Download Liu - Blumberg Lab

CRISPRi-based genome-scale
identification of functional long
noncoding RNA loci in human cells
Presented by Nur Ata Bruss and Xinyi Ma
Background
• long noncoding RNA loci
• CRISPR interaction (CRISPRi)
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
Signification - lncRNA
Background-CRISPRi
• Loss of function study
– A tool for inhibiting lncRNA gene activity
• Highly specific and scalable
Background-CRISPRi
• Loss of function study
– A tool for inhibiting lncRNA gene activity
• Highly specific and scalable
In this study,
• Growth modifier lncRNA (16,401)
• 7 human cell types:
• 10 sgRNA per TSS
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
• Build a library: CRiNCL
• Merge 3 major ncTranscriptome annotations  Prioritize genes based on
expression in cell lines

Develop 10 sgRNAs per TSS
• Rate the impact of loss lncRNA on cell growth
γ • +: positive impact on cell growth caused by knockdown
• -: negative impact on cell growth caused by knockdown
Scatter plot:
• Non-targeting sgRNA:
distributed around 0
• Targeting sgRNA:
Pearson r =0.34-0.90
---- reproducible phenotype
Volcano plot:
• Negative control: non-targeting sgRNA
phenotype
• Hits: if combined phenotype effect size
and p-value > threshold (gray dot line)
Statistic significant phenotype
Final!!
Knockdown of target lncRNA?
or
Inhibition of neighboring coding gene?
Using Flow Cytometry to validate the lncRNA
knockdown phenotypes
Hits gene
Screen vs. Flow cytometry
Proto-oncogen:
progrowth phenotype
Previous finding vs. flow cytometry vs. CRISPRi screen
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
Assess the repression efficiency
Non-hits genes
• True negative? or ineffective repression?
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
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.
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
Independent Mechanisms of lncRNA
Across Cell Lines
•
•
Equivalent ChIP-seq data
Differing RNA-seq data
–
Knockout of LINC002263
expresses different
transcriptome phenotypes
across cell lines, showing
differences in transcriptional
networks.
Independent Mechanisms of lncRNA
Across Cell Lines
• Used a ribonuclease dependent inhibition method on of LINC002263 to
replicate the results
• Saw the same decrease in Transcript number, same trends in cell growth
and relatively stable levels of cell cycle representation
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)
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
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
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)
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 16491 suggests that this study
arguably missed a large number of hits based on its design
• Combinations of specific lncRNAs should be assessed
Further Reading
• Further reading:
• General lncRNA:
J. L. Rinn, H. Y. Chang, Genome regulation by long noncoding RNAs.
Annu. Rev. Biochem. 81,145–166 (2012). doi: 10.1146/ annurevbiochem-051410-0929025.
• More on CRISPRi:
L. A. Gilbert et al., CRISPR-mediated modular RNA-guided regulation of
transcription in eukaryotes. Cell 154, 442–451 (2013). doi:
10.1016/j.cell.2013.06.044