Improved MALDI-MS method for the highly sensitive and reproducible detection of biomarker peaks for the proteotyping of Salmonella serotypes.

The rapid identification and classification of pathogenic microorganisms, including Salmonella enterica, is important for the surveillance and prevention of foodborne diseases. Matrix-assisted laser desorption\ionization time-of-flight mass spectrometry (MALDI-TOFMS) has been shown to be an effective tool for the rapid identification of microorganisms. In a previous report, a mass database consisting of 12 biomarker proteins, S8, L15, L17, L21, L25, S7, superoxide dismutase (SodA), peptidylprolyl cis-trans isomerase C, Gns, YibT, YaiA, and YciF, was introduced for the serotyping of S. enterica via MALDI-MS (Applied Microbiology and Biotechnology, 2017, 101, 8557-8569). However, the reproducibility of peak detection of biomarkers such as SodA at m\z 23 000 was poor. We report here an optimized MALDI-MS method for detecting these biomarkers with high sensitivity and reproducibility. The issue was solved by controlling the bacterial concentration at 1 × 10 to 1 × 102 MFU (3 × 106 to 3 × 107 CFU\µL, as calculated from the MFU), using the colony suspension supernatant obtained by centrifugation, and using matrix additives such as methylenediphosphonic acid and N-decyl-ß-D-maltopyranoside. We propose that the method including the above steps is one of the best for detecting biomarkers with high sensitivity and reproducibility.

Optimal inter-batch normalization method for GC/MS/MS-based targeted metabolomics with special attention to centrifugal concentration.

This study investigated the optimal inter-batch normalization method for gas chromatography/tandem mass spectrometry (GC/MS/MS)-based targeted metabolome analysis of rodent blood samples. The effect of centrifugal concentration on inter-batch variation was also investigated. Six serum samples prepared from a mouse and 2 quality control (QC) samples from pooled mouse serum were assigned to each batch, and the 3 batches were analyzed by GC/MS/MS at different days. The following inter-batch normalization methods were applied to metabolome data: QC-based methods with quadratic (QUAD)- or cubic spline (CS)-fitting, total signal intensity (TI)-based method, median signal intensity (MI)-based method, and isotope labeled internal standard (IS)-based method. We revealed that centrifugal concentration was a critical factor to cause inter-batch variation. Unexpectedly, neither the QC-based normalization methods nor the IS-based method was able to normalize inter-batch variation, though MI- or TI-based normalization methods were effective in normalizing inter-batch variation. For further validation, 6 disease model rat and 6 control rat plasma were evenly divided into 3 batches, and analyzed as different batches. Same as the results above, MI- or TI-based methods were able to normalize inter-batch variation. In particular, the data normalized by TI-based method showed similar metabolic profiles obtained from their intra-batch analysis. In conclusion, the TI-based normalization method is the most effective to normalize inter-batch variation for GC/MS/MS-based metabolome analysis. Graphical abstract.

MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) experiments require a suitable match of the matrix and target compounds to achieve a selective and sensitive analysis. However, it is still difficult to predict which metabolites are ionizable with a given matrix and which factors lead to an efficient ionization. In the present study, we extracted structural properties of metabolites that contribute to their ionization in MALDI-MS analyses exploiting our experimental data set. The MALDI-MS experiment was performed for 200 standard metabolites using 9-aminoacridine (9-AA) as the matrix. We then developed a prediction model for the ionization profiles (both the ionizability and ionization efficiency) of metabolites using a quantitative structure-property relationship (QSPR) approach. The classification model for the ionizability achieved a 91% accuracy, and the regression model for the ionization efficiency reached a rank correlation coefficient of 0.77. An analysis of the descriptors contributing to such model construction suggested that the proton affinity is a major determinant of the ionization, whereas some substructures hinder efficient ionization. This study will lead to the development of more rational and predictable MALDI-MS analyses.