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sgrna plasmid library  (Addgene inc)


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    Addgene inc sgrna plasmid library
    Sgrna Plasmid Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 98/100, based on 14370 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc sgrna plasmid library
    Sgrna Plasmid Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc lentiviral sgrna library backbone plasmid
    (A) Summary of gene deletion vs. gene overexpression approaches. (B) Rationale for the present study. (C) Experimental outline of the present study. (D) HEK293 and Jurkat cells were inoculated with different volumes of Ebola or rabies pseudovirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This revealed that Jurkat cells are largely refractory to Ebola or rabies pseudovirus entry, relative to HEK293 cells. (E) Construction of a clonal Jurkat cell line, known as “Jurkat C6”, stably expressing a degron-tagged CRISPRa construct ( left ). Jurkat C6 cells were transduced with <t>sgRNA</t> targeting the endogenous human CD19 gene, in the presence or absence of TMP (1 μM) for 3 days, followed by flow cytometry to detect hCD19 expression ( right ). (F) At each of the indicated points of the genome-wide CRISPRa screen, the cell population was challenged with Ebola or rabies pseudovirus, and cell infectivity was evaluated by flow cytometry. Upon successive rounds of the screen, the cell population became progressively more susceptible to infection to either Ebola or rabies pseudovirus, respectively. (G) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for Ebola pseudovirus entry. (H) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for rabies pseudovirus entry.
    Lentiviral Sgrna Library Backbone Plasmid, supplied by Addgene inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc library expression vector pu6 sgrna ef1alpha puro t2a gfp
    (A) Summary of gene deletion vs. gene overexpression approaches. (B) Rationale for the present study. (C) Experimental outline of the present study. (D) HEK293 and Jurkat cells were inoculated with different volumes of Ebola or rabies pseudovirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This revealed that Jurkat cells are largely refractory to Ebola or rabies pseudovirus entry, relative to HEK293 cells. (E) Construction of a clonal Jurkat cell line, known as “Jurkat C6”, stably expressing a degron-tagged CRISPRa construct ( left ). Jurkat C6 cells were transduced with <t>sgRNA</t> targeting the endogenous human CD19 gene, in the presence or absence of TMP (1 μM) for 3 days, followed by flow cytometry to detect hCD19 expression ( right ). (F) At each of the indicated points of the genome-wide CRISPRa screen, the cell population was challenged with Ebola or rabies pseudovirus, and cell infectivity was evaluated by flow cytometry. Upon successive rounds of the screen, the cell population became progressively more susceptible to infection to either Ebola or rabies pseudovirus, respectively. (G) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for Ebola pseudovirus entry. (H) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for rabies pseudovirus entry.
    Library Expression Vector Pu6 Sgrna Ef1alpha Puro T2a Gfp, supplied by Addgene inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc genome wide ko eko sgrna library
    <t>Genome-wide</t> <t>CRISPR-Cas9</t> screen identifies genetic determinants of GTE-induced cytotoxicity. (A) Schematic overview of the experimental workflow for the genome-wide CRISPR knockout screen in NALM-6 cells treated with green tea extract (GTE), aimed at identifying genes modulating compound sensitivity. (B) Distribution of CRANKS scores for 19,034 genes following GTE treatment. Negative scores (red arrows, CRANKS ≤ −2) indicate sensitizer genes whose knockout enhances compound sensitivity, while positive scores (green arrows, CRANKS ≥2) represent rescuers whose knockout confers resistance. The 2D scatter plot is aligned with a one-dimensional frequency distribution, illustrating the thresholds used to define top hits. (C) Volcano plot showing CRANKS scores plotted against –log 10 p -values. Genes above the horizontal dashed line marks the p -threshold ( p < 0.05), while those beyond the vertical dashed lines exceed CRANKS thresholds of ±2, identifying 30 filtered genes ( p ≤ 0.01) labeled by gene symbol. Red points represent sensitizers; green points represent rescuers. Listed starred gene (∗) indicate top hits. (D) KEGG pathway enrichment analysis of highest CRANKS-filtered candidates (|CRANKS| ≥ 2; p ≤ 0.01; set including all top hits). Bubble color reflects false discovery rate (FDR), ranging from light green (FDR = 9 × 10 −7 ) to dark blue (FDR = 4 × 10 −3 ), and bubble size corresponds to the number of genes in each pathway. Pathways are ranked by significance on the y-axis. Analysis was performed using the STRING database with whole-genome background, requiring a minimum of two genes per pathway and FDR ≤0.05.
    Genome Wide Ko Eko Sgrna Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc human pooled genome wide sgrna library brunello sequences addgene addgene
    <t>Genome-wide</t> <t>CRISPR-Cas9</t> screen identifies genetic determinants of GTE-induced cytotoxicity. (A) Schematic overview of the experimental workflow for the genome-wide CRISPR knockout screen in NALM-6 cells treated with green tea extract (GTE), aimed at identifying genes modulating compound sensitivity. (B) Distribution of CRANKS scores for 19,034 genes following GTE treatment. Negative scores (red arrows, CRANKS ≤ −2) indicate sensitizer genes whose knockout enhances compound sensitivity, while positive scores (green arrows, CRANKS ≥2) represent rescuers whose knockout confers resistance. The 2D scatter plot is aligned with a one-dimensional frequency distribution, illustrating the thresholds used to define top hits. (C) Volcano plot showing CRANKS scores plotted against –log 10 p -values. Genes above the horizontal dashed line marks the p -threshold ( p < 0.05), while those beyond the vertical dashed lines exceed CRANKS thresholds of ±2, identifying 30 filtered genes ( p ≤ 0.01) labeled by gene symbol. Red points represent sensitizers; green points represent rescuers. Listed starred gene (∗) indicate top hits. (D) KEGG pathway enrichment analysis of highest CRANKS-filtered candidates (|CRANKS| ≥ 2; p ≤ 0.01; set including all top hits). Bubble color reflects false discovery rate (FDR), ranging from light green (FDR = 9 × 10 −7 ) to dark blue (FDR = 4 × 10 −3 ), and bubble size corresponds to the number of genes in each pathway. Pathways are ranked by significance on the y-axis. Analysis was performed using the STRING database with whole-genome background, requiring a minimum of two genes per pathway and FDR ≤0.05.
