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Identifying Cell Subpopulations Using Transcriptional and Epigenetic Analysis
- 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. Disclosure of financial conflicts of interest None Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 2
- 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. 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. 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. 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. 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. 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. 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. 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. PAGODA overview: assess expression within annotated pathways and de novo gene sets Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 11
- 12. PAGODA overview: assess expression within annotated pathways and de novo gene sets Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 12
- 13. PAGODA overview: Identify pathways and gene sets exhibiting coordinated over dispersion Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 13
- 14. PAGODA overview: Remove redundancy pathways and gene sets, and visualize Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 14
- 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. 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. 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. PAGODA applied to human cortical cells identifies and characterizes subpopulations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 18 Xiaochang Zhang Chris Walsh
- 19. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 19
- 20. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 20
- 21. PAGODA integrated with MISO identifies alternative splicing in pure pooled single cells Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 21
- 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. 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. PAGODA identifies known cell types in fetal cortices confirmed by marker genes Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 24
- 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. 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. 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. 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. 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. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 30
- 31. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 31
- 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. Study overview: droplet based transcriptomics and DNA accessibility assays from same tissues Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 33
- 34. scTHS-seq identified many neuronal subtypes across cortical tissues based on DNA accessibility Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 34
- 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. Integrative approach overview: predict differential accessibility using differential expression to refine scTHS-seq populations Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 36
- 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. GBM model trained on Oli vs. Ast to learn general feature importance Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 38
- 39. Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 39
- 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. 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. Study overview: pool within discovered subpopulations to discover cell-type specific properties Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 42
- 43. Integrative analysis enables identification of cell-type specific TFs Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 43
- 44. Integrating GWAS implicates cell types in neuro-related diseases Jean Fan / Kharchenko Lab / HMS DBMI - SfN 2017 44
- 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. 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
Hinweis der Redaktion
- Actually identify subpopulations
- DCX = neuronal maturation marker Previous FACs rely on just one marker PAGODA builds on these error models Rather than variability of genes, coordinated variability of genes within a pathway or gene set The general intuition… you can image if I have many cells one gene red is high blue is low
- PAGODA builds on these error models Rather than variability of genes, coordinated variability of genes within a pathway or gene set The general intuition… you can image if I have many cells one gene red is high blue is low
- PAGODA builds on these error models Rather than variability of genes, coordinated variability of genes within a pathway or gene set The general intuition… you can image if I have many cells one gene red is high blue is low
- After error modeling… Explain green and orange Red and green split de novo and top section Given annotations from MsigDB, GO, or other ontologies we integrate the error models previously mentioned and use weighted PCA to capture the variability of a gene set in principle components where weights are derived from our error modeling because annotations are limited, we also derive ‘de novo’ gene sets based on correlated expression patterns we observe directly from the data Capturing the patterns of variability
- because annotations are limited, we also derive ‘de novo’ gene sets based on correlated expression patterns we observe directly from the data
- We focus on the pathways and gene sets that exhibit significantly coordinated variability Statistical significance of the λ1 eigenvalues obtained for each gene set was evaluated based on the Tracy-Widom F1 distribution F1(m,ne ), where m is the number of genes in a given set s, and ne is the effective number of cells, determined to fit the distribution of the randomly sampled gene sets (containing the same number of genes as the actual gene sets).
- But many pathways and gene sets share genes or show similar patterns of variability across cells we further collapse these redundancies into pathway clusters Ultimately finally providing a cell clustering along with an interactive browser to explore these results Label middle heatmap
- But many pathways and gene sets share genes or show similar patterns of variability across cells we further collapse these redundancies into pathway clusters Ultimately finally providing a cell clustering along with an interactive browser to explore these results Label middle heatmap
- We applied PAGODA to identify subpopulations CLS
- Look at known marker genes for interpretation. Indeed, we've identified radial glials or mature neurons... Instead of looking at gene expression, let's look at alternative splicing
- Look at known marker genes for interpretation. Indeed, we've identified radial glials or mature neurons... Instead of looking at gene expression, let's look at alternative splicing
- Chris Burge’s lab at MIT
- Create in silico mini-bulks
- Look at known marker genes for interpretation. Indeed, we've identified radial glials or mature neurons... Instead of looking at gene expression, let's look at alternative splicing
- Sashimi plots
- Bulk microdissection See same trends Reviewers were initially concerned about purity of bulk
- Bulk microdissection See same trends Reviewers were initially concerned about purity of bulk