This document discusses using super-enhancer RNA profiles to identify cell states. The presenter hypothesizes that each cell state can be represented by a distinct super-enhancer RNA profile. They analyze a dataset containing various cell types and conditions to construct super-enhancer RNA profiles. Non-negative matrix factorization is used to decompose the data and reveal hidden cell states. Their results suggest super-enhancer RNA profiles can effectively classify cell types and identify state transitions, such as during induced pluripotent stem cell differentiation. Future work involves using these methods to study core regulatory circuits and cell state changes, especially related to tumorigenesis.

1. Identification of Cell States Using
Super-Enhancer RNA
Speaker: Yueh-Hua Tu (杜岳華)
Advisor: Hsuan-Cheng Huang

2. Enhancer
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3. Super-enhancer (SE)
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Pott, S., & Lieb, J. D. (2014). What are super-enhancers? Nature Genetics, 47(1), 8–12.
ChIP-seqsignal
Super-enhancer
A B

4. Super-enhancers are cell-specific
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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. 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
Kim, T.-K., et al (2010). Widespread transcription at neuronal activity-regulated
enhancers. Nature, 465(7295), 182–7.
Enhancer RNA (eRNA)

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
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8. Purpose
• To identify cell states using super-enhancer (SE)
RNA.
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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
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FANTOM5: http://fantom.gsc.riken.jp/

10. CAGE sequencing
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11. Method
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eRNA profile
Gene expression
profile
Super-enhancer
RNA profile
From FANTOM5

12. Expression of SE and proximal gene
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13. Clustering cell types using super-
enhancer RNA
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14. Cell type classification power
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15. Apply non-negative matrix
factorization
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16. Non-negative matrix factorization
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Customer
rating
matrix
n movies
mcustomers
Super-
enhancer
RNA
profile
n samples
msuper-enhancerRNA
𝑀 = 𝑂𝑅

17. Non-negative matrix factorization
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W H×
k peferences
mcustomers
n movies
kpeferences
k states
msuper-enhancerRNA
n samples
kstates
𝑀 =

18. NMF on all cell types
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kstates
n samples

19. iPS differentiate to neuron
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20. iPS differentiate to neuron
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21. iPS differentiate to neuron
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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
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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.
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24. Thank you for attention
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