    Human Pooled Genome Wide Sgrna Library Brunello Sequences Addgene Addgene, supplied by Addgene inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc custom crispr sgrna library
    <t>Genome-wide</t> <t>CRISPR-Cas9</t> screen identifies genetic determinants of GTE-induced cytotoxicity. (A) Schematic overview of the experimental workflow for the genome-wide CRISPR knockout screen in NALM-6 cells treated with green tea extract (GTE), aimed at identifying genes modulating compound sensitivity. (B) Distribution of CRANKS scores for 19,034 genes following GTE treatment. Negative scores (red arrows, CRANKS ≤ −2) indicate sensitizer genes whose knockout enhances compound sensitivity, while positive scores (green arrows, CRANKS ≥2) represent rescuers whose knockout confers resistance. The 2D scatter plot is aligned with a one-dimensional frequency distribution, illustrating the thresholds used to define top hits. (C) Volcano plot showing CRANKS scores plotted against –log 10 p -values. Genes above the horizontal dashed line marks the p -threshold ( p < 0.05), while those beyond the vertical dashed lines exceed CRANKS thresholds of ±2, identifying 30 filtered genes ( p ≤ 0.01) labeled by gene symbol. Red points represent sensitizers; green points represent rescuers. Listed starred gene (∗) indicate top hits. (D) KEGG pathway enrichment analysis of highest CRANKS-filtered candidates (|CRANKS| ≥ 2; p ≤ 0.01; set including all top hits). Bubble color reflects false discovery rate (FDR), ranging from light green (FDR = 9 × 10 −7 ) to dark blue (FDR = 4 × 10 −3 ), and bubble size corresponds to the number of genes in each pathway. Pathways are ranked by significance on the y-axis. Analysis was performed using the STRING database with whole-genome background, requiring a minimum of two genes per pathway and FDR ≤0.05.
    Custom Crispr Sgrna Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc human gecko v2 pooled sgrna library
    Generation of gemcitabine-resistant GBC cell lines and genome-wide CRISPR screen for therapeutic targets ( A ) Dose-response curves and IC 50 values of gemcitabine in parental (NOZ and GBC-SD) and gemcitabine-resistant (NOZ-R and GBC-SD-R) cells, assessed by CellTiter-Glo viability assays after 5 days treatment. Data are presented as mean ± SD from three independent experiments ( B ) Volcano plots of differentially expressed genes (DEGs): NOZ-R versus NOZ (left) and GBC-SD-R versus GBC-SD (right). Significantly upregulated (red) and downregulated (blue) genes are defined by log 2 fold-change > 1 or < −1 and adjusted p < 0.05 ( C ) Venn diagrams illustrating the overlap of significantly upregulated (top) and downregulated (bottom) DEGs in NOZ-R and GBC-SD-R cells ( D ) Genome-wide CRISPR-Cas9 loss-of-function screen in NOZ-R cells under Olaparib versus DMSO treatment, highlighting top candidate genes with significant depletion (negative selection, left) or enrichment (positive selection, right) based on fold-change and p -value ( E ) Normalized <t>sgRNA</t> counts for the top 10 candidate genes identified from the CRISPR screen, with six sgRNAs per gene ( F ) Validation of selected screen hits (E2F8, PHF12, PTCH1 and C2orf73) by CRISPR-mediated knockout in NOZ-R and GBC-SD-R cells, followed by treatment with Olaparib (1 µM) or DMSO for 5 days. Cell viability was measured using CellTiter-Glo. Data represent mean ± SD from three independent experiments. Statistical significance was determined using two-tailed Student’s t-test (* p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant)
    Human Gecko V2 Pooled Sgrna Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Addgene inc human gecko v2 pooled sgrna library 185
    Generation of gemcitabine-resistant GBC cell lines and genome-wide CRISPR screen for therapeutic targets ( A ) Dose-response curves and IC 50 values of gemcitabine in parental (NOZ and GBC-SD) and gemcitabine-resistant (NOZ-R and GBC-SD-R) cells, assessed by CellTiter-Glo viability assays after 5 days treatment. Data are presented as mean ± SD from three independent experiments ( B ) Volcano plots of differentially expressed genes (DEGs): NOZ-R versus NOZ (left) and GBC-SD-R versus GBC-SD (right). Significantly upregulated (red) and downregulated (blue) genes are defined by log 2 fold-change > 1 or < −1 and adjusted p < 0.05 ( C ) Venn diagrams illustrating the overlap of significantly upregulated (top) and downregulated (bottom) DEGs in NOZ-R and GBC-SD-R cells ( D ) Genome-wide CRISPR-Cas9 loss-of-function screen in NOZ-R cells under Olaparib versus DMSO treatment, highlighting top candidate genes with significant depletion (negative selection, left) or enrichment (positive selection, right) based on fold-change and p -value ( E ) Normalized <t>sgRNA</t> counts for the top 10 candidate genes identified from the CRISPR screen, with six sgRNAs per gene ( F ) Validation of selected screen hits (E2F8, PHF12, PTCH1 and C2orf73) by CRISPR-mediated knockout in NOZ-R and GBC-SD-R cells, followed by treatment with Olaparib (1 µM) or DMSO for 5 days. Cell viability was measured using CellTiter-Glo. Data represent mean ± SD from three independent experiments. Statistical significance was determined using two-tailed Student’s t-test (* p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant)
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    (A) Summary of gene deletion vs. gene overexpression approaches. (B) Rationale for the present study. (C) Experimental outline of the present study. (D) HEK293 and Jurkat cells were inoculated with different volumes of Ebola or rabies pseudovirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This revealed that Jurkat cells are largely refractory to Ebola or rabies pseudovirus entry, relative to HEK293 cells. (E) Construction of a clonal Jurkat cell line, known as “Jurkat C6”, stably expressing a degron-tagged CRISPRa construct ( left ). Jurkat C6 cells were transduced with sgRNA targeting the endogenous human CD19 gene, in the presence or absence of TMP (1 μM) for 3 days, followed by flow cytometry to detect hCD19 expression ( right ). (F) At each of the indicated points of the genome-wide CRISPRa screen, the cell population was challenged with Ebola or rabies pseudovirus, and cell infectivity was evaluated by flow cytometry. Upon successive rounds of the screen, the cell population became progressively more susceptible to infection to either Ebola or rabies pseudovirus, respectively. (G) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for Ebola pseudovirus entry. (H) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for rabies pseudovirus entry.

