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Using Single-Cell Analysis to Identify Subpopulations and Variability in Gene Expression
Research and Development, Life Technologies, 2130 Woodward St, Texas, Austin
BioTechniques, Vol. 51, No. 4, October 2011, pp. 278–279
Full Text (PDF)


When gene expression from a population of cells is analyzed en masse, the resulting profile represents an average of all the cells in the group. This means that differences between individual cells are masked, and subpopulations cannot be distinguished. Even more importantly, small populations are lost, and many times it is these cells that are critical to understanding the complexity of the biological system under study. For example, a small number of undifferentiated stem cells within a group of differentiated cells or a population of circulating cancer stem cells can have significant clinical impact. Even with the knowledge that these differences exist, RNA is still routinely isolated from bulk cultured cells or entire organs for gene expression analysis. Consequently, the data obtained represent the gene expression profile of the population, not individual cells. Traditionally, tools for the analysis of single cells have not been easy to use, but the development and optimization of products in four key areas of the workflow (single-cell capture, sample preparation, amplification and detection, and data analysis) put single-cell analysis within reach.

Materials and Methods

We used the Single Cell-to-CT™ Kit (Life Technologies) to investigate differences in gene expression among individual embryonic stem cells. CV25 (olig2-EGFP; a derivative of BG01) was our source of hESCs. Single cells were isolated using a BD FACSAria™ instrument (BD Biosciences). The Single Cell-to-CT Kit contains all reagents needed for cell lysis and stabilization, cDNA synthesis using Single Cell VILO™ with SuperScript® III RT, and preamplification of selected genes using Single Cell TaqMan® PreAmp Master Mix and Assays-on-Demand™ products. TaqMan® Gene Expression Master Mix was used for qPCR. One hundred genes were selected for preamplification. Of these, 10 were selected for initial screening: hESC-specific genes POU5F1 (OCT4), UTF1, and ZFP42 (REX1); NSC-specific genes SOX1, NES (nestin), and PAX6; motor neuron– specific TUBB3; cardiac-specific T; astrocyte-specific GFAP; control gene GAPDH. JMP® and Partec® software were used for data analysis.

One advantage of this kit is that the entire reaction is carried out in a single tube, without the need to transfer the sample, which can result in loss of the minute amounts of template. The kit is simple to use, minimizing errors, and is flexible enough to allow for multiple stopping points throughout the workflow.


To verify sensitivity and reproducibility, 100-cell samples were analyzed through the workflow, and little variability was observed, based on an endogenous control gene (Figure 1). Single-cell equivalents (samples diluted from 100 lysed cells) were also analyzed using the same the workflow, and variability similar to that observed with the100-cell sample was noted, demonstrating that the kit does not introduce variability at this low template level. Sensitivity was also demonstrated, since the Cts of the single-cell equivalents and 100-cell samples are approximately 6.6 cycles apart. It is important to note the large variability seen in single-cell analysis. Because our results ruled out kit-introduced variability, the variability in expression seen among individual cells can be attributed to biological variability.

The kit was also compared to three other previously published protocols (Figure 2). Using 10-cell samples, the deleterious effects of salt, divalent cations, and heating are shown (note that the Single Cell-to-CT Kit protocol is performed at room temperature). The importance of inactivating cellular RNases is also shown by the reduced sensitivity in samples that were frozen and thawed in PBS.

Eighty-four single hESCs and 100-cell samples were analyzed for ACTB and OCT4 expression, and qPCR results are shown as amplification plots (Figure 3). For ACTB, the 100-cell samples showed very little variation in expression levels. Single-cell samples formed two distinct populations, with high and low expression levels showing greater than a 5000-fold difference. However, for OCT4 only one population existed, with expression levels spread over a larger range than in either of the ACTB populations. Levels for ACTB and OCT4 did not correlate, i.e., some of the cells with high ACTB levels expressed OCT4 at a high level and others at a low level. This suggests that the expression profile for each cell is unique. Also, there was a small subpopulation of cells that expressed OCT4 at a very low level (Figure 3, right panel, amplification plots farthest to the right). These cells also expressed some of the lineage-specific markers, suggesting these cells might already have started to differentiate. Analyzing gene expression profiles en masse in the 100-cell samples gave an average profile that obscured this distinctive OCT4 subpopulation.

When analyzing multiple genes within the population, expression levels varied for each gene (Figure 4), suggesting that not all genes are regulated equally. More examples of subpopulations were also observed, such as the small number of cells that expressed PAX6 at a high level, even though the majority of the population did not express this gene at all.


The Single Cell-to-CT Kit employs an optimized workflow that allows single-cell expression analysis using qPCR to look at expression levels of different genes within a population, and identify and characterize subpopulations. Since the technical variation introduced by the workflow was very low, we were able to show that the expression levels of different genes can vary significantly from cell to cell (Figure 4), and that cell size, which may change during the cell cycle, does not account for the large variation. Single-cell profiles are also heterogeneous and may be so due to transcriptional variation and cell-to-cell variation. In the future, analysis of multiple genes from individual cells during ESC differentiation may clarify relationships between individual genes and allow the identification of novel markers for different lineage-specific cell types.