"V体育ios版" Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The . gov means it’s official. Federal government websites often end in . gov or . mil VSports app下载. Before sharing sensitive information, make sure you’re on a federal government site. .

Https

The site is secure V体育官网. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. .

. 2017 Jan 6;355(6320):aah7111.
doi: 10.1126/science.aah7111. Epub 2016 Dec 15.

CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells

S John Liu  1   2 , Max A Horlbeck  3   4   5   6 , Seung Woo Cho  7 , Harjus S Birk  1   2 , Martina Malatesta  1   2 , Daniel He  1   2 , Frank J Attenello  1   2 , Jacqueline E Villalta  3   4   5   6 , Min Y Cho  3   4   5   6 , Yuwen Chen  3   4   5   6 , Mohammad A Mandegar  3 , Michael P Olvera  3 , Luke A Gilbert  3   4   5   6 , Bruce R Conklin  3   8   9 , Howard Y Chang  7 , Jonathan S Weissman  10   4   5   6 , Daniel A Lim  11   2   12
Affiliations

CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells (V体育官网)

S John Liu et al. Science. .

V体育官网 - Abstract

The human genome produces thousands of long noncoding RNAs (lncRNAs)-transcripts >200 nucleotides long that do not encode proteins. Although critical roles in normal biology and disease have been revealed for a subset of lncRNAs, the function of the vast majority remains untested. We developed a CRISPR interference (CRISPRi) platform targeting 16,401 lncRNA loci in seven diverse cell lines, including six transformed cell lines and human induced pluripotent stem cells (iPSCs). Large-scale screening identified 499 lncRNA loci required for robust cellular growth, of which 89% showed growth-modifying function exclusively in one cell type. We further found that lncRNA knockdown can perturb complex transcriptional networks in a cell type-specific manner. These data underscore the functional importance and cell type specificity of many lncRNAs VSports手机版. .

