Establishing drug resistance in microorganisms by mass spectrometry.

A rapid method to determine drug resistance in bacteria based on mass spectrometry is presented. In it, a mass spectrum of an intact microorganism grown in drug-containing stable isotope-labeled media is compared with a mass spectrum of the intact microorganism grown in non-labeled media without the drug present. Drug resistance is determined by predicting characteristic mass shifts of one or more microorganism biomarkers using bioinformatics algorithms. Observing such characteristic mass shifts indicates that the microorganism is viable even in the presence of the drug, thus incorporating the isotopic label into characteristic biomarker molecules. The performance of the method is illustrated on the example of intact E. coli, grown in control (unlabeled) and (13)C-labeled media, and analyzed by MALDI TOF MS. Algorithms for data analysis are presented as well.

Enhanced in-source fragmentation in MALDI-TOF-MS of oligonucleotides using 1,5-diaminonapthalene.

The capability to rapidly and confidently determine or confirm the sequences of short oligonucleotides, including native and chemically-modified DNA and RNA, is important for a number of fields. While matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS) has been used previously to sequence short oligonucleotides, the typically low fragmentation efficiency of in-source or post-source decay processes necessitates the accumulation of a large number of spectra, thus limiting the throughput of these methods. Here we introduce a novel matrix, 1,5-diaminonapthalene (DAN), for facile in-source decay (ISD) of DNA and RNA molecular anions, which allows for rapid sequence confirmation. d-, w-, and y-series ions are prominent in the spectra, complementary to the (a-B)- and w- ions that are typically produced by MALDI post-source decay (PSD). Results are shown for several model DNA and RNA oligonucleotides, including combinations of DAN-induced fragmentation with true tandem TOF MS (MS/MS) for pseudo-MS(3) and "activated-ion PSD."

Arrayed time-of-flight mass spectrometry for time-critical detection of hazardous agents.

The design and operation of an arrayed time-of-flight (TOF) mass spectrometer for simultaneous data acquisition from multiple samples is described. Versions of the instrument employ sets of two or four linear or reflectron mass analyzers. They are housed in the same vacuum chamber and utilize the same laser for ion desorption. Instrument performance is illustrated in the example of a two-linear-mass-analyzer array using MALDI-MS for mixtures of commercially available proteins as well as intact microorganisms. We also describe the properties of a novel short delay time (<170 ns) pulsed extraction method for linear TOF analyzers. This configuration allows uniform resolution improvements to be achieved in a wide m/z range. In addition, we present multiplexed sample preparation methods, using different reagents prior to mass analysis in the arrayed system, to increase the overall sensitivity of the MS method and to allow wider and more efficient detection across the entire range of potentially hazardous agents. In addition to the multifold increase in data collection rates, arrayed TOF-MS configurations provide a high degree of redundancy, critical for rapid, high confidence agent identification as well as for reduction in false alarm rates.

Microorganism identification by matrix-assisted laser/desorption ionization mass spectrometry and model-derived ribosomal protein biomarkers.

An improved data analysis method is described for rapid identification of intact microorganisms from MALDI-TOF-MS data. The method makes no use of mass spectral fingerprints. Instead, a microorganism database is automatically generated that contains biomarker masses derived from ribosomal protein sequences and a model of N-terminal Met loss. We quantitatively validate the method via a blind study that seeks to identify microorganisms with known ribosomal protein sequences. We also include in the database microorganisms with incompletely known sets of ribosomal proteins to test the specificity of the method. With an optimal MALDI protocol, and at the 95% confidence level, microorganisms represented in the database with 20 or more biomarkers (i.e., those with complete or nearly completely sequenced genomes) are correctly identified from their spectra 100% of the time, with no incorrect identifications. Microorganisms with seven or less biomarkers (i.e., incompletely sequenced genomes) are either not identified or misidentified. Robustness with respect to variations in sample preparation protocol and mass analysis protocol is demonstrated by collecting data with two different matrixes and under two different ion-mode configurations. Statistical analysis suggests that, even without further improvement, the method described here would successfully scale up to microorganism databases with roughly 1000 microorganisms. The results demonstrate that microorganism identification based on proteome data and modeling can perform as well as methods based on mass spectral fingerprinting.