fAter being released from transcription sites, messenger ribonucleoprotein particles (mRNPs) must reach the nuclear pore complexes in order to be translocated to the cytoplasm. Whether the intranuclear movement of mRNPs results largely from Brownian motion or involves molecular motors remains unknown. Here we have used quantitative photobleaching techniques to monitor the intranuclear mobility of protein components of mRNPs tagged with GFP. The results show that the diffusion coefficients of the poly(A)-binding protein II (PABP2) and the export factor TAP are significantly reduced when these proteins are bound to mRNP complexes, as compared with nonbound proteins. The data further show that the mobility of wild-type PABP2 and TAP, but not of a point mutant variant of PABP2 that fails to bind to RNA, is significantly reduced when cells are ATP depleted or incubated at 22°C. Energy depletion has only minor effects on the intranuclear mobility of a 2,000-kD dextran (which corresponds approximately in size to 40S mRNP particles), suggesting that the reduced mobility of PABP2 and TAP is not caused by a general alteration of the nuclear environment. Taken together, the data suggest that the mobility of mRNPs in the living cell nucleus involves a combination of passive diffusion and ATP-dependent processes.

The spliceosomal snRNAs U1, U2, U4, and U5 are synthesized in the nucleus, exported to the cytoplasm to assemble with Sm proteins, and reimported to the nucleus as ribonucleoprotein particles. Recently, two novel proteins involved in biogenesis of small nuclear ribonucleoproteins (snRNPs) were identified, the Spinal muscular atrophy disease gene product (SMN) and its associated protein SIP1. It was previously reported that in HeLa cells, SMN and SIP1 form discrete foci located next to Cajal (coiled) bodies, the so-called “gemini of coiled bodies” or “gems.” An intriguing feature of gems is that they do not appear to contain snRNPs. Here we show that gems are present in a variable but small proportion of rapidly proliferating cells in culture. In the vast majority of cultured cells and in all primary neurons analyzed, SMN and SIP1 colocalize precisely with snRNPs in the Cajal body. The presence of SMN and SIP1 in Cajal bodies is confirmed by immunoelectron microscopy and by microinjection of antibodies that interfere with the integrity of the structure. The association of SMN with snRNPs and coilin persists during cell division, but at the end of mitosis there is a lag period between assembly of new Cajal bodies in the nucleus and detection of SMN in these structures, suggesting that SMN is targeted to preformed Cajal bodies. Finally, treatment of cells with leptomycin B (a drug that blocks export of U snRNAs to the cytoplasm and consequently import of new snRNPs into the nucleus) is shown to deplete snRNPs (but not SMN or SIP1) from the Cajal body. This suggests that snRNPs flow through the Cajal body during their biogenesis pathway.

Confocal laser scanning microscopy (CLSM) has been widely used in the life sciences for the characterization of cell processes because it allows the recording of the distribution of fluorescence-tagged macromolecules on a section of the living cell. It is in fact the cornerstone of many molecular transport and interaction quantification techniques where the identification of regions of interest through image segmentation is usually a required step. In many situations, because of the complexity of the recorded cellular structures or because of the amounts of data involved, image segmentation either is too difficult or inefficient to be done by hand and automated segmentation procedures have to be considered. Given the nature of CLSM images, statistical segmentation methodologies appear as natural candidates. In this work we propose a model to be used for statistical unsupervised CLSM image segmentation. The model is derived from the CLSM image formation mechanics and its performance is compared to the existing alternatives. Results show that it provides a much better description of the data on classes characterized by their mean intensity, making it suitable not only for segmentation methodologies with known number of classes but also for use with schemes aiming at the estimation of the number of classes through the application of cluster selection criteria.

No abstract