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gata6 expression vector  (Addgene inc)


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    Addgene inc gata6 expression vector
    TET3 transcriptionally represses <t>GATA6</t> through histone deacetylation. A) Western blot analysis of GATA6 protein levels in wild‐type (WT) and TET3 knockout (KO) PANC‐1 cells. B) UMAP visualization showing the expression distribution of TET3 and GATA6 in epithelial cells from scRNA‐seq of 25 PDAC patients ( GSE242230 ). C) Violin plots showing the expression levels of GATA6 in type 1 and type 2 ductal cells previously identified in PDAC patients (CRA001160; n = 24). D) RT‐qPCR analysis of GATA6 mRNA in TET3 knockout PANC‐1 cells transduced with doxycycline‐inducible wild‐type TET3 (TET3 wt , unfilled bars) or catalytically inactive mutant TET3 (TET3 mut , striped bars), treated with (purple) or without (gray) doxycycline (1 µg mL −1 ) (n = 3). E) ChIP‐qPCR assay of H3K27ac levels in WT and KO PANC‐1 cells (n = 3). Primers targeted regions upstream or downstream of the GATA6 transcription start site, as indicated in Figure , Supporting Information. F) RT‐qPCR measuring GATA6 mRNA expression in wild‐type PANC‐1 cells treated with SAHA at 0, 5, or 10 µM for 24 or 48 h (n = 3). G) Western blot analysis of GATA6 protein levels in PANC‐1 cells treated with SAHA (0, 5, 10 µM) for 24 h. H) ChIP‐qPCR assay of V5 in PANC‐1 cells ectopically expressing V5‐tagged TET3 (n = 3). qPCR primers are the same as those used in (E). I) Immunoprecipitation of V5‐tagged TET3 in PANC‐1 cells, followed by immunoblotting for HDAC1 and HDAC2 using an anti‐V5 antibody. Data represent mean ± SD. Statistical significance was determined by two‐tailed Wilcoxon test (C), one‐way ANOVA (D, F) or two‐tailed unpaired t ‐test (E, H). * p < 0.05, ** p < 0.01, *** p < 0.001.
    Gata6 Expression Vector, supplied by Addgene inc, used in various techniques. Bioz Stars score: 93/100, based on 3 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/barcode/EF1a_GATA6_P2A_Hygro_Barcode+(Plasmid+%23120445)/pmc12499388-223-0-3
    Average 93 stars, based on 3 article reviews
    gata6 expression vector - by Bioz Stars, 2026-11
    93/100 stars

    Images

    1) Product Images from "The TET3/GATA6 Axis Drives Lipid Metabolism and Therapeutic Vulnerabilities in Pancreatic Ductal Adenocarcinoma"

    Article Title: The TET3/GATA6 Axis Drives Lipid Metabolism and Therapeutic Vulnerabilities in Pancreatic Ductal Adenocarcinoma

