"No two cells are the same” is a saying that many scientists have used to explain the importance of single cell analysis. When looking at cell populations as a whole, the differences between cells are masked and averaged into a single profile. Even though there are many distinct types of cells in a tissue, we routinely grind up the entire tissue to isolate DNA, RNA, and proteins.
Interestingly, the scientific community has been doing single cell analysis for a long time. Microscopy has allowed us to see things like differences in morphology, expression levels, and cell health in single cells. Flow cytometry has been able to do the same, but at a higher throughput using fluorescent dyes and light scattering.
Most dramatically, cell-to-cell differences can be seen with the output from flow cytometry. Often, multiple “populations” of cells are found within each sample of cells. However, use of both microscopy and flow cytometry has been limited to a small number of analytes that can be interrogated at once, as cell size does not necessarily correlate with the amount of DNA in the nucleus.
The isolation of single cells is also a key part of single cell analysis. Fortunately, multiple methods, such as fluorescently activated cell sorting and laser capture microscopy, have evolved to a point which enables all researchers to isolate single cells. Newer techniques such as microfluidics provide researchers with other methods to perform this key analysis step.
The analysis of RNA from large populations of cells has allowed us to interrogate many targets at once. Moreover, with the development of technologies such as quantitative PCR (qPCR), microarrays, and next-generation sequencing, RNA expression analysis has become easy and quantitative. This has made it possible to identify and correlate multiple targets from a single sample. However, the ability to do this type of RNA expression analysis on single cells has been limited by technology. For example, the tools used for the stabilization and detection of 2 µg of RNA from a single sample don’t work for 20 pg of RNA from a single cell. This has caused researchers to turn to techniques such as boiling samples in reverse transcriptase (RT) buffer or freezing and thawing cells, all of which can damage RNA, but are the only available options.
An Improved Set of Tools
Many of these technology limitations have been addressed by the Single Cell-to-CT Kit for the analysis of RNA, miRNA, and DNA by qPCR from Life Technologies. Stabilization and lysis of cells is simplified by using a proprietary lysis buffer. Reverse transcription of these limited samples is performed using SuperScript® III RT, and the limited template is amplified using a preamplification master mix. The workflow solves many of the problems found with previous methods. All of the reactions are performed in the same tube with the addition of each subsequent reagent. This has minimized the loss of material on tips and tubes. The entire cell is transferred to each subsequent reaction, minimizing the loss when samples are split. Optimized volumes have allowed the reactions to remain small, and even fit into 384-well plates.
Testing the Tools
To verify sensitivity and reproducibility of the kit, 100-cell samples were analyzed through the workflow using an endogenous control gene, and little variability was observed. To evaluate the workflow with low input, single cell equivalents (samples diluted from 100 lysed cells) were also analyzed, and variability similar to that observed with the 100-cell sample was noted, demonstrating that the kit does not introduce variability at this low template level. Sensitivity was also demonstrated, since the qPCR results from single cell equivalents showed RNA at a 100-fold lower level than the 100 cell samples. To test stability after lysis, samples were left for different lengths of time and then frozen and thawed. No deterioration of signal was seen from freezing and thawing, and samples were stable for extended periods of time at room temperature, important when obtaining hundreds of rare cells by fluorescence-activated cell sorting (FACS).
Applications of Single Cell Analysis
We differentiated embryonic stem cells (ESCs) into neural stem cells over a 24-day period, and every 3 days isolated single cells and 100 cell samples by FACS. We analyzed a number of genes from each cell at each time point by qPCR following use of the Single Cell-to-CT Kit and found a significant amount of heterogeneity between single cells at each time point. The 100 cell samples had very little variation in expression levels from sample to sample. At day 0, the single cells split into 2 subpopulations of cells, one expressing a high level of ACTB and the other expressing a low level. There was up to a 5,000-fold difference in expression levels between subpopulations. However, only one population of cells was seen when OCT4 was analyzed. Interestingly, the level of OCT4 expression did not always coincide with levels of ACTB, suggesting that overall transcriptional activity was not responsible for the differences. Another interesting observation was a small population of cells that did not express OCT4 at all. These cells expressed a few of the lineage-specific differentiation markers, suggesting these cells might already have started to differentiate, which supports some models for ESC biology. When analyzing multiple genes from each cell we also found many different expression patterns, suggesting that not all genes are regulated in the same way in the population. Differences also demonstrated that no two cells had exactly the same expression profile. After clustering gene expression profiles at the different time points, it was also seen that cells differentiate at different rates, and that at any one time point cells at different stages are present.
The Single Cell-to-CT Kit employs an easy-to-use workflow for the expression analysis of single cells by qPCR. It allows the analysis of different genes within a population and the identification and characterization of 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 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 due to transcriptional variation and cell-to-cell variation. In the future, analysis of multiple genes from many individual cells may clarify relationships between individual genes and allow the identification of novel cell types.