Search

Despite recent advances in analytical and computational chemistry, lipid identification remains a significant challenge in lipidomics. Ion-mobility spectrometry provides an accurate measure of the molecules' rotationally averaged collision cross-section (CCS) in the gas phase and is thus related to ionic shape. Here, we investigate the use of CCS as a highly specific molecular descriptor for identifying lipids in biological samples. Using traveling wave ion mobility mass spectrometry (MS), we measured the CCS values of over 200 lipids within multiple chemical classes. CCS values derived from ion mobility were not affected by instrument settings or chromatographic conditions, and they were highly reproducible on instruments located in independent laboratories (interlaboratory RSD < 3% for 98% of molecules). CCS values were used as additional molecular descriptors to identify brain lipids using a variety of traditional lipidomic approaches. The addition of CCS improved the reproducibility of analysis in a liquid chromatography-MS workflow and maximized the separation of isobaric species and the signal-to-noise ratio in direct-MS analyses (e.g., "shotgun" lipidomics and MS imaging). These results indicate that adding CCS to databases and lipidomics workflows increases the specificity and selectivity of analysis, thus improving the confidence in lipid identification compared to traditional analytical approaches. The CCS/accurate-mass database described here is made publicly available.

Despite recent advances in NMR and mass spectrometry, the structural identification of organic compounds in complex biofluids remains a significant analytical challenge. For mass spectroscopy applications, chemical identification is generally limited to determination of elemental formula. Here we test the hypothesis that unknown chemical structures can be determined by matching their experimental collision-induced dissociation (CID) fragmentation spectra with computational fragmentation spectra of compounds retrieved from chemical databases. The monoisotopic molecular weights (MIMW [+ or -] 10 ppm) of 102 'test' compounds were used to download 102 'bins' from the PubChem database. Each bin contained the corresponding test compound and, on average, 272 other candidate compounds, including 158 compounds having the same elemental formula as the test compound. Commercially available software was used to generate fragmentation spectra for all compounds in each of the 102 bins. Experimental CID spectra for each of the 102 test compounds were then compared to the computational spectra in order to rank candidate compounds based on number of fragment MIMW matches. This method returned the test compound as the highest ranking (or tied with the highest ranking) compound for 65 of the 102 bins. The test compound was ranked within the top 20 candidate compounds for 87 bins. In addition, the correct elemental formula was ranked first for 98 of 102 bins. Thus, matching experimental with computational fragmentation spectra is a valid method for rapidly discriminating among compounds having the same elemental formula and provides a novel approach for querying chemical databases for structural information.

Abstract Background The discovery of biomarkers is an important step towards the development of criteria for early diagnosis of disease status. Recently electrospray ionization (ESI) and matrix assisted laser desorption (MALDI) time-of-flight (TOF) mass spectrometry have been used to identify biomarkers both in proteomics and metabonomics studies. Data sets generated from such studies are generally very large in size and thus require the use of sophisticated statistical techniques to glean useful information. Most recent attempts to process these types of data model each compound's intensity either discretely by positional (mass to charge ratio) clustering or through each compounds' own intensity distribution. Traditionally data processing steps such as noise removal, background elimination and m/z alignment, are generally carried out separately resulting in unsatisfactory propagation of signals in the final model. Results In the present study a novel semi-parametric approach has been developed to distinguish urinary metabolic profiles in a group of traumatic patients from those of a control group consisting of normal individuals. Data sets obtained from the replicates of a single subject were used to develop a functional profile through Dirichlet mixture of beta distribution. This functional profile is flexible enough to accommodate variability of the instrument and the inherent variability of each individual, thus simultaneously addressing different sources of systematic error. To address instrument variability, all data sets were analyzed in replicate, an important issue ignored by most studies in the past. Different model comparisons were performed to select the best model for each subject. The m/z values in the window of the irregular pattern are then further recommended for possible biomarker discovery. Conclusion To the best of our knowledge this is the very first attempt to model the physical process behind the time-of flight mass spectrometry. Most of the state of the art techniques does not take these physical principles in consideration while modeling such data. The proposed modeling process will apply as long as the basic physical principle presented in this paper is valid. Notably we have confined our present work mostly within the modeling aspect. Nevertheless clinical validation of our recommended list of potential biomarkers will be required. Hence, we have termed our modeling approach as a "framework" for further work.