    Journal: bioRxiv

    Article Title: Elucidating genes sufficient for viral entry into cells through sequential genome-wide CRISPR activation screens

    doi: 10.64898/2026.03.06.710083

    Figure Lengend Snippet: (A) Summary of gene deletion vs. gene overexpression approaches. (B) Rationale for the present study. (C) Experimental outline of the present study. (D) HEK293 and Jurkat cells were inoculated with different volumes of Ebola or rabies pseudovirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This revealed that Jurkat cells are largely refractory to Ebola or rabies pseudovirus entry, relative to HEK293 cells. (E) Construction of a clonal Jurkat cell line, known as “Jurkat C6”, stably expressing a degron-tagged CRISPRa construct ( left ). Jurkat C6 cells were transduced with sgRNA targeting the endogenous human CD19 gene, in the presence or absence of TMP (1 μM) for 3 days, followed by flow cytometry to detect hCD19 expression ( right ). (F) At each of the indicated points of the genome-wide CRISPRa screen, the cell population was challenged with Ebola or rabies pseudovirus, and cell infectivity was evaluated by flow cytometry. Upon successive rounds of the screen, the cell population became progressively more susceptible to infection to either Ebola or rabies pseudovirus, respectively. (G) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for Ebola pseudovirus entry. (H) sgRNA distribution upon successive rounds of the genome-wide CRISPRa screen for rabies pseudovirus entry.

