Single cell gene expression analysis is a powerful technique that provides a unique and insightful perspective on biological pathways and processes. Here we present a robust workflow that enables fast and accurate analysis of up to 100 genes in isolated single cells. Our workflow is highly sensitive, by assessing RNA reference standards we find that a single RNA transcript is detected with about 80% efficiency. We used this workflow to study differentiation in cultured NTera2 cells (NT2), a human embryonic stem cell model system. We analyzed untreated NT2 cells, and NT2 cells treated with low and high doses of retinoic acid (RA) for 10 days to initiate differentiation to a neuronal lineage. The expression levels of 16 genes were quantified in 164 single cells by multiplex real-time qPCR with two technical replicates. The entire experiment, from cultured cells to results, can be completed in 2-3 days and requires four 384-well qPCR plates for gene expression quantification. We find that control cells and cells treated with a high dose of RA (10 uM) are relatively homogeneous in the expression levels of the targeted genes. However cells treated with a low dose of RA (0.25 uM) exhibit significant heterogeneity with respect to gene expression; about half of the cells are similar to the high-dose RA cells, the other cells exhibit a wide range of partial differentiation. Interestingly, we find that LEFTY2 expression is almost exclusive to the low dose RA cells and strongly correlates with partial differentiation. A time-course study analyzing cell populations reveals that LEFTY2 is only transiently expressed in differentiating NT2 cells with peak expression at 3 days of high dose RA treatment. These findings imply that, in NT2 cells, LEFTY2 is a potential biomarker of early differentiation. In summary, we present an accurate, sensitive and robust single cell analysis procedure that uses standard reagents and platforms. We envision that this workflow will enable researchers to investigate cell heterogeneity in biological pathways in a cost-effective way.
Citation Format: Steven T. Okino, Michelle Kong, Jason Ma, Joshua Fenrich, Luca Boveri, Yann Jouvenot, Yan Wang. High throughput single cell gene expression profiling by multiplex qPCR. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2891.