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首页> 外文期刊>Biophysical Chemistry: An International Journal Devoted to the Physical Chemistry of Biological Phenomena >Precise quantification of transcription factors in a surface-based single-molecule assay
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Precise quantification of transcription factors in a surface-based single-molecule assay

机译:基于表面的单分子测定中的转录因子的精确定量

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Biosensors have recognized a rapid development the last years in both industry and science. Recently, a single-molecule assay based on alternating laser excitation has been established for the quantitative detection of transcription factors. These proteins specifically recognize and bind DNA and play an important role in controlling gene expression. We implemented this assay format on a total internal reflection fluorescence microscope to detect transcription factors with immobilized single-molecule DNA biosensors. We quantify transcription factors via colocalization of the two halves of their binding site with immobilized single molecules of a two-color DNA biosensor. We could detect a model transcription factor, the bacterial lactose repressor, at different concentrations down to 150 pM. We found that robust modeling of stoichiometry derived TIRF data is achieved with Student's t-distributions and nonlinear least-squares estimation with weights equal to the inverse of the expected number of bin entries. This significantly improved transcription factor concentration estimates with respect to distribution modeling with Gaussians without adding notable computational effort. The proposed model may enhance the precision of other single-molecule assays quantifying molecular distributions. Our measurements reliably confirm that the immobilized biosensor format is more sensitive than a previously published solution based approach.
机译:生物传感器已经认识到了在工业和科学的最后几年快速发展。最近,已经建立了基于交替激光激发的单分子测定用于定量检测转录因子。这些蛋白质特异性识别和结合DNA并在控制基因表达中起重要作用。我们在全内反射荧光显微镜上实施了该测定格式,以检测具有固定化的单分子DNA生物传感器的转录因子。我们通过与双色DNA生物传感器的固定的单分子的固定的单分子分子来定量转录因子。我们可以检测模型转录因子,细菌乳糖阻遏物,不同浓度下降至150μm。我们发现,通过学生的T分布和非线性最小二乘估计来实现化学计量衍生的TIRF数据的鲁棒建模,其重量等于预期的箱子条目数的倒数。这显着改善了与高斯分布建模的转录因子浓度估计,而无需添加显着的计算工作。所提出的模型可以增强其他单分子测定量化分子分配的精度。我们的测量值可靠地证实,固定化的生物传感器格式比以前公布的基于解决方案的方法更敏感。

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