Direct gene transfer to the adult brain is dependent on vectors that transduce non-dividing cells, such as lentiviral vectors. Another aspect of the development of gene therapy to the brain is the need for cell-specific transgene expression. Expression from vesicular stomatitis virus G-protein (VSV-G) pseudotyped lentiviral vectors has been reported to be mainly neuron specific in the brain. We constructed cell-specific lentiviral vectors using the neuron-specific enolase (rNSE) or the glial fibrillary acidic protein (hGFAP) promoters and compared them to the ubiquitous human cytomegalovirus promoter (hCMV), a hybrid CMV/beta-actin promoter (CAG) and the promoter for human elongation factor 1 alpha (EF1 alpha). Our results showed that the hGFAP promoter was expressed only in glial cells, whereas rNSE was purely neuron specific, showing that VSV-G is pantropic in the rat striatum. We conclude that the VSV-G allows transduction of both glial and neuronal cells and the promoter dictates in what cell type the transgene will be expressed. The expression of transgenes exclusively in astrocytes would allow for local delivery of secreted transgene products, such as glial cell line-derived neurotrophic factor (GDNF), circumventing the anterograde transport that may induce unwanted side effects.

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.

Membrane proteins are fairly refractory to digestion especially by trypsin, and less specific proteases, such as elastase and pepsin, are much more effective. However, database searching using nontryptic peptides is much less effective because of the lack of charge localization at the N and C termini and the absence of sequence specificity. We describe a method for N-terminal-specific labeling of peptides from nontryptic digestions of membrane proteins, which facilitates Mascot database searching and can be used for relative quantitation. The conditions for digestion have been optimized to obtain peptides of a suitable length for mass spectrometry (MS) fragmentation. We show the effectiveness of the method using a plasma membrane preparation from a leukemia cell line and demonstrate a large increase in the number of membrane proteins, with small extra-membranar domains being identified in comparison to previous published methods.