Evaluation and validation of a robust single cell RNA-amplification protocol through transcriptional profiling of enriched lung cancer initiating cells.

Background: Although profiling of RNA in single cells has broadened our understanding of development, cancer biology and mechanisms of disease dissemination, it requires the development of reliable and flexible methods. Here we demonstrate that the EpiStem RNA-Amp™ methodology reproducibly generates microgram amounts of cDNA suitable for RNA-Seq, RT-qPCR arrays and Microarray analysis.

Results: Initial experiments compared amplified cDNA generated by three commercial RNA-Amplification protocols (Miltenyi μMACS™ SuperAmp™, NuGEN Ovation® One-Direct System and EpiStem RNA-Amp™) applied to single cell equivalent levels of RNA (25-50 pg) using Affymetrix arrays. The EpiStem RNA-Amp™ kit exhibited the highest sensitivity and was therefore chosen for further testing. A comparison of Affymetrix array data from RNA-Amp™ cDNA generated from single MCF7 and MCF10A cells to reference controls of unamplified cDNA revealed a high degree of concordance. To assess the flexibility of the amplification system single cell RNA-Amp™ cDNA was also analysed using RNA-Seq and high-density qPCR, and showed strong cross-platform correlations. To exemplify the approach we used the system to analyse RNA profiles of small populations of rare cancer initiating cells (CICs) derived from a NSCLC patient-derived xenograft. RNA-Seq analysis was able to identify transcriptional differences in distinct subsets of CIC, with one group potentially enriched for metastasis formation. Pathway analysis revealed that the distinct transcriptional signatures demonstrated in the CIC subpopulations were significantly correlated with published stem-cell and epithelial-mesenchymal transition signatures.

Conclusions: The combined results confirm the sensitivity and flexibility of the RNA-Amp™ method and demonstrate the suitability of the approach for identifying clinically relevant signatures in rare, biologically important cell populations.