    Article Snippet: To achieve CRISPRa-mediated overexpression, sgRNA sequences were cloned into the lentiviral sgRNA library backbone plasmid (Addgene, 84832) using the restriction sites BstX1 and BlpI.

    Techniques: Over Expression, Marker, Mutagenesis, Flow Cytometry, Infection, Stable Transfection, Expressing, Construct, Transduction, Genome Wide

    A) Plasmids used to pseudotype non-replicating lentiviruses with either Ebola or rabies envelope proteins. EBOV-GP: glycoprotein of Ebola virus, Makona variant. RABV-GP N2C: glycoprotein of rabies virus, N2C variant. B) HEK293 and Jurkat cells were inoculated with different volumes of VSV envelope protein-pseudotyped lentivirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This served as a positive control to confirm that Jurkat cells and HEK293 cells are both susceptible to VSV pseudovirus entry. C) Jurkat C6 cells were transduced with sgRNA targeting the endogenous human CD19 gene, in the presence of different TMP concentrations (0-4 μM) for 2-3 days, followed by flow cytometry to detect human CD19 expression.

    Journal: bioRxiv

    Article Title: Elucidating genes sufficient for viral entry into cells through sequential genome-wide CRISPR activation screens

    doi: 10.64898/2026.03.06.710083

    Figure Lengend Snippet: A) Plasmids used to pseudotype non-replicating lentiviruses with either Ebola or rabies envelope proteins. EBOV-GP: glycoprotein of Ebola virus, Makona variant. RABV-GP N2C: glycoprotein of rabies virus, N2C variant. B) HEK293 and Jurkat cells were inoculated with different volumes of VSV envelope protein-pseudotyped lentivirus encoding a cell-surface marker (mCD19t; a truncated mutant of mouse CD19), followed by flow cytometry to determine the percentage of infected cells. This served as a positive control to confirm that Jurkat cells and HEK293 cells are both susceptible to VSV pseudovirus entry. C) Jurkat C6 cells were transduced with sgRNA targeting the endogenous human CD19 gene, in the presence of different TMP concentrations (0-4 μM) for 2-3 days, followed by flow cytometry to detect human CD19 expression.

    Article Snippet: To achieve CRISPRa-mediated overexpression, sgRNA sequences were cloned into the lentiviral sgRNA library backbone plasmid (Addgene, 84832) using the restriction sites BstX1 and BlpI.

    Techniques: Virus, Variant Assay, Marker, Mutagenesis, Flow Cytometry, Infection, Positive Control, Transduction, Expressing

    A) NGFR was expressed in Jurkat C6 cells using CRISPRa, or alternatively, Jurkat cells using cDNA expression. NGFR -expressing or control cells were then inoculated with rabies pseudovirus encoding mCD19t. Flow cytometry was then performed to determine the percentage of infected cells. This revealed that NGFR expression significantly increased the susceptibility of Jurkat cells to rabies pseudovirus infection. As positive controls, flow cytometry was used to confirm successful delivery of the sgRNA construct as part of the CRISPRa workflow (as denoted by BFP expression) and that NGFR was expressed (upon cDNA expression). B) L-SIGN or DC-SIGN were expressed in primary human CD4 + T cells using cDNA expression. L-SIGN -expressing, DC-SIGN -expressing, or control cells were inoculated with rabies pseudovirus encoding hEGFRt (a truncated mutant of human EGFR). Flow cytometry was then performed to determine the percentage of infected cells. This revealed that L-SIGN or DC-SIGN expression significantly increased the susceptibility of Jurkat cells and primary T cells to Ebola pseudovirus infection. Cells expressing the highest levels of L-SIGN and DC-SIGN were preferentially infected by Ebola pseudovirus. C) L-SIGN or DC-SIGN were expressed in primary human CD4 + T cells using cDNA expression, and then L-SIGN -expressing, DC-SIGN -expressing, or control cells were inoculated with GFP -expressing Ebola virus or zsGreen -expressing Sudan virus under BSL4 containment. On days 0, 1, and 2 post-infection, flow cytometry was performed to determine the percentage of infected cells and qPCR was performed on cell culture supernatants to quantify viral genome replication. This revealed that L-SIGN or DC-SIGN expression enabled authentic Ebola and Sudan virus entry into primary human T cells, but viral genome replication was impaired, perhaps reflective of cell-intrinsic restriction factors.

