Abstract
The characterization of molecular changes in diseased tissues gives insight into pathophysiological mechanisms and is important for therapeutic development. Genome-wide gene expression analysis has proven valuable for identifying biological processes in neurodegenerative diseases using post mortem human brain tissue and numerous datasets are publically available. However, many studies utilize heterogeneous tissue samples consisting of multiple cell types, all of which contribute to global gene expression values, confounding biological interpretation of the data. In particular, changes in numbers of neuronal and glial cells occurring in neurodegeneration confound transcriptomic analyses, particularly in human brain tissues where sample availability and controls are limited. To identify cell specific gene expression changes in neurodegenerative disease, we have applied our recently published computational deconvolution method, population specific expression analysis (PSEA). PSEA estimates cell-type-specific expression values using reference expression measures, which in the case of brain tissue comprises mRNAs with cell-type-specific expression in neurons, astrocytes, oligodendrocytes and microglia. As an exercise in PSEA implementation and hypothesis development regarding neurodegenerative diseases, we applied PSEA to Parkinson's and Huntington's disease (PD, HD) datasets. Genes identified as differentially expressed in substantia nigra pars compacta neurons by PSEA were validated using external laser capture microdissection data. Network analysis and Annotation Clustering (DAVID) identified molecular processes implicated by differential gene expression in specific cell types. The results of these analyses provided new insights into the implementation of PSEA in brain tissues and additional refinement of molecular signatures in human HD and PD.
Highlights
Identifying changes in gene or protein expression has the potential to focus attention on key molecular mechanisms underlying a given degenerative process
AND CONCLUSIONS Our study has further demonstrated the applicability and utility of Population-Specific Expression Analysis (PSEA) to refine analyses of interesting human disease datasets
These further demonstrate the technical soundness of the method and show solutions for applying PSEA in cases where modeling all resident cell populations simultaneously is unfeasible
Summary
Identifying changes in gene or protein expression has the potential to focus attention on key molecular mechanisms underlying a given degenerative process (e.g., disease or aging effects on the brain). Neurodegenerative diseases often lead to progressive changes in brain parenchyma composition, typically comprising a decline in the number of neuronal cells, together with an increase of glial cell number (astrocytes, oligodendrocytes and/or microglia) (Figure 1) These changes in the relative proportions of different cell populations can confound the ability to detect the molecular changes occurring in specific cell types. When analyzing genome wide expression data from central nervous system (CNS) tissues it is important to use methods that can reliably account for changes in cell numbers to allow correct interpretations. To overcome this problem, we have recently developed a method called Population-Specific Expression Analysis (PSEA) and shown its potential to successfully identify novel gene expression changes in human HD caudate (Kuhn et al, 2011). We reasoned that implementation of PSEA has the potential to create new, improved analyses of other brain datasets describing neurodegenerative processes
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