ddx6 rabbit polyclonal antibody  (Proteintech)

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

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

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: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Schematic representation of in vitro and in vivo CRISPR screenings. Created in BioRender. Bi, H. (2025) https://BioRender.com/5y4p2p4 . b – d The normalized median CRISPR scores of the 101 genes in vitro CRISPR screening with Mono-mac-6 AML cells ( b ), in vitro CRISPR screening with AML PDX cells ( c ), and in vivo CRISPR screening with AML PDX mouse model ( d ). e – g Normalized CRISPR scores of the top 5 overlapped genes in vitro CRISPR screening with Mono-mac-6 AML cells ( e ), in vitro CRISPR screening with AML PDX cells ( f ), and in vivo CRISPR screening with AML PDX mouse model ( g ). Data are the mean ± SEM of n = 24 (Positive control), 40 (Negative control), and 20 ( EIF5A , DDX6 , CNOT3 , EIF4E , PABPC1 ) independent sgRNAs. Simple one-way ANOVA. Box plots show the median (center line), the first and third quartiles (bounds of the box). Whiskers are chosen to show 1.5 of the Interquartile Range. h The PS-Self scores of the top 5 RBPs. PS-Self: self-assembling phase-separating. i The PS-Part score of the top 5 RBPs. PS-Part: partner-dependent phase-separating. j Representative images showing the impact of KO of CNOT3, EIF4E, PABPC1 , and DDX6 on PB assembly in Mono-mac-6 AML cells. Green: Dcp1b. k Statistical results of PB numbers in Mono-mac-6 AML cells following CNOT3, EIF4E, PABPC1 , and DDX6 KO (n = 11 [sgNS]; 12 [sg DDX6 ], or 13 [sg PABPC1 , sg CNOT3 , and sg EIF4E ]). l Relative expression levels of DDX6 in various AML subtypes compared to healthy controls. m , n Protein levels of DDX6 in AML cells ( m ), PDX cells ( n ), and healthy controls. o Relative growth of Mono-mac-6 AML cells following DDX6 KO. p Relative growth of AML PDX cells following DDX6 KO and overexpression (OE). sg DDX6 -1 was utilized for ( j and p ). o and p : mean ± SEM (n = 3 independent experiments); m and n: n = 3 independent experiments with consistent results; Unpaired two-tailed Student’s t-test was utilized for all analyses. Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: In Vitro, In Vivo, CRISPR, Positive Control, Negative Control, Expressing, Over Expression, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Prediction of DDX6 IDR domains with IUPred3. b Schematic representation of DDX6-WT, -CT, -NT, and -ΔIDR. c Representative images showing the colocalization between DDX6-WT and PB marker Dcp1b in HEK293T cells. d Time-lapse images from in cellulo FRAP assay with HEK293T cells. The DDX6-RFP granules before and after photobleaching were highlighted. e FRAP curves for in cellulo DDX6-RFP granules. f Time-lapse images from in vitro FRAP assay with purified DDX6-EGFP protein. g FRAP curves for in vitro DDX6-EGFP droplets. h The phase diagram of DDX6 in the presence of varying NaCl concentrations, showing that salt reduces the LLPS potential of the protein. Green circles indicate the presence of protein droplets, while unfilled circles denote the absence of droplets in the buffer. The result was derived from Fig. 2i. i In vitro droplet formation of recombinant DDX6-EGFP proteins at varying concentrations in the presence of varying NaCl concentrations. j Dynamic motion trajectory of DDX6-EGFP droplet. k In vitro droplet formation of 10 µM recombinant DDX6-WT-EGFP, DDX6-NT-EGFP, DDX6-CT-EGFP, and DDX6-ΔIDR-EGFP in the absence or presence of 200 ng/μl poly(U)-RNA and poly(C)-RNA. l Quantification of the total integrated intensity of DDX6-WT-EGFP, DDX6-NT-EGFP, DDX6-CT-EGFP, and DDX6-ΔIDR-EGFP droplets. e , g: mean ± SEM (n = 3 independent experiments); l: mean ± SEM (n = 3 independent experiments); Unpaired two-tailed Student’s t-test was utilized for ( l ). Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: Marker, FRAP Assay, In Vitro, Purification, Derivative Assay, Recombinant, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Schematic outline of MLL-AF9-driven malignant transformation of Lin - HSCs. Created in BioRender. Bi, H. (2025) https://BioRender.com/ijytw0r . b Ddx6 KO efficacy in MLL-AF9-transduced murine Lin - HSCs. c Effect of Ddx6 KO on the colony-forming ability of MLL-AF9-transduced Lin - HSCs. d Representative images of colonies from MLL-AF9-transduced Lin - HSCs following Ddx6 KO. e Effect of DDX6 KO on the colony-forming ability of PDX cells. f DDX6 KO and OE efficacy in Mono-mac-6 cells as determined by Western blotting. g – i Effects of DDX6 KO and OE on the growth of THP-1 ( g ), Mono-mac-6 ( h ), and MA9.3RAS ( i ) cells. MTT assay was used. j Effects of DDX6 KO and OE on myeloid differentiation of MA9.3RAS cells. Flow cytometry