Improved cell typing by charge-state deconvolution of matrix-assisted laser desorption/ionization mass spectra.

Robust, specific, and rapid identification of toxic strains of bacteria and viruses, to guide the mitigation of their adverse health effects and optimum implementation of other response actions, remains a major analytical challenge. This need has driven the development of methods for classification of microorganisms using mass spectrometry, particularly matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), that allows high-throughput analyses with minimum sample preparation. We describe a novel approach to cell typing based on pattern recognition of MALDI mass spectra, which involves charge-state deconvolution in conjunction with a new correlation analysis procedure. The method is applicable to both prokaryotic and eukaryotic cells. Charge-state deconvolution improves the quantitative reproducibility of spectra because multiply charged ions resulting from the same biomarker attaching a different number of protons are recognized and their abundances are combined. This allows a clearer distinction of bacterial strains or of cancerous and normal liver cells. Improved class distinction provided by charge-state deconvolution was demonstrated by cluster spacing on canonical variate score charts and by correlation analyses. Deconvolution may enhance detection of early disease state or therapy progress markers in various tissues analyzed by MALDI-MS.

Compilation of a MALDI-TOF mass spectral database for the rapid screening and characterisation of bacteria implicated in human infectious diseases.

A database of MALDI-TOF mass spectrometry (MS) profiles has been developed with the aim of establishing a high throughput system for the characterisation of microbes. Several parameters likely to affect the reproducibility of the mass spectrum of a taxon were exhaustively studied. These included such criteria as sample preparation, growth phase, culture conditions, sample storage, mass range of ions, reproducibility between instruments and the methodology prior to database entry. Replicates of 12 spectra per sample were analysed using a 96-well target plate containing central wells for peptide standards to correct against mass drift during analysis. The quality of the data was assessed statistically prior to database addition using root mean squared values of <3.0 as the criterion for rejection. Cluster analysis using a nearest neighbour algorithm also enabled subsets of data to be compared. This was achieved using the bespoke MicrobeLynx trade mark software. Columbia blood agar was used to standardise all procedures for the database, since it permitted the culture of most human pathogens and also produced spectra with a broad range of mass ions. In some instances, alternative media such as CLED were used in specific studies with greater success. Following standardisation of the procedure, a database was developed comprising ca. 3500 spectra with multiple strain entries for most species. The results to date show unequivocally that as the number of strains per species increased, so too did the success of species matching. The technique demonstrated unique mass spectral profiles for each genus/species, with the variation in mass ions among strains/species being dependent on the intra-specific diversity. The success of identification against the database for wild-type strains ranged between 33 and 100%; the lower percentage results being generally associated with poor representation of some species within the database. These findings provide a new dimension for the rapid and high throughput characterisation of human pathogens with potentially broad applications across the field of microbiology.