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2023-10-03-FOGBoston

 2023-10-03-FOGBoston

Leonardo Collado-Torres

October 02, 2023
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  1. @lcolladotor
    lcolladotor.github.io
    lcolladotor.github.io/bioc_team_ds
    Lessons Learned from
    Spatially-Resolved
    Transcriptomics of Postmortem
    Human Brain Data Projects
    Leonardo Collado Torres, Investigator
    Festival of Genomics & Biodata
    October 3 2023
    Slides available at speakerdeck.com/lcolladotor

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  2. doi.org/10.1016/j.biopsych.2020.06.005
    Michael Gandal
    @mikejg84
    Transcriptomic
    Insight Into the
    Polygenic
    Mechanisms
    Underlying
    Psychiatric
    Disorders

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  3. Background: Human DLPFC
    3
    slideshare.net
    Louise A Huuki-Myers
    @lahuuki

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  4. Zoom in:
    snRNA-seq → deconvolution of
    bulk RNA-seq
    Matthew N Tran
    @mattntran
    Kristen R Maynard
    @kr_maynard
    Louise A Huuki-Myers
    @lahuuki
    Keri Martinowich
    @martinowk
    Stephanie C Hicks
    @stephaniehicks

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  5. What is Deconvolution?
    ● Inferring the composition of
    different cell types in a bulk
    RNA-seq data
    Louise A Huuki-Myers
    @lahuuki

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  6. Sean Maden
    @MadenSean
    Sang Ho Kwon
    @sanghokwon17
    #deconvochallenge doi.org/10.48550/arXiv.2305.06501

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  7. #deconvochallenge
    Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes
    using single cell RNA-sequencing datasets
    doi.org/10.48550/arXiv.2305.06501
    Sean Maden
    @MadenSean

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  8. Zoom in: spatial omics
    Kristen R Maynard
    @kr_maynard
    Keri Martinowich
    @martinowk
    Stephanie C Hicks
    @stephaniehicks
    Andrew E Jaffe
    @andrewejaffe
    Stephanie C Page
    @CerceoPage

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  9. Visium Platform for Spatial Gene Expression
    Image from 10x Genomics
    - A slide contains 4 capture areas, each full of thousands of 55um-wide “spots”
    (often containing 1-10 cells)
    - Unique barcodes in each spot bind to particular genes; after sequencing, gene
    expression can be tied back to exact spots, forming a spatial map
    Kristen R. Maynard
    9

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  10. bioconductor.org/packages/spatialLIBD
    Pardo et al, 2022 DOI 10.1186/s12864-022-08601-w
    Maynard, Collado-Torres, 2021 DOI 10.1038/s41593-020-00787-0
    Brenda Pardo Abby Spangler
    @PardoBree @abspangler
    Louise A. Huuki-Myers
    @lahuuki

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  11. 2 pairs spatial adjacent replicates x subject = 12 sections
    11
    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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  12. “Pseudo-bulking” collapses data: spot to layer level
    12
    Maynard, Collado-Torres, et al, Nat Neuro, 2021

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  13. DOI: 10.1038/s41593-020-00787-0
    twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29
    Andrew E Jaffe
    @andrewejaffe
    Kristen R Maynard
    @kr_maynard
    Keri Martinowich
    @martinowk

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  14. DOI: 10.1038/s41593-020-00787-0
    twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29
    DOI 10.1093/bioinformatics/btac299
    Since Feb 2020
    spatialLIBD::fetch_data()
    provides access to
    SpatialExperiment
    R/Bioconductor objects
    Stephanie C Hicks
    @stephaniehicks
    Lukas M Weber
    @lmweber

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  15. DOI: 10.1038/s41593-020-00787-0
    twitter.com/lcolladotor/status/1233661576433061888 from 2020-02-29
    twitter.com/CrowellHL/status/1597579271945715717
    DOI 10.1093/bioinformatics/btac299
    Since Feb 2020
    spatialLIBD::fetch_data()
    provides access to
    SpatialExperiment
    R/Bioconductor objects

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  16. #spatialDLPFC
    16
    doi.org/10.1101/2023.02.15.528722
    Louise A
    Huuki-Myers
    @lahuuki
    Abby Spangler
    @abspangler
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)