    Journal: bioRxiv

    Article Title: Elucidating genes sufficient for viral entry into cells through sequential genome-wide CRISPR activation screens

    doi: 10.64898/2026.03.06.710083

    Figure Lengend Snippet: A) NGFR was expressed in Jurkat C6 cells using CRISPRa, or alternatively, Jurkat cells using cDNA expression. NGFR -expressing or control cells were then inoculated with rabies pseudovirus encoding mCD19t. Flow cytometry was then performed to determine the percentage of infected cells. This revealed that NGFR expression significantly increased the susceptibility of Jurkat cells to rabies pseudovirus infection. As positive controls, flow cytometry was used to confirm successful delivery of the sgRNA construct as part of the CRISPRa workflow (as denoted by BFP expression) and that NGFR was expressed (upon cDNA expression). B) L-SIGN or DC-SIGN were expressed in primary human CD4 + T cells using cDNA expression. L-SIGN -expressing, DC-SIGN -expressing, or control cells were inoculated with rabies pseudovirus encoding hEGFRt (a truncated mutant of human EGFR). Flow cytometry was then performed to determine the percentage of infected cells. This revealed that L-SIGN or DC-SIGN expression significantly increased the susceptibility of Jurkat cells and primary T cells to Ebola pseudovirus infection. Cells expressing the highest levels of L-SIGN and DC-SIGN were preferentially infected by Ebola pseudovirus. C) L-SIGN or DC-SIGN were expressed in primary human CD4 + T cells using cDNA expression, and then L-SIGN -expressing, DC-SIGN -expressing, or control cells were inoculated with GFP -expressing Ebola virus or zsGreen -expressing Sudan virus under BSL4 containment. On days 0, 1, and 2 post-infection, flow cytometry was performed to determine the percentage of infected cells and qPCR was performed on cell culture supernatants to quantify viral genome replication. This revealed that L-SIGN or DC-SIGN expression enabled authentic Ebola and Sudan virus entry into primary human T cells, but viral genome replication was impaired, perhaps reflective of cell-intrinsic restriction factors.

    Article Snippet: To achieve CRISPRa-mediated overexpression, sgRNA sequences were cloned into the lentiviral sgRNA library backbone plasmid (Addgene, 84832) using the restriction sites BstX1 and BlpI.

    Techniques: Expressing, Control, Flow Cytometry, Infection, Construct, Mutagenesis, Virus, Cell Culture

    Genome-wide CRISPR-Cas9 screen identifies genetic determinants of GTE-induced cytotoxicity. (A) Schematic overview of the experimental workflow for the genome-wide CRISPR knockout screen in NALM-6 cells treated with green tea extract (GTE), aimed at identifying genes modulating compound sensitivity. (B) Distribution of CRANKS scores for 19,034 genes following GTE treatment. Negative scores (red arrows, CRANKS ≤ −2) indicate sensitizer genes whose knockout enhances compound sensitivity, while positive scores (green arrows, CRANKS ≥2) represent rescuers whose knockout confers resistance. The 2D scatter plot is aligned with a one-dimensional frequency distribution, illustrating the thresholds used to define top hits. (C) Volcano plot showing CRANKS scores plotted against –log 10 p -values. Genes above the horizontal dashed line marks the p -threshold ( p < 0.05), while those beyond the vertical dashed lines exceed CRANKS thresholds of ±2, identifying 30 filtered genes ( p ≤ 0.01) labeled by gene symbol. Red points represent sensitizers; green points represent rescuers. Listed starred gene (∗) indicate top hits. (D) KEGG pathway enrichment analysis of highest CRANKS-filtered candidates (|CRANKS| ≥ 2; p ≤ 0.01; set including all top hits). Bubble color reflects false discovery rate (FDR), ranging from light green (FDR = 9 × 10 −7 ) to dark blue (FDR = 4 × 10 −3 ), and bubble size corresponds to the number of genes in each pathway. Pathways are ranked by significance on the y-axis. Analysis was performed using the STRING database with whole-genome background, requiring a minimum of two genes per pathway and FDR ≤0.05.

    Journal: Redox Biology

    Article Title: CRISPR-based chemogenomic profiling reveals redox vulnerabilities to epigallocatechin-3-gallate and green tea polyphenol extract

