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ddx6 rabbit polyclonal antibody  (Proteintech)


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    Structured Review

    Proteintech ddx6 rabbit polyclonal antibody
    Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing <t>DDX6-GFP</t> were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.
    Ddx6 Rabbit Polyclonal Antibody, supplied by Proteintech, used in various techniques. Bioz Stars score: 93/100, based on 27 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/ddx6+rabbit+polyclonal+antibody/pmc12925565-289-5-9?v=Proteintech
    Average 93 stars, based on 27 article reviews
    ddx6 rabbit polyclonal antibody - by Bioz Stars, 2026-07
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    Images

    1) Product Images from "Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds"

    Article Title: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds

    Journal: iScience

    doi: 10.1016/j.isci.2026.114866

    Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing DDX6-GFP were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.
    Figure Legend Snippet: Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing DDX6-GFP were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.

    Techniques Used: Stable Transfection, Expressing, Imaging, Single Cell, Extraction

    Quantification and mechanisms of action analysis of selected drugs (A) A simulation model of intracellular P-body was constructed to generate synthetic P-body distributions with ground truth annotations. (B) A YOLO-v7 architecture trained on synthetic datasets was implemented for automated identification and quantitative analysis of P-body formation. (C) Example of P-body detection, achieving >95% agreement with manual annotations . (D) P-body numbers per cell in the time course under different drug treatment groups. (E) DDX6-GFP intensity (a.u.) per cell under different drug treatment groups. Error bars represent the STD of three independent analyses for (D) and (E). (F) Quantitative analysis of P-body numbers at 6 h post-treatment across different drug groups. (G) Quantitative analysis of DDX6-GFP intensity (a.u.) at 6 h post-treatment across different drug groups. The p -values were determined using the two-tailed Mann-Whitney U test for (F) and (G). The statistical significance compared with DMSO was indicated as ∗∗∗ p < 0.001; ∗ p < 0.05; ns, no significant difference. Data points that lay outside the 15%–85% range were deemed outliers and excluded from the statistical analysis. (H and I) Mechanism of action (MOA) profiling for drugs in Groups 1 and 3.
    Figure Legend Snippet: Quantification and mechanisms of action analysis of selected drugs (A) A simulation model of intracellular P-body was constructed to generate synthetic P-body distributions with ground truth annotations. (B) A YOLO-v7 architecture trained on synthetic datasets was implemented for automated identification and quantitative analysis of P-body formation. (C) Example of P-body detection, achieving >95% agreement with manual annotations . (D) P-body numbers per cell in the time course under different drug treatment groups. (E) DDX6-GFP intensity (a.u.) per cell under different drug treatment groups. Error bars represent the STD of three independent analyses for (D) and (E). (F) Quantitative analysis of P-body numbers at 6 h post-treatment across different drug groups. (G) Quantitative analysis of DDX6-GFP intensity (a.u.) at 6 h post-treatment across different drug groups. The p -values were determined using the two-tailed Mann-Whitney U test for (F) and (G). The statistical significance compared with DMSO was indicated as ∗∗∗ p < 0.001; ∗ p < 0.05; ns, no significant difference. Data points that lay outside the 15%–85% range were deemed outliers and excluded from the statistical analysis. (H and I) Mechanism of action (MOA) profiling for drugs in Groups 1 and 3.

