Use of physiological information based on grayscale images to improve mass spectrometry imaging data analysis from biological tissues.

The characterization of cancer tissues by matrix-assisted laser desorption ionization-mass spectrometry images (MALDI-MSI) is of great interest because of the power of MALDI-MS to understand the composition of biological samples and the imaging side that allows for setting spatial boundaries among tissues of different nature based on their compositional differences. In tissue-based cancer research, information on the spatial location of necrotic/tumoral cell populations can be approximately known from grayscale images of the scanned tissue slices. This study proposes as a major novelty the introduction of this physiologically-based information to help in the performance of unmixing methods, oriented to extract the MS signatures and distribution maps of the different tissues present in biological samples. Specifically, the information gathered from grayscale images will be used as a local rank constraint in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the analysis of MALDI-MSI of cancer tissues. The use of this constraint, setting absence of certain kind of tissues only in clear zones of the image, will help to improve the performance of MCR-ALS and to provide a more reliable definition of the chemical MS fingerprint and location of the tissues of interest. The general strategy to address the