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Society for Neuroscience November 2017 - snDropseq scTHSseq talk

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Minisymposium - After the Data Deluge: Grappling With Transcriptional Complexity in the Brain.

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Society for Neuroscience November 2017 - snDropseq scTHSseq talk

  1. 1. Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 1 Classifying and characterizing single cells using transcriptional and epigenetic analysis Jean Fan Kharchenko Lab Bioinformatics and Integrative Genomics PhD Department of Biomedical Informatics Harvard Medical School / Harvard University
  2. 2. Disclosure of financial conflicts of interest None Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 2
  3. 3. Motivation: Characterize heterogeneity and identify cell subpopulations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 3 Greig LC, Woodworth MB, Galazo MJ, Padmanabhan H, Macklis JD. Molecular logic of neocortical projection neuron specification, development and diversity. Nat Rev Neurosci. 2013;14(11):755-69. NPCs
  4. 4. Technological advancements in single cell sequencing enables scRNA-seq Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 4 Microfluidic Chips Droplet Microfluidics 1000s of genes in 100s and 100,000s of cells -> need computational methods
  5. 5. Talk Outline ◦ How can we identify transcriptional subpopulations in a way that is robust and takes into consideration technical artefacts from single cell RNA-seq? ◦ Beyond expression heterogeneity, how can we use single-cell RNA-seq data to identify patterns of alternative splicing important to neuronal development? ◦ How can we connect transcriptional heterogeneity to epigenetic heterogeneity (accessibility) ◦ What insights can such integrative analysis provide about cell-type specific regulation and neuro-psychiatric disease? Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 5
  6. 6. Talk Outline ◦ How can we identify transcriptional subpopulations in a way that is robust and takes into consideration technical artefacts from single cell RNA-seq? ◦ Beyond expression heterogeneity, how can we use single-cell RNA-seq data to identify patterns of alternative splicing important to neuronal development? ◦ How can we connect transcriptional heterogeneity to epigenetic heterogeneity (accessibility) ◦ What insights can such integrative analysis provide about cell-type specific regulation and neuro-psychiatric disease? Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 6
  7. 7. PAGODA (Pathway And Geneset OverDispersion Analysis) uses pathways to identify transcriptional subpopulations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 7 Nature Methods 13, 241–244 (2016) doi:10.1038/nmeth.3734
  8. 8. PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets ◦ Rather than relying on a few genes, look for broader patterns of variability ◦ Coordinated patterns of variability of genes linked to function/phenotype == stronger signal -> increases statistical power Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 8
  9. 9. PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets ◦ Rather than relying on a few genes, look for broader patterns of variability ◦ Coordinated patterns of variability of genes linked to function/phenotype == stronger signal -> increases statistical power Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 9
  10. 10. PAGODA intuition: Improve statistical sensitivity by taking advantage of pathways and gene sets ◦ Rather than relying on a few genes, look for broader patterns of variability ◦ Coordinated patterns of variability of genes linked to function/phenotype == stronger signal -> increases statistical power Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 10
  11. 11. PAGODA overview: assess expression within annotated pathways and de novo gene sets Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 11
  12. 12. PAGODA overview: assess expression within annotated pathways and de novo gene sets Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 12
  13. 13. PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 13
  14. 14. PAGODA overview: Remove redundancy pathways and gene sets, and visualize Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 14
  15. 15. PAGODA overview: Remove redundancy pathways and gene sets, and visualize Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 15 PAGODA leverages pathway annotations and de novo gene sets to identify robust transcriptionally distinct subpopulations
  16. 16. Increasing throughput of single cell sequencing requires lighter computational solutions -> PAGODA2 Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 16 github.com/hms-dbmi/pagoda2
  17. 17. Talk Outline ◦ How can we identify transcriptional subpopulations in a way that is robust and takes into consideration technical artefacts from single cell RNA-seq? ◦ Beyond expression heterogeneity, how can we use single-cell RNA-seq data to identify patterns of alternative splicing important to neuronal development? ◦ How can we connect transcriptional heterogeneity to epigenetic heterogeneity (accessibility) ◦ What insights can such integrative analysis provide about cell-type specific regulation and neuro-psychiatric disease? Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 17
