Gene expression-based approaches to understanding huntington's disease and new tools for the interpretation of expression datasets

Abstract

Expression profiling has become a well-established and widely utilized approach to generate and support hypotheses regarding biological processes. Etiopathologic mechanisms of Huntingtin toxicity include transcriptional dysregulation [reviewed in Luthi-Carter (Drug Dis Today Dis Mech 4:111-119, 2007)], and a strikingly characteristic set of HD-related changes in gene expression have been informative in assessing model systems and underlying pathological (and compensatory) mechanisms [reviewed in Seredenina and Luthi-Carter (Neurobiol Dis 45:83-98, 2012)]. Here we provide details of two strategies that we have developed to improve gene expression analyses in Huntington's disease (HD). The first subsection of this chapter describes an approach we have termed "Population-Specific Expression Analysis" (PSEA). This computational method was developed to be able to assign expression measures to specific cell types within complex tissues. The second subsection demonstrates the implementation of a simple measure that we have termed the "concordance coefficient" to compare the similarities of disease-related effects across different systems. These methods are both applicable to a variety of experimental contexts and molecular data types.