    Techniques Used: Construct, Two Tailed Test, MANN-WHITNEY

    Perturbation of JAK leads to enhanced P-bodies (A) HCT116 cells were knocked down using JAK1 and JAK2 siRNA, and immunostained for P-body components DDX6 (magenta) and EDC4 (green). The nuclei were visualized with DAPI (blue). Scale bar, 10 μm. (B) Quantification of P-body number per cell across three experimental groups. Statistical significance determined by an unpaired t test was indicated as ∗∗∗ p < 0.001. (C) Model of JAK-STAT signaling pathway-mediated P-body regulation. JAK is activated when cytokines or growth factors bind to their respective receptors, leading to receptor dimerization, JAK and STAT phosphorylation, and subsequent transcriptional regulation. Inhibition of the pathway by knockdown of JAK1/2 leads induction of P-body formation. (D) Summary of JAK inhibitors identified in this work that modulate P-body formation.
    Figure Legend Snippet: Perturbation of JAK leads to enhanced P-bodies (A) HCT116 cells were knocked down using JAK1 and JAK2 siRNA, and immunostained for P-body components DDX6 (magenta) and EDC4 (green). The nuclei were visualized with DAPI (blue). Scale bar, 10 μm. (B) Quantification of P-body number per cell across three experimental groups. Statistical significance determined by an unpaired t test was indicated as ∗∗∗ p < 0.001. (C) Model of JAK-STAT signaling pathway-mediated P-body regulation. JAK is activated when cytokines or growth factors bind to their respective receptors, leading to receptor dimerization, JAK and STAT phosphorylation, and subsequent transcriptional regulation. Inhibition of the pathway by knockdown of JAK1/2 leads induction of P-body formation. (D) Summary of JAK inhibitors identified in this work that modulate P-body formation.

    Techniques Used: Phospho-proteomics, Inhibition, Knockdown



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    (A) Widefield fluorescence images of HEK-FUCCI cells with <t>DDX6-labeled</t> PBs. Left panel: nuclear Cdt1-mVenus signal indicates cells in G1 or G2 phase, while cytoplasmic PBs were immunostained with anti-DDX6 antibodies (both in green in the merge). Middle panels: nuclear mCherry-Gem signal indicates cells in S or G2 phase (in red in the merge). Cell cycle phases are indicated on the left. Right insets present enlargements of a representative PB. Scale bars, 10 and 1 µm in the merge panels and insets, respectively. (B) Scatter plot representation of PB size and DDX6 intensity across the cell cycle (from 2 independent experiments). (C) Main steps of the FAPS procedure. (D) Cell cycle analysis from representative synchronized samples. The percentage of cells in the targeted cell cycle phase is displayed in each panel. (E) Representative FAPS profiles of pre-sorting lysate (middle-left panel) and sorted PBs (rightmost panel) from GFP-LSM14A cells synchronized at the G1S transition. Associated controls (buffer and lysate from GFP-LSM14Δ cells) are shown in left panels. The window used to sort PBs is outlined. (F) Representative widefield fluorescence images of lysates before and after sorting. PBs were labelled with GFP-LSM14A (in green) and contaminants were revealed by non-specific ethidium bromide (EtBr) staining (in red). Scale bar, 10 µm. (G) Boxplot showing the enrichment or depletion of several protein groups after sorting (ratio of MS area after/before sorting) in cells at the G1S transition. For each group, the number of detected proteins (protein area >1) is indicated in brackets. (H) Pairwise comparison of the abundance (MS area) of known PB proteins in the PB fraction between successive cell cycle stages.
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    Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing <t>DDX6-GFP</t> were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.
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    Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing <t>DDX6-GFP</t> were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.
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    Figure 1. <t>DDX6</t> functions as a sensor of rare codon-triggered mRNA decay in human cells. (A) Schematic representation of the reporters used in panels (B, D). (B) Wild-type (WT) and DDX6 KO HEK293T cells were transfected with indicated reporter plasmids. After 48 hr, cells were treated with actinomycin D (ActD) and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (C) Relative reporter mRNA levels from panel B at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the standard deviation (SD) (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each mRNA in WT and DDX6 KO cells is represented as the mean ± SD. (D) HEK293T cells were transfected with MBP or POP2 dominant negative mutant (POP2 DE-AA) and indicated reporter plasmids. After 48 hr, cells were treated with ActD and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (E) Relative reporter mRNA levels from panel D at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the SD (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each reporter mRNA in WT and POP2 DE-AA overexpressing cells is represented as the mean ± SD.
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    Fig. 5 | PPIA regulates protein phase separation of its substrates. a, Stress- granule formation was visualized and quantified with G3BP1 staining after stress induction with sodium arsenite in HeLa control or PPIA Kd cells. DAPI, blue; G3BP1, green. Scale bars, 50 µm. PPIA knockdown was partially rescued by the reintroduction of knockdown-resistant PPIA. Cell viability was measured on an automated cell counter with acridine orange/propidium iodide staining solution using n = 6 independently treated replicates per group. Data are means ± s.d.; **P < 0.01, ****P < 0.0001; two-sided Wilcoxon rank-sum test; n = 616 (control), n = 656 (control + PPIA), n = 254 (PPIA knockdown) and n = 293 (PPIA knockdown + PPIA) cells were analysed following blinding. Data are representative of three independent experiments. b, Staining for <t>DDX6</t> revealed significantly fewer P-bodies in HeLa cells following PPIA knockdown. Scale bars, 20 μm. The arrowhead indicates a representative P-body. ****P < 0.0001; two-sided Wilcoxon rank-sum
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    Image Search Results