  18. 18. PAGODA applied to human cortical cells identifies and characterizes subpopulations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 18 Xiaochang Zhang Chris Walsh
  19. 19. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 19
  20. 20. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 20
  21. 21. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 21
  22. 22. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 22 Needs bulk
  23. 23. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 23 Needs bulk -> pool single cells
  24. 24. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 24
  25. 25. Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 25
  26. 26. Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 26
  27. 27. Pure pooled RGs vs neurons lend credence to potential purity concerns with bulk CP vs. VZ Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 27 PAGODA enables generation of pure in-silico mini-bulks
  28. 28. Talk Outline ◦ How can we identify transcriptional subpopulations in a way that is robust and takes into consideration technical artefacts from single cell RNA-seq? ◦ Beyond expression heterogeneity, how can we use single-cell RNA-seq data to identify patterns of alternative splicing important to neuronal development? ◦ How can we connect transcriptional heterogeneity to epigenetic heterogeneity (accessibility) ◦ What insights can such integrative analysis provide about cell-type specific regulation and neuro-psychiatric disease? Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 28
  29. 29. Integrative Single-Cell Analysis By Transcriptional And Epigenetic States In Human Adult Brain Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 29 Blue Lake Brandon Sos Song Chen Kun Zhang Just accepted into Nature Biotech!
  30. 30. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 30
  31. 31. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 31
  32. 32. snDrop-seq identified many neuronal subtypes across cortical tissues based on gene expression Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 32 Clustering with tSNE in PAGODA2
  33. 33. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 33
  34. 34. scTHS-seq identified many neuronal subtypes across cortical tissues based on DNA accessibility Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 34
  35. 35. snDrop-seq and scTHS-seq identified many neuronal subtypes within the visual cortex Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 35 Visual Cortex snDrop-seq (expression) scTHS-seq (accessibility)
  36. 36. Integrative approach overview: predict differential accessibility using differential expression to refine scTHS-seq populations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 36
  37. 37. Integrative approach overview: predict differential accessibility using differential expression to refine scTHS-seq populations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 37
  38. 38. GBM model trained on Oli vs. Ast to learn general feature importance Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 38
  39. 39. Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 39
  40. 40. Cell-types confirmed using marker genes (promoter accessibility, gene expression, tissue staining) Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 40 Promoter Accessibility Gene Expression Spatial Localization
  41. 41. Cell-types confirmed using marker genes (promoter accessibility, gene expression, tissue staining) Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 41 RORB RORBRORB ExL4 ExL4
  42. 42. Study overview: pool within discovered subpopulations to discover cell-type specific properties Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 42
  43. 43. Integrative analysis enables identification of cell-type specific TFs Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 43
  44. 44. Integrating GWAS implicates cell types in neuro-related diseases Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 44
  45. 45. Summary ◦ PAGODA allows us to leverage pathway-level information to identify transcriptional subpopulations from single cell RNA-seq ◦ Beyond expression heterogeneity, we can pool single-cell RNA-seq data to create in-silico mini-bulks to identify patterns of alternative splicing ◦ Integrative analysis of snDrop-seq and scTHS-seq data allows us to connect transcriptional heterogeneity to epigenetic heterogeneity (accessibility) and identify potentially important TFs and implicate cell subtypes in disease using GWAS Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 45
  46. 46. Thanks and happy to take questions! Kharchenko Lab Peter Kharchenko Joseph Herman Nikolas Barkas Ruslan Soldatov Zhang Lab Kun Zhang Blue Lake Brandon Sos Song Chen Chun Lab Jerold Chun Gwen Kaeser Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 46 Funding Wu Lab Catherine Wu Lili Wang Ken Livak Shuqiang Li Park Lab Peter Park Soo Lee Semin Lee SGI Woong-yang Park Hae-Ock Lee Walsh Lab Chris Walsh Xiaochang Zhang Find me online! Web: http://JEF.works Github: JEFworks Twitter: @JEFworks jeanfan@fas.harvard.edu Many others CZ Zhang Angela Brooks DAC Nir Hacohen Soumya Raychaudhuri Rafael Irizarry

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