    Journal: Advanced Science

    doi: 10.1002/advs.202501774

    TET3 transcriptionally represses GATA6 through histone deacetylation. A) Western blot analysis of GATA6 protein levels in wild‐type (WT) and TET3 knockout (KO) PANC‐1 cells. B) UMAP visualization showing the expression distribution of TET3 and GATA6 in epithelial cells from scRNA‐seq of 25 PDAC patients ( GSE242230 ). C) Violin plots showing the expression levels of GATA6 in type 1 and type 2 ductal cells previously identified in PDAC patients (CRA001160; n = 24). D) RT‐qPCR analysis of GATA6 mRNA in TET3 knockout PANC‐1 cells transduced with doxycycline‐inducible wild‐type TET3 (TET3 wt , unfilled bars) or catalytically inactive mutant TET3 (TET3 mut , striped bars), treated with (purple) or without (gray) doxycycline (1 µg mL −1 ) (n = 3). E) ChIP‐qPCR assay of H3K27ac levels in WT and KO PANC‐1 cells (n = 3). Primers targeted regions upstream or downstream of the GATA6 transcription start site, as indicated in Figure , Supporting Information. F) RT‐qPCR measuring GATA6 mRNA expression in wild‐type PANC‐1 cells treated with SAHA at 0, 5, or 10 µM for 24 or 48 h (n = 3). G) Western blot analysis of GATA6 protein levels in PANC‐1 cells treated with SAHA (0, 5, 10 µM) for 24 h. H) ChIP‐qPCR assay of V5 in PANC‐1 cells ectopically expressing V5‐tagged TET3 (n = 3). qPCR primers are the same as those used in (E). I) Immunoprecipitation of V5‐tagged TET3 in PANC‐1 cells, followed by immunoblotting for HDAC1 and HDAC2 using an anti‐V5 antibody. Data represent mean ± SD. Statistical significance was determined by two‐tailed Wilcoxon test (C), one‐way ANOVA (D, F) or two‐tailed unpaired t ‐test (E, H). * p < 0.05, ** p < 0.01, *** p < 0.001.
    Figure Legend Snippet: TET3 transcriptionally represses GATA6 through histone deacetylation. A) Western blot analysis of GATA6 protein levels in wild‐type (WT) and TET3 knockout (KO) PANC‐1 cells. B) UMAP visualization showing the expression distribution of TET3 and GATA6 in epithelial cells from scRNA‐seq of 25 PDAC patients ( GSE242230 ). C) Violin plots showing the expression levels of GATA6 in type 1 and type 2 ductal cells previously identified in PDAC patients (CRA001160; n = 24). D) RT‐qPCR analysis of GATA6 mRNA in TET3 knockout PANC‐1 cells transduced with doxycycline‐inducible wild‐type TET3 (TET3 wt , unfilled bars) or catalytically inactive mutant TET3 (TET3 mut , striped bars), treated with (purple) or without (gray) doxycycline (1 µg mL −1 ) (n = 3). E) ChIP‐qPCR assay of H3K27ac levels in WT and KO PANC‐1 cells (n = 3). Primers targeted regions upstream or downstream of the GATA6 transcription start site, as indicated in Figure , Supporting Information. F) RT‐qPCR measuring GATA6 mRNA expression in wild‐type PANC‐1 cells treated with SAHA at 0, 5, or 10 µM for 24 or 48 h (n = 3). G) Western blot analysis of GATA6 protein levels in PANC‐1 cells treated with SAHA (0, 5, 10 µM) for 24 h. H) ChIP‐qPCR assay of V5 in PANC‐1 cells ectopically expressing V5‐tagged TET3 (n = 3). qPCR primers are the same as those used in (E). I) Immunoprecipitation of V5‐tagged TET3 in PANC‐1 cells, followed by immunoblotting for HDAC1 and HDAC2 using an anti‐V5 antibody. Data represent mean ± SD. Statistical significance was determined by two‐tailed Wilcoxon test (C), one‐way ANOVA (D, F) or two‐tailed unpaired t ‐test (E, H). * p < 0.05, ** p < 0.01, *** p < 0.001.

    Techniques Used: Western Blot, Knock-Out, Expressing, Quantitative RT-PCR, Transduction, Mutagenesis, ChIP-qPCR, Immunoprecipitation, Two Tailed Test