    (A) Widefield fluorescence images of HEK-FUCCI cells with DDX6-labeled PBs. Left panel: nuclear Cdt1-mVenus signal indicates cells in G1 or G2 phase, while cytoplasmic PBs were immunostained with anti-DDX6 antibodies (both in green in the merge). Middle panels: nuclear mCherry-Gem signal indicates cells in S or G2 phase (in red in the merge). Cell cycle phases are indicated on the left. Right insets present enlargements of a representative PB. Scale bars, 10 and 1 µm in the merge panels and insets, respectively. (B) Scatter plot representation of PB size and DDX6 intensity across the cell cycle (from 2 independent experiments). (C) Main steps of the FAPS procedure. (D) Cell cycle analysis from representative synchronized samples. The percentage of cells in the targeted cell cycle phase is displayed in each panel. (E) Representative FAPS profiles of pre-sorting lysate (middle-left panel) and sorted PBs (rightmost panel) from GFP-LSM14A cells synchronized at the G1S transition. Associated controls (buffer and lysate from GFP-LSM14Δ cells) are shown in left panels. The window used to sort PBs is outlined. (F) Representative widefield fluorescence images of lysates before and after sorting. PBs were labelled with GFP-LSM14A (in green) and contaminants were revealed by non-specific ethidium bromide (EtBr) staining (in red). Scale bar, 10 µm. (G) Boxplot showing the enrichment or depletion of several protein groups after sorting (ratio of MS area after/before sorting) in cells at the G1S transition. For each group, the number of detected proteins (protein area >1) is indicated in brackets. (H) Pairwise comparison of the abundance (MS area) of known PB proteins in the PB fraction between successive cell cycle stages.

    Journal: bioRxiv

    Article Title: Cell cycle-dependent mRNA localization in P-bodies

    doi: 10.1101/2024.04.16.589748

    Figure Lengend Snippet: (A) Widefield fluorescence images of HEK-FUCCI cells with DDX6-labeled PBs. Left panel: nuclear Cdt1-mVenus signal indicates cells in G1 or G2 phase, while cytoplasmic PBs were immunostained with anti-DDX6 antibodies (both in green in the merge). Middle panels: nuclear mCherry-Gem signal indicates cells in S or G2 phase (in red in the merge). Cell cycle phases are indicated on the left. Right insets present enlargements of a representative PB. Scale bars, 10 and 1 µm in the merge panels and insets, respectively. (B) Scatter plot representation of PB size and DDX6 intensity across the cell cycle (from 2 independent experiments). (C) Main steps of the FAPS procedure. (D) Cell cycle analysis from representative synchronized samples. The percentage of cells in the targeted cell cycle phase is displayed in each panel. (E) Representative FAPS profiles of pre-sorting lysate (middle-left panel) and sorted PBs (rightmost panel) from GFP-LSM14A cells synchronized at the G1S transition. Associated controls (buffer and lysate from GFP-LSM14Δ cells) are shown in left panels. The window used to sort PBs is outlined. (F) Representative widefield fluorescence images of lysates before and after sorting. PBs were labelled with GFP-LSM14A (in green) and contaminants were revealed by non-specific ethidium bromide (EtBr) staining (in red). Scale bar, 10 µm. (G) Boxplot showing the enrichment or depletion of several protein groups after sorting (ratio of MS area after/before sorting) in cells at the G1S transition. For each group, the number of detected proteins (protein area >1) is indicated in brackets. (H) Pairwise comparison of the abundance (MS area) of known PB proteins in the PB fraction between successive cell cycle stages.