was used. k , l Effects of DDX6 KO on myeloid differentiation of THP1 cells as determined by Wright-Giemsa staining ( k ) and flow cytometry ( l ). m Statistical results showing the effects of DDX6 KO on myeloid differentiation of THP-1 cells. n , o Effect of DDX6 KO and rescued expression with WT and truncated DDX6 on cell viability ( n ) and myeloid differentiation ( o ) in THP-1 cells. p DDX6 KO and OE efficacy in THP-1 as determined by Western blotting. q Effects of DDX6 KO and OE ΔIDR on the growth of Mono-mac-6 cells as assessed by MTT assay. r Effects of DDX6 KO and LSM14A OE on the growth of Mono-mac-6 cells. s Effects of DDX6 KO and LSM14A OE on the apoptosis of Mono-mac-6 cells as assessed by flow cytometry. t Effects of DDX6 KO and LSM14A OE on PBs number in Mono-mac-6 cells. sg DDX6 -1 was utilized for ( f , h – t ). b , f , p: n = 3 independent experiments with consistent results; Data were represented as mean ± SEM (n = 5 for ( e ); n = 3 for ( g – j , m – o , and q – s ; independent experiments); Unpaired two-tailed Student’s t-test was utilized for all analysis. Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: Transformation Assay, Western Blot, MTT Assay, Flow Cytometry, Staining, Expressing, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Schematic outline of the design of Ddx6 cKO mice. b Genotyping results of Ddx6 cKO mice. c Ddx6 KO efficacy in Lin - HSCs as determined by Western blotting. d Schematic overview of poly (I:C)-induced Ddx6 cKO in mice. Created in BioRender. Bi, H. (2025) https://BioRender.com/qqp9hro . e – j Peripheral blood analysis of Ddx6 WT, heterozygous KO, and homozygous KO mice. The levels of WBC ( e ), LYM ( f ), PLT ( g ), RBC ( h ), HGB ( i ), and NEU ( j ) were displayed. k The percentage of Lin - , LK, and LSK cells in the BM of Ddx6 WT, heterozygous KO, and homozygous KO mice. l Representative flow cytometric plots of LK and LSK cell populations in the BM of WT and Ddx6 KO mice. m Representative images of colonies in Lin - HSCs following Ddx6 heterozygous and homozygous KO. n Effect of Ddx6 heterozygous and homozygous KO on the colony-forming ability of Lin - HSCs. o Statistical results of PB numbers in Ddx6 WT and homozygous KO Lin - HSC cells. p Frequencies of T-lymphoid (CD3 + ), B-lymphoid (B220 + ), myeloid (Mac1 + Gr1 + ), and erythroid (Ter119 + ) cells in the spleen of Ddx6 WT, heterozygous KO, and homozygous KO mice. q Representative spleen images. Scale bar, 1 cm. r The spleen weight of Ddx6 WT, heterozygous KO, and homozygous KO mice. b , c: n = 3 independent experiments with consistent results; Data in ( e – k , o , p , and r were presented as mean ± SEM (n = 5 WT mice, n = 5 heterozygous mice, n = 5 homozygous mice); Unpaired two-tailed Student’s t-test was utilized for all the statistical analysis. Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: Western Blot, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Principal component analysis (PCA) of RNA-seq data in Mono-mac-6 AML cells following DDX6 KO. b Schematic overview of the classification of mRNAs into four subgroups: (1) total mRNAs, (2) DDX6-binding mRNAs, (3) DDX6-binding & PB-depleted mRNAs, and (4) DDX6-binding & PB-enriched mRNAs. Created in BioRender. Bi, H. (2025) https://BioRender.com/3aoczy3 . c – f MA plots illustrating the expression levels of total mRNAs ( c ), DDX6-binding mRNAs ( d ), DDX6-binding & PB-depleted mRNAs ( e ), and DDX6-binding & PB-enriched mRNAs ( f ) in Mono-mac-6 cells following DDX6 KO. Significantly increased mRNAs following DDX6 KO are shown in red, while significantly decreased mRNAs are displayed in blue. g Cumulative-distribution-function (CDF) plot depicting the GC content of DDX6-binding & PB-depleted mRNAs and DDX6-binding & PB-enriched mRNAs. h CDF plot depicting GC content of DDX6 KO_Up & DDX6-binding & PB-depleted mRNAs and DDX6 KO_Down & DDX6-binding & PB-enriched mRNAs. i Hockey stick plot representing GC content of the 226 DDX6 KO_Down & DDX6-binding & PB-enriched mRNAs (see Fig. 5f). j GSEA analysis of the DDX6 KO_Down & DDX6-binding & PB-enriched mRNAs with GC content ≤ 45% (see Fig. 5i). The top 10 significantly enriched pathways and the -log 10 (P) value for each pathway were shown. k Sankey diagram showing the top 30 most significantly downregulated core-enriched mRNAs and their corresponding pathways. l Heatmap illustrating the expression levels of the top 30 most significantly downregulated core-enriched mRNAs in Mono-mac-6 cells following DDX6 KO. m , n Pearson correlation between expression levels of DDX6 and top 30 core-enriched mRNAs in AML cell lines ( m ) and the TCGA cohort ( n ). r, Pearson correlation coefficient. The p- values for Pearson correlation are shown. Unpaired two-tailed Student’s t-test was utilized for ( g , h ).

