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A genome-wide CRISPR library for high-throughput genetic screening in Drosophila cells.

Bassett AR, Kong L, Liu JL - J Genet Genomics (2015)

Bottom Line: The simplicity of the CRISPR/Cas9 system of genome engineering has opened up the possibility of performing genome-wide targeted mutagenesis in cell lines, enabling screening for cellular phenotypes resulting from genetic aberrations.The ability of CRISPR to generate genetic loss of function mutations not only increases the magnitude of any effect over currently employed RNAi techniques, but allows analysis over longer periods of time which can be critical for certain phenotypes.Moreover, we describe strategies to monitor the population of guide RNAs by high throughput sequencing (HTS).

View Article: PubMed Central - PubMed

Affiliation: MRC Functional Genomics Unit, University of Oxford, Department of Physiology, Anatomy and Genetics, South Parks Road, Oxford, OX1 3PT, United Kingdom; Genome Engineering Oxford, Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, United Kingdom. Electronic address: andrew.bassett@path.ox.ac.uk.

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Related in: MedlinePlus

Optimisation of library screening conditions and pilot screen.A: Principal component analysis (PCA). sgRNA distributions in each condition were anlaysed by PCA. PC1 explained 98.6% and PC2 1.8% of the variance in the samples. B: Heat map of gene enrichment and depletion. Unsupervised hierarchical clustering of log2 fold change in sgRNA abundance for genes across different conditions. a and b correspond to biological replicates. Note that this does not include the entire gene set analysed, only the region that shows enrichment or depletion. C: Differential sgRNA abundance analysis. DESeq2 was used to identify statistically significant changes in sgRNA counts for all genes with ≥3 sgRNAs between samples at 1 day and 10 days (d) at a 1:100 dilution. MA plot (left panel) shows log2 fold change against sgRNA counts, with significant changes highlighted in red. Right panel shows the significantly enriched (red) or depleted (purple) genes, and functional enrichment as determined by DAVID. Lysosomal genes are indicated in bold red type.
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fig5: Optimisation of library screening conditions and pilot screen.A: Principal component analysis (PCA). sgRNA distributions in each condition were anlaysed by PCA. PC1 explained 98.6% and PC2 1.8% of the variance in the samples. B: Heat map of gene enrichment and depletion. Unsupervised hierarchical clustering of log2 fold change in sgRNA abundance for genes across different conditions. a and b correspond to biological replicates. Note that this does not include the entire gene set analysed, only the region that shows enrichment or depletion. C: Differential sgRNA abundance analysis. DESeq2 was used to identify statistically significant changes in sgRNA counts for all genes with ≥3 sgRNAs between samples at 1 day and 10 days (d) at a 1:100 dilution. MA plot (left panel) shows log2 fold change against sgRNA counts, with significant changes highlighted in red. Right panel shows the significantly enriched (red) or depleted (purple) genes, and functional enrichment as determined by DAVID. Lysosomal genes are indicated in bold red type.

Mentions: We performed all transfections into around 20 million cells in biological duplicates, and analysed the sgRNA populations by principal component analysis (PCA) to look for clustering of the samples (Fig. 5A). This demonstrated that most of the samples clustered with the original, untransfected library. However, two samples were separated from the remainder, which represented the biological duplicates at the longest time point (10 days) and the highest library dilution (1:100). Consistent with this, if we analyse the samples at the longest time point (10 days) at the lower dilutions of library (1:10 and 1:1), they show a similar effect, but the magnitude is smaller. This suggests that the cells need to be grown for extended periods of time for the effects of gene disruption to be observed, and that higher library dilutions give a stronger signal to noise ratio.


A genome-wide CRISPR library for high-throughput genetic screening in Drosophila cells.

Bassett AR, Kong L, Liu JL - J Genet Genomics (2015)

Optimisation of library screening conditions and pilot screen.A: Principal component analysis (PCA). sgRNA distributions in each condition were anlaysed by PCA. PC1 explained 98.6% and PC2 1.8% of the variance in the samples. B: Heat map of gene enrichment and depletion. Unsupervised hierarchical clustering of log2 fold change in sgRNA abundance for genes across different conditions. a and b correspond to biological replicates. Note that this does not include the entire gene set analysed, only the region that shows enrichment or depletion. C: Differential sgRNA abundance analysis. DESeq2 was used to identify statistically significant changes in sgRNA counts for all genes with ≥3 sgRNAs between samples at 1 day and 10 days (d) at a 1:100 dilution. MA plot (left panel) shows log2 fold change against sgRNA counts, with significant changes highlighted in red. Right panel shows the significantly enriched (red) or depleted (purple) genes, and functional enrichment as determined by DAVID. Lysosomal genes are indicated in bold red type.
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fig5: Optimisation of library screening conditions and pilot screen.A: Principal component analysis (PCA). sgRNA distributions in each condition were anlaysed by PCA. PC1 explained 98.6% and PC2 1.8% of the variance in the samples. B: Heat map of gene enrichment and depletion. Unsupervised hierarchical clustering of log2 fold change in sgRNA abundance for genes across different conditions. a and b correspond to biological replicates. Note that this does not include the entire gene set analysed, only the region that shows enrichment or depletion. C: Differential sgRNA abundance analysis. DESeq2 was used to identify statistically significant changes in sgRNA counts for all genes with ≥3 sgRNAs between samples at 1 day and 10 days (d) at a 1:100 dilution. MA plot (left panel) shows log2 fold change against sgRNA counts, with significant changes highlighted in red. Right panel shows the significantly enriched (red) or depleted (purple) genes, and functional enrichment as determined by DAVID. Lysosomal genes are indicated in bold red type.
Mentions: We performed all transfections into around 20 million cells in biological duplicates, and analysed the sgRNA populations by principal component analysis (PCA) to look for clustering of the samples (Fig. 5A). This demonstrated that most of the samples clustered with the original, untransfected library. However, two samples were separated from the remainder, which represented the biological duplicates at the longest time point (10 days) and the highest library dilution (1:100). Consistent with this, if we analyse the samples at the longest time point (10 days) at the lower dilutions of library (1:10 and 1:1), they show a similar effect, but the magnitude is smaller. This suggests that the cells need to be grown for extended periods of time for the effects of gene disruption to be observed, and that higher library dilutions give a stronger signal to noise ratio.

Bottom Line: The simplicity of the CRISPR/Cas9 system of genome engineering has opened up the possibility of performing genome-wide targeted mutagenesis in cell lines, enabling screening for cellular phenotypes resulting from genetic aberrations.The ability of CRISPR to generate genetic loss of function mutations not only increases the magnitude of any effect over currently employed RNAi techniques, but allows analysis over longer periods of time which can be critical for certain phenotypes.Moreover, we describe strategies to monitor the population of guide RNAs by high throughput sequencing (HTS).

View Article: PubMed Central - PubMed

Affiliation: MRC Functional Genomics Unit, University of Oxford, Department of Physiology, Anatomy and Genetics, South Parks Road, Oxford, OX1 3PT, United Kingdom; Genome Engineering Oxford, Sir William Dunn School of Pathology, University of Oxford, South Parks Road, Oxford, OX1 3RE, United Kingdom. Electronic address: andrew.bassett@path.ox.ac.uk.

No MeSH data available.


Related in: MedlinePlus