    Article Snippet: To label DDX6, we used a rabbit polyclonal anti-DDX6 antibody recognizing its C-ter extremity (BIOTECHNE, NB200-192) diluted 1/1000 in PBS, coupled to a secondary F(ab)2 goat anti-rabbit antibody labeled with AF488 (Life technologies, A1107) diluted 1/1000 in PBS.

    Techniques: Fluorescence, Labeling, Cell Cycle Assay, Staining, Comparison

    Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing DDX6-GFP were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.

    Journal: iScience

    Article Title: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds

    doi: 10.1016/j.isci.2026.114866

    Figure Lengend Snippet: Overview of PB-scope: an unsupervised deep learning-based framework for large-scale phenotypic screening on P-bodies (A) HCT116 cells stably expressing DDX6-GFP were plated in 96-well plates, treated with 280 compounds at 10 μM concentrations, and subjected to high-content imaging using the CQ1 confocal quantitative imaging system. (B) The analyzed images consist of four channels: (1) bright-field image for cellular morphology, (2) mitochondrial network, (3) processing body, and (4) nucleus. Merged composite demonstrates spatial relationships between these subcellular compartments. Scale bar, 10 μm. (C) Mitochondrial channels were processed through Cellpose 3.0 to generate a curated dataset containing over 400,000 high-quality single-cell images. (D) A contrastive clustering framework was implemented for unsupervised feature extraction, followed by UMAP dimensionality reduction to identify compounds with analogous mechanism-of-action (MOA) profiles through cluster localization analysis. (E) Quantitative analysis of P-body formation followed by drug treatment. (F) Mechanistic evaluation of lead compounds via imaging analysis.

    Article Snippet: As primary antibodies, we used DDX6 rabbit polyclonal antibody (Proteintech, 14632-1-AP) and EDC4 mouse monoclonal antibody (Santa Cruz Biotechnology, sc-376382).

    Techniques: Stable Transfection, Expressing, Imaging, Single Cell, Extraction

    Quantification and mechanisms of action analysis of selected drugs (A) A simulation model of intracellular P-body was constructed to generate synthetic P-body distributions with ground truth annotations. (B) A YOLO-v7 architecture trained on synthetic datasets was implemented for automated identification and quantitative analysis of P-body formation. (C) Example of P-body detection, achieving >95% agreement with manual annotations . (D) P-body numbers per cell in the time course under different drug treatment groups. (E) DDX6-GFP intensity (a.u.) per cell under different drug treatment groups. Error bars represent the STD of three independent analyses for (D) and (E). (F) Quantitative analysis of P-body numbers at 6 h post-treatment across different drug groups. (G) Quantitative analysis of DDX6-GFP intensity (a.u.) at 6 h post-treatment across different drug groups. The p -values were determined using the two-tailed Mann-Whitney U test for (F) and (G). The statistical significance compared with DMSO was indicated as ∗∗∗ p < 0.001; ∗ p < 0.05; ns, no significant difference. Data points that lay outside the 15%–85% range were deemed outliers and excluded from the statistical analysis. (H and I) Mechanism of action (MOA) profiling for drugs in Groups 1 and 3.

