Genomics methods like expression profiling and epigenomic profiling hold enormous potential benefit to parse the effects of drugs and assess patient response to therapy in the clinic. However, the lack of advanced sample preparation methods is a blocking issue in translating informative genomic techniques for application in chemical biology and clinical medicine. Even advanced single-cell genomics methods require large total input quantities and struggle to produce more than one type of genomic profile, which may not be sufficiently informative or predictive. Powerful computational tools to integrate multiple layers of `omic information are available and ready to accept data if available from the right samples. Thus, the need to prepare multiple sequence library types at the same time (eg co-profiling epigenome and transcriptome) from small numbers of primary cells in a single streamlined system is a crucial missing link in the advancement of chemical biology and realizing the potential impact of genomic medicine. The proposed project will solve this sample preparation challenge by establishing simultaneous epigenomic and expression co-profiling sample preparation method from low biomass samples in an automated microfluidic device. The co-profiling sample preparation system will constitute a single microdevice able to accept small multiple primary cell samples and automatically produce multiple sequence library types in a single run. We will apply this capability to dissect the pleiotropic effects of an important new drug class, histone deacetylase inhibitors (HDACi). HDACi have (currently) unpredictable effects on gene activity, but show promise for therapeutic modulation of proliferative, inflammatory, autoimmune, and neurological processes. To evaluate potential therapeutic effects and safety (regarding immunity to infection) of HDACi treatment, we will co-profile HDACi-treated human primary cells.
Prescription drugs have complex effects on cells, and affect different people in different ways. A number of advanced analytical technologies exist with the power to unravel these effects and help predict how a given person will respond to a given drug, however it is currently difficult to apply these technologies to a patient sample. This project will test a new, more efficient lab-on-chip system that will enable analysis of key patient sample types and apply this system to study a promising new drug class.
