A signature-based method to distinguish time-of-flight secondary-ion mass spectra from biological samples

Time-Of-Flight Mass Spectrometry (TOF-SIMS) was used to determine elemental and biomolecular ions from isolated protein samples. We identified a set of 23 mass-to-charge ratio (m/z) peaks that represent signatures for distinguishing biological samples. The 23 peaks were identified by Singular Value Decomposition (SVD) and Canonical Analysis (CA) to find the underlying structure in the complex mass-spectra data sets. From this modified data, SVD was used to identify sets of m/z peaks, and we used these patterns from the TOF-SIMS data to predict the biological source from which individual mass spectra were generated. The signatures were validated using an additional data set different from the initial training set used to identify the signatures. We present a simple method to identify multiple variables required for sample classification based on mass spectra that avoids overfit. This is important in a variety of studies using mass spectrometry, including the ability to identify proteins in complex mixtures and for the identification of new biomarkers.     

A pilot study of salivary N-glycome in HBV-induced chronic hepatitis,cirrhosis, and hepatocellular carcinoma

Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus (HBV), which can lead to chronic liver disease and put people at high risk of death from cirrhosis of the liver and liver cancer. However, little is known about the correlation of salivary N-linked glycans related to HBV-infected liver diseases. Here we investigated N-linked glycome in saliva from 200 subjects (50 healthy volunteers (HV), 40 HBV-infected patients (HB), 50 cirrhosis patients (HC), and 60 hepatocellular carcinoma patients (HCC) using MALDI-TOF/TOF-MS. Representative MS spectra of N-glycans with signal-to-noise ratios >6 were annotated using the GlycoWorkbench program. A total of 40, 47, 29, and 33 N-glycan peaks were identified and annotated from HV, HB, HC, and HCC groups, respectively. There were 15 N-glycan peaks (e.g., m/z 1647.587, 1688.613 and 2101.755) were present in all groups. Three N-glycan peaks (m/z 2596.925, 2756.962, and 2921.031) were unique in HV group, 2 N-glycan peaks (m/z 1898.676 and 1971.692) were unique in HB group, 5 N-glycan peaks (m/z 1954.677, 2507.914, 2580.930, 2637.952, and 3092.120) were unique in HC group, and 3 N-glycan peaks (m/z 2240.830, 2507.914, and 3931.338) were unique in HCC group. The proportion of fucosylated N-glycans was apparently increased in the HCC group (84.8%) than in any other group (73.1% ± 0.01), however, the proportion of sialylated N-glycans was decreased in HCC group (12.1%) than in any other group (17.23% ± 0.003). Our data provide pivotal information to distinguish between HBV-associated hepatitis, cirrhosis and HCC, and facilitate the discovery of biomarkers for HCC during its early stages based on precise alterations of N-linked glycans in saliva.     

Internal calibrants allow high accuracy peptide matching between MALDI imaging MS and LC-MS/MS

One of the important challenges for MALDI imaging mass spectrometry (MALDI-IMS) is the unambiguous identification of measured analytes. One way to do this is to match tryptic peptide MALDI-IMS m/z values with LC-MS/MS identified m/z values. Matching using current MALDI-TOF/TOF MS instruments is difficult due to the variability of in situ time-of-flight (TOF) m/z measurements. This variability is currently addressed using external calibration, which limits achievable mass accuracy for MALDI-IMS and makes it difficult to match these data to downstream LC-MS/MS results. To overcome this challenge, the work presented here details a method for internally calibrating data sets generated from tryptic peptide MALDI-IMS on formalin-fixed paraffin-embedded sections of ovarian cancer. By calibrating all spectra to internal peak features the m/z error for matches made between MALDI-IMS m/z values and LC-MS/MS identified peptide m/z values was significantly reduced. This improvement was confirmed by follow up matching of LC-MS/MS spectra to in situ MS/MS spectra from the same m/z peak features. The sum of the data presented here indicates that internal calibrants should be a standard component of tryptic peptide MALDI-IMS experiments.     

Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model.We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/ yudpaw.     

Synthesis and characterization of hexadecadienyl compounds with a conjugated diene system,sex pheromone of the persimmon fruit moth and related compounds

Hexadecadien-1-ol and the derivatives (acetate and aldehyde) with a conjugated diene system have recently been identified from a pheromone gland extract of the persimmon fruit moth (Stathmopoda masinissa), a pest insect of persimmon fruits distributed in East Asia. The alcohol and acetate showed their base peaks at m/z 79 in a GC-MS analysis by electron impact ionization, but the aldehyde produced a unique base peak at m/z 84, suggesting a 4,6-diene structure. To confirm this inference, four geometrical isomers of each 4,6-hexadecadienyl compound were synthesized by two different routes in which one of two double bonds was furnished in a highly stereoselective manner. Separation of the two isomers synthesized together by each route was facilely accomplished by preparative HPLC. Their mass spectra coincided well with those of natural components, indicating that they were available for use as authentic standards for determining the configuration of the natural pheromone. Furthermore, other hexadecadienyl compounds, including the conjugated diene system between the 3- and 10-positions, were synthesized to accumulate the spectral data of pheromone candidates. 5,7-Hexadecadienal interestingly showed the base peak at m/z 80; meanwhile, the base peaks of its alcohol and acetate were detected at m/z 79 like the corresponding 4,6-dienes. The base peaks of all 6,8-, 7,9-, and 8,10-dienes universally appeared at m/z 67 like 9,11-, 10,12-, and 13,15-dienes, the spectra of which have already been published. Although 3,5-hexadecadienal was not prepared, base peaks at m/z 67 and 79 were recorded for the alcohol and acetate, respectively.     

Precision enhancement of MALDI-TOF MS using high resolution peak detection and label-free alignment

We have developed an automated procedure for aligning peaks in multiple TOF spectra that eliminates common timing errors and small variations in spectrometer output. Our method incorporates high-resolution peak detection, re-binning, and robust linear data fitting in the time domain. This procedure aligns label-free (uncalibrated) peaks to minimize the variation in each peak's location from one spectrum to the next, while maintaining a high number of degrees of freedom. We apply our method to replicate pooled-serum spectra from multiple laboratories and increase peak precision (t/sigma(t)) to values limited only by small random errors (with sigma(t) less than one time count in 89 out of 91 instances, 13 peaks in seven datasets). The resulting high precision allowed for an order of magnitude improvement in peak m/z reproducibility. We show that the CV for m/z is 0.01% (100 ppm) for 12 out of the 13 peaks that were observed in all datasets between 2995 and 9297 Da.     

Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer

A classification and regression tree (CART) model was trained to classify 41 clinical specimens as disease/nondisease based on 26 variables computed from the mass-to-charge ratio (m/z) and peak heights of proteins identified by mass spectroscopy. The CART model built on all of the specimens (no cross-validation) had an error rate of 4/41 = 10%. The CART model suggests that mass spectra peaks in the 8000-10,000, 20,000-30,000, 45,000-60, 000, and >125,000 m/z ranges may be valuable in distinguishing between the disease/nondisease specimens. The area under the receiver operating characteristics curve was 0.80 +/- 0.07 for leave-one-out cross-validation.     

Biomarkers of HIV-1 associated dementia: proteomic investigation of sera

Background

Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.

Methods

Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.

Results

A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).

Conclusion

Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.     