    Journal: iScience

    Article Title: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds

    doi: 10.1016/j.isci.2026.114866

    Figure Lengend Snippet: Quantification and mechanisms of action analysis of selected drugs (A) A simulation model of intracellular P-body was constructed to generate synthetic P-body distributions with ground truth annotations. (B) A YOLO-v7 architecture trained on synthetic datasets was implemented for automated identification and quantitative analysis of P-body formation. (C) Example of P-body detection, achieving >95% agreement with manual annotations . (D) P-body numbers per cell in the time course under different drug treatment groups. (E) DDX6-GFP intensity (a.u.) per cell under different drug treatment groups. Error bars represent the STD of three independent analyses for (D) and (E). (F) Quantitative analysis of P-body numbers at 6 h post-treatment across different drug groups. (G) Quantitative analysis of DDX6-GFP intensity (a.u.) at 6 h post-treatment across different drug groups. The p -values were determined using the two-tailed Mann-Whitney U test for (F) and (G). The statistical significance compared with DMSO was indicated as ∗∗∗ p < 0.001; ∗ p < 0.05; ns, no significant difference. Data points that lay outside the 15%–85% range were deemed outliers and excluded from the statistical analysis. (H and I) Mechanism of action (MOA) profiling for drugs in Groups 1 and 3.

    Article Snippet: As primary antibodies, we used DDX6 rabbit polyclonal antibody (Proteintech, 14632-1-AP) and EDC4 mouse monoclonal antibody (Santa Cruz Biotechnology, sc-376382).

    Techniques: Construct, Two Tailed Test, MANN-WHITNEY

    Perturbation of JAK leads to enhanced P-bodies (A) HCT116 cells were knocked down using JAK1 and JAK2 siRNA, and immunostained for P-body components DDX6 (magenta) and EDC4 (green). The nuclei were visualized with DAPI (blue). Scale bar, 10 μm. (B) Quantification of P-body number per cell across three experimental groups. Statistical significance determined by an unpaired t test was indicated as ∗∗∗ p < 0.001. (C) Model of JAK-STAT signaling pathway-mediated P-body regulation. JAK is activated when cytokines or growth factors bind to their respective receptors, leading to receptor dimerization, JAK and STAT phosphorylation, and subsequent transcriptional regulation. Inhibition of the pathway by knockdown of JAK1/2 leads induction of P-body formation. (D) Summary of JAK inhibitors identified in this work that modulate P-body formation.

    Journal: iScience

    Article Title: Contrastive learning of dynamic processing body formation reveals undefined mechanisms of approved compounds

    doi: 10.1016/j.isci.2026.114866

    Figure Lengend Snippet: Perturbation of JAK leads to enhanced P-bodies (A) HCT116 cells were knocked down using JAK1 and JAK2 siRNA, and immunostained for P-body components DDX6 (magenta) and EDC4 (green). The nuclei were visualized with DAPI (blue). Scale bar, 10 μm. (B) Quantification of P-body number per cell across three experimental groups. Statistical significance determined by an unpaired t test was indicated as ∗∗∗ p < 0.001. (C) Model of JAK-STAT signaling pathway-mediated P-body regulation. JAK is activated when cytokines or growth factors bind to their respective receptors, leading to receptor dimerization, JAK and STAT phosphorylation, and subsequent transcriptional regulation. Inhibition of the pathway by knockdown of JAK1/2 leads induction of P-body formation. (D) Summary of JAK inhibitors identified in this work that modulate P-body formation.

    Article Snippet: As primary antibodies, we used DDX6 rabbit polyclonal antibody (Proteintech, 14632-1-AP) and EDC4 mouse monoclonal antibody (Santa Cruz Biotechnology, sc-376382).

    Techniques: Phospho-proteomics, Inhibition, Knockdown

    Journal: eLife

    Article Title: Human DCP1 is crucial for mRNA decapping and possesses paralog-specific gene regulating functions

    doi: 10.7554/eLife.94811

    Figure Lengend Snippet:

    Article Snippet: Antibody , Anti-DDX6 (rabbit polyclonal) , Bethyl , Bethyl #A300-461Z , WB (1:5000); IF (1:1000).

