Zipporah E Binita, Govarthanan Kavitha, Shyamsunder Pavithra, Verma Rama S
Stem Cell and Molecular Biology Lab, Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India.
Cancer Science Institute, NUS, Singapore, Singapore.
Methods Mol Biol. 2018;1783:243-258. doi: 10.1007/978-1-4939-7834-2_12.
Gene expression analysis mainly helps to study gene quantification methods by using various downstream detection approaches like imaging, amplification, probe hybridization, or sequencing. With respect to DNA, which is less static, mRNA levels vary over time, between cell types under divergent conditions. Gene expression analysis is principally focused on determination of mRNA levels transcribed from DNA. DNA microarrays are one of the robust and powerful tools to detect changes in multiple transcripts in larger cohorts in parallel. The basic principle of DNA microarray hybridization is complementary base pairing of single-stranded nucleic-acid sequences. On a microarray platform (also called a chip), known sequences called targets are attached at fixed locations (spots) to a solid surface such as glass using robotic spotting. Since a large number of samples (variables) are used in a typical hybridization experiment, which often leads to impreciseness for example, target mRNA transcribed from the same source should be identical every time. In such cases, developing an optimized protocol for microarray platform to study the expression profiling of differentially regulated genes is a challenging task. Thus genome-wide expression array analysis yields data about candidate genes that may be involved in disease acquisition progression, and helps in better understanding the pathophysiology of the disease. In this chapter we describe in detail the microarray technique, a well-accepted method for understanding the development and progression of Fanconi anemia (FA), a genetic disorder which is characterized by progressive bone marrow failure and a predisposition to cancer.
基因表达分析主要通过使用各种下游检测方法，如成像、扩增、探针杂交或测序，来帮助研究基因定量方法。对于相对不稳定的DNA，mRNA水平会随时间变化，并且在不同条件下的细胞类型之间也存在差异。基因表达分析主要侧重于测定从DNA转录的mRNA水平。DNA微阵列是在更大队列中并行检测多个转录本变化的强大有力工具之一。DNA微阵列杂交的基本原理是单链核酸序列的互补碱基配对。在微阵列平台（也称为芯片）上，使用机器人点样将称为靶标的已知序列固定在诸如玻璃的固体表面的固定位置（斑点）上。由于在典型的杂交实验中使用大量样本（变量），这常常会导致不精确性，例如，从同一来源转录的靶标mRNA每次都应相同。在这种情况下，为微阵列平台开发一个优化方案以研究差异调节基因的表达谱是一项具有挑战性的任务。因此，全基因组表达阵列分析可产生有关可能参与疾病发生发展的候选基因的数据，并有助于更好地理解疾病的病理生理学。在本章中，我们将详细描述微阵列技术，这是一种被广泛接受的用于了解范可尼贫血（FA）的发生和发展的方法，范可尼贫血是一种遗传性疾病，其特征是进行性骨髓衰竭和易患癌症。