    GATA6 suppresses lipogenic gene expression and limits tumor growth. A) Western blot showing knockout efficiency of GATA6 in TET3 knockout (KO) PANC‐1 cells. B) RT‐qPCR analysis of lipid metabolic gene expression in PANC‐1 cells with wild‐type (WT), KO, and TET3 / GATA6 double knockout (KO‐sgGATA6) (n = 3). C) Western blot showing overexpression efficiency of GATA6 (GATA6 OE ) in wild‐type PANC‐1 cells. D) RT‐qPCR analysis of lipid metabolic gene expression in PANC‐1 cells with constitutive GATA6 overexpression (GATA6 OE ) (n = 3). E) Representative images of subcutaneous xenograft tumors (left) and tumor weight at 8 weeks post‐implantation (right) for the indicated cell lines. Nude mice were transplanted with WT (n = 5), KO (n = 5), or KO‐sgGATA6 PANC‐1 cells (n = 5). F) Cell viability of wild‐type PANC‐1 cells treated for 24 or 48 h with gemcitabine (1 µM), gemcitabine + SAHA (5 µM), gemcitabine + Erastin (1 µM), or a triple combination of gemcitabine, SAHA, and Erastin (n = 3). G) Representative images of xenografts (left) and tumor weight at 7 weeks post‐implantation (right) following treatment beginning at week 4 with the indicated agents. Nude mice were transplanted with wild‐type PANC‐1 cells. Data represent mean ± SD. Statistical significance was determined by one‐way ANOVA (B, E, F, G) or two‐tailed unpaired t ‐test (D). * p < 0.05, ** p < 0.01, *** p < 0.001.
    Figure Legend Snippet: GATA6 suppresses lipogenic gene expression and limits tumor growth. A) Western blot showing knockout efficiency of GATA6 in TET3 knockout (KO) PANC‐1 cells. B) RT‐qPCR analysis of lipid metabolic gene expression in PANC‐1 cells with wild‐type (WT), KO, and TET3 / GATA6 double knockout (KO‐sgGATA6) (n = 3). C) Western blot showing overexpression efficiency of GATA6 (GATA6 OE ) in wild‐type PANC‐1 cells. D) RT‐qPCR analysis of lipid metabolic gene expression in PANC‐1 cells with constitutive GATA6 overexpression (GATA6 OE ) (n = 3). E) Representative images of subcutaneous xenograft tumors (left) and tumor weight at 8 weeks post‐implantation (right) for the indicated cell lines. Nude mice were transplanted with WT (n = 5), KO (n = 5), or KO‐sgGATA6 PANC‐1 cells (n = 5). F) Cell viability of wild‐type PANC‐1 cells treated for 24 or 48 h with gemcitabine (1 µM), gemcitabine + SAHA (5 µM), gemcitabine + Erastin (1 µM), or a triple combination of gemcitabine, SAHA, and Erastin (n = 3). G) Representative images of xenografts (left) and tumor weight at 7 weeks post‐implantation (right) following treatment beginning at week 4 with the indicated agents. Nude mice were transplanted with wild‐type PANC‐1 cells. Data represent mean ± SD. Statistical significance was determined by one‐way ANOVA (B, E, F, G) or two‐tailed unpaired t ‐test (D). * p < 0.05, ** p < 0.01, *** p < 0.001.

    Techniques Used: Gene Expression, Western Blot, Knock-Out, Quantitative RT-PCR, Double Knockout, Over Expression, Two Tailed Test

    TET3 promotes invasive PDAC through activation of TGF‐β signaling pathway. A) mRNA expression levels of TET3 in normal pancreatic tissues (n = 7), IPMA tissues (n = 6), and invasive PDAC tissues (n = 3) from GSE19650 (n = 16). B) Representative images and quantification of transwell invasion and migration assays in wild‐type (WT) and TET3 knockout (KO) PANC‐1 cells (n = 3). Scale bars = 200 µm. C) Western blot analysis of epithelial‐mesenchymal transition (EMT) markers E‐cadherin, N‐cadherin, and vimentin in WT and KO PANC‐1 cells. D) Western blot analysis of TGF‐β pathway proteins in WT, KO, and TET3 / GATA6 double knockout (KO‐sgGATA6) PANC‐1 cells. E) Western blot analysis of TGF‐β signaling proteins in wild‐type PANC‐1 cells constitutively overexpressing GATA6 (GATA6 OE ). F) Representative images and quantification of transwell invasion and migration assays in WT and KO in CFPAC‐1 cells (n = 3). G) Survival analysis of TCGA‐PAAD patients stratified by SMAD4 expression (high: top 50%, n = 89; low: bottom 50%, n = 89) and further subdivided by TET3 expression (high: top 25%, n = 22; low: bottom 25%, n = 22). Data are shown as mean ± SD. Statistical significance was assessed by one‐way ANOVA (A), two‐tailed unpaired t ‐test (B, F), or log‐rank Mantel‐Cox test (G). * p < 0.05, ** p < 0.01, *** p < 0.001.
    Figure Legend Snippet: TET3 promotes invasive PDAC through activation of TGF‐β signaling pathway. A) mRNA expression levels of TET3 in normal pancreatic tissues (n = 7), IPMA tissues (n = 6), and invasive PDAC tissues (n = 3) from GSE19650 (n = 16). B) Representative images and quantification of transwell invasion and migration assays in wild‐type (WT) and TET3 knockout (KO) PANC‐1 cells (n = 3). Scale bars = 200 µm. C) Western blot analysis of epithelial‐mesenchymal transition (EMT) markers E‐cadherin, N‐cadherin, and vimentin in WT and KO PANC‐1 cells. D) Western blot analysis of TGF‐β pathway proteins in WT, KO, and TET3 / GATA6 double knockout (KO‐sgGATA6) PANC‐1 cells. E) Western blot analysis of TGF‐β signaling proteins in wild‐type PANC‐1 cells constitutively overexpressing GATA6 (GATA6 OE ). F) Representative images and quantification of transwell invasion and migration assays in WT and KO in CFPAC‐1 cells (n = 3). G) Survival analysis of TCGA‐PAAD patients stratified by SMAD4 expression (high: top 50%, n = 89; low: bottom 50%, n = 89) and further subdivided by TET3 expression (high: top 25%, n = 22; low: bottom 25%, n = 22). Data are shown as mean ± SD. Statistical significance was assessed by one‐way ANOVA (A), two‐tailed unpaired t ‐test (B, F), or log‐rank Mantel‐Cox test (G). * p < 0.05, ** p < 0.01, *** p < 0.001.

