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goat il1β  (R&D Systems)


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    Structured Review

    R&D Systems goat il1β
    Goat Il1β, supplied by R&D Systems, used in various techniques. Bioz Stars score: 97/100, based on 953 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Mathematical Modeling Reveals Differential Control of TNF-α and IL-1β Transcription (A) Differential expression of IL-1β and TNF-α mRNA. Shown is the cumulative distribution function of mRNA counts in RAW 264.7 macrophages stimulated with 500 ng/mL of lipid A for 3 h. A total of 718 cells were measured for <t>IL1β</t> , and 356 for TNF-α , and pooled across at least three smFISH experiments, respectively, and expressed as log 10 (mRNA+1). (B) Characteristics of single-cell mRNA expression. Shown is the CV, burst size ( b m ), and frequency ( f m ) calculated based on moments of the mRNA count data from (A) (expressed as mean ± SD from experimental replicates). “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (C) Distribution of transcription sites is gene dependent. (Left) de-convolved wide-field microscopy image of cells with TNF-α and IL-1β smFISH, revealing Tx through an aggregation of multiple mRNA molecules in the nucleus (insert). Scale bar represents 5 μm. (Middle) the fraction of cells with 0–4 Tx calculated from (A). “ ∗ ” denotes a result of the Fisher exact test (p < 0.05) for difference in the Tx site distributions. (Right) the number of nascent mRNA per Tx. Shown are individual Tx site data, together with the mean and SD of the pooled distribution. “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (D) TNF-α transcription conforms to a one-step stochastic model. The comparison between measured and fitted TNF-α mRNA distributions at 3 h after 500 ng/mL lipid A treatment. In black: a Kaplan-Meier estimator of the measured cumulative distribution functions (CDF) (with 95% confidence intervals); and in red: a family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right. (E) IL-1β transcription conforms to a two-step stochastic model. The comparison between measured and fitted IL-1β mRNA distributions at 3 h after 500 ng/mL lipid A treatment for the depicted model. In black: Kaplan-Meier estimator of measured CDF (with 95% confidence intervals); and in red: family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right.
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    Mathematical Modeling Reveals Differential Control of TNF-α and IL-1β Transcription (A) Differential expression of IL-1β and TNF-α mRNA. Shown is the cumulative distribution function of mRNA counts in RAW 264.7 macrophages stimulated with 500 ng/mL of lipid A for 3 h. A total of 718 cells were measured for <t>IL1β</t> , and 356 for TNF-α , and pooled across at least three smFISH experiments, respectively, and expressed as log 10 (mRNA+1). (B) Characteristics of single-cell mRNA expression. Shown is the CV, burst size ( b m ), and frequency ( f m ) calculated based on moments of the mRNA count data from (A) (expressed as mean ± SD from experimental replicates). “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (C) Distribution of transcription sites is gene dependent. (Left) de-convolved wide-field microscopy image of cells with TNF-α and IL-1β smFISH, revealing Tx through an aggregation of multiple mRNA molecules in the nucleus (insert). Scale bar represents 5 μm. (Middle) the fraction of cells with 0–4 Tx calculated from (A). “ ∗ ” denotes a result of the Fisher exact test (p < 0.05) for difference in the Tx site distributions. (Right) the number of nascent mRNA per Tx. Shown are individual Tx site data, together with the mean and SD of the pooled distribution. “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (D) TNF-α transcription conforms to a one-step stochastic model. The comparison between measured and fitted TNF-α mRNA distributions at 3 h after 500 ng/mL lipid A treatment. In black: a Kaplan-Meier estimator of the measured cumulative distribution functions (CDF) (with 95% confidence intervals); and in red: a family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right. (E) IL-1β transcription conforms to a two-step stochastic model. The comparison between measured and fitted IL-1β mRNA distributions at 3 h after 500 ng/mL lipid A treatment for the depicted model. In black: Kaplan-Meier estimator of measured CDF (with 95% confidence intervals); and in red: family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right.
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    Mathematical Modeling Reveals Differential Control of TNF-α and IL-1β Transcription (A) Differential expression of IL-1β and TNF-α mRNA. Shown is the cumulative distribution function of mRNA counts in RAW 264.7 macrophages stimulated with 500 ng/mL of lipid A for 3 h. A total of 718 cells were measured for IL1β , and 356 for TNF-α , and pooled across at least three smFISH experiments, respectively, and expressed as log 10 (mRNA+1). (B) Characteristics of single-cell mRNA expression. Shown is the CV, burst size ( b m ), and frequency ( f m ) calculated based on moments of the mRNA count data from (A) (expressed as mean ± SD from experimental replicates). “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (C) Distribution of transcription sites is gene dependent. (Left) de-convolved wide-field microscopy image of cells with TNF-α and IL-1β smFISH, revealing Tx through an aggregation of multiple mRNA molecules in the nucleus (insert). Scale bar represents 5 μm. (Middle) the fraction of cells with 0–4 Tx calculated from (A). “ ∗ ” denotes a result of the Fisher exact test (p < 0.05) for difference in the Tx site distributions. (Right) the number of nascent mRNA per Tx. Shown are individual Tx site data, together with the mean and SD of the pooled distribution. “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (D) TNF-α transcription conforms to a one-step stochastic model. The comparison between measured and fitted TNF-α mRNA distributions at 3 h after 500 ng/mL lipid A treatment. In black: a Kaplan-Meier estimator of the measured cumulative distribution functions (CDF) (with 95% confidence intervals); and in red: a family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right. (E) IL-1β transcription conforms to a two-step stochastic model. The comparison between measured and fitted IL-1β mRNA distributions at 3 h after 500 ng/mL lipid A treatment for the depicted model. In black: Kaplan-Meier estimator of measured CDF (with 95% confidence intervals); and in red: family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right.

