Gene Expression Analysis in Stem Cell-derived Cortical Neuronal Cultures Using Multi-well SYBR Green Quantitative PCR Arrays.

To optimize differentiation protocols for stem cell-based modeling applications, it is essential to assess the change in gene expression during the differentiation process. This allows controlling its differentiation efficiency into the target cell types. While RNA transcriptomics provides detail at a larger scale, timing and cost are prohibitive to include such analyses in the optimization process. In contrast, expression analysis of individual genes is cumbersome and lengthy. Here, we developed a versatile and cost-efficient SYBR Green array of 27 markers along with two housekeeping genes to quickly screen for differentiation efficiency of human induced pluripotent stem cells (iPSCs) into excitatory cortical neurons. We first identified relevant pluripotency, neuroprogenitor, and neuronal markers for the array by literature search, and designed primers with a product size of 80-120 bp length, an annealing temperature of 60°C, and minimal predicted secondary structures. We spotted combined forward and reverse primers on 96-well plates and dried them out overnight. These plates can be prepared in advance in batches and stored at room temperature until use. Next, we added the SYBR Green master mix and complementary DNA (cDNA) to the plate in triplicates, ran quantitative PCR (qPCR) on a Quantstudio 6 Flex, and analyzed results with QuantStudio software. We compared the expression of genes for pluripotency, neuroprogenitor cells, cortical neurons, and synaptic markers in a 96-well format at four different time points during the cortical differentiation. We found a sharp reduction of pluripotency genes within the first three days of pre-differentiation and a steady increase of neuronal markers and synaptic markers over time. In summary, we built a gene expression array that is customizable, fast, medium-throughput, and cost-efficient, ideally suited for optimization of differentiation protocols for stem cell-based modeling.