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: RNA Sequencing, Binding Assay, Expressing, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a The direct interaction of BCAT1-1 mRNA with DDX6-WT or DDX6-ΔIDR in THP-1 cells. b Colocalization between DDX6 protein and BCAT1 mRNA in HEK293T cells. c In vitro (cell-free) RNA pull-down workflow. d The 6×His-DDX6-EGFP protein was purified from E. coli . e Western blotting shows the cell-free binding between 6×His-DDX6-EGFP protein and biotin-labeled BCAT1 mRNA. f Protein levels of DDX6 in THP-1 cells following DDX6 KO. g Effect of DDX6 KO on the stability of BCAT1 mRNAs in THP-1 cells. h Effect of DDX6 KO and OE on the steady-state levels of BCAT1 mRNA in MA9.3RAS cells. i Protein levels of DDX6 and BCAT1 in MA9.3RAS cells following DDX6 KO and/or BCAT1 OE. j Rescue effects of BCAT1 OE on DDX6 KO-mediated growth inhibition in THP-1 cells. MTT assay was used. k Protein levels of DDX6 and BCAT1 in THP-1 cells following DDX6 KO and BCAT1 OE. l Effect of BCAT1 OE on OCR in THP-1 cells, Seahorse assay was used. m Effect of BCAT1 OE on basal and maximal mitochondrial respiration in THP-1 cells. n Rescue effects of BCAT1 OE on DDX6 KO-mediated OCR reduction in THP-1 cells. o Effect of DDX6 KO and BCAT1 OE on basal and maximal mitochondrial respiration in THP-1 cells. p Volcano diagram displaying relative levels of 13 C- or 15 N-labeled metabolites in Mono-mac-6 cells upon DDX6 KO. q Schematic overview of 13 C, 15 N-leucine tracing assay. r – u Relative levels of 13 C- or 15 N-labeled NEAAs ( r ), nucleotide metabolites ( s ), and TCA cycle metabolites ( t–u ) in Mono-mac-6 cells upon DDX6 KO. Created in BioRender. Bi, H. (2025) https://BioRender.com/65u2jr6 ( c , q ). sg DDX6 -1 was utilized for ( h – k , n – p , and r – u ). Data were presented as mean ± SEM (n = 3 for ( a , g , h , j , n , o , and r – u ); n = 7 for l, m ; independent experiments); Unpaired two-tailed Student’s t-test was utilized for all the statistical analysis. Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: In Vitro, Purification, Western Blot, Binding Assay, Labeling, Inhibition, MTT Assay, Two Tailed Test

Journal: Nature Communications

Article Title: DDX6 undergoes phase separation to modulate metabolic plasticity and chemoresistance

doi: 10.1038/s41467-025-66966-4

Figure Lengend Snippet: a Effect of Ara-C plus DDX6 KO on U-937 cell viability. The cells were treated with 50 nM Ara-C for 96 h. The assay was conducted on day 10 following lentivirus transduction. b Effect of Ara-C plus BCAT1 KD on Mono-mac-6 cell viability. The cells were treated with 50 nM Ara-C for 96 h. The assay was conducted on day 10 following lentivirus transduction. c Effect of DDX6 KO and BCAT1 OE on the sensitivity to Ara-C in Mono-mac-6 cells. The cells were treated with 50 nM Ara-C for 96 h. sgDDX6-1 was utilized. The assay was conducted on day 10 following lentivirus transduction. d Effect of DDX6 KO on the IC 50 value with Ara-C treatment for 96 h in Mono-mac-6 cells. e In vivo bioluminescent images of AML PDX mouse models following DDX6 KO and/or Ara-C treatment. sg DDX6 -1 was utilized. f Kaplan-Meier curves showing the effect of DDX6 KO and/or Ara-C treatment on the overall survival of AML PDX models. n = 6 mice per group. g Kaplan-Meier curves showing the effect of DDX6 KO and/or Ara-C treatment on the overall survival of AML xenograft models (with Mono-mac 6). n = 6 mice per group. h H&E staining of spleen and liver from each group of the AML PDX models. The samples were collected at the endpoints. i Percentage of CD45 AML donor cells in the BM of recipient mice. a , c , and i : mean ± SEM (n = 3 independent experiments; b : mean ± SEM (n = 4 independent experiments); Extra-sum-of-squares F test ( d , n = 4 independent experiments); Log-rank test ( f , g ); Unpaired two-tailed Student’s t-test ( a – c and i ). Source data are provided as a Source Data file.

Article Snippet: The Ddx6 conditional knockout (cKO) mouse models ( Ddx6 fl/+ and Ddx6 fl/fl ; genetic background: C57BL/6 J [CD45.2]) were created with CRISPR/Cas-mediated genome engineering by Cyagen.

Techniques: Transduction, In Vivo, Staining, Two Tailed Test