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In the last decade mass-spectrometry-based proteomics has become an indispensable analytical tool for molecular biology, cellular biology and, lately, for the emerging systems biology. This review summarises the evolution and great potential of analytical methods based on elemental mass-spectrometric detection for quantitative proteomic analysis.

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In order to estimate metal distribution patterns in biomolecules of different sizes and their possible relationship with environmental heavy metal contamination, multi-elemental distributions in different fractions of the cytosols of mussels were studied. To do so, samples were collected from natural populations of two coastal regions in Spain: a wild (uncontaminated) coast and an industrialised (contaminated) area in Asturias. Moreover, some commercial mussels from the Galicia coast were also investigated for comparison. Aliquots of the mussel cytosol extracts from each sample were applied to a calibrated Sephadex G-75 column (100 x 1 cm) and forty 3 ml fractions were obtained. After suitable dilution, 18 trace metals were determined by double focusing inductively coupled plasma mass spectrometry (DF-ICP-MS). The use of DF-ICP-MS detection allowed the resolution of several spectral interferences that cannot be resolved by quadrupole ICP-MS. Accurate results for ultratrace elements at basal levels are possible even after sample dilution to prevent matrix effects. After biomolecule-metal association pattern has been established, quantitative analysis of mussel cytosols from the three coastal areas was carried out, using external aqueous calibration plus standard additions to correct for possible matrix effects. Results showed that total metal contents increased following the expected order: wild coast < Galicia coast < industrial area coast. Speciation of Cu, Zn, Ca, U, Ni, Mo, Mn, Cr, V, Cd, Al and Sb showed a similar distribution pattern among cytosolic ligands for all the studied samples. Conversely, Fe, Pb, Sn, Co, Hg and Ag were found to exhibit different speciation patterns when samples from industrialised (contaminated) and non-industrialised areas were compared.

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