In recent years, different technologies have been used to measure genome-wide gene expression levels and to study the transcriptome across many types of tissues and in response to in vitro treatments. However, a full understanding of gene regulation in any given cellular and environmental context combination is still missing. This is partly because analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g. some cell types may not respond to certain treatments).
To circumvent these challenges, researchers from Wayne State University have established a new two-step high-throughput and cost-effective RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g. largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more profitable allocation of resources for the deep sequencing of re-pooled libraries in step two. They have applied this approach to study the response to 26 treatments in three lymphoblastoid cell line samples and we show that it is applicable for other high-throughput transcriptome profiling requiring iterative refinement or screening.
Heatmap and hierarchical clustering of gene expression levels. Gene expression levels (FPKMs) were clustered for each sample (row coloring) and treatment (column coloring, row labeling) combination. The dendrogram shows the Euclidian distance between samples, while the heatmap shows the Pearson correlation (red = 1, blue = -1).