Compound specific stable isotope analysis of aromatics in diesel fuel to identify potential cocktailing.

Estimates suggest billions of dollars are lost annually in the US due to fuel tax fraud. One method of fuel fraud is called "cocktailing" and involves blending products that are non-taxed, lower value, taxed at a lower rate, or unwanted/less-refined petroleum to diesel fuels. The goal of this study was to investigate compound specific isotope analysis (CSIA) using isotope ratio mass spectrometry (IRMS) for small aromatics contained in diesel fuel to determine whether this approach could be used to identify cocktailing and potentially fingerprint possible sources. However, the high chemical complexity of diesel fuels complicates CSIA owing to the need to fully separate individual compounds for effective isotope analysis. Therefore, different methods were investigated to selectively isolate aromatics for CSIA and evaluate these methods for isotopic fractionation. Analyses indicate that there is enough variability in isotopic ratios (δ2H and δ13C) between toluene samples obtained from different sources to use CSIA to differentiate/identify the origin of potential fuel adulterants. Three isolation methods were identified that provided sufficiently pure aromatic fractions for CSIA: selective solvent extraction, ionic liquid coated solid phase microextraction (SPME), and a combination of the two. However, due to the labor-intensive nature of selective solvent extraction, ionic liquid coated SPME represents the best method to quickly isolate aromatics from diesel fuel, without sacrificing selectivity or sensitivity. All methods tested can result in isotopic fractionation, but this can be compensated for by applying a correction factor. Furthermore, the chemical composition of a sample appeared to be important in the degree to which fractionation occurred during isolation. While the tested approaches for aromatic extraction from diesel showed promise, additional studies are required to refine and validate the methods prior to routine use in fuel cocktailing investigations.

Probabilistic Limit of Detection for Ricin Identification Using a Shotgun Proteomics Assay.

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/µL for a 2-µL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Applications and challenges of forensic proteomics.

Mass spectrometry-based proteomics has been a useful tool for addressing numerous questions in basic biology research for many years. This success, combined with the maturity of mass spectrometric instrumentation, the ever-increasing availability of protein sequence databases derived from genome sequencing, and the growing sophistication of data analysis methods, places proteomics in a position to have an important role in biological forensics. Because proteins contain information about genotype (sequence) and phenotype (expression levels), proteomics methods can both identify biological samples and characterize the conditions that produced them. In addition to serving as a valuable orthogonal method to genomic analyses, proteomics can be used in cases where nucleic acids are absent, degraded, or uninformative. Mass spectrometry provides both broad applicability and exquisite specificity, often without customized detection reagents like primers or antibodies. This review briefly introduces proteomics methods, and surveys a variety of forensic applications (including criminal justice, historical, archaeological, and national security areas). Finally, challenges and crucial areas for further research are addressed.

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics.

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide "hits" (i.e., confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven to be successful in laboratory studies; however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world forensic samples where conditions vary and large databases may be needed. In this work, we propose statistical criteria that overcome the problem of signal erosion and can be applied regardless of the sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result of an organism or toxin identification. We test the proposed criteria on 919 LC-MS/MS data sets originating from 2 toxins and 32 bacterial strains acquired using multiple data collection platforms. Results reveal a > 95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Characterization of diesel fuel by chemical separation combined with capillary gas chromatography (GC) isotope ratio mass spectrometry (IRMS).