PubMed Disclaimer

"VSports app下载" Figures

Figure 1
Figure 1. CRISPRi screens identify lncRNA genes that modify cell growth
A) Schematic of CRISPRi library design strategy. Three lncRNA annotation sets were merged, prioritized by expression in the indicated cell lines, and targeted by 10 sgRNAs per TSS using the hCRISPRi-v2.1 algorithm. Heatmap represents expression as z-score of fragments per kilobase million (FPKM) within each cell line (see Figure S1 for TPM values). B) Schematic of growth screens performed in 7 different cell lines, and formula for calculation of the growth phenotype (γ). C) Scatter plot of sgRNA phenotypes from two independent replicates of a CRISPRi screen performed in iPSCs. D) Volcano plot of gene γ and p-value. Screen replicates were averaged, and sgRNAs targeting the same gene were collapsed into a growth phenotype for each gene by the average of the 3 top scoring sgRNAs by absolute value, and assigned a p-value by the Mann-Whitney test of all 10 sgRNAs compared to the non-targeting controls. Negative control genes were randomly generated from the set of non-targeting sgRNAs, and dashed lines represents a threshold for calling hits by screen score (see Methods). Neighbor hits are not displayed for clarity (see Figure S3A,B). E) Summary table of all CRISPRi growth screens performed.
Figure 2
Figure 2. Validation of screen results shows reproducible phenotypes, correlated transcriptome responses, and robust knockdown of target transcripts
A) Individual sgRNA phenotypes from internally-controlled growth assays (B,C) compared to sgRNA phenotypes from screens. Individual growth phenotypes were calculated from relative fraction of sgRNA-containing cells at the endpoint, divided by the number of doublings from 4 days post-infection. Screen growth phenotypes represent the replicate average phenotype from the indicated cell line. B) Internally-controlled growth assays performed with sgRNAs targeting lncRNA hit genes in U87 and K562. Cells were infected with lentivirus of the sgRNA expression vector (including a BFP marker gene) and passaged for 20 days. The fraction of sgRNA-containing cells was measured as the fraction of high-BFP-expressing cells by flow cytometry, and expressed relative to the fraction at 4 days post infection. Points represent the mean and standard deviation of 3 biological replicates. C) Internally-controlled growth assays of PVT1-targeting sgRNAs in 5 cell lines. Assays were performed as in (B). Asterisks represent t-test p-values compared to the non-targeting (NT) sgRNA at the assay endpoint (* < 0.05, ** < 0.01, *** < 0.001). D) Boxplot of sgRNA growth phenotypes from tiling screen of PVT1 in U87 cells. TSS represents all sgRNAs within 1kb of the PVT1 “p1” and “p2” TSSs as annotated by FANTOM, exon represents sgRNAs targeting any PVT1 exon annotated by Ensembl, and intron represents all other sgRNAs (see Figure S7B). sgRNA γs are the average of two replicates. E) Pairwise correlation of gene expression profiles for independent sgRNAs. Expression profiles were measured by RNA-seq and correlations were calculated from transcripts per million (TPM) of genes with significant variation of expression (see Methods). “All” represents every sgRNA pair from the same cell line with the same phenotype direction, except same-sgRNA and same-gene pairs. F) Relative RNA abundance in K562 of lncRNA genes that were not hits in any cell line. RNA abundance for all 10 sgRNAs targeting the indicated genes in the CRiNCL library was measured by qPCR. Each bar represents the mean and standard deviation of 3 biological replicates, and is ordered by decreasing activity as predicted by the hCRISPRi-v2.1 algorithm. G) Correlation of lncRNA repression in K562 and U87. Points represent mean values from (F) and Figure S7C.
Figure 3
Figure 3. Growth modifier lncRNA function is highly cell type-specific
A) Numbers of lncRNA hits for each set of cell types in the complete library and (B) common sublibrary (lncRNAs that were expressed and screened in all cell types). Blue bars indicate total number of lncRNA hits in each cell type. C) Cumulative distribution function for the proportion of cell types in which each gene is a hit. Protein coding hits were obtained from Hart et al. 2015 using the authors’ 5% FDR Bayes Factor threshold. D) Distributions of the maximum 1 - Jenson Shannon distance (JSD) metric of cell type-specificity for lncRNA hit screen scores and expression values. Horizontal lines – median. E) Distributions of screen scores across all cell types for lncRNAs that were hits in iPSCs. Dashed line represents screen score threshold for calling hit genes. F) Distributions of screen scores across both replicates of iPS cells, for lncRNAs that would be called as hits in replicate 1 (left) and in replicate 2 (right).
Figure 4
Figure 4. Dissection of cell type-specific growth modifier lncRNA LINC00263
A) Internally-controlled growth assays for 2 independent sgRNAs targeting the TSS of LINC00263 and non-targeting sgRNA in U87, K562, HeLa, and MCF7 cells. B) ChIP-seq against H3K9me3 in replicates of U87 and HeLa cells infected with non-targeting sgRNAs or LINC00263 sgRNAs. Values represent normalized reads. C) Volcano plots for ChIP-seq samples in (B), representing genome-wide differential enrichment of H3K9me3 at promoter regions. Fold changes are between LINC00263 sgRNAs over non-targeting sgRNAs. D) Volcano plots for RNA-seq differential expression following infection of LINC00263 sgRNAs compared to infection of non-targeting sgRNAs. E) qPCR of ASO knockdown of LINC00263 in U87 and HeLa cells. F) Proportion of cells at 13 days post ASO transfection, relative to control ASO. G) Percentage of cells in S or G2/M phases following ASO knockdown of LINC00263. * indicates p = 0.0029.
Figure 5
Figure 5. Machine learning identifies genomic features of growth modifier lncRNAs
A) Results from logistic regression model using 18 classes of genomic data as possible predictors of growth modifier lncRNAs. Cell type dependent variables are marked. Odds ratios represent relative impact of 1 standard deviation increase of given variable. Significant variables (p < 0.01) are bolded. Results of 10-fold cross validation are represented as the % of cross validation iterations where the given variable is significant. B) ROC curves for full model compared to model using only expression data. C) Density plot of expression levels for lncRNAs that scored as hits and non-hits, aggregated across all cell types. D) Percentage of non-hit (red) and hit (blue) lncRNAs whose gene bodies resided < 1 kb from an annotated FANTOM enhancer. E) Percentage of non-hit (red) and hit (blue) lncRNAs whose gene bodies resided < 5 kb from a cancer associated SNP. F) Cumulative distribution function of number of exons for non-hit (red) and hit (blue) lncRNAs transcripts.
See this image and copyright information in PMC

"V体育安卓版" Comment in

References

    1. Djebali S, et al. Landscape of transcription in human cells. Nature. 2012;489:101–108. - PMC - PubMed
    1. FANTOM Consortium and the RIKEN PMI and CLST (DGT) et al. A promoter-level mammalian expression atlas. Nature. 2014;507:462–470. - PMC - PubMed
    1. Ulitsky I, Bartel DP. lincRNAs: genomics, evolution, and mechanisms. Cell. 2013;154:26–46. - PMC - PubMed
    1. Rinn JL, Chang HY. Genome regulation by long noncoding RNAs. Annu Rev Biochem. 2012;81:145–166. - PMC (V体育2025版) - PubMed
    1. Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell. 2009;136:629–641. - PubMed

V体育官网 - Publication types

MeSH terms