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  17. BayesSpace clustering with batch correction worked
    best for multiple samples
    17
    doi.org/10.1101/2023.02.15.528722
    twitter.com/CrowellHL/status/1597579271945715717

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  18. Spatial Registration Adds Anatomical Context
    ● Validate detection of laminar structure
    ● Correlate enrichment t-statistics for top marker genes of reference
    ○ Cluster vs. manual annotation
    ● Annotate with strongly associated histological layer
    18
    Sp
    k
    D
    d
    ~L
    doi.org/10.1101/2023.02.15.528722

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  19. Spatial Registration of
    Spatial Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    19
    doi.org/10.1101/2023.02.15.528722

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  20. Identify Layer Associated Neuron Populations
    20
    ● Apply Spatial Registration with
    manual layers
    ● 13 layer-level cell types
    ○ Assign Excitatory Neurons
    histological layers
    ○ Pool other cell type groups
    Kelsey D Montgomery

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  21. Spot Deconvolution
    21
    Cell 1 Cell 2 … Cell N
    Gene 1 0 0 … 0
    Gene 2 2 5 … 3
    … … … … …
    Gene i 1 0 … 0
    Spot 1 Spot 2 … Spot M
    Gene 1 1 0 … 3
    Gene 2 0 1 … 0
    … … … … …
    Gene j 4 2 … 2
    Astro Excit … Inhib
    Spot 1 1 1 … 1
    Spot 2 …
    … … … … …
    Spot N 1 0 … 2
    Single- Nucleus
    Spatial
    Deconvolved Results
    Spot 1
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)

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  22. Existing Spot Deconvolution Software
    - Explored 3 novel software methods from the literature
    Software name Overall
    approach
    Input Cell
    Counts
    Output
    Tangram
    (Biancalani et al.)
    Mapping
    individual cells
    Every spot Integer counts
    Cell2location
    (Kleshchevnikov et al.)
    Matching
    gene-expression
    profile
    Average across
    spots
    Decimal
    counts
    SPOTlight
    (Elosua-Bayes et al.)
    Matching
    gene-expression
    profile
    Not used Proportions
    22
    Excit L5 Counts

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  23. Visium Spatial
    Proteogenomics
    (SPG) Images as an
    Orthogonal
    Measurement
    23

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  24. Visium Spatial Proteogenomics (Visium-SPG)
    Visium-SPG = Visium SRT + immunofluorescence
    (using identical tissue samples)
    Sang Ho Kwon
    @sanghokwon17

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  25. Visium Spatial Proteogenomics (Visium-SPG)
    - Gene expression captured like ordinary Visium
    - Multi-channel fluorescent images captured of the
    same tissue
    - Channels measure proteins marking for specific cell types
    Kristen R. Maynard
    25 Sang Ho Kwon
    Visium-SPG = Visium SRT + immunofluorescence
    (using identical tissue samples)
    Fluorescent Protein Cell Type
    TMEM119 Microglia
    Neun Neurons
    OLIG2 Oligodendrocytes
    GFAP Astrocytes

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  26. 26
    Max across layers
    Not max
    Benchmark Results:
    Leverage Prior Knowledge
    Nicholas
    @Nick-Eagl

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  27. Benchmark Results: Leverage Prior Knowledge
    27

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  28. Benchmark Summary
    28
    Metric Tangram Cell2location SPOTlight Metric Type
    Avg. cor (spot-level) 0.31 0.30 0.21 Orthogonal measurements
    Avg. RMSE (spot-level) 1.35 1.24 1.3 Orthogonal measurements
    Overall prop.: (KL Div.) 0.44 0.49 0.41 Orthogonal measurements
    Overall prop.: (cor.) 0.46 0.37 0.47 Orthogonal measurements
    Overall prop.: (RMSE) 3020 3890 3040 Orthogonal measurements
    Histological mapping 0.69 0.77 0.23 Leverage known biology
    Broad vs. layer (cor.) 1.00 0.77 -0.36 Self-consistency of results
    Broad vs. layer (RMSE) 102 4200 4220 Self-consistency of results
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

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  29. How Spot
    Deconvolution
    Results Were
    Used
    A. Better characterize unsupervised
    spatial domains
    B. Cell-cell communication;
    cell-type-informed ligand-receptor
    interactions in the context of
    schizophrenia risk
    A
    29
    Boyi Guo Melissa Grant-Peters

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  30. Visium spatial clustering works for variables with high %
    variance explained. But what about other ones?
    DOI: 10.1038/s41593-020-00787-0

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  31. twitter.com/sanghokwon17/status/1650589385379962881 from 2023-04-24
    Sang Ho Kwon
    @sanghokwon17
    DOI: 10.1101/2023.04.20.537710
    #Visium_SPG_AD

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  32. Experimental design & study overview
    Braak V-VI & CERAD frequent
    Sang Ho Kwon

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  33. AD pathology signal is too small to detect by
    spatially-resolved gene expression alone research.libd.org/Visium_SPG_AD/

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  34. Identifying transcriptional signatures of AD-related neuropathology
    Sang Ho Kwon

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  35. Some challenges ⚠ with
    Visium
    35

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  36. sc/snRNA-seq QC metrics such as # detected genes, # UMI,
    mitochondria expression % are likely biologically related!

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  37. Prashanthi Ravichandran
    @prashanthi-ravichandran (GH)
    Artifacts in general are normalized away by library size,
    though there are caveats

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  38. Diffusion issues: could be related to
    permeabilization step

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  39. Having more data is useful to provide context!
    Here 4 new samples have low sequencing saturation (outliers) but
    are within range of good samples from other studies

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  40. Having more data is useful to provide context!
    Those 4 samples have great median UMI counts per spot ^_^

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  41. Software keeps evolving and as leaders in the field we aim to
    use the best methods
    41
    Moses, L., Pachter, L. Museum of spatial transcriptomics. Nat Methods 19,
    534–546 (2022). https://doi.org/10.1038/s41592-022-01409-2

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  42. 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
    https://github.com/LieberInstitute/jhpce_mod_source
    https://github.com/LieberInstitute/jhpce_module_config
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

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  43. 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
    Nicholas J Eagles
    @Nick-Eagles (GitHub)

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  44. Documentation + wrapper functions + tests (GitHub Actions +
    Bioconductor)
    44
    bioconductor.org/packages/spatialLIBD

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  45. More challenges
    ahead
    Working with multiple
    capture areas per tissue
    Nicholas J Eagles
    @Nick-Eagles (GitHub)
    Prashanthi Ravichandran
    @prashanthi-ravichandran (GH)
    Spot
    diameter
    error:
    ~1.8 →
    ~1.1
    Another
    pair:
    ~2.8 →
    ~0.76

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  46. lcolladotor.github.io/#projects
    ● Every assay has caveats
    ● We re-use tricks:
    think adding 0, multiplying by 1
    ● It nearly always takes a team
    ● Data sharing accelerates science +
    democratizes access to it
    ● Zooming in allows us to reduce the
    heterogeneity
    ● We can learn from each other: from
    uniformly processing our data & re-using
    it → replicate / validate?

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  47. @MadhaviTippani
    Madhavi Tippani
    @HeenaDivecha
    Heena R Divecha
    @lmwebr
    Lukas M Weber
    @stephaniehicks
    Stephanie C Hicks
    @abspangler
    Abby Spangler
    @martinowk
    Keri Martinowich
    @CerceoPage
    Stephanie C Page
    @kr_maynard
    Kristen R Maynard
    @lcolladotor
    Leonardo Collado-Torres
    @Nick-Eagles (GH)
    Nicholas J Eagles
    Kelsey D Montgomery
    Sang Ho Kwon
    Image Analysis
    Expression Analysis
    Data Generation
    Thomas M Hyde
    @lahuuki
    Louise A Huuki-Myers
    @BoyiGuo
    Boyi Guo
    @mattntran
    Matthew N Tran
    @sowmyapartybun
    Sowmya Parthiban
    Slides available at
    speakerdeck.com
    /lcolladotor
    + Many more LIBD, JHU, and
    external collaborators
    @mgrantpeters
    Melissa Grant-Peters
    @prashanthi-ravichandran (GH)
    Prashanthi Ravichandran

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  48. lcolladotor.github.io
    @lcolladotor

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