    doi: 10.1016/j.redox.2026.104047

    Figure Lengend Snippet: Genome-wide CRISPR-Cas9 screen identifies genetic determinants of GTE-induced cytotoxicity. (A) Schematic overview of the experimental workflow for the genome-wide CRISPR knockout screen in NALM-6 cells treated with green tea extract (GTE), aimed at identifying genes modulating compound sensitivity. (B) Distribution of CRANKS scores for 19,034 genes following GTE treatment. Negative scores (red arrows, CRANKS ≤ −2) indicate sensitizer genes whose knockout enhances compound sensitivity, while positive scores (green arrows, CRANKS ≥2) represent rescuers whose knockout confers resistance. The 2D scatter plot is aligned with a one-dimensional frequency distribution, illustrating the thresholds used to define top hits. (C) Volcano plot showing CRANKS scores plotted against –log 10 p -values. Genes above the horizontal dashed line marks the p -threshold ( p < 0.05), while those beyond the vertical dashed lines exceed CRANKS thresholds of ±2, identifying 30 filtered genes ( p ≤ 0.01) labeled by gene symbol. Red points represent sensitizers; green points represent rescuers. Listed starred gene (∗) indicate top hits. (D) KEGG pathway enrichment analysis of highest CRANKS-filtered candidates (|CRANKS| ≥ 2; p ≤ 0.01; set including all top hits). Bubble color reflects false discovery rate (FDR), ranging from light green (FDR = 9 × 10 −7 ) to dark blue (FDR = 4 × 10 −3 ), and bubble size corresponds to the number of genes in each pathway. Pathways are ranked by significance on the y-axis. Analysis was performed using the STRING database with whole-genome background, requiring a minimum of two genes per pathway and FDR ≤0.05.

    Article Snippet: Briefly, a human NALM-6 (pre-B ALL lymphocytes) clone bearing an integrated inducible Cas9 expression cassette generated by lentiviruses made from pCW-Cas9 (Addgene #50661) was transduced with the genome-wide KO EKO sgRNA library (278,754 different sgRNAs).

    Techniques: Genome Wide, CRISPR, Knock-Out, Labeling

    EGCG-induced pro-oxidant mechanisms: Insights from genome-wide CRISPR screening. (1) EGCG undergoes auto-oxidation generating reactive oxygen species (ROS) such as hydrogen peroxide (H 2 O 2 ) and quinone intermediates. A genome-wide CRISPR-Cas9 screen identified key genes modulating cellular response to EGCG-induced oxidative stress. (2) Knockouts of glutathione biosynthesis genes ( GCLC , GCLM , GSS ) impair H 2 O 2 detoxification, sensitizing cells to ferroptosis. (3) Peroxisomal genes ( PEX1 , PEX6 , PEX12 , PEX14 ) regulate ROS metabolism; their disruption compromises H 2 O 2 clearance via catalase ( CAT ) and peroxiredoxin-1 ( PRDX1 ). (4) The KEAP1-NRF2 axis controls antioxidant gene expression, inducing enzymes (e.g., CAT, PRDX1, GCLs) that mitigate ROS toxicity. KEAP1 knockout enhances NRF2 signaling and confers resistance. (5) Additional modulators include ABCC1 (drug efflux), SLC7A11 (cysteine transporter) and BAK1 (apoptosis). Sensitizer hits (red labels) indicate knockouts that heighten EGCG toxicity, while resistance hits (green labels) protect against cell death. Color intensity reflects CRANKS scores relative to the highest scoring genes. Together, these findings illustrate how EGCG's pro-oxidant activity can overwhelm cancer cell defenses when redox-regulating pathways are genetically compromised.

    Journal: Redox Biology

    Article Title: CRISPR-based chemogenomic profiling reveals redox vulnerabilities to epigallocatechin-3-gallate and green tea polyphenol extract

    doi: 10.1016/j.redox.2026.104047

    Figure Lengend Snippet: EGCG-induced pro-oxidant mechanisms: Insights from genome-wide CRISPR screening. (1) EGCG undergoes auto-oxidation generating reactive oxygen species (ROS) such as hydrogen peroxide (H 2 O 2 ) and quinone intermediates. A genome-wide CRISPR-Cas9 screen identified key genes modulating cellular response to EGCG-induced oxidative stress. (2) Knockouts of glutathione biosynthesis genes ( GCLC , GCLM , GSS ) impair H 2 O 2 detoxification, sensitizing cells to ferroptosis. (3) Peroxisomal genes ( PEX1 , PEX6 , PEX12 , PEX14 ) regulate ROS metabolism; their disruption compromises H 2 O 2 clearance via catalase ( CAT ) and peroxiredoxin-1 ( PRDX1 ). (4) The KEAP1-NRF2 axis controls antioxidant gene expression, inducing enzymes (e.g., CAT, PRDX1, GCLs) that mitigate ROS toxicity. KEAP1 knockout enhances NRF2 signaling and confers resistance. (5) Additional modulators include ABCC1 (drug efflux), SLC7A11 (cysteine transporter) and BAK1 (apoptosis). Sensitizer hits (red labels) indicate knockouts that heighten EGCG toxicity, while resistance hits (green labels) protect against cell death. Color intensity reflects CRANKS scores relative to the highest scoring genes. Together, these findings illustrate how EGCG's pro-oxidant activity can overwhelm cancer cell defenses when redox-regulating pathways are genetically compromised.

