Using confocal laser scanning and double immunogold electron microscopy, we demonstrate that reggie-1 and -2 are colocalized in Յ0.1-m plasma membrane microdomains of neurons and astrocytes. In astrocytes, reggie-1 and -2 do not occur in caveolae but clearly outside these structures. Microscopy and coimmunoprecipitation show that reggie-1 and -2 are associated with fyn kinase and with the glycosylphosphatidyl inositol-anchored proteins Thy-1 and F3 that, when activated by antibody cross-linking, selectively copatch with reggie. Jurkat cells, after crosslinking of Thy-1 or GM1 (with the use of cholera toxin), exhibit substantial colocalization of reggie-1 and -2 with Thy-1, GM1, the T-cell receptor complex and fyn. This, and the accumulation of reggie proteins in detergent-resistant membrane fractions containing F3, Thy-1, and fyn imparts to reggie-1 and -2 properties of raft-associated proteins. It also suggests that reggie-1 and -2 participate in the formation of signal transduction centers. In addition, we find reggie-1 and -2 in endolysosomes. In Jurkat cells, reggie-1 and -2 together with fyn and Thy-1 increase in endolysosomes concurrent with a decrease at the plasma membrane. Thus, reggie-1 and -2 define raft-related microdomain signaling centers in neurons and T cells, and the protein complex involved in signaling becomes subject to degradation.

In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.