    

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20. 20
    High accessions, citations,
    AltMetric, …
    This data is way more
    challenging than the mouse:
    mouse you are looking at
    different brain regions
    

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21. 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
    

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22. 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
    

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23. Documentation + wrapper functions + tests (GitHub Actions +
    Bioconductor)
    23
    http://bioconductor.org/packages/spatialLIBD
    http://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/
    inst/doc/TenX_data_download.html
    

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24. 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
    

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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
    WM
    L6
    L5
    L4
    L3
    L2
    L1
    PE.U
    p
    PE.D
    ow
    n
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    p
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    ow
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    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)
    25
    Maynard, Collado-Torres, et al, Nat Neuro, 2021
    

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26. 26
    Adopted and modified from B Wang (2018) and the Brain from the Top to Bottom in McGill University
    Progressive neurodegeneration
    in Alzheimer’s disease
    

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27. Integration of proteomic and transcriptomic data
    with Visium-Immunofluorescence (Visium-IF)
    27
    Can we define pathology-associated changes in gene
    expression in Alzheimer’s Disease in human brain?
    

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28. 28
    Visium-IF AD Study Design (Inferior Temporal Cortex)
    Sang Ho Kwon
    @sanghokwon17
    (Kwon et al., in preparation)
    

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29. 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
    

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30. 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
    

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31. 31
    Registering pathology maps with gene expression spots
    Madhavi Tippani
    @MadhaviTippani
    (Kwon et al., in preparation)
    Prop IF/Spot
    VistoSeg now supports Visium-IF
    

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32. Annotating and pseudo-bulking spots by pathology
    for differential expression analyses
    32 Sowmya Parthiban
    @sowmyapartybun (Kwon et al., in preparation)
    

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33. Identification of genes associated with AD pathology
    33
    (Kwon et al., in preparation)
    p<0.05 in
    targeted
    sequencing
    panel
    

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34. 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
    34
    

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35. 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)
    35
    

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36. 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
    

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37. #spatialLIBD is a supportive LIBD & JHU team
    37
    Check for your yourself at
    https://twitter.com/lcolladotor/status/1516587531369811971
    https://lcolladotor.github.io/team_surveys/
    

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38. We are hiring! https://www.libd.org/careers/
    38
    

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