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Identification of Cell States Using
Super-Enhancer RNA
Speaker: Yueh-Hua Tu (杜岳華)
Advisor: Hsuan-Cheng Huang
Enhancer
2
Super-enhancer (SE)
3
Pott, S., & Lieb, J. D. (2014). What are super-enhancers? Nature Genetics, 47(1), 8–12.
ChIP-seqsign...
Super-enhancers are cell-specific
4
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., … Y...
Super-enhancer v.s. typical
enhancer
5
Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., ...
6
Kim, T.-K., et al (2010). Widespread transcription at neuronal activity-regulated
enhancers. Nature, 465(7295), 182–7.
E...
Rationale & Hypothesis
• Define super-enhancer RNA using eRNA
• There exist different cell states between cell types
• Eac...
Purpose
• To identify cell states using super-enhancer (SE)
RNA.
8
FANTOM5 dataset
• Why use this dataset?
• Cap Analysis of Gene Expression (CAGE-seq) can detect
eRNA (without poly-A tail)...
CAGE sequencing
10
Method
11
eRNA profile
Gene expression
profile
Super-enhancer
RNA profile
From FANTOM5
Expression of SE and proximal gene
12
Clustering cell types using super-
enhancer RNA
13
Cell type classification power
14
Apply non-negative matrix
factorization
15
Non-negative matrix factorization
16
Customer
rating
matrix
n movies
mcustomers
Super-
enhancer
RNA
profile
n samples
msup...
Non-negative matrix factorization
17
W H×
k peferences
mcustomers
n movies
kpeferences
k states
msuper-enhancerRNA
n sampl...
NMF on all cell types
18
kstates
n samples
iPS differentiate to neuron
19
iPS differentiate to neuron
20
iPS differentiate to neuron
21
Conclusions
• Our proposed super-enhancer RNA can act as a
good alternative for classification of cell type
specification,...
Future work
• Find out the core regulatory circuitry in each cell or
cancer types.
• How cell migrate from one state to an...
Thank you for attention
24
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20170715 北Bio meetup

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Identification of Cell States Using Super-Enhancer RNA

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20170715 北Bio meetup

  1. 1. Identification of Cell States Using Super-Enhancer RNA Speaker: Yueh-Hua Tu (杜岳華) Advisor: Hsuan-Cheng Huang
  2. 2. Enhancer 2
  3. 3. Super-enhancer (SE) 3 Pott, S., & Lieb, J. D. (2014). What are super-enhancers? Nature Genetics, 47(1), 8–12. ChIP-seqsignal Super-enhancer A B
  4. 4. Super-enhancers are cell-specific 4 Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., … Young, R. A. (2013). Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes. Cell, 153(2), 307–319.
  5. 5. Super-enhancer v.s. typical enhancer 5 Whyte, W. A., Orlando, D. A., Hnisz, D., Abraham, B. J., Lin, C. Y., Kagey, M. H., … Young, R. A. (2013). Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes. Cell, 153(2), 307–319.
  6. 6. 6 Kim, T.-K., et al (2010). Widespread transcription at neuronal activity-regulated enhancers. Nature, 465(7295), 182–7. Enhancer RNA (eRNA)
  7. 7. Rationale & Hypothesis • Define super-enhancer RNA using eRNA • There exist different cell states between cell types • Each cell state can be represented by distinct super-enhancer RNA profile 7
  8. 8. Purpose • To identify cell states using super-enhancer (SE) RNA. 8
  9. 9. FANTOM5 dataset • Why use this dataset? • Cap Analysis of Gene Expression (CAGE-seq) can detect eRNA (without poly-A tail) • 1829 samples containing cell lines, primary cells, tissues, time-course (785 samples, 12 cell types) • Contains stimulated and unstimulated profiles 9 FANTOM5: http://fantom.gsc.riken.jp/
  10. 10. CAGE sequencing 10
  11. 11. Method 11 eRNA profile Gene expression profile Super-enhancer RNA profile From FANTOM5
  12. 12. Expression of SE and proximal gene 12
  13. 13. Clustering cell types using super- enhancer RNA 13
  14. 14. Cell type classification power 14
  15. 15. Apply non-negative matrix factorization 15
  16. 16. Non-negative matrix factorization 16 Customer rating matrix n movies mcustomers Super- enhancer RNA profile n samples msuper-enhancerRNA 𝑀 = 𝑂𝑅
  17. 17. Non-negative matrix factorization 17 W H× k peferences mcustomers n movies kpeferences k states msuper-enhancerRNA n samples kstates 𝑀 =
  18. 18. NMF on all cell types 18 kstates n samples
  19. 19. iPS differentiate to neuron 19
  20. 20. iPS differentiate to neuron 20
  21. 21. iPS differentiate to neuron 21
  22. 22. Conclusions • Our proposed super-enhancer RNA can act as a good alternative for classification of cell type specification, without complicated measurements of histone modifications by ChIP-seq. • Super-enhancer RNA profiles can be used to identify cell states between cell types. • Non-negative matrix factorization is a good method for decomposing large biological data to reveal the interpretable hidden states 22
  23. 23. Future work • Find out the core regulatory circuitry in each cell or cancer types. • How cell migrate from one state to another state? • Use Markov model or Bayesian network to construct and organize cell states. • How core regulatory circuitry behave during migration? • Construct a virtual cell with dynamic gene regulatory circuitry. • If we know how it behave, we can get deeper into tumorigenesis. 23
  24. 24. Thank you for attention 24

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