    Journal: Cell Systems

    Article Title: Gene-Specific Linear Trends Constrain Transcriptional Variability of the Toll-like Receptor Signaling

    doi: 10.1016/j.cels.2020.08.007

    Figure Lengend Snippet: Mathematical Modeling Reveals Differential Control of TNF-α and IL-1β Transcription (A) Differential expression of IL-1β and TNF-α mRNA. Shown is the cumulative distribution function of mRNA counts in RAW 264.7 macrophages stimulated with 500 ng/mL of lipid A for 3 h. A total of 718 cells were measured for IL1β , and 356 for TNF-α , and pooled across at least three smFISH experiments, respectively, and expressed as log 10 (mRNA+1). (B) Characteristics of single-cell mRNA expression. Shown is the CV, burst size ( b m ), and frequency ( f m ) calculated based on moments of the mRNA count data from (A) (expressed as mean ± SD from experimental replicates). “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (C) Distribution of transcription sites is gene dependent. (Left) de-convolved wide-field microscopy image of cells with TNF-α and IL-1β smFISH, revealing Tx through an aggregation of multiple mRNA molecules in the nucleus (insert). Scale bar represents 5 μm. (Middle) the fraction of cells with 0–4 Tx calculated from (A). “ ∗ ” denotes a result of the Fisher exact test (p < 0.05) for difference in the Tx site distributions. (Right) the number of nascent mRNA per Tx. Shown are individual Tx site data, together with the mean and SD of the pooled distribution. “ ∗ ” denotes a result of a two-sample Mann-Whitney U test between groups (p < 0.01). (D) TNF-α transcription conforms to a one-step stochastic model. The comparison between measured and fitted TNF-α mRNA distributions at 3 h after 500 ng/mL lipid A treatment. In black: a Kaplan-Meier estimator of the measured cumulative distribution functions (CDF) (with 95% confidence intervals); and in red: a family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right. (E) IL-1β transcription conforms to a two-step stochastic model. The comparison between measured and fitted IL-1β mRNA distributions at 3 h after 500 ng/mL lipid A treatment for the depicted model. In black: Kaplan-Meier estimator of measured CDF (with 95% confidence intervals); and in red: family of 50 models fitted to the data. Fitted parameter values (means ± SD) listed on the right.

    Article Snippet: Samples were incubated in the presence of 1:100 anti-IL1β primary antibody (abcam; ab9722) for 1 h at room temperature, washed and further incubated for 30 mins in the presence of 1:500 secondary antibody (abcam; ab150077) before a final PBS wash.

    Techniques: Control, Quantitative Proteomics, Expressing, MANN-WHITNEY, Microscopy, Comparison

    Journal: Cell Systems

    Article Title: Gene-Specific Linear Trends Constrain Transcriptional Variability of the Toll-like Receptor Signaling

    doi: 10.1016/j.cels.2020.08.007

    Figure Lengend Snippet:

    Article Snippet: Samples were incubated in the presence of 1:100 anti-IL1β primary antibody (abcam; ab9722) for 1 h at room temperature, washed and further incubated for 30 mins in the presence of 1:500 secondary antibody (abcam; ab150077) before a final PBS wash.

    Techniques: Recombinant, Plasmid Preparation, DNA Library Preparation, Sequencing, Software