Transcriptional Profiling of Small Samples in the Central Nervous System

We describe a comparative mathematical framework for two genome-scale expression data sets. This framework formulates expression as superposition of the effects of regulatory programs, biological processes, and experimental artifacts common to both data sets, as well as those that are exclusive to one data set or the other, by using generalized singular value decomposition. This framework enables comparative reconstruction and classification of the genes and arrays of both data sets. We illustrate this framework with a comparison of yeast and human cell-cycle expression data sets.

Dysfunction of cholinergic basal forebrain (CBF) neurons of the nucleus basalis (NB) is a cardinal feature of Alzheimer's disease (AD) and correlates with cognitive decline. Survival of CBF neurons depends upon binding of nerve growth factor (NGF) with high-affinity (trkA) and low-affinity (p75(NTR)) neurotrophin receptors produced within CBF neurons. Since trkA and p75(NTR) protein levels are reduced within CBF neurons of people with mild cognitive impairment (MCI) and mild AD, trkA and/or p75(NTR) gene expression deficits may drive NB degeneration. Using single cell expression profiling methods coupled with custom-designed cDNA arrays and validation with real-time quantitative PCR (qPCR) and in situ hybridization, individual cholinergic NB neurons displayed a significant down regulation of trkA, trkB, and trkC expression during the progression of AD. An intermediate reduction was observed in MCI, with the greatest decrement in mild to moderate AD as compared to controls. Importantly, trk down regulation is associated with cognitive decline measured by the Global Cognitive Score (GCS) and the Mini-Mental State Examination (MMSE). In contrast, there is a lack of regulation of p75(NTR) expression. Thus, trk defects may be a molecular marker for the transition from no cognitive impairment (NCI) to MCI, and from MCI to frank AD.

There are a variety of options for making microarrays and obtaining microarray data. Here, we describe the building and use of two microarray facilities in academic settings. In addition to specifying technical detail, we comment on the advantages and disadvantages of components and approaches, and provide a protocol for hybridization. The fact that we are now making and using microarrays to answer biological questions demonstrates that the technology can be implemented in a university environment.