Abstract
Single-cell analysis enables detailed molecular characterization of cells in relation to cell type, genotype, cell state, temporal variations, and microenvironment. These studies often include the analysis of individual genes and networks of genes. The total amount of RNA also varies between cells due to important factors, such as cell type, cell size, and cell cycle state. However, there is a lack of simple and sensitive methods to quantify the total amount of RNA, especially mRNA. Here, we developed a method to quantify total mRNA levels in single cells based on global reverse transcription followed by quantitative PCR. Standard curve analyses of diluted RNA and sorted cells showed a wide dynamic range, high reproducibility, and excellent sensitivity. Single-cell analysis of three sarcoma cell lines and human fibroblasts revealed cell type variations, a lognormal distribution of total mRNA levels, and up to an eight-fold difference in total mRNA levels among the cells. The approach can easily be combined with targeted or global gene expression profiling, providing new means to study cell heterogeneity at an individual gene level and at a global level. This method can be used to investigate the biological importance of variations in the total amount of mRNA in healthy as well as pathological conditions.
Highlights
Gene expression profiling is widely used in both research and medicine for the characterization of different biological and pathological conditions
To quantify the amount of polyadenylated RNA in individual cells, we developed a fast and simple approach based on full-length reverse transcription of RNA, followed by quantitative PCR (qPCR) with SYBR
We developed a method to quantify the amount of polyadenylated RNA in single cells, which can be used to profile global transcript differences among cell types as well as to monitor the effects of intrinsic and extrinsic factors
Summary
Gene expression profiling is widely used in both research and medicine for the characterization of different biological and pathological conditions. These experiments are performed on bulk samples that include populations of cells. It is well-known that there exist large variations in gene expression levels between individual cells caused by cell type, cell state, genotype, temporal variations in gene expression, and microenvironment [1]. Single-cell gene expression profiling has been applied to a variety of different biological and clinical applications, including cell type characterization [4], hierarchical organization of hematopoietic progenitors [5], the immune response to bacterial infection [6], therapy resistance in cancer [7], and
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