Strategy for purification and mass spectrometry identification of SELDI peaks corresponding to low-abundance plasma and serum proteins

Analysis by SELDI-TOF-MS of low abundance proteins makes it possible to select peaks as candidate biomarkers. Our aim was to define a purification strategy to optimise identification by MS of peaks detected by SELDI-TOF-MS from plasma or serum, regardless of any treatment by a combinatorial peptide ligand library (CPLL). We describe 2 principal steps in purification. First, choosing the appropriate sample containing the selected peak requires setting up a databank that records all the m/z peaks detected from samples in different conditions. Second, the specific purification process must be chosen: separation was achieved with either chromatographic columns or liquid-phase isoelectric focusing, both combined when appropriate with reverse-phase chromatography. After purification, peaks were separated by gel electrophoresis and the candidate proteins were analyzed by nano-liquid-chromatography-MS/MS. We chose 4m/z peaks (9400, 13,571, 13,800 and 15,557) selected for their differential expression between two conditions, as examples to explain the different strategies of purification, and we successfully identified 3 of them. Despite some limitations, our strategy to purify and identify peaks selected from SELDI-TOF-MS analysis was effective.     

Analysis of polar lipids from some representative enterobacteria, Plesiomonas and Acinetobacter by fast atom bombardment-mass spectrometry.

Fast atom bombardment-mass spectrometry (FAB-MS) was used to analyse lipid extracts of bacteria to assess its usefulness for analysing anionic phospholipids of potential chemotaxonomic value. The following micro-organisms were tested: Acinetobacter calcoaceticus, Acinetobacter sp., Citrobacter freundii, Enterobacter cloacae (2 strains), Escherichia coli (3 strains), Hafnia alvei, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Plesiomonas shigelloides, Proteus mirabilis (3 strains), Serratia liquefaciens and Serratia marcescens. Negative-ion spectra provide data for twenty-seven major carboxylate anions (m/z 209-325) and for thirty-seven major phospholipid anions (m/z 645-774). Generally, the largest carboxylate peaks were due to 16:1, 16:0, cyc17 and 18:1 while the largest phospholipid anion peaks were due to PE(32:1), PE(33:1), PE(34:1), PE(34:2), PG(30:2), PG(31:2), PG(32:2), PG(34:1) and PS(33:0). However, quantitative differences were observed. For example, Acinetobacter lacked PE (33:1) but had exceptionally high peaks at m/z 748, PS(33:0), and m/z 281, octadecanoate. Unknown 'carboxylate' peaks were detected at m/z 254, 256, 261, 268, 282 and 301. In some cases, unknown peaks appeared to constitute possible homologous series being separated by delta m/z of 14(identical to methylene). For chemotaxonomic purposes, the complexity of the data required numerical analysis. Using the Pearson coefficient of linear correlation, as a measure of association, it was possible to compare all strains analysed. Typical results for strain comparisons were as follows: Ent. cloacae vs Ent. cloacae, r = 0.90 (Ent. cloacae vs Ac. calcoaceticus, r = 0.46). Thus FAB-MS represents an excellent means of obtaining large quantities of data on polar lipids of a range of bacterial isolates, which may be suitable for chemotaxonomic purposes.     

Analysis of polar lipids from some representative enterobacteria, Plesiomonas and Acinetobacter by fast atom bombardment-mass spectrometry