    Techniques Used: Activation Assay, Expressing, Migration, Knock-Out, Western Blot, Double Knockout, Two Tailed Test

    Schematic representation of the TET3/GATA6 axis in regulating lipogenic metabolism and promoting tumor growth and invasion in pancreatic cancer.
    Figure Legend Snippet: Schematic representation of the TET3/GATA6 axis in regulating lipogenic metabolism and promoting tumor growth and invasion in pancreatic cancer.

    Techniques Used:

    Related Articles

    Expressing:

    Article Title: The TET3/GATA6 Axis Drives Lipid Metabolism and Therapeutic Vulnerabilities in Pancreatic Ductal Adenocarcinoma
    Article Snippet: .. GATA6 expression vector (Addgene, #120445) was packaged into a lentivirus transduction system and used to infect cancer cells. .. Empty vector (Addgene, #120426) was transduced into wild‐type cells as a control group.

    Plasmid Preparation:

    Article Title: The TET3/GATA6 Axis Drives Lipid Metabolism and Therapeutic Vulnerabilities in Pancreatic Ductal Adenocarcinoma
    Article Snippet: .. GATA6 expression vector (Addgene, #120445) was packaged into a lentivirus transduction system and used to infect cancer cells. .. Empty vector (Addgene, #120426) was transduced into wild‐type cells as a control group.

    Transduction:

    Article Title: The TET3/GATA6 Axis Drives Lipid Metabolism and Therapeutic Vulnerabilities in Pancreatic Ductal Adenocarcinoma
    Article Snippet: .. GATA6 expression vector (Addgene, #120445) was packaged into a lentivirus transduction system and used to infect cancer cells. .. Empty vector (Addgene, #120426) was transduced into wild‐type cells as a control group.

    other:

    Article Title: A Reflux Linked GATA Factor Fulcrum Dictates Lineage Commitment Through GPRC5B During the Esophageal Dysplastic Transition
    Article Snippet: GATA4 overexpression plasmid (Addgene #120444) added was used for GATA4 expression EF1a_GATA4_P2A_Hygro.



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    New England Biolabs barcode mixture a
    A. Schematic of the <t>Swap-seq</t> workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a <t>barcode</t> cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.
    Barcode Mixture A, supplied by New England Biolabs, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    multicolor barcode array  (Exosome Diagnostics)
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    Exosome Diagnostics multicolor barcode array
    A. Schematic of the <t>Swap-seq</t> workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a <t>barcode</t> cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.
    Multicolor Barcode Array, supplied by Exosome Diagnostics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    feature barcode technology  (10X Genomics)
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    10X Genomics feature barcode technology
    A. Schematic of the <t>Swap-seq</t> workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a <t>barcode</t> cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.
    Feature Barcode Technology, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    feature barcode kit  (10X Genomics)
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    10X Genomics feature barcode kit
    A. Schematic of the <t>Swap-seq</t> workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a <t>barcode</t> cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.
    Feature Barcode Kit, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    barcode r  (Sangon Biotech)
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    A. Schematic of the <t>Swap-seq</t> workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a <t>barcode</t> cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.
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    Image Search Results


    A. Schematic of the Swap-seq workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a barcode cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.