The purpose of this study was to perform a preliminary investigation of compound-specific isotope analysis (CSIA) of diesel fuels to evaluate whether the technique could distinguish diesel samples from different sources/locations. The ability to differentiate or correlate diesel samples could be valuable for discovering fuel tax evasion schemes or for environmental forensic studies. Two urea adduction-based techniques were used to isolate the n-alkanes from the fuel. Both carbon isotope ratio (δ(13)C) and hydrogen isotope ratio (δD) values for the n-alkanes were then determined by CSIA in each sample. The samples investigated had δ(13)C values that ranged from -30.1‰ to -26.8‰, whereas δD values ranged from -83‰ to -156‰. Plots of δD versus δ(13)C with sample n-alkane points connected in order of increasing carbon number gave well-separated clusters with characteristic shapes for each sample. Principal components analysis (PCA) with δ(13)C, δD, or combined δ(13)C and δD data was applied to extract the maximum information content. PCA scores plots could clearly differentiate the samples, thereby demonstrating the potential of this approach for distinguishing (e.g., fingerprinting) fuel samples using δ(13)C and δD values.

Residual agar determination in bacterial spores by electrospray ionization mass spectrometry.

Presented here is an analytical method to detect residual agar from a bacterial spore sample as an indication of culturing on an agar plate. This method is based on the resolubilization of agar polysaccharide from a bacterial spore sample, enzymatic digestion, followed by electrospray ionization tandem mass spectrometry (ESI-MS(n)) analysis for detection of a specific agar fragment ion. A range of Bacillus species and strains were selected to demonstrate the effectiveness of this approach. The characteristic agar fragment ion was detected in the spores grown on agar that were washed from 1 to 5 times, irradiated or nonirradiated, and not in the spores grown in broth. A sample containing approximately 10(8) spores is currently needed for confident detection of residual agar from culture on agar plates in the presence of bacterial spores with a limit of detection of approximately 1 ppm agar spiked into a broth-grown spore sample. The results of a proficiency test with 42 blinded samples are presented demonstrating the utility of this method with no false positives and only three false negatives for samples that were below the detection level of the method as documented.

Bayesian-integrated microbial forensics.

In the aftermath of the 2001 anthrax letters, researchers have been exploring ways to predict the production environment of unknown-source microorganisms. Culture medium, presence of agar, culturing temperature, and drying method are just some of the broad spectrum of characteristics an investigator might like to infer. The effects of many of these factors on microorganisms are not well understood, but the complex way in which microbes interact with their environments suggests that numerous analytical techniques measuring different properties will eventually be needed for complete characterization. In this work, we present a Bayesian statistical framework for integrating disparate analytical measurements. We illustrate its application to the problem of characterizing the culture medium of Bacillus spores using three different mass spectral techniques. The results of our study suggest that integrating data in this way significantly improves the accuracy and robustness of the analyses.

Stable isotope ratios and forensic analysis of microorganisms.

In the aftermath of the anthrax letters of 2001, researchers have been exploring various analytical signatures for the purpose of characterizing the production environment of microorganisms. One such signature is stable isotope ratios, which in heterotrophs, are a function of nutrient and water sources. Here we discuss the use of stable isotope ratios in microbial forensics, using as a database the carbon, nitrogen, oxygen, and hydrogen stable isotope ratios of 247 separate cultures of Bacillus subtilis 6051 spores produced on a total of 32 different culture media. In the context of using stable isotope ratios as a signature for sample matching, we present an analysis of variations between individual samples, between cultures produced in tandem, and between cultures produced in the same medium but at different times. Additionally, we correlate the stable isotope ratios of carbon, nitrogen, oxygen, and hydrogen for growth medium nutrients or water with those of spores and show examples of how these relationships can be used to exclude nutrient or water samples as possible growth substrates for specific cultures.

Differentiation of spores of Bacillus subtilis grown in different media by elemental characterization using time-of-flight secondary ion mass spectrometry.

We demonstrate the use of time-of-flight secondary ion mass spectrometry (TOF-SIMS) in a forensics application to distinguish Bacillus subtilis spores grown in various media based on the elemental signatures of the spores. Triplicate cultures grown in each of four different media were analyzed to obtain TOF-SIMS signatures comprised of 16 elemental intensities. Analysis of variance was unable to distinguish growth medium types based on 40Ca-normalized signatures of any single normalized element. Principal component analysis proved successful in separating the spores into groups consistent with the media in which they were prepared. Confusion matrices constructed using nearest-neighbor classification of the PCA scores confirmed the predictive utility of TOF-SIMS elemental signatures in identifying sporulation medium. Theoretical calculations based on the number and density of spores in an analysis area indicate an analytical sample size of about 1 ng, making this technique an attractive method for bioforensics applications.

Comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data.

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

A study of spectral integration and normalization in NMR-based metabonomic analyses.

Metabonomics involves the quantitation of the dynamic multivariate metabolic response of an organism to a pathological event or genetic modification [J.K. Nicholson, J.C. Lindon, E. Holmes, Xenobiotica 29 (1999) 1181-1189]. The analysis of these data involves the use of appropriate multivariate statistical methods; Principal Component Analysis (PCA) has been documented as a valuable pattern recognition technique for 1H NMR spectral data [J.T. Brindle, H. Antti, E. Holmes, G. Tranter, J.K. Nicholson, H.W. Bethell, S. Clarke, P.M. Schofield, E. McKilligin, D.E. Mosedale, D.J. Grainger, Nat. Med. 8 (2002) 1439-1444; B.C. Potts, A.J. Deese, G.J. Stevens, M.D. Reily, D.G. Robertson, J. Theiss, J. Pharm. Biomed. Anal. 26 (2001) 463-476; D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; L.C. Robosky, D.G. Robertson, J.D. Baker, S. Rane, M.D. Reily, Comb. Chem. High Throughput Screen. 5 (2002) 651-662]. Prior to PCA the raw data is typically processed through four steps; (1) baseline correction, (2) endogenous peak removal, (3) integration over spectral regions to reduce the number of variables, and (4) normalization. The effect of the size of spectral integration regions and normalization has not been well studied. The variability structure and classification accuracy on two distinctly different datasets are assessed via PCA and a leave-one-out cross-validation approach under two normalization approaches and an array of spectral integration regions. The first dataset consists of urine from 15 male Wistar-Hannover rats dosed with ANIT measured at five time points, mimicking drug-induced cholangiolitic hepatitis [D.G. Robertson, M.D. Reily, R.E. Sigler, D.F. Wells, D.A. Paterson, T.K. Braden, Toxicol. Sci. 57 (2000) 326-337; J.P. Shockcor, E. Holmes, Curr. Top. Med. Chem. 2 (2002) 35-51; N.J. Waters, E. Holmes, A. Williams, C.J. Waterfield, R.D. Farrant, J.K. Nicholson, Chem. Res. Toxicol. 14 (2001) 1401-1412]. The second data is serum samples from young male C57BL/6 mice subjected to instillation of pancreatic elastase producing emphysema type symptoms [C. Kuhn, S.Y. Yu, M. Chraplyvy, H.E. Linder, R.M. Senior, Lab. Invest. 34 (1976) 372-380; C. Kuhn, R.M. Senior, Lung 155 (1978) 185-197]. This study indicates that independent of the normalization method the classification accuracy achieved from metabonomic studies is not highly sensitive to the size of the spectral integration region. Additionally, both datasets scaled to mean zero and unity variance (auto-scaled) have higher variability within classification accuracy over spectral integration window widths than data scaled to the total intensity of the spectrum. Of the top 10 latent variables for the ANIT dataset the auto-scale normalization has standard deviations larger than the total-scale in seven cases. In the case of the elastase all standard deviations are larger for the auto-scaling.

Bacterial analysis by MALDI-TOF mass spectrometry: an inter-laboratory comparison.