    Techniques: Enzyme-linked Immunosorbent Assay, Transfection, Construct, Plasmid Preparation, Sequencing, Software

    Figure 1. DDX6 functions as a sensor of rare codon-triggered mRNA decay in human cells. (A) Schematic representation of the reporters used in panels (B, D). (B) Wild-type (WT) and DDX6 KO HEK293T cells were transfected with indicated reporter plasmids. After 48 hr, cells were treated with actinomycin D (ActD) and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (C) Relative reporter mRNA levels from panel B at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the standard deviation (SD) (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each mRNA in WT and DDX6 KO cells is represented as the mean ± SD. (D) HEK293T cells were transfected with MBP or POP2 dominant negative mutant (POP2 DE-AA) and indicated reporter plasmids. After 48 hr, cells were treated with ActD and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (E) Relative reporter mRNA levels from panel D at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the SD (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each reporter mRNA in WT and POP2 DE-AA overexpressing cells is represented as the mean ± SD.

    Journal: eLife

    Article Title: Human DDX6 regulates translation and decay of inefficiently translated mRNAs

    doi: 10.7554/elife.92426

    Figure Lengend Snippet: Figure 1. DDX6 functions as a sensor of rare codon-triggered mRNA decay in human cells. (A) Schematic representation of the reporters used in panels (B, D). (B) Wild-type (WT) and DDX6 KO HEK293T cells were transfected with indicated reporter plasmids. After 48 hr, cells were treated with actinomycin D (ActD) and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (C) Relative reporter mRNA levels from panel B at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the standard deviation (SD) (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each mRNA in WT and DDX6 KO cells is represented as the mean ± SD. (D) HEK293T cells were transfected with MBP or POP2 dominant negative mutant (POP2 DE-AA) and indicated reporter plasmids. After 48 hr, cells were treated with ActD and harvested at the indicated time points. Reporter mRNA levels were analyzed by northern blotting. 18 S rRNA ethidium bromide staining shows equal loading. (E) Relative reporter mRNA levels from panel D at time point zero (before ActD addition) were defined as 100%. Relative reporter mRNA levels were plotted as a function of time. Circles represent the mean value and error bars the SD (n=3). The decay curves were fitted to an exponential decay with a single component (dotted lines). R2 values are indicated for each curve. The half-life of each reporter mRNA in WT and POP2 DE-AA overexpressing cells is represented as the mean ± SD.

    Article Snippet: Endogenous human RPS3A was detected using a polyclonal anti- RPS3A antibody (abcam, ab264368, 1:1,000), CNOT1 was detected using a rabbit anti- CNOT1 antibody (in- house, 1:1,000), DDX6 was detected using a rabbit polyclonal anti- DDX6 antibody (Bethyl, A300- 461Z, 1:1000), Tubulin was detected using a mouse monoclonal anti- Tubulin antibody (Sigma Aldrich, T6199, 1:3000), and V5- SBP- MBP- MS2 was detected using a mouse monoclonal anti- V5 antibody BioRad, MCA1360GA, 1:5000.

    Techniques: Transfection, Northern Blot, Staining, Standard Deviation, Dominant Negative Mutation

    Figure 2. DDX6 interacts with ribosomal proteins in human cells. (A) The interaction between the recombinant NusA-Strep-DDX6 and purified human ribosomal proteins was analyzed by SDS-PAGE and stained with Coomassie blue. Input lysate (1%) and bound fractions (20%) were loaded. (B) Western blot showing the interaction between GFP-tagged DDX6 full-length/N-ter/C-ter with HA-tagged RPL22 and endogenous RPS3A in human HEK293T

    Journal: eLife

    Article Title: Human DDX6 regulates translation and decay of inefficiently translated mRNAs

    doi: 10.7554/elife.92426

    Figure Lengend Snippet: Figure 2. DDX6 interacts with ribosomal proteins in human cells. (A) The interaction between the recombinant NusA-Strep-DDX6 and purified human ribosomal proteins was analyzed by SDS-PAGE and stained with Coomassie blue. Input lysate (1%) and bound fractions (20%) were loaded. (B) Western blot showing the interaction between GFP-tagged DDX6 full-length/N-ter/C-ter with HA-tagged RPL22 and endogenous RPS3A in human HEK293T

    Article Snippet: Endogenous human RPS3A was detected using a polyclonal anti- RPS3A antibody (abcam, ab264368, 1:1,000), CNOT1 was detected using a rabbit anti- CNOT1 antibody (in- house, 1:1,000), DDX6 was detected using a rabbit polyclonal anti- DDX6 antibody (Bethyl, A300- 461Z, 1:1000), Tubulin was detected using a mouse monoclonal anti- Tubulin antibody (Sigma Aldrich, T6199, 1:3000), and V5- SBP- MBP- MS2 was detected using a mouse monoclonal anti- V5 antibody BioRad, MCA1360GA, 1:5000.

