1. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells
Presented by Nur Ata Bruss and Xinyi Ma

2. Background
long noncoding RNA loci
CRISPR interaction (CRISPRi)

3. Background: lncRNA Non-protein coding transcripts > 200 nucleotides
Plays critical roles in normal biology and disease
However, few is known to function in fundamental aspects of cell biology
Which means, we don’t know which lncRNA is expressed in what kind of cell and for what.
Also, why is it important to know that?

4. Signification - lncRNA
It is becoming evident that RNA, long considered to be platform of protein production, may in fact play a major role in, although not all of them, in gene regulation. E.g. MicroRNA & lncRNA.

5. Background-CRISPRi Loss of function study Highly specific and scalable
A tool for inhibiting lncRNA gene activity
Highly specific and scalable

6. Background-CRISPRi Loss of function study Highly specific and scalable
A tool for inhibiting lncRNA gene activity
Highly specific and scalable
TSS: transcription starting site
sgRNA: single guide RNA, match the targeted sequences and guide the dcas-9 repressor fusion protein

7. In this study, Growth modifier lncRNA (16,401) 7 human cell types:
10 sgRNA per TSS
3. Guaratee?

8. & & & Using CRISPRi screen to identify lncRNA genes
Figure 1
Using Flow Cytometry to validate the lncRNA knockdown phenotypes
&
Figure 2A-D
Using RNA-seq to test the transcriptome responses b/w different lncRNA
&
Figure 2E
Assess the repression efficiency
Figure 2F-G
Finding that growth modifier lncRNA is highly cell type-specific
Figure 3
&
Even expressed in different cell type, the same lncRNA involves in different mechanism (a specific example)
Figure 4
Genomic features aided by machine can be used to find lncRNA
Figure 5

9. Build a library: CRiNCL
Merge 3 major ncTranscriptome annotations 
Rate the impact of loss lncRNA on cell growth
Prioritize genes based on expression in cell lines 
Develop 10 sgRNAs per TSS
+: positive impact on cell growth caused by knockdown
-: negative impact on cell growth caused by knockdown γ

10. Scatter plot:
Non-targeting sgRNA: distributed around 0
Targeting sgRNA: Pearson r =
---- reproducible phenotype
Volcano plot:
Negative control: non-targeting sgRNA phenotype
Hits: if combined phenotype effect size and p-value > threshold (gray dot line)

12. Final!! Statistic significant phenotype Knockdown of target lncRNA? or
Inhibition of neighboring coding gene?

14. Using Flow Cytometry to validate the lncRNA knockdown phenotypes
Hits gene
Flow cytometry is an internally-controlled growth assay, which can measure the cells infected with sgRNA over time.
Screen vs. Flow cytometry

15. Previous finding vs. flow cytometry vs. CRISPRi screen
Proto-oncogen:
progrowth phenotype
Above 4: knockdown of PVT1, another lncRNA, has PROGROWTH phenotype
Previous finding vs. flow cytometry vs. CRISPRi screen

16. Using RNA-seq to test the transcriptome responses b/w different lncRNA
42 hits in 3 cell lines
Same lncRNA is more likely to function the same way among cell lines
Different lncRNA tends to have different molecular mechanism although they have similar phenotypes
In other words, to better understand the consequences of CRISPRi

17. Assess the repression efficiency
Non-hits genes
True negative? or ineffective repression?

18. Assess the repression efficiency
Non-hits genes
True negative? or ineffective repression?
Answer: lncRNA that did not appear as a sceen hit produce transcrips that are not essential for robust growth

19. Cell Type Specific Effects of lncRNA
No more than 8 individual hits between multiple cell lines, with no hits across all of them.
iPS had an disproportionately large number of hits.
Same general trend across the complete and common libraries.

20. Independent Mechanisms of lncRNA Across Cell Lines
Individual analysis of LINC002263
expressed in all cell types
Negative growth phenotype in U87, no significant change in K562, HeLa and MCF7
confirmed equivalent and specific CRISPRi targeting via ChIP-seq

21. Independent Mechanisms of lncRNA Across Cell Lines
Equivalent ChIP-seq data
Differing RNA-seq data
Knockout of LINC expresses different transcriptome phenotypes across cell lines, showing differences in transcriptional networks.

22. Independent Mechanisms of lncRNA Across Cell Lines
Used a ribonuclease dependent inhibition method on of LINC to replicate the results
Saw the same decrease in Transcript number, same trends in cell growth and relatively stable levels of cell cycle representation

23. Machine learning as a prediction method
goal: to use large data sets to distinguish nonhit lncRNAs from hit lncRNA.
Compared 18 classes of genomic data such as: enhancer maps, expression levels, chromosomal looping data, conservation and CNV from 3 different data sets
Utilized 3 different data sets (ENCODE, FANTOM, Vista)

25. Machine learning as a prediction method
significant predictors of lncRNA hits:
-Expression levels within a cell line
- within 1kb of FANTOM enhancer
- 5kb of cancer associated SNP site
- exon number of lncRNA gene
consistent with the theory that lncRNA splicing effects function, however screen did identify multiple single exon hits
no genomic factor alone provides a strong predictor

26. Review
Review:
Developed a new CRISPRi platform which can be used to assess lncRNA function, specifically in terms of population growth effects.
Exemplifies the necessity of validating results with previously accepted methods (Flow cytometry, ChIP-seq, RNA-seq, ASO)
Identified 499 lncRNA genes required for cell growth
89% are cell type specific, this differs in nature from most functional molecules across cell lines stressing the importance of cellular context
Identified variation in transcriptional networks across cell lines under the same conditions
Generated general prediction factors for lncRNA hits using machine learning

27. Why does any of this matter?
made possible due to CRISPR sgRNA technology
very cost effective in comparison to other DNA binding domains
relatively straightforward techniques, feasible by most labs
Provides a scalable method that can be used to study lncRNA on an “omic” level
- effective within the nucleus where most transcriptome activity occurs (vs RNAi)

28. Critique and Further Research
Only assessed robust growth differences as a factor for lncRNA hits, and drew many conclusions from just this criteria, other aspects of cellular function should be assesed such as metabolism, DNA damage response, lethality, cell fate…etc
499 functional lncRNAs out of suggests that this study arguably missed a large number of hits based on its design
Combinations of specific lncRNAs should be assessed

29. Further Reading Further reading: General lncRNA:
General lncRNA:
J. L. Rinn, H. Y. Chang, Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81,145–166 (2012). doi: / annurev- biochem
More on CRISPRi:
L. A. Gilbert et al., CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013). doi: /j.cell ­­