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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for <t>poly(A)-mRNA</t> sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.
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(A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for poly(A)-mRNA sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.

Journal: bioRxiv

Article Title: Multi-omics and biophysical phosphoproteomics upon BRAF inhibition uncover functional networks of BRAFV600E-driven signaling

doi: 10.64898/2026.02.09.704793

Figure Lengend Snippet: (A) Overview of the time-resolved transcriptomic profiling workflow. A2058, COLO-201 and MNT-1 cells (results here shown for A2058) were treated with 100 nM Dabrafenib or DMSO, and samples were collected at 0, 2, 4, and 8 hours for poly(A)-mRNA sequencing. Transcript abundance dynamics were used to infer the transcriptional programs downstream of BRAF inhibition. (B) PCA of gene counts of the top 10% most variable genes in Dabrafenib- (green) and DMSO-treated (grey) A2058 cells. (C) Inference of transcription factor (TF) activity using gene expression signatures upon Dabrafenib treatment (2-8 h) of known targets (normalized weighted mean test with CollecTRI database, p-value < 0.05). Heatmap shows scores of top TFs across time points for A2058 cells, highlighting both upregulated (red) and downregulated (blue) TFs. (D) Venn diagram showing the overlap between TFs with significantly altered phosphorylation in the Dabrafenib phosphoproteomics time course and TFs with inferred changes in activity upon Dabrafenib treatment based on the transcriptomics data. (E) Schematic overview of the thermal proteome profiling (TPP) experiment. A2058 cells treated with 100 nM Dabrafenib or DMSO for 4 hours were subjected to a temperature gradient, followed by lysis and multiplexed MS-based quantification of soluble proteins across the temperature range. (F) Volcano plot of differential protein thermal stability upon Dabrafenib treatment (100 nM, 4 h) relative to DMSO. Blue and red dots denote stabilizing and destabilizing responses, respectively (moderated t-test, adjusted p-value < 0.05). (G) Pathway enrichment analysis of proteins showing altered thermal stability upon Dabrafenib treatment (normalized weighted mean test, with GO terms and Reactome pathways, p-value < 0.05). (H) Venn diagram showing overlap between proteins with altered phosphorylation in the Dabrafenib phosphoproteomics time course and those with altered thermal stability in TPP upon Dabrafenib treatment.

Article Snippet: For library preparation an automated version of the NEBNext® UltraTM II Directional RNA Library Prep Kit was used, following section 1 - Protocol for use with NEBNext Poly(A) mRNA Magnetic Isolation Module .

Techniques: Sequencing, Inhibition, Activity Assay, Gene Expression, Phospho-proteomics, Transcriptomics, Lysis