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 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?

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.

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

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

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

& & & 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  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

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)

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

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

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

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

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:   General lncRNA: J. L. Rinn, H. Y. Chang, Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81,145–166 (2012). doi: 10.1146/ annurev- biochem-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