    Article Snippet: Briefly, a human NALM-6 (pre-B ALL lymphocytes) clone bearing an integrated inducible Cas9 expression cassette generated by lentiviruses made from pCW-Cas9 (Addgene #50661) was transduced with the genome-wide KO EKO sgRNA library (278,754 different sgRNAs).

    Techniques: Genome Wide, CRISPR, Disruption, Gene Expression, Knock-Out, Activity Assay

    Generation of gemcitabine-resistant GBC cell lines and genome-wide CRISPR screen for therapeutic targets ( A ) Dose-response curves and IC 50 values of gemcitabine in parental (NOZ and GBC-SD) and gemcitabine-resistant (NOZ-R and GBC-SD-R) cells, assessed by CellTiter-Glo viability assays after 5 days treatment. Data are presented as mean ± SD from three independent experiments ( B ) Volcano plots of differentially expressed genes (DEGs): NOZ-R versus NOZ (left) and GBC-SD-R versus GBC-SD (right). Significantly upregulated (red) and downregulated (blue) genes are defined by log 2 fold-change > 1 or < −1 and adjusted p < 0.05 ( C ) Venn diagrams illustrating the overlap of significantly upregulated (top) and downregulated (bottom) DEGs in NOZ-R and GBC-SD-R cells ( D ) Genome-wide CRISPR-Cas9 loss-of-function screen in NOZ-R cells under Olaparib versus DMSO treatment, highlighting top candidate genes with significant depletion (negative selection, left) or enrichment (positive selection, right) based on fold-change and p -value ( E ) Normalized sgRNA counts for the top 10 candidate genes identified from the CRISPR screen, with six sgRNAs per gene ( F ) Validation of selected screen hits (E2F8, PHF12, PTCH1 and C2orf73) by CRISPR-mediated knockout in NOZ-R and GBC-SD-R cells, followed by treatment with Olaparib (1 µM) or DMSO for 5 days. Cell viability was measured using CellTiter-Glo. Data represent mean ± SD from three independent experiments. Statistical significance was determined using two-tailed Student’s t-test (* p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant)

    Journal: Journal of Experimental & Clinical Cancer Research : CR

    Article Title: Targeting E2F8 sensitizes gemcitabine-resistant gallbladder cancer to PARP inhibitors by disrupting RRM2-driven DNA repair

    doi: 10.1186/s13046-025-03586-2

    Figure Lengend Snippet: Generation of gemcitabine-resistant GBC cell lines and genome-wide CRISPR screen for therapeutic targets ( A ) Dose-response curves and IC 50 values of gemcitabine in parental (NOZ and GBC-SD) and gemcitabine-resistant (NOZ-R and GBC-SD-R) cells, assessed by CellTiter-Glo viability assays after 5 days treatment. Data are presented as mean ± SD from three independent experiments ( B ) Volcano plots of differentially expressed genes (DEGs): NOZ-R versus NOZ (left) and GBC-SD-R versus GBC-SD (right). Significantly upregulated (red) and downregulated (blue) genes are defined by log 2 fold-change > 1 or < −1 and adjusted p < 0.05 ( C ) Venn diagrams illustrating the overlap of significantly upregulated (top) and downregulated (bottom) DEGs in NOZ-R and GBC-SD-R cells ( D ) Genome-wide CRISPR-Cas9 loss-of-function screen in NOZ-R cells under Olaparib versus DMSO treatment, highlighting top candidate genes with significant depletion (negative selection, left) or enrichment (positive selection, right) based on fold-change and p -value ( E ) Normalized sgRNA counts for the top 10 candidate genes identified from the CRISPR screen, with six sgRNAs per gene ( F ) Validation of selected screen hits (E2F8, PHF12, PTCH1 and C2orf73) by CRISPR-mediated knockout in NOZ-R and GBC-SD-R cells, followed by treatment with Olaparib (1 µM) or DMSO for 5 days. Cell viability was measured using CellTiter-Glo. Data represent mean ± SD from three independent experiments. Statistical significance was determined using two-tailed Student’s t-test (* p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant)

    Article Snippet: A genome-wide CRISPR-Cas9 loss-of-function screen was performed using the human GeCKO v2 pooled sgRNA library (Addgene, #52961), which encodes both sgRNAs and Cas9 in a single lentiviral construct.