Fast atom bombardment-mass spectrometry (FAB-MS) was used to analyse lipid extracts of bacteria to assess its usefulness for analysing anionic phospholipids of potential chemotaxonomic value. The following micro-organisms were tested: Acinetobacter calcoaceticus, Acinetobacter sp., Citrobacter freundii, Enterobacter cloacae (2 strains), Escherichia coli (3 strains), Hafnia alvei, Klebsiella oxytoca, Klebsiella pneumoniae, Morganella morganii, Plesiomonas shigelloides, Proteus mirabilis (3 strains), Serratia liquefaciens and Serratia marcescens. Negative-ion spectra provide data for twenty-seven major carboxylate anions (m/z 209–325) and for thirty-seven major phospholipid anions (m/z 645–774). Generally, the largest carboxylate peaks were due to 16: 1, 16: 0, cyc17 and 18: 1 while the largest phospholipid anion peaks were due to PE(32: 1), PE(33: 1), PE(34: 1), PE(34: 2), PG(30: 2), PG(31: 2), PG(32: 2), PG(34: 1) and PS (33: 0). However, quantitative differences were observed. For example, Acinetobacter lacked PE (33: 1) but had exceptionally high peaks at m/z 748, PS(33: 0), and m/z 281, octadecanoate. Unknown 'carboxylate' peaks were detected at m/z 254, 256, 261, 268, 282 and 301. In some cases, unknown peaks appeared to constitute possible homologous series being separated by Δ m/z of 14(≡ methylene). For chemotaxonomic purposes, the complexity of the data required numerical analysis. Using the Pearson coefficient of linear correlation, as a measure of association, it was possible to compare all strains analysed. Typical results for strain comparisons were as follows: Ent. cloacae vs Ent. cloacae, r = 0.90 ( Ent. cloacae vs Ac. calcoaceticus, r = 0.46). Thus FAB-MS represents an excellent means of obtaining large quantities of data on polar lipids of a range of bacterial isolates, which may be suitable for chemotaxonomic purposes.     

Characterization of fatty amides produced by lipase-catalyzed amidation of multi-hydroxylated fatty acids

Novel multi-hydroxylated primary fatty amides produced by direct amidation of 7,10-dihydroxy-8(E)-octadecenoic acid and 7,10,12-trihydroxy-8(E)-octadecenoic acid were characterized by GC-MS and NMR. The amidation reactions were catalyzed by immobilized Pseudozyma (Candida) antarctica lipase B (Novozym 435) in organic solvent with ammonium carbamate. The mass spectra of the underivatized products exhibited characteristic primary amide peaks at m/z 59 and m/z 72 that differed in peak intensities. Other peaks present were consistent with cleavage next to the hydroxyl groups. The mass spectra of the silylated amidation products showed the correct molecular weight and the typical fragmentation pattern of silylated hydroxy compounds. The mass spectra, together with proton and 13C NMR data, suggest that the products of lipase-catalyzed direct amidation of 7,10-dihydroxy-8(E)-octadecenoic acid and 7,10,12-trihydroxy-8(E)-octadecenoic acid are, 7,10-dihydroxy-8(E)-octadecenamide and 7,10,12-trihydroxy-8(E)-octadecenamide acid, respectively. Amidation of multi-hydroxylated fatty acids had increased the melting point, but reduced the surface active property of the resulting primary amides.     

Spectroscopic detection and identification of infected cells with herpes viruses

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and Fourier transform infrared (FTIR) microspectroscopy were previously applied for the identification of various biological samples. In the present study, normal cells in culture and cells infected with herpes simplex virus type 2 (HSV-2) or varicella-zoster virus (VZV) were analyzed by MALDI-TOF and FTIR microscopy. Specific spectral biomarkers for rapid and reliable monitoring and identification of infected cells and probably for the discrimination between these viruses were searched. The results show consistent spectral peaks in all examined normal uninfected human fibroblast cells both in MALDI-T0F and FTIR microscopy. In HSV-2- or VZV-infected cells, two unique peaks for each appeared at m/z 5397 and 5813 or at m/z 3501 and 4951, respectively, in MALDI-TOF spectra. In addition, several peaks that appeared in control uninfected cells at the region m/z 13,000-20,000 disappeared completely in all examined infected samples. When these infected cells were examined by FTIR microscopy, a band at 859 cm(-1) in control uninfected cells was significantly shifted to 854 cm(-1) in both HSV2- and VZV-infected cells. In addition, phosphate levels were considerably increased in all infected cells compared to normal uninfected cells. These parameters could be used as a basis for developing a spectral method for the detection and identification of cells infected with herpes viruses.     

