Microfluidics for single-cell noise measurements

微流体和延时显微镜的结合是一个强大的组合，具有在整个细胞群中产生单细胞测量时间序列数据的潜力。该项目旨在利用这些工具来开发技术和策略，以促进单细胞水平上随机事件的研究。工作的重点是微流体和图像分析技术的发展。这个项目中的这些进展正被应用于研究酵母GCN4转录调节因子表达中的噪声的具体例子。这是激活氨基酸饥饿期间，通过一个新的转录后调控系统。迄今为止，对基因表达噪声的研究大多集中在转录噪声上，因此对转录后调控对噪声的影响的了解相对较少。生物系统在宏观层面上表现出许多明显的确定性行为，但其潜在机制往往是随机的。基因表达中的噪声在克隆细胞群体中产生异质性，并且是许多细胞过程（例如昼夜节律）的重要因素。即使在众所周知的“再压器”合成基因电路中，噪声也对电路输出产生了重大影响。因此，对基因表达的随机性的认识对于全面理解生物学是至关重要的，对于成功设计未来的合成生物系统也是必不可少的。因此，该项目属于EPSRC合成生物学研究领域。生物工程总是会产生不完全相同的细胞群，任何设计生物系统的尝试都必须考虑到这种异质性。通过利用微流体技术研究面包酵母（Saccharomyces cerevisiae）中自然发生的和工程的细胞间异质性，该项目旨在定量了解这种真核生物中基因表达的随机性

The combination of microfluidics and time-lapse microscopy is a powerful one, with the potential for the generation of time-series data for single-cell measurements across a whole population of cells. This project aims to employ these tools for the development of technologies and strategies that facilitate the study of stochastic events at the single-cell level. A key focus of the work is the development of microfluidic and image analysis techniques.Such developments within this project are being applied to study the specific example of noise within the expression of the yeast GCN4 transcriptional regulator. This is activated during amino acid starvation, through a novel post-transcriptional regulatory system. The majority of research into gene expression noise thus far has focused on transcriptional noise, so the impact of post-transcriptional regulation on noise is relatively poorly understood.Biological systems show many apparently deterministic behaviours at the macroscopic level, but their underlying mechanisms are often stochastic in nature. Noise in gene expression generates heterogeneity across clonal cell populations, and is an important factor in many cellular processes (e.g. circadian rhythms). Even within the well-known "repressilator" synthetic gene circuit, noise had a significant impact on the circuit output. An appreciation of stochasticity in gene expression is therefore crucial for a complete understanding of biology, and will also be essential for the successful design of future synthetic biological systems. Consequently, this project falls within the EPSRC research area of synthetic biology. Engineering of living organisms will always create populations of cells that are not all identical, and any attempt to engineer biological systems must take this heterogeneity into account. By utilising microfluidics to study both naturally occurring and engineered cell-to-cell heterogeneity in baker's yeast (Saccharomyces cerevisiae), this project aims to provide a quantitative understanding of gene expression stochasticity in this eukaryotic organism