    Techniques: Genome Wide, CRISPR, Biomarker Discovery, Selection, Knock-Out, Two Tailed Test

    E2F8 deficiency sensitizes gemcitabine-resistant gallbladder cancer cells to PARP inhibition. ( A ) Knockdown of E2F8 enhances sensitivity to Olaparib and gemcitabine in NOZ-R cells. Cells were transduced with control sgRNA (sgNC) or two independent sgRNAs targeting E2F8 (sgE2F8 #1 and sgE2F8 #2), followed by treatment with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM). Cell viability was measured at the indicated time points using the CellTiter-Glo assay. Data are shown as mean ± SD from three independent experiments (two-tailed t-test; * p < 0.05, ** p < 0.01, *** p < 0.001) ( B ) Apoptosis analysis of NOZ-R cells transduced with sgNC or sgE2F8 (#1 and #2) and treated with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM) for three days. Apoptotic cells were detected by Annexin V/PI staining followed by flow cytometry. Data are presented as mean ± SD from three independent experiments (t-test; *** p < 0.001) ( C ) Quantification of apoptotic cells in GBC-SD-R cells transduced with sgNC or sgE2F8 (#1 and #2) and treated with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM) for three days, as determined by Annexin V/PI staining and flow cytometry ( D - E ) Apoptosis assays in NOZ-R ( D ) and GBC-SD-R ( E ) cells stably expressing control (shNC) or E2F8-targeting shRNA (shE2F8), with or without E2F8 overexpression, followed by treatment with DMSO or Olaparib (1 µM). Apoptotic cells were quantified by flow cytometry after Annexin V/PI staining. E2F8 expression levels were confirmed by western blotting ( F ) Western blot analysis of γ-H2AX levels in NOZ, NOZ-R, GBC-SD, and GBC-SD-R cells pretreated with gemcitabine (0.5 µM) for 30 min and harvested 4 h later. Quantitative of γ-H2AX levels was performed using ImageJ software and normalized to total H2AX ( G ) Immunofluorescence detection and quantification of γ-H2AX foci per nucleus in the same panel of cell lines treated as in (F). Foci were quantified using Image-Pro Plus software. Scatter dot plots represent mean ± SD ( n = 3 independent experiments, unpaired t-test; *** p < 0.001)

    Journal: Journal of Experimental & Clinical Cancer Research : CR

    Article Title: Targeting E2F8 sensitizes gemcitabine-resistant gallbladder cancer to PARP inhibitors by disrupting RRM2-driven DNA repair

    doi: 10.1186/s13046-025-03586-2

    Figure Lengend Snippet: E2F8 deficiency sensitizes gemcitabine-resistant gallbladder cancer cells to PARP inhibition. ( A ) Knockdown of E2F8 enhances sensitivity to Olaparib and gemcitabine in NOZ-R cells. Cells were transduced with control sgRNA (sgNC) or two independent sgRNAs targeting E2F8 (sgE2F8 #1 and sgE2F8 #2), followed by treatment with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM). Cell viability was measured at the indicated time points using the CellTiter-Glo assay. Data are shown as mean ± SD from three independent experiments (two-tailed t-test; * p < 0.05, ** p < 0.01, *** p < 0.001) ( B ) Apoptosis analysis of NOZ-R cells transduced with sgNC or sgE2F8 (#1 and #2) and treated with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM) for three days. Apoptotic cells were detected by Annexin V/PI staining followed by flow cytometry. Data are presented as mean ± SD from three independent experiments (t-test; *** p < 0.001) ( C ) Quantification of apoptotic cells in GBC-SD-R cells transduced with sgNC or sgE2F8 (#1 and #2) and treated with DMSO, Olaparib (1 µM), or gemcitabine (0.1 µM) for three days, as determined by Annexin V/PI staining and flow cytometry ( D - E ) Apoptosis assays in NOZ-R ( D ) and GBC-SD-R ( E ) cells stably expressing control (shNC) or E2F8-targeting shRNA (shE2F8), with or without E2F8 overexpression, followed by treatment with DMSO or Olaparib (1 µM). Apoptotic cells were quantified by flow cytometry after Annexin V/PI staining. E2F8 expression levels were confirmed by western blotting ( F ) Western blot analysis of γ-H2AX levels in NOZ, NOZ-R, GBC-SD, and GBC-SD-R cells pretreated with gemcitabine (0.5 µM) for 30 min and harvested 4 h later. Quantitative of γ-H2AX levels was performed using ImageJ software and normalized to total H2AX ( G ) Immunofluorescence detection and quantification of γ-H2AX foci per nucleus in the same panel of cell lines treated as in (F). Foci were quantified using Image-Pro Plus software. Scatter dot plots represent mean ± SD ( n = 3 independent experiments, unpaired t-test; *** p < 0.001)

    Article Snippet: A genome-wide CRISPR-Cas9 loss-of-function screen was performed using the human GeCKO v2 pooled sgRNA library (Addgene, #52961), which encodes both sgRNAs and Cas9 in a single lentiviral construct.

    Techniques: Inhibition, Knockdown, Transduction, Control, Glo Assay, Two Tailed Test, Staining, Flow Cytometry, Stable Transfection, Expressing, shRNA, Over Expression, Western Blot, Software, Immunofluorescence