    Techniques: Recombinant, Purification, SDS Page, Staining, Western Blot

    Figure 3. DDX6 controls mRNA abundance and translational efficiency in human cells. (A) Comparative analysis of translational efficiency (TE) in wild-type (WT) HEK293T and DDX6 KO cells. Genes with significantly (FDR <0.005) increased (n=1707 genes) and decreased (n=1484 genes) mRNA abundance are colored in red and blue, respectively. (B) Comparative analysis of TE in WT HEK293T and DDX6 KO cells. Genes with significantly (FDR <0.005) increased (n=260 genes) and decreased (n=38 genes) TE are highlighted in salmon and cyan, respectively. The top 20 (total 89) of translationally

    Journal: eLife

    Article Title: Human DDX6 regulates translation and decay of inefficiently translated mRNAs

    doi: 10.7554/elife.92426

    Figure Lengend Snippet: Figure 3. DDX6 controls mRNA abundance and translational efficiency in human cells. (A) Comparative analysis of translational efficiency (TE) in wild-type (WT) HEK293T and DDX6 KO cells. Genes with significantly (FDR <0.005) increased (n=1707 genes) and decreased (n=1484 genes) mRNA abundance are colored in red and blue, respectively. (B) Comparative analysis of TE in WT HEK293T and DDX6 KO cells. Genes with significantly (FDR <0.005) increased (n=260 genes) and decreased (n=38 genes) TE are highlighted in salmon and cyan, respectively. The top 20 (total 89) of translationally

    Article Snippet: Endogenous human RPS3A was detected using a polyclonal anti- RPS3A antibody (abcam, ab264368, 1:1,000), CNOT1 was detected using a rabbit anti- CNOT1 antibody (in- house, 1:1,000), DDX6 was detected using a rabbit polyclonal anti- DDX6 antibody (Bethyl, A300- 461Z, 1:1000), Tubulin was detected using a mouse monoclonal anti- Tubulin antibody (Sigma Aldrich, T6199, 1:3000), and V5- SBP- MBP- MS2 was detected using a mouse monoclonal anti- V5 antibody BioRad, MCA1360GA, 1:5000.

    Techniques:

    Figure 4. DDX6 is required for ribosome-stalling mRNA degradation. (A) Schematic representation of the reporters used in panels (B, C). (B) Representative northern blots showing the decay of androgen receptor (AR) reporter mRNAs in HEK293T wild-type (WT) or DDX6 KO cells. Cells were transfected with indicated reporter plasmids and monitored after the inhibition of transcription using actinomycin D (ActD) for the indicated time. 18 S rRNA ethidium bromide staining shows equal loading. (C) Relative reporter mRNA levels from panel B at time point zero (before ActD addition)

    Journal: eLife

    Article Title: Human DDX6 regulates translation and decay of inefficiently translated mRNAs

    doi: 10.7554/elife.92426

    Figure Lengend Snippet: Figure 4. DDX6 is required for ribosome-stalling mRNA degradation. (A) Schematic representation of the reporters used in panels (B, C). (B) Representative northern blots showing the decay of androgen receptor (AR) reporter mRNAs in HEK293T wild-type (WT) or DDX6 KO cells. Cells were transfected with indicated reporter plasmids and monitored after the inhibition of transcription using actinomycin D (ActD) for the indicated time. 18 S rRNA ethidium bromide staining shows equal loading. (C) Relative reporter mRNA levels from panel B at time point zero (before ActD addition)