Bacterial analysis by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry has been demonstrated in numerous laboratories, and a few attempts have been made to compare results from different laboratories on the same organism. It has been difficult to understand the causes behind the observed differences between laboratories when different instruments, matrices, solvents, etc. are used. In order to establish this technique as a useful tool for bacterial identification, additional efforts in standardizing the methods by which MALDI mass spectra are obtained and comparisons of spectra from different instruments with different operators are needed. Presented here is an extension of our previous single-laboratory reproducibility study with three different laboratories in a controlled experiment with aliquots of the same bacterial culture, matrix stock solution, and calibrant standards. Using automated spectral collection of whole-cell bacteria and automated data processing and analysis algorithms, fingerprints from three different laboratories were constructed and compared. Nine of the ions appeared reproducibly within all three laboratories, with additional unique ions observed within each of the laboratories. An initial evaluation of the ability to use a fingerprint generated within one laboratory for bacterial identification of a sample from another laboratory is presented, and strategies for improving identification rates between laboratories is discussed.

Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data.

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.

High-speed peak matching algorithm for retention time alignment of gas chromatographic data for chemometric analysis.

A rapid retention time alignment algorithm was developed as a preprocessing utility to be used prior to chemometric analysis of large datasets of diesel fuel profiles obtained using gas chromatography (GC). Retention time variation from chromatogram-to-chromatogram has been a significant impediment against the use of chemometric techniques in the analysis of chromatographic data due to the inability of current chemometric techniques to correctly model information that shifts from variable to variable within a dataset. The alignment algorithm developed is shown to increase the efficacy of pattern recognition methods applied to diesel fuel chromatograms by retaining chemical selectivity while reducing chromatogram-to-chromatogram retention time variations and to do so on a time scale that makes analysis of large sets of chromatographic data practical. Two sets of diesel fuel gas chromatograms were studied using the novel alignment algorithm followed by principal component analysis (PCA). In the first study, retention times for corresponding chromatographic peaks in 60 chromatograms varied by as much as 300 ms between chromatograms before alignment. In the second study of 42 chromatograms, the retention time shifting exhibited was on the order of 10 s between corresponding chromatographic peaks, and required a coarse retention time correction prior to alignment with the algorithm. In both cases, an increase in retention time precision afforded by the algorithm was clearly visible in plots of overlaid chromatograms before and then after applying the retention time alignment algorithm. Using the alignment algorithm, the standard deviation for corresponding peak retention times following alignment was 17 ms throughout a given chromatogram, corresponding to a relative standard deviation of 0.003% at an average retention time of 8 min. This level of retention time precision is a 5-fold improvement over the retention time precision initially provided by a state-of-the-art GC instrument equipped with electronic pressure control and was critical to the performance of the chemometric analysis. This increase in retention time precision does not come at the expense of chemical selectivity, since the PCA results suggest that essentially all of the chemical selectivity is preserved. Cluster resolution between dissimilar groups of diesel fuel chromatograms in a two-dimensional scores space generated with PCA is shown to substantially increase after alignment. The alignment method is robust against missing or extra peaks relative to a target chromatogram used in the alignment, and operates at high speed, requiring roughly 1 s of computation time per GC chromatogram.

Analysis of microbial mixtures by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

Many different laboratories are currently developing mass-spectrometric techniques to analyze and identify microorganisms. However, minimal work has been done with mixtures of bacteria. To demonstrate that microbial mixtures could be analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), mixed bacterial cultures were analyzed in a double-blind fashion. Nine different bacterial species currently in our MALDI-MS fingerprint library were used to generate 50 different simulated mixed bacterial cultures similar to that done for an initial blind study previously reported (Jarman, K. H.; Cebula, S. T.; Saenz, A. J.; Petersen, C. E.; Valentine, N. B.; Kingsley, M. T.; Wahl, K. L. Anal. Chem. 2000, 72, 1217-1223). The samples were analyzed by MALDI-MS with automated data extraction and analysis algorithms developed in our laboratory. The components present in the sample were identified correctly to the species level in all but one of the samples. However, correctly eliminating closely related organisms was challenging for the current algorithms, especially in differentiating Serratia marcescens, Escherichia coli, and Yersinia enterocolitica, which have some similarities in their MALDI-MS fingerprints. Efforts to improve the specificity of the algorithms are in progress.