    Journal: bioRxiv

    Article Title: Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens

    doi: 10.64898/2026.10.08.756856

    Figure Lengend Snippet: A. Schematic of the Swap-seq workflow for measuring the effects of DNA sequence deletions on gene expression. Each Swap-seq edit uses a pair of pegRNAs to delete a 50- to 250-bp target sequence and replace it with a barcode cassette containing an edit-specific 8-bp barcode together with sequences common to all edits, including PCR primer-binding sites (yellow) and a 3′ overlap sequence (purple). In a Swap-seq screen, hundreds of such edits are designed to perturb sequences surrounding a gene of interest. Cells are infected with a lentiviral pegRNA-pair library at low multiplicity of infection (MOI); after 14 days of editing, cells are cultured without doxycycline for 7 days before undergoing FlowFISH sorting and quantification of changes in gene expression. B. Individual Swap-seq editing test in three regions: the PPIF promoter, a candidate enhancer with high accessibility and H3K27ac signal (enhancer e2 in G ), and a region with low accessibility (hg19: chr10:81,069,000-81,069,517). Error bars: 95% CI for the mean. C. Correlation of effect sizes between two biological replicates, after removing edits that did not pass the 0.001% frequency threshold. Effects for each edit are averaged among barcodes. (Pearson’s r = 0.97, n = 117 edits). D. Editing efficiency and number of cells assayed for each barcode. A barcode passed the threshold in a given technical replicate if aligned reads exceeded 0.001% of all reads. Bottom axis, mean allele frequency calculated as the percent of reads aligned to the barcode averaged across replicates; y-axis, number of barcodes; top axis, number of cells assayed. E. Volcano plot of Swap-seq edits passing the frequency threshold. Edits were considered significant at BH-corrected p < 0.001 (horizontal dashed line). Error bars: 95% CI for the mean. F. Swap-seq screen candidate elements at the PPIF locus. In total, 24 candidate regulatory elements were targeted and the PPIF promoter was included as a positive control. *Two elements with no edits above the frequency threshold of 0.001%. G. Swap-seq effects on PPIF expression. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits. H. Swap-seq edits spanning the promoter, splice junction, and enhancer g1. Red, significant edits (BH-corrected p < 0.001, one-sample t-test); gray, non-significant edits.

    Article Snippet: Following extraction, we split genomic DNA into PCR replicates and amplified the Swap-seq barcode cassettes with Q5 High-Fidelity 2X Master Mix (New England Biolabs #M0492L) and 0.5 uM of each amplification primer ( table S1 ) with the following program: 98°C for 1 minute and then 26 cycles of 98°C for 10 seconds, 69°C for 20 seconds, 72°C for 20 seconds, followed by a final 72°C extension for 2 minutes.

    Techniques: Sequencing, Gene Expression, Binding Assay, Infection, Cell Culture, Positive Control, Expressing, Polymerase Chain Reaction, ChIP-sequencing

    A. Comparison of regulatory elements identified by Swap-seq and CRISPRi. An element was classified as significant in Swap-seq if at least one edit within the element was significant (one-sample t-test, BH-corrected p < 0.001). CRISPRi used the average effects of all gRNAs targeting the same region (one-sample t-test, BH-corrected p < 0.05). Elements were grouped according to the hypothesized mechanisms based on their candidate element categories and effects on PPIF gene expression. B. Correlation between Swap-seq and CRISPRi effect sizes. Elements within the PPIF gene body, which are not testable by CRISPRi, were excluded from this analysis; when an element contains several significant edits, the one with the strongest effect was used for comparison with CRISPRi (Pearson’s r = 0.75, n = 18 elements). Error bars: 95% CI for the mean. C. Swap-seq edits overlap key transcription factor binding sites (TFBS) within regulatory elements. Close-up views of four elements with significant effects on PPIF expression (left to right: e2, a PPIF enhancer with high H3K27ac; e5, a low-H3K27ac enhancer; g1, an enhancer in intron 1 of PPIF ; c2, a CTCF binding site). ChromBPNet attribution scores are plotted together with TF-MoDISco motif annotations. All edits that passed the frequency threshold are plotted with width corresponding to the deleted region. Significance determined by one-sample t-test: red, significant edits (BH-corrected p < 0.001); gray, non-significant edits. Error bars: 95% CI for the mean. D. Schematic of the downstream lentiMPRA. Candidate regulatory elements were cloned downstream of the promoter and an ORF–barcode–poly(A) cassette (ORF-BC-pA). Cells were infected with the lentiviral library at high MOI, and barcodes were sequenced from both DNA and RNA; CRE activity was calculated as the ratio of RNA to DNA barcode abundance. E. LentiMPRA validation of candidate enhancers. Each dot represents a barcode from two biological replicates. A sequence lacking predicted TFBS (noTFBS) served as a negative control for basal promoter strength. Significance determined by two-sample t-test: *p < 0.05, **p < 0.01, ***p < 0.001. Error bars: 95% CI for the mean. F. CTCF deletion alters 3D genome contacts at the PPIF locus. Contact frequencies were averaged across region-capture Micro-C (RCMC) measurements from three independent clones, and log2 fold-change was calculated as log2(c2-deleted clones / WT clones) at 1.6 kb bin sizes. G. Quantification of contact changes associated with CTCF deletion, with the viewpoint on the PPIF promoter. Bars show changes in contact frequency between the PPIF promoter and each regulatory element, averaged across three clonal lines at 1.6 kb binsize. Significance determined by two-sample t-test: *p < 0.05, **p < 0.01, ***p < 0.001. Error bars: 95% CI for the mean.

