MicroRNA (miRNAs) are short (18–24 nucleotides), non-coding RNAs that regulate gene expression by both disrupting messenger RNA (mRNA) stability and inhibiting mRNA translation. The expression of miRNA species in cellular populations is thought to drive downstream gene expression and protein functionality. Our goal was to determine the variability in miRNA expression at the single cell level using a microfluidic system which automates single cell capture and miRNA pre-amplification for downstream expression analysis. We have developed a simple, modular workflow for streamlined analysis of cell populations down to the single-cell level (Figure 1). The workflow is centered on two key components: the C1TM Single Cell Auto Prep System (Figure 1a: Sample Prep, including cell isolation and cDNA preparation from miRNA species) and the Dynamic Array™ IFC and BiomarkTM HD System (Figure 1b: Read out, for highly parallel expression analysis). The Specific Target Amplification (STA) chemistry performed on each individual cell captured on the C1TM IFC borrows components from the Single Cell-to-Ct™ kit (Life Technologies) for the lysis and preamplification steps and components from the TaqMan® MicroRNA Reverse Transcription Kit (Life Technologies) for the Reverse Transcription step (Figure 2).

Using the Dynamic Array IFCs and the Biomark HD System, up to 96 cDNA samples preamplified from the 96 single cells are each analyzed in parallel with up to 96 microRNA TaqMan expression assays. Principal Component Analysis (PCA) of the data using Fluidigm’s SINGuLAR™ Analysis Toolset v2.0 reveals significant variations in the expression of discrete miRNA species in a population of single cells from a single phenotype (Figure 3, 4, and 5). Comparison of phenotypically distinct populations (human embryonic fibroblasts, human induced Pluripotent Stem Cells (iPS), human Neural Progenitor Cells (NPC) derived from the iPS, and fully differentiated human neurons (HN)) demonstrate more dramatic differences in addition to the heterogeneity of expression within each group.

Results

Figure 1: Integrated workflow for miRNA analysis in single cells

The C1 Single-Cell Auto Prep System performs Specific Target Amplification (STA) of miRNA transcripts from single cells using the reagents and a protocol developed for this purpose by Life Technologies (protocol “Single-cell MicroRNA expression analysis”). The whole process, from loading the cell suspension on the C1 Integrated Fluidic Circuit (IFC) to full data analysis of the data can be accomplished in less than 24 hours.

Figure 2. C1 MicroRNA STA experimental workflow

Figure 3. Analysis: Single iPS cells and their NPC progeny

A) Unsupervised clustering of the data clearly distinguishes iPS cells from their NPC progeny obtained using small molecules1. Subpopulations are also revealed within each group of cells. B) PCA shows a clear difference between the two phenotypically distinct cell populations. C) Violin plots show differential expression of miRNAs in different subpopulations and reveal the main contributors to Principal Components 1 and 2 (in order from top left to right). The variations in expression of a set of five miRNAs between iPS and NPC shows the same trends as microarray measurements obtained with Embryonic Stem cells (ES) and their NPC progeny (unpublished data, courtesy of Yao Shuyuan).

Figure 4. Human Neurons, iPS and NPC cells

A) Unsupervised clustering of the data obtained with iPS, NPC and mature neurons (HN) clearly distinguishes HN cells from iPS and NPC cells and also reveals subpopulations within each cell type. miR-9 is more frequently and more highly expressed in mature neurons (HN). B) PCA clearly distinguishes between the three cell types based on miRNA expression.The expression of miR-20a, 19b, 17 & 106a is lower in HN, as expected based on neural differentiation and aging data2,3 .

Figure 5. Embryonic fibroblasts at different passage number

A) Unsupervised clustering of the data from two different cultures of BJ embryonic fibroblasts obtained at difference passage numbers (P13 and P24) is not able to distinguish the populations from one another, even though it can reveal different miRNA expression patterns between individual cells. B) PCA analysis of the miRNA expression data from P13 and P24 cells confirms that the two cell populations are undistinguishable based on miRNA expression. C) When the passage numbers are more distant (P7 vs. P24), PCA analysis of the miRNA data (heatmap not shown) distinguishes passage number P7 from P24.

Conclusion

•We have developed a streamlined protocol on the C1TSingle-Cell Auto Prep System to analyze the expression patterns of miRNA species in up to 96 individual cells processed in parallel with minimum hands-on time, in less than 24 hours.