    Article Snippet: Endogenous human RPS3A was detected using a polyclonal anti- RPS3A antibody (abcam, ab264368, 1:1,000), CNOT1 was detected using a rabbit anti- CNOT1 antibody (in- house, 1:1,000), DDX6 was detected using a rabbit polyclonal anti- DDX6 antibody (Bethyl, A300- 461Z, 1:1000), Tubulin was detected using a mouse monoclonal anti- Tubulin antibody (Sigma Aldrich, T6199, 1:3000), and V5- SBP- MBP- MS2 was detected using a mouse monoclonal anti- V5 antibody BioRad, MCA1360GA, 1:5000.

    Techniques: Northern Blot, Transfection, Inhibition, Staining

    Fig. 5 | PPIA regulates protein phase separation of its substrates. a, Stress- granule formation was visualized and quantified with G3BP1 staining after stress induction with sodium arsenite in HeLa control or PPIA Kd cells. DAPI, blue; G3BP1, green. Scale bars, 50 µm. PPIA knockdown was partially rescued by the reintroduction of knockdown-resistant PPIA. Cell viability was measured on an automated cell counter with acridine orange/propidium iodide staining solution using n = 6 independently treated replicates per group. Data are means ± s.d.; **P < 0.01, ****P < 0.0001; two-sided Wilcoxon rank-sum test; n = 616 (control), n = 656 (control + PPIA), n = 254 (PPIA knockdown) and n = 293 (PPIA knockdown + PPIA) cells were analysed following blinding. Data are representative of three independent experiments. b, Staining for DDX6 revealed significantly fewer P-bodies in HeLa cells following PPIA knockdown. Scale bars, 20 μm. The arrowhead indicates a representative P-body. ****P < 0.0001; two-sided Wilcoxon rank-sum

    Journal: Nature cell biology

    Article Title: Cyclophilin A supports translation of intrinsically disordered proteins and affects haematopoietic stem cell ageing.

    doi: 10.1038/s41556-024-01387-x

    Figure Lengend Snippet: Fig. 5 | PPIA regulates protein phase separation of its substrates. a, Stress- granule formation was visualized and quantified with G3BP1 staining after stress induction with sodium arsenite in HeLa control or PPIA Kd cells. DAPI, blue; G3BP1, green. Scale bars, 50 µm. PPIA knockdown was partially rescued by the reintroduction of knockdown-resistant PPIA. Cell viability was measured on an automated cell counter with acridine orange/propidium iodide staining solution using n = 6 independently treated replicates per group. Data are means ± s.d.; **P < 0.01, ****P < 0.0001; two-sided Wilcoxon rank-sum test; n = 616 (control), n = 656 (control + PPIA), n = 254 (PPIA knockdown) and n = 293 (PPIA knockdown + PPIA) cells were analysed following blinding. Data are representative of three independent experiments. b, Staining for DDX6 revealed significantly fewer P-bodies in HeLa cells following PPIA knockdown. Scale bars, 20 μm. The arrowhead indicates a representative P-body. ****P < 0.0001; two-sided Wilcoxon rank-sum

    Article Snippet: Western blots were performed with a rat monoclonal anti-HA high-affinity antibody (clone 3F10, Millipore Sigma), a rabbit polyclonal anti-histone H3 antibody (ab1791, Abcam), a rabbit polyclonal anti-cyclophilin A antibody (2175, Cell Signaling Technology), a rabbit polyclonal anti-PABPC1 antibody (4992, Cell Signaling Technology), a rabbit polyclonal anti-DDX6 antibody (14632-1-AP, Proteintech), a rabbit polyclonal anti-G3BP1 antibody (13057-2-AP, Proteintech), a rabbit polyclonal anti-NPM1 antibody (10306-1-AP, Proteintech), a mouse monoclonal anti-β-tubulin antibody (86298, Cell Signaling Technology) and a mouse monoclonal anti-glyceraldehyde 3-phosphate dehydrogenase (anti-GAPDH) antibody (ab204481, Abcam).

    Techniques: Staining, Control, Knockdown