    Journal: bioRxiv

    Article Title: Systematic mapping of enhancers, silencers, and topological elements with prime editing deletion screens

    doi: 10.64898/2026.10.08.756856

    Figure Lengend Snippet: A. Comparison of regulatory elements identified by Swap-seq and CRISPRi. An element was classified as significant in Swap-seq if at least one edit within the element was significant (one-sample t-test, BH-corrected p < 0.001). CRISPRi used the average effects of all gRNAs targeting the same region (one-sample t-test, BH-corrected p < 0.05). Elements were grouped according to the hypothesized mechanisms based on their candidate element categories and effects on PPIF gene expression. B. Correlation between Swap-seq and CRISPRi effect sizes. Elements within the PPIF gene body, which are not testable by CRISPRi, were excluded from this analysis; when an element contains several significant edits, the one with the strongest effect was used for comparison with CRISPRi (Pearson’s r = 0.75, n = 18 elements). Error bars: 95% CI for the mean. C. Swap-seq edits overlap key transcription factor binding sites (TFBS) within regulatory elements. Close-up views of four elements with significant effects on PPIF expression (left to right: e2, a PPIF enhancer with high H3K27ac; e5, a low-H3K27ac enhancer; g1, an enhancer in intron 1 of PPIF ; c2, a CTCF binding site). ChromBPNet attribution scores are plotted together with TF-MoDISco motif annotations. All edits that passed the frequency threshold are plotted with width corresponding to the deleted region. Significance determined by one-sample t-test: red, significant edits (BH-corrected p < 0.001); gray, non-significant edits. Error bars: 95% CI for the mean. D. Schematic of the downstream lentiMPRA. Candidate regulatory elements were cloned downstream of the promoter and an ORF–barcode–poly(A) cassette (ORF-BC-pA). Cells were infected with the lentiviral library at high MOI, and barcodes were sequenced from both DNA and RNA; CRE activity was calculated as the ratio of RNA to DNA barcode abundance. E. LentiMPRA validation of candidate enhancers. Each dot represents a barcode from two biological replicates. A sequence lacking predicted TFBS (noTFBS) served as a negative control for basal promoter strength. Significance determined by two-sample t-test: *p < 0.05, **p < 0.01, ***p < 0.001. Error bars: 95% CI for the mean. F. CTCF deletion alters 3D genome contacts at the PPIF locus. Contact frequencies were averaged across region-capture Micro-C (RCMC) measurements from three independent clones, and log2 fold-change was calculated as log2(c2-deleted clones / WT clones) at 1.6 kb bin sizes. G. Quantification of contact changes associated with CTCF deletion, with the viewpoint on the PPIF promoter. Bars show changes in contact frequency between the PPIF promoter and each regulatory element, averaged across three clonal lines at 1.6 kb binsize. Significance determined by two-sample t-test: *p < 0.05, **p < 0.01, ***p < 0.001. Error bars: 95% CI for the mean.

    Article Snippet: Following extraction, we split genomic DNA into PCR replicates and amplified the Swap-seq barcode cassettes with Q5 High-Fidelity 2X Master Mix (New England Biolabs #M0492L) and 0.5 uM of each amplification primer ( table S1 ) with the following program: 98°C for 1 minute and then 26 cycles of 98°C for 10 seconds, 69°C for 20 seconds, 72°C for 20 seconds, followed by a final 72°C extension for 2 minutes.

    Techniques: Comparison, Gene Expression, Analysis, Binding Assay, Expressing, Clone Assay, Infection, Activity Assay, Biomarker Discovery, Sequencing, Negative Control