The last decade has seen remarkable advances in our ability to analyze patterns of mRNA expression at the genome-wide level. Microarray and SAGE technologies now are applied effectively to study changes in expression among large populations of transcripts. Changes in expression profiles also are regularly employed to define the phenotypes of single cell types. These approaches are now used for clinical studies as well as basic research. Work in the laboratory of David R. Morris and elsewhere has extended these technologies to measure the association of individual transcripts with the translational machinery, and other aspects of mRNA metabolism as well. Quantitative evaluation of the association of transcripts with the translational apparatus has allowed transcriptome-wide estimates of the rates of synthesis of individual proteins.
Current gene expression analysis requires access to a pure population of cells. An important remaining challenge is to explore the intricate developmental and physiological relationships in expression patterns that exist between multiple cell types within a complex tissue. Evaluation of gene expression in the mammalian central nervous system is a prominent example of the challenges presented by this type of analysis.
In situ hybridization approaches have been notably successful in defining qualitative differences in expression of individual transcripts in different cell types in the brain. However, monumental barriers stand in the way of adapting this in situ technology for quantitative analysis at the genome-wide scale. Various alternate approaches to anatomical dissection, including Laser Capture Micro-dissection (LCM) and cell sorting by Fluorescence Activated Cell Sorting (FACS), have been employed to isolate cells from the brain for expression analysis. However, isolation of pure cell populations from tissues often requires extended periods of enzymatic digestion or environmental stress that likely alter patterns of gene expression within the cells of interest. Given the intertwined cellular networks within the brain, this tissue is an extreme example of the difficulties in isolation and separation of individual cell types using available methods.
In our research in collaboration with Dr. David R. Morris, we propose to develop a new approach to genome-wide expression analysis of complex cellular mixtures by methods that do not rely on prior cell separations. Furthermore, this experimental approach selectively reports only those cell-specific transcripts that are actively being translated. The results will relate more directly to protein production and therefore to cellular phenotype. In order to carry out this determination, we propose to replace a wild-type ribosomal protein with an epitope-tagged version specifically in cell types of interest. Thus, starting with a whole tissue homogenate, we will isolate the specifically tagged ribosomes along with their associated cell-specific complement of transcripts. Since association of mRNAs with translating ribosomes is stable to freezing, it will be possible to analyze actively translated, cell-specific transcripts from snap-frozen tissue samples. This methodological advance should obviate the perturbations in transcript patterns that are likely to arise during cell separations and would also be far less time consuming. An experimental mouse carrying the conditionally tagged ribosomal protein is a powerful tool. Once a mouse homozygous for the conditionally-tagged ribosomal protein allele is generated, this mouse will have wide applicability to many cell types, limited only by the rapidly lengthening list of mice carrying the Cre recombinase gene driven by cell-specific promoters.
