Quantification of multiple gene expression in individual cells
Quantification of multiple gene expression in individual cells
复制标题
DOI：
10.1101/gr.2890204
复制
发表时间：
2004-10-01
期刊：
影响因子：
7
通讯作者：
Veiga-Fernandes, H
中科院分区：
文献类型：
--
作者：
Peixoto, A;Monteiro, M;Veiga-Fernandes, H
定量基因表达分析旨在确定决定细胞行为的基因表达模式。到目前为止，这些评估只能在人口层面上进行。因此，他们决定了一个群体内的平均基因表达，忽略了可能导致不同细胞行为/细胞命运的细胞间异质性。了解单个细胞的行为需要对单个细胞进行多个基因表达分析，这可能是理解所有类型的生物事件和/或分化过程的基础。我们在这里描述了一种新的逆转录聚合酶链式反应(RT-PCR)方法，允许同时定量同一单个细胞中20个基因的表达。这种方法在不同物种和任何类型的基因组合中都有广泛的应用。对RT效率进行了评估。为所有基因提供了统一和最大化的扩增条件。保持了丰度关系，允许精确量化每个细胞的mRNA分子的绝对数量，每个基因的范围从2到1.28x10(9)。我们通过研究所有明显同质的群体(抗原刺激后4天恢复的单克隆性T细胞)，或者使用这种方法，或者使用传统的实时RT-PCR，评估了这种方法对功能遗传读出的影响。单细胞研究揭示了相当大的细胞间差异：并不是所有的T细胞都表达所有的单个基因。基因共表达模式异质性很强。不同转录本和不同细胞中的mRNA拷贝数不同。因此，这种单细胞分析引入了关于功能基因组读出的新的和基本的信息。通过比较，我们还表明，传统的确定总体平均水平的定量分析提供的信息不够充分，甚至可能具有高度误导性。
Quantitative gene expression analysis aims to define the gene expression patterns determining cell behavior. So far, these assessments can only be performed at the population level. Therefore, they determine the average gene expression within a population, overlooking possible cell-to-cell heterogeneity that Could lead to different cell behaviors/cell fates. Understanding individual cell behavior requires multiple gene expression analyses of single cells, and may be fundamental for the understanding of all types of biological events and/or differentiation processes. We here describe a new reverse transcription-polymerase chain reaction (RT-PCR) approach allowing the simultaneous quantification of the expression of 20 genes in the same single cell. This method has broad application, in different species and any type of gene combination. RT efficiency is evaluated. Uniform and maximized amplification conditions for all genes are provided. Abundance relationships are maintained, allowing the precise quantification of the absolute number of mRNA molecules per cell, ranging from 2 to 1.28x10(9) for each individual gene. We evaluated the impact of this approach on functional genetic read-outs by studying ail apparently homogeneous population (monoclonal T cells recovered 4 d after antigen stimulation), using either this method or conventional real-time RT-PCR. Single-cell studies revealed considerable cell-to-cell variation: All T cells did not express all individual genes. Gene coexpression patterns were very heterogeneous. mRNA copy numbers varied between different transcripts and in different cells. As a consequence, this single-cell assay introduces new and fundamental information regarding functional genomic read-outs. By comparison, we also show that conventional quantitative assays determining population averages supply insufficient information, and may even be highly misleading.