A robust biomarker discovery pipeline for high-performance mass spectrometry data

A high-throughput software pipeline for analyzing high-performance mass spectral data sets has been developed to facilitate rapid and accurate biomarker determination. The software exploits the mass precision and resolution of high-performance instrumentation, bypasses peak-finding steps, and instead uses discrete m/z data points to identify putative biomarkers. The technique is insensitive to peak shape, and works on overlapping and non-Gaussian peaks which can confound peak-finding algorithms. Methods are presented to assess data set quality and the suitability of groups of m/z values that map to peaks as potential biomarkers. The algorithm is demonstrated with serum mass spectra from patients with and without ovarian cancer. Biomarker candidates are identified and ranked by their ability to discriminate between cancer and noncancer conditions. Their discriminating power is tested by classifying unknowns using a simple distance calculation, and a sensitivity of 95.6% and a specificity of 97.1% are obtained. In contrast, the sensitivity of the ovarian cancer blood marker CA125 is approximately 50% for stage I/II and approximately 80% for stage III/IV cancers. While the generalizability of these markers is currently unknown, we have demonstrated the ability of our analytical package to extract biomarker candidates from high-performance mass spectral data.     

Evaluation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry proteomic profiling: identification of alpha 2-HS glycoprotein B-chain as a biomarker of diet

Biomarkers have the potential to impact a wide range of public health concerns, including early detection of diseases, drug discovery, and improved accuracy of monitoring effects of interventions. Given new technological developments, broad-based screening approaches will likely advance biomarker discovery at an accelerated pace. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) allows for the elucidation of individual protein masses from a complex mixture with high throughput. We have developed a method for identifying serum biomarkers using MALDI-TOF and statistical analysis. However, before applying this approach to screening of complex diseases, we evaluated the approach in a controlled dietary intervention study. In this study, MALDI-TOF spectra were generated using samples from a randomized controlled trial. During separate feeding periods, 38 participants ate a basal diet devoid of fruits and vegetables and a basal diet supplemented with cruciferous (broccoli) family vegetables. Serum samples were obtained at the end of each 7-day feeding period and treated to remove large, abundant proteins. MALDI-TOF spectra were analyzed using peak picking algorithms and logistic regression models. Our bioinformatics methods identified two significant peaks at m/z values of 2740 and 1847 that could classify participants based on diet (basal vs. cruciferous) with 76% accuracy. The 2740 m/z peak was identified as the B-chain of alpha 2-HS glycoprotein, a serum protein previously found to vary with diet and be involved in insulin resistance and immune function.     

Machine learning approaches to lung cancer prediction from mass spectra

We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten-fold cross-validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676-mass data set and on a prescreened 124-mass data set. Highest predictive accuracies (1-2 off-sample misclassifications) were achieved by a multilayer perceptron and Na?ve Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.     

Analysis of Human Proteome Organization Plasma Proteome Project (HUPO PPP) reference specimens using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectrometry: multi-institution correlation of spectra and identification of biomarkers

We report on a multicenter analysis of HUPO reference specimens using SELDI-TOF MS. Eight sites submitted data obtained from serum and plasma reference specimen analysis. Spectra from five sites passed preliminary quality assurance tests and were subjected to further analysis. Intralaboratory CVs varied from 15 to 43%. A correlation coefficient matrix generated using data from these five sites demonstrated high level of correlation, with values >0.7 on 37 of 42 spectra. More than 50 peaks were differentially present among the various sample types, as observed on three chip surfaces. Additionally, peaks at approximately 9200 and approximately 15,950 m/z were present only in select reference specimens. Chromatographic fractionation using anion-exchange, membrane cutoff, and reverse phase chromatography, was employed for protein purification of the approximately 9200 m/z peak. It was identified as the haptoglobin alpha subunit after peptide mass fingerprinting and high-resolution MS/MS analysis. The differential expression of this protein was confirmed by Western blot analysis. These pilot studies demonstrate the potential of the SELDI platform for reproducible and consistent analysis of serum/plasma across multiple sites and also for targeted biomarker discovery and protein identification. This approach could be exploited for population-based studies in all phases of the HUPO PPP.     

