Single-Molecule Detection in the Study of Gene Expression
Single-Molecule Detection in the Study of Gene Expression
复制标题
基因表达研究中的单分子检测
DOI：
10.1017/9781108525909.015
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Y.
中科院分区：
文献类型：
--
作者：
Kumar;V.;Leclerc;S.;Taniguchi;Y.
确定基因表达调控的规则是预测细胞如何解码基因组序列以产生各种表型的重要一步。成像技术的最新进展揭示了基因表达的随机性质，即在具有相同基因组序列的细胞中可以产生不同数量的信使核糖核酸和蛋白质分子(Elowitz，2002；Kaufmann和van Oudenaarden，2007)。早期的一项研究表明，这种随机性是由两个因素造成的：内在和外在的噪音。前者是由于基因表达过程中瞬间的随机化学反应，而后者是由于较长时间的基因表达整合而产生的细胞特有的分子状态(Elowitz，2002；Kaufmann和van Oudenaarden，2007)。这一发现启发了研究，以探索细胞如何在这种随机性下确定性地导致强健的表型。相比之下，这也促使人们研究细胞如何利用这种随机性为神经发育(Johnson等人，2015年)、细菌耐药性的出现(S-罗梅罗和CasadeúS，2014年)或癌症发展(Marusyk等人，2012年；Junttila和De Sauvage，2013年)等过程产生不同类型的表型。表征和预测单细胞中基因表达的异质性是揭示随机基因表达机制的关键方法，但它需要多个领域的研究。为了实验检测基因表达的随机行为，先进的荧光成像方法是必不可少的。特别是，具有单一mRNA或蛋白质敏感性的成像方法对于测量任何丰度的随机性都至关重要，包括由于相对较少的mRNA和蛋白质数量而出现的泊松行为(Paulsson，2005)。为了定量描述和预测随机基因表达，理论建模方法是必要的。通常，当开发模型时，诸如RNA聚合酶和核糖体反应等构成基因表达的分子过程被假设由用过渡方案描述的随机过程序列来表示，
Determining rules for gene expression regulation is an important step toward predicting how cells are decoding the genome sequence to create a wide variety of phenotypes. Recent advances in imaging technologies revealed the stochastic nature of gene expression, in which different numbers of mRNA and protein molecules can be created in cells that have the same genome sequence (Elowitz, 2002); Kaufmann and van Oudenaarden, 2007). An early research revealed that this stochasticity is yielded by two factors: intrinsic and extrinsic noise. While the former is due to instant random chemical reactions in gene expression process, the latter is caused by cell specific molecular states emerging from the integration of gene expression over a longer time (Elowitz, 2002); Kaufmann and van Oudenaarden 2007). This finding inspired studies to investigate how cells deterministically cause robust phenotypes under such stochasticity. In contrast, this also motivated investigations on how cells utilize this stochasticity to generate different kinds of phenotypes for processes such as neural development (Johnson et al., 2015), emergence of bacterial resistance (Sánchez-Romero and Casadesús, 2014), or cancer development (Marusyk et al., 2012; Junttila and de Sauvage, 2013).Characterizing and predicting heterogeneity of gene expression in single cells is a key approach to reveal mechanisms involved in stochastic gene expression, but it requires multiple fields of research. To experimentally detect stochastic behaviors of gene expression, advanced fluorescence imaging methods are essential. Especially imaging methods that have sensitivity of single mRNA or protein are crucial to measure stochasticity at any abundance, including Poissonian behaviors that emerge due to the relatively small number of mRNAs and proteins (Paulsson, 2005). To quantitatively characterize and predict stochastic gene expressions, theoretical modeling approaches are necessary. Typically, when developing a model, molecular processes constituting gene expression, such as RNA polymerase and ribosome reactions, are hypothesized to be represented by a sequence of stochastic processes described with a transition scheme,