Biomedical subjects

Franco Basile

Publications and source records attributed to Franco Basile.

4 recordsLinked to original sources

PubMed2005

Interface for direct and continuous sample-matrix deposition onto a MALDI probe for polymer analysis by thermal field flow fractionation and off-line MALDI-MS.

A simple interface based on an oscillating capillary nebulizer (OCN) is described for direct deposition of eluate from a thermal field-flow fractionation (ThFFF) system onto a matrix-assisted laser desorption/ionization (MALDI) probe. In this study, the polymer-containing eluent from the ThFFF system was mixed on-line with MALDI matrix solution and deposited directly onto a moving MALDI probe. The result was a continuous sample track representative of the fractionation process. Subsequent off-line MALDI-mass spectrometry analysis was performed in automated and manual modes. Polystyrene samples of broad polydispersity were used to characterize the overall system performance. The OCN interface is easy to build and operate without the use of heaters or high voltages and is compatible with any MALDI probe format.

Fractionation, Field Flow↗

PubMed2004

A simple and inexpensive approach to interfacing high-performance liquid chromatography and matrix-assisted laser desorption/ionization-time of flight-mass spectrometry.

The ability to obtain the accurate mass of a protein in a complex sample mixture aids in determining its correct in vivo form. This is important when identifying post-translationally modified proteins, protein variants or isoforms. The central technique used to separate proteins, 2-dimensional gel electrophoresis offers excellent separation capabilities but does not provide adequate mass accuracy. In this study, an alternative method, liquid chromatography (LC) coupled with matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF)-MS (LC-MALDI) is described. LC-MALDI-MS was used to separate and determine the mass of proteins and peptides in a complex biological sample (i.e., human pituitary gland homogenate). Peptides and proteins were first separated by capillary chromatography and the eluent mixed post-column with sinapinic acid matrix. The flow was then deposited directly onto a standard MALDI target via a capillary nebulizer. In addition to offering high mass accuracy, this method can be applied to peptide and protein quantification.

Chromatography↗

PubMed2002

Simultaneous multiple substrate tag detection with ESI-ion trap MS for in vivo bacterial enzyme activity profiling.

A bacterial identification method in which multiple enzyme activities are measured simultaneously and in vivo with electrospray ionization-mass spectrometry (ESI-MS) is described. Whole-cell bacteria are immobilized onto a filter support and incubated with a mixture of substrates. Each substrate is chosen to measure a specific enzyme activity of a targeted bacterium and to produce a tag of unique molecular weight. After a predetermined incubation time, the solution is filtered, and the supernatant consisting of a mixture of released tags and unhydrolyzed substrates is directly analyzed, without chromatographic separation, by ESI-MS. Bacteria remain viable on the filter for further analyses. The method was tested by measuring the aminopeptidase activity of the bacteria Escherichia coli, Bacillus subtilis, Bacillus cereus, and Pseudomonas aeruginosa. The resulting aminopeptidase enzyme profiles allowed the differentiation between the four bacteria tested. The method is rapid, since a multiplex advantage is realized when assaying for multiple enzymes, and it is amenable to automation via a flow injection analysis setup.

Aminopeptidases↗

PubMed2002

Validation using sensitivity and target transform factor analyses of neural network models for classifying bacteria from mass spectra.

Temperature constrained cascade correlation networks (TCCCNs) are computational neural networks that configure their own architecture, train rapidly, and give reproducible prediction results. TCCCN classification models were built using the Latin-partition method for five classes of pathogenic bacteria. Neural networks are problematic in that the relationships among the inputs (i.e., mass spectra) and the outputs (i.e., the bacterial identities) are not apparent. In this study, neural network models were constructed that successfully classified the targeted bacteria and the classification model was validated using sensitivity and target transformation factor analysis (TTFA). Without validation of the classification model, it is impossible to ascertain whether the bacteria are classified by peaks in the mass spectrum that have no causal relationships with the bacteria, but instead randomly correlate with the bacterial classes. Multiple single output network models did not offer any benefits when compared to single network models that had multiple outputs. A multiple output TCCCN model achieved classification accuracies of 96 +/- 2% and exhibited improved performance over multiple single output TCCCN models. Chemical ionization mass spectra were obtained from in situ thermal hydrolysis methylation of freeze-dried bacteria. Mass spectral peaks that pertain to the neural network classification model of the pathogenic bacterial classes were obtained by sensitivity analysis. A significant number of mass spectral peaks that had high sensitivity corresponded to known biomarkers, which is the first time that the significant peaks used by a neural network model to classify mass spectra have been divulged. Furthermore, TTFA furnishes a useful visual target as to which peaks in the mass spectrum correlate with the bacterial identities.

Bacteria↗