rabbit anti ddx6 antibody  (Novus Biologicals)

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