Drug response expression profiling has emerged as a powerful method for characterizing the cellular response to drug treatment at a molecular level. In this approach, cells are treated with various drugs and changes in expression compared to negative control are measured. Using this method, it is possible to gain insight into the mode of action (MOA) of drugs: distinguish direct from indirect targets and also assess off target effects. It is also possible to use the data for drug repositioning, i.e. finding novel therapeutic targets for existing drugs. The major resources currently available are the Connectivity Map (CMAP) and the Library of Integrated Cellular Signatures (LINCS). Using the drug response expression profiling approach, there are many cases of successful drug repositioning, especially for cancer drugs, as well as novel insights into MOA.
Although useful, the existing resources lack in several important aspects, regarding the resolution they can achieve and which kinds of cell types can be profiled. Firstly, the response measure is biased by only considering a fixed set of genes to profile. This may leave out genes that are very specific to the pathways involved, and also RNAs not commonly present on microarrays such as lncRNAs. By using a sequencing based method it would be possible to get an unbiased response measure, and also get specific response profiles at different genomic elements such as enhancers and promoters. Secondly, studies of drug response in bulk cell culture do not address the heterogeneity in the drug response. Several studies have shown that cells do not react to drug treatment in a uniform way, including at the transcriptional level. If done at the single cell level, it is possible to get a more precise characterization of the response, and to identify genes and pathways that enable or disable an efficient response, by using advanced gene network reverse-engineering approaches, which require multiple expression profiles to work properly. Thirdly, existing resources are mainly done in cancer cell lines, which are not always good models for in vivo situations. In particular, rare cell types are impossible to profile using bulk cell approaches.
In this project, we measure the transcriptional drug response at promoters and enhancers using C1 CAGE, a newly developed method for doing CAGE in single cells. CAGE is the only validated technology that robustly detects expression at enhancers and promoters in a single experiment at genome wide scale. By using C1 CAGE, we can address the shortcomings of currently used methods outlined above, and in addition to achieving unbiased expression measurements with higher genomic resolution, we will also be able to assess population response heterogeneity as well as profile rare cell types.