Optimal construction of theoretical spectra for MS/MS spectra identification

We derive the optimal number of peaks (defined as the minimum number that provides the required efficiency of spectra identification) in the theoretical spectra as a function of (i) the experimental accuracy, sigma, of the measured ratio m/z; (ii) experimental spectrum density; (iii) size of the database; (iv) number of peaks in the theoretical spectra; and (v) types of ions that the peaks represent. We show that if theoretical spectra are constructed including b and y ions alone, then for sigma = 0.5, which is typical for high-throughput data, peptide chains of eight amino acids or longer can be identified based on the positions of peaks alone, at a rate of false identification below 1%. To discriminate between shorter peptides, additional (e.g., intensity-inferred) information is necessary. We derive the dependence of the probability of false identification on the number of peaks in the theoretical spectra and on the types of ions that the peaks represent. Our results suggest that the class of mass spectrum identification problems, for which more elaborate development of fragmentation rules (such as intensity model) is required, can be reduced to the problems that involve homologous peptides.     

Identifying the characteristic secondary ions of lignin polymer using ToF-SIMS

The chemical structure of lignin, a complex, irregular polymer of phenylpropane units that occurs in plant cell walls, was investigated using time-of-flight secondary ion mass spectrometry (ToF-SIMS). The positive ToF-SIMS spectra of lignin isolated from pine and beech wood showed prominent secondary ions possessing guaiacyl (at m/z 137 and 151) or syringyl (at m/z 167 and 181) rings, which are the basic building units of lignin polymer. This shows that ToF-SIMS is a useful tool for lignin structural analysis. The peaks at m/z 137 and 167 were assigned as the C6-C1 ion, and the peaks at m/z 151 and 181 may be double-component, the C6-C1 ion and the C6-C2 ion. We confirmed the characteristic guaiacyl ions using a synthetic lignin model compound, dehydrogenation polymer (DHP), which was formed by polymerizing of unlabeled and deuterium-labeled coniferyl alcohols. The formation mechanism of the main secondary ions was deduced by labeling specific positions of coniferyl alcohols with a stable isotope to study the relationship between chemical structure and secondary ion formation in ToF-SIMS.     

Algorithms and tools for analysis and management of mass spectrometry data

Mass spectrometry (MS) is a technique that is used for biological studies. It consists in associating a spectrum to a biological sample. A spectrum consists of couples of values (intensity, m/z), where intensity measures the abundance of biomolecules (as proteins) with a mass-to-charge ratio (m/z) present in the originating sample. In proteomics experiments, MS spectra are used to identify pattern expressions in clinical samples that may be responsible of diseases. Recently, to improve the identification of peptides/proteins related to patterns, MS/MS process is used, consisting in performing cascade of mass spectrometric analysis on selected peaks. Latter technique has been demonstrated to improve the identification and quantification of proteins/peptide in samples. Nevertheless, MS analysis deals with a huge amount of data, often affected by noises, thus requiring automatic data management systems. Tools have been developed and most of the time furnished with the instruments allowing: (i) spectra analysis and visualization, (ii) pattern recognition, (iii) protein databases querying, (iv) peptides/proteins quantification and identification. Currently most of the tools supporting such phases need to be optimized to improve the protein (and their functionalities) identification processes. In this article we survey on applications supporting spectrometrists and biologists in obtaining information from biological samples, analyzing available software for different phases. We consider different mass spectrometry techniques, and thus different requirements. We focus on tools for (i) data preprocessing, allowing to prepare results obtained from spectrometers to be analyzed; (ii) spectra analysis, representation and mining, aimed to identify common and/or hidden patterns in spectra sets or in classifying data; (iii) databases querying to identify peptides; and (iv) improving and boosting the identification and quantification of selected peaks. We trace some open problems and report on requirements that represent new challenges for bioinformatics.