LCG20

Transcript

1. @lcolladotor
   lcolladotor.github.io
   lcolladotor.github.io/bioc_team_ds
   Studying the human prefrontal
   cortex transcriptome at different
   resolutions
   Leonardo Collado Torres, Investigator
   #LCG20 at LCG-UNAM 󰐏
   August 31 2023
   Slides available at speakerdeck.com/lcolladotor
   

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2. Interesadxs → Usarixs → Desarrolladores
   ¿Cómo le hacemos para fomentar este paso
   en México y Latino América?
   

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3. • 2017:
   ○ idea en BioC2017 e inicio de la fundación de CDSB
   • 2018:
   ○ primer taller ^_^, con instructores de Bioconductor: Martin Morgan &
   Benilton Carvalho
   • 2019:
   ○ BioC2019: apoyo a solicitud de becas
   ○ Taller con materiales adaptados de RStudio
   • 2020:
   ○ regutools: primer paquete en Bioconductor
   ○ Taller con RStudio & Bioconductor
   • 2021:
   ○ primera vez con 2 talleres https://comunidadbioinfo.github.io/
   

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5. https://doi.org/10.1093/bioinformatics/btaa575
   

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6. Junta Directiva CDSB hasta 2019
   

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7. comunidadbioinfo.github.io
   

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8. @lcolladotor
   lcolladotor.github.io
   lcolladotor.github.io/bioc_team_ds
   Studying the human prefrontal
   cortex transcriptome at different
   resolutions
   Leonardo Collado Torres, Investigator
   #LCG20 at LCG-UNAM 󰐏
   August 31 2023
   Slides available at speakerdeck.com/lcolladotor
   

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9. 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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10. Background: Human DLPFC
    10
    slideshare.net
    Louise A Huuki-Myers
    @lahuuki
    

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11. Zoom in: base pair resolution
    Jeff Leek
    @jtleek
    Ph.D. advisor
    Andrew E Jaffe
    @andrewejaffe
    Ph.D. co-advisor
    

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12. lcolladotor.github.io/talk/lcg2014/
    #LCG10 fue organizado por
    Esperanza Martínez Romero
    et al.
    

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13. Flexible expressed region analysis for RNA-seq with #derfinder
    doi.org/10.1093/nar/gkw852
    Jeff Leek
    @jtleek
    Ph.D. advisor
    

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14. Fetal Infant
    Child Teen
    Adult 50+
    6 / group, N = 36
    Discovery data
    Postmortem Human Brain Samples
    Fetal Infant
    Child Teen
    Adult 50+
    6 / group, N = 36
    Replication data
    Andrew E Jaffe
    @andrewejaffe
    Ph.D. co-advisor
    Developmental regulation of
    human cortex transcription and its clinical relevance at single base resolution
    doi.org/10.1038/nn.3898
    github.com/leekgroup/libd_n36
    

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15. doi.org/10.1038/nn.3898
    Developmental regulation of
    human cortex transcription and its clinical relevance at single base resolution
    github.com/leekgroup/libd_n36
    

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16. Zoom in: more data!
    Ben Langmead
    @BenLangmead
    Abhinav Nellore
    @nellore (GitHub)
    Christopher Wilks
    @chrisnwilks
    Shannon Ellis
    @Shannon_E_Ellis
    Kasper Daniel Hansen
    @KasperDHansen
    Andrew E Jaffe
    @andrewejaffe
    Ph.D. co-advisor
    + LIBD former
    boss
    Jeff Leek
    @jtleek
    Ph.D. advisor
    

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17. doi.org/10.1038/543007a
    

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18. expression data for ~70,000 human samples
    samples
    phenotypes
    ?
    GTEx
    N=9,962
    TCGA
    N=11,284
    SRA
    N=49,848
    samples
    expression
    estimates
    gene
    exon
    junctions
    ERs
    Answer meaningful
    questions about
    human biology and
    expression
    slide adapted from Shannon Ellis
    Reproducible RNA-seq analysis using #recount2
    + Improving the value of public RNA-seq expression data by phenotype prediction
    doi.org/10.1038/nbt.3838
    doi.org/10.1093/nar/gky102
    

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19. recount3: over 700,000 human and mouse RNA-seq samples
    #recount3: summaries and queries for large-scale RNA-seq expression and splicing
    Christopher Wilks
    @chrisnwilks
    research.libd.org/recount3-docs/
    doi.org/10.1186/s13059-021-02533-6
    

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20. 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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21. 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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22. Interaction eQTLs with cell type proportions
    github.com/LieberInstitute/goesHyde_mdd_rnaseq/tree/master/eqtl/code
    

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23. Reference Single Cell
    Data
    23
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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24. 10x snRNA-seq Reference Data
    AMY DLPFC HPC NAc sACC
    Astro 1638 782 1170 1099 907
    Endo 31 0 0 0 0
    Macro 0 10 0 22 0
    Micro 1168 388 1126 492 784
    Mural 39 18 43 0 0
    Oligo 6080 5455 5912 6134 4584
    OPC 1459 572 838 669 911
    Tcell 31 9 26 0 0
    Excit 443 2388 623 0 4163
    Inhib 3117 1580 366 11476 3974
    @mattntran
    Matthew N Tran
    doi.org/10.1016/j.neuron.2021.09.001
    

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25. Marker Finding
    25
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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26. 1vAll Markers vs. Mean Ratio Markers
    26
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/
    

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27. 1vAll Markers vs. Mean Ratio Markers
    27
    Louise A
    Huuki-Myers
    @lahuuki
    research.libd.org/DeconvoBuddies/
    

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28. Methods
    28
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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29. Which Method is the Most Accurate?
    ● Benchmarking shows that different methods perform best on
    different data sets (Cobos et al, Nature Communications, 2020)
    ● Benchmarking results from different papers on “real” data
    ○ MuSiC paper: MuSiC > NNLS > BSEQ-sx > CIBERSORT
    ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 2a)
    ○ Bisque paper: Bisque > MuSiC > CIBERSORT
    ■ DLPFC: Microglia vs. Braak stage, Neuron vs. Cognitive diagnostic category
    (Fig 4)
    ○ SCDC paper: SCDC > MuSiC > Bisque > DWLS > CIBERSORT
    ■ Pancreatic Islet: Beta cells vs. HbA1c (Fig 4b)
    ○ Cobos benchmark: DWLS > MuSiC > Bisque > deconvoSeq
    ■ Human PMBC flow sorted (Fig 7)
    29
    Louise A
    Huuki-Myers
    @lahuuki
    

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30. Results + Validation
    30
    deconvolution(Y, Z) = Proportion of Cell Types
    Louise A Huuki-Myers
    @lahuuki
    

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31. Mean Proportions By Region: Tran et al, bioRxiv, 2020 (5 donors, 6 cell types)
    Louise A Huuki-Myers
    @lahuuki
    

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32. Peric =
    Mural + Endo
    Mean Proportions By Region: Tran et al, Neuron, 2021 (8 donors, 10 cell types)
    Louise A Huuki-Myers
    @lahuuki
    

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

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

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36. research.libd.org/TREG/
    doi.org/10.1101/2022.04.28.489923
    Louise A
    Huuki-Myers
    @lahuuki
    

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37. 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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38. 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
    38
    

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39. 2 pairs spatial adjacent replicates x subject = 12 sections
    39
    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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40. “Pseudo-bulking” collapses data: spot to layer level
    40
    Maynard, Collado-Torres, et al, Nat Neuro, 2021
    

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41. 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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42. 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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43. 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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44. 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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45. #spatialDLPFC
    45
    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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46. Different Resolutions of
    BayesSpace Clustering
    k = number of clusters
    ● k=2: separate white vs. grey matter
    ● k=9: best reiterated histological layers
    ● k=16: data-driven optimal k based on
    fast H+ statistic
    46
    More Clusters = More Complexity
    doi.org/10.1101/2023.02.15.528722
    

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47. 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
    47
    Sp
    k
    D
    d
    ~L
    doi.org/10.1101/2023.02.15.528722
    

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48. Spatial Registration of
    Spatial Domains
    ● Map SpDs to Maynard et al. manual
    annotated layers
    ● Highlight most strongly associated
    histological layer to add biological
    context
    48
    doi.org/10.1101/2023.02.15.528722
    

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49. Identify Layer Associated Neuron Populations
    49
    ● 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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50. Layer-level Cell Type Marker genes
    50
    Louise A
    Huuki-Myers
    @lahuuki
    

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51. Spot Deconvolution
    51
    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 M 1 0 … 2
    Single- Nucleus
    Spatial
    Deconvolved Results
    Spot 1
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    

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52. 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
    52
    Excit L5 Counts
    

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53. Benchmarking Spot Deconvolution Software: Theory
    - How do we measure performance or accuracy of cell-type
    predictions?
    - Make orthogonal measurements*: image-derived counts
    - Leverage prior knowledge: neurons localize to gray matter?
    - Self-consistency of results: broad vs. fine cell-type results
    53
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    doi.org/10.1101/2023.02.15.528722
    

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54. Visium Spatial
    Proteogenomics
    (SPG) Images as an
    Orthogonal
    Measurement
    54
    Nicholas J Eagles
    @Nick-Eagles
    (GitHub)
    

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

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56. 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
    56 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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57. 57
    Max across layers
    Not max
    Benchmark Results:
    Leverage Prior Knowledge
    

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58. Benchmark Results: Leverage Prior Knowledge
    58
    

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59. Benchmark Summary
    59
    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
    

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60. Viewing Spot Deconvolution Results: Samui Browser
    - View:
    - Fluorescence
    channels
    - Spot deconvo results
    - Segmented cells
    - Gene expression
    - Interactive
    - Quickly zoom/scroll
    - Full-resolution
    images
    samuibrowser.com/from?url=data2.loopybrowser.com/VisiumIF/&s=Br2720_Ant_IF&s=Br6432_Ant_IF&s=Br6522_Ant_IF&s=Br8667_Post_IF
    Sriworarat, 2023.
    60
    

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61. Viewing Spot
    Deconvolution
    Results: spatialLIBD
    apps
    - View:
    - spot deconvolution results
    - spatial domains/ clusters
    - gene expression
    - Huge amount of
    aesthetic customization
    61
    https://libd.shinyapps.io/spatialDLPFC_Visium_SPG/
    

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62. 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
    62
    Boyi Guo Melissa Grant-Peters
    

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63. 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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64. 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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65. Experimental design & study overview
    Braak V-VI & CERAD frequent
    Sang Ho Kwon
    

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

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

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68. Some challenges ⚠ with
    Visium
    68
    

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

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

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71. Diffusion issues: could be related to
    permeabilization step
    

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

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74. Software keeps evolving and as leaders in the field we aim to
    use the best methods
    74
    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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75. 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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76. 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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77. Documentation + wrapper functions + tests (GitHub Actions +
    Bioconductor)
    77
    bioconductor.org/packages/spatialLIBD
    

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78. 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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79. 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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80. @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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81. lcolladotor.github.io
    @lcolladotor
    

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