In the nervous system, there is a critical need to develop molecular biology tools that are accurate at the single cell level, simply because the tissue in the nervous system is such a mixture of different cell types. Even if you isolate a very small fraction of tissue - say 1 mm3 - you will find a tremendous mix of neurons, astrocytes, endothelial cells, etc.. In Jim's lab they have developed a method to capture the contents of an individual cell, amplify the aRNA, and use this amplified aRNA to generate a gene expression profile of that individual cell. Click here for a review article that we published recently that describes this technique. Because you can use the technique on an individual cell (or a relatively small number of pooled cells), you can preselect a certain cell type, or a cell that has certain features. For example, it's easy to label neurons with an acridine orange stain. If you then also label with a marker for apoptotic cells, you can pick cells that are positive for both labels, giving you a pool of material to identify the 'molecular signature' of apoptotic neurons. Obviously, you can compare this to the profile generated from healthy neurons that are picked from the same section of tissue and see if there is a relative increase or decrease in the expression of different gene families. We have used microarray based profiling to detect the genes differentially expressed between cortical and hippocampal neurons (see link). We work with the bioinformatics group here at Penn to help us interpret these arrays, and see if these changes are consistent across different experiments. In this way, we are beginning to determine how many different changes in transcription are occurring under the different conditions that we study in the lab - injury, growth, or development.
The changes in the genes expressed under these conditions are only the beginning of the story. There is a great interest in finding out if these transcriptional changes are translated into different protein levels within the cell. Again, the Eberwine lab has developed a new technique - immuno-based detection by attachment to T7 polymerase (IDAT) - that uses an amplification strategy to detect specific protein levels in very small samples (see the recent paper in Neuron). It's the early stages of what can be considered single cell proteomics, which can be an extremely useful tool for people in neuroscience. It will be an extremely important area for us as we try to understand how changes at the genomic level in injury are being transferred to changes in the protein levels within cells.
