target capture probes  (Twist Bioscience)

Journal: bioRxiv

Article Title: Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

doi: 10.1101/2025.01.25.634869

Figure Lengend Snippet: Overview of Decoy-seq. a, Graphical illustration of the design of Decoy-seq and experimental outline in this study. b, Schematic summary of the grouping of miRNAs into miRNA families based on the shared seed sequence determined by TargetScan, and the grouping of tRFs into clusters based on sequence similarity determined by ClustalW alignment. Clusters are denoted by the initial tRF in the naming whereas the initial tRNA in the naming indicates a unique non-clustered tRF. c, Summary information of the miRNA and tRF screens. d, Box-and-whisker plots of log-normalized expression of the predicted target genes (DTL, SLC2A1 and TGM2 from the left to right panel) in single cells expressing TuDs targeting other miRNAs or miR-629-3p, miR-92a-1-5p, and miR-17-5p/20-5p/93-5p/106-5p/519-3p family, respectively, in the miRNA screen. P-values calculated using two-tailed Student’s t-test.

Article Snippet: Predicted conserved targets (mRNA) for miRNAs were obtained from TargetScan 6.0, and a panel of hybrid capture probes against miRNA target genes were designed and purchased from Twist Bioscience.

Techniques: Sequencing, Whisker Assay, Expressing, Two Tailed Test

Journal: bioRxiv

Article Title: Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

doi: 10.1101/2025.01.25.634869

Figure Lengend Snippet: Decoy-seq delineates miRNA-mediated transcriptomic phenotypes and regulatory networks. a, Heatmap displaying the Spearman’s correlation of each miRNA perturbation based on the aggregated pseudo-bulk expression of 3000 highly variable genes. Each row and column represent an individual miRNA perturbation. Red and blue colors denote positive and negative correlations, respectively. b, Graphical illustration of the analytical framework, which incorporates principal component analysis (PCA) followed by varimax rotation to identify perturbations with significant phenotypic changes and assign the biological function. RC: “rotated component”. c, Dotplot showing miRNA perturbations with significant shifts in the transcriptomic states across a varimax-rotated principal component. The x-axis represents the RCs and the y-axis represents the miRNA perturbations that are best correlated to the variations in transcriptomic states captured by the respective RCs. Log-likelihood ratio test was used to obtain p-values (adjusted using Benjamini-Hochberg procedure); values passing FDR of 25% plotted. d, Dotplot showing a gene ontology (GO) enrichment analysis of the same RCs shown in c using Enrichr. e, (Left) barplots of normalized gene expression count in hexose import and glycolysis markers SLC2A5 and FBP1 from relevant gene-sets highlighted in d from the bulk RNA-seq data of MDA-MB-231 cells transduced with a non-targeting (NT) control TuD or a TuD targeting miR-34a-5p. P-values calculated with DESeq2 using the two-tailed Wald test and adjusted with the Benjamini-Hochberg procedure. (Right) Gene set enrichment analysis (GSEA) of differentially expressed genes of the glycolytic process GO geneset shown. Plotted is the relative expression or the running enrichment score of all sequenced genes ordered by upregulated to downregulated via the DESeq2 Wald test statistic. P-value calculated by odds of peak enrichment score out of GSEA simulations done with random gene order, adjusted for all gene-set comparisons by Benjamini-Hochberg procedure.

Article Snippet: Predicted conserved targets (mRNA) for miRNAs were obtained from TargetScan 6.0, and a panel of hybrid capture probes against miRNA target genes were designed and purchased from Twist Bioscience.

Techniques: Expressing, Gene Expression, RNA Sequencing, Transduction, Control, Two Tailed Test

Journal: bioRxiv

Article Title: Decoy-seq unlocks scalable genetic screening for regulatory small noncoding RNAs

doi: 10.1101/2025.01.25.634869

Figure Lengend Snippet: Decoy-seq reveals the multifaceted regulatory roles of tRNA-derived fragments. a, Heatmap displaying the Spearman’s correlation of each tRF perturbation based on the aggregated pseudo-bulk expression of 3000 highly variable genes. Each row and column represent an individual miRNA perturbation. Red and blue colors denote positive and negative correlations, respectively. b, Dotplot showing tRF perturbations with significant shifts in the transcriptomic states across varimax-rotated principal components. The x-axis represents the rotated components (RCs) and the y-axis represents the tRF perturbations that are best correlated to the variations in transcriptomic states captured by the respective RCs. Log-likelihood ratio test used to obtain p-values for best linear fit (adjusted using Benjamini-Hochberg procedure); values passing FDR of 25% plotted. c, Dotplot showing a gene ontology (GO) enrichment analysis of the same RCs shown in b using Enrichr. d, (Left) barplots of normalized gene expression count in mRNA 3’ processing and polyadenylation markers NUDT21 and CPSF7 from relevant gene-sets highlighted in c from the bulk RNA-seq data of MDA-MB-231 cells transduced with a non-targeting (NT) control TuD or a TuD targeting tRNA-Val-CAC-2-1.rh. P-values for change in expression calculated in DESeq2 using the two-tailed Wald test and adjusted with the Benjamini-Hochberg procedure. (Top-right) tRNA-Val-CAC-2-1 with tRF sequence tRNA-Val-CAC-2-1.rh highlighted in red and their subgrouping, indicating their origin within the tRNA from which it was derived. (Bottom-right) GSEA analysis of differentially expressed genes of the GO mRNA 3’-end processing gene-set shown. Plotted is the relative expression or the running enrichment score of all sequenced genes ordered by upregulated to downregulated via the DESeq2 Wald test statistic. P-value calculated by odds of peak enrichment score out of GSEA simulations done with random gene order, adjusted for all gene-set comparisons by Benjamini-Hochberg procedure. e, Volcano plot depicting differential usage of polyadenylation sites in 3’UTRs, comparing tRNA-Val-CAC-2-1.rh TuD versus non-targeting TuD pseudo-bulk cell profiles. Red dots indicate 175 3’UTR lengthening events, blue dots represent 257 3’UTR shortening events, and grey dots show non-significant changes. Statistical significance was determined with thresholds set at adjusted p-value < 0.05 and absolute log2(fold change) > 1. Key genes showing significant changes are labeled. f, Kernel density estimation (KDE) plots illustrating read distribution patterns across polyadenylation sites in the 3’UTR regions of two representative genes: GSTZ1 and SMC2. The x-axis shows genomic coordinates for each gene’s 3’UTR region (5’ to 3’ direction), while the y-axis represents relative usage of polyadenylation sites. Gray peaks represent non-targeting TuD cells, while blue peaks show tRNA-Val-CAC-2-1.rh TuD cells. Numbers above peaks indicate the number of reads assigned to each polyadenylation site (PAS) by MAAPER.

Article Snippet: Predicted conserved targets (mRNA) for miRNAs were obtained from TargetScan 6.0, and a panel of hybrid capture probes against miRNA target genes were designed and purchased from Twist Bioscience.

Techniques: Derivative Assay, Expressing, Gene Expression, RNA Sequencing, Transduction, Control, Two Tailed Test, Sequencing, Labeling