PROINNOVA2023

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1. @lcolladotor lcolladotor.github.io lcolladotor.github.io/bioc_team_ds Metodologías para análisis de RNA-seq aplicadas al cerebro 🧠 Leonardo Collado Torres Investigator, LIEBER INSTITUTE for BRAIN DEVELOPMENT Asst. Prof. Dept. of Biostatistics, Johns Hopkins Bloomberg School of Public Health #PROINNOVA 󰐏 Octubre 23 2023 Slides available at speakerdeck.com/lcolladotor
2. Interesadxs → Usarixs → Desarrolladores ¿Cómo le hacemos para fomentar este paso en México y Latino América?
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/
5. https://doi.org/10.1093/bioinformatics/btaa575
6. Junta Directiva CDSB hasta 2019
7. comunidadbioinfo.github.io
8. doi.org/10.1016/j.biopsych.2020.06.005 Michael Gandal @mikejg84 Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders
9. Background: Human DLPFC 9 slideshare.net Louise A Huuki-Myers @lahuuki
10. Zoom in: base pair resolution Jeff Leek @jtleek Ph.D. advisor Andrew E Jaffe @andrewejaffe Ph.D. co-advisor
11. Flexible expressed region analysis for RNA-seq with #derfinder doi.org/10.1093/nar/gkw852 Jeff Leek @jtleek Ph.D. advisor
12. 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
13. 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
14. 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
15. doi.org/10.1038/543007a
16. 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
17. 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
18. 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
19. What is Deconvolution? • Inferring the composition of different cell types in a bulk RNA-seq data Louise A Huuki-Myers @lahuuki
20. Sean Maden @MadenSean Sang Ho Kwon @sanghokwon17 #deconvochallenge doi.org/10.48550/arXiv.2305.06501
21. #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
22. Zoom in: spatial omics Kristen R Maynard @kr_maynard Keri Martinowich @martinowk Stephanie C Hicks @stephaniehicks Andrew E Jaffe @andrewejaffe Stephanie C Page @CerceoPage
23. 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 23
24. 2 pairs spatial adjacent replicates x subject = 12 sections 24 Subject 1 Subject 2 Subject 3 Adjacent spatial replicates (0μm) Adjacent spatial replicates (300μm) PCP4 Maynard, Collado-Torres, et al, Nat Neuro, 2021
25. “Pseudo-bulking” collapses data: spot to layer level 25 Maynard, Collado-Torres, et al, Nat Neuro, 2021
26. 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
27. 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
28. 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
29. 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
30. #spatialDLPFC 30 doi.org/10.1101/2023.02.15.528722 Louise A Huuki-Myers @lahuuki Abby Spangler @abspangler Nicholas J Eagles @Nick-Eagles (GitHub)
31. BayesSpace clustering with batch correction worked best for multiple samples 31 doi.org/10.1101/2023.02.15.528722 twitter.com/CrowellHL/status/1597579271945715717
32. 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 32 Sp k D d ~L doi.org/10.1101/2023.02.15.528722
33. Spatial Registration of Spatial Domains • Map SpDs to Maynard et al. manual annotated layers • Highlight most strongly associated histological layer to add biological context 33 doi.org/10.1101/2023.02.15.528722
34. Identify Layer Associated Neuron Populations 34 • Apply Spatial Registration with manual layers • 13 layer-level cell types ◦ Assign Excitatory Neurons histological layers ◦ Pool other cell type groups Kelsey D Montgomery
35. Spot Deconvolution 35 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)
36. 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 36 Excit L5 Counts
37. Visium Spatial Proteogenomics (SPG) Images as an Orthogonal Measurement 37
38. Visium Spatial Proteogenomics (Visium-SPG) Visium-SPG = Visium SRT + immunofluorescence (using identical tissue samples) Sang Ho Kwon @sanghokwon17
39. 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 39 Sang Ho Kwon Visium-SPG = Visium SRT + immunofluorescence (using identical tissue samples) Fluorescent Protein Cell Type TMEM119 Microglia Neun Neurons OLIG2 Oligodendrocytes GFAP Astrocytes
40. 40 Max across layers Not max Benchmark Results: Leverage Prior Knowledge Nicholas @Nick-Eagl
41. Benchmark Results: Leverage Prior Knowledge 41
42. Benchmark Summary 42 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)
43. 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 43 Boyi Guo Melissa Grant-Peters
44. Visium spatial clustering works for variables with high % variance explained. But what about other ones? DOI: 10.1038/s41593-020-00787-0
45. twitter.com/sanghokwon17/status/1650589385379962881 from 2023-04-24 Sang Ho Kwon @sanghokwon17 DOI: 10.1101/2023.04.20.537710 #Visium_SPG_AD
46. Experimental design & study overview Braak V-VI & CERAD frequent Sang Ho Kwon
47. AD pathology signal is too small to detect by spatially-resolved gene expression alone research.libd.org/Visium_SPG_AD/
48. Identifying transcriptional signatures of AD-related neuropathology Sang Ho Kwon
49. 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?
50. @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
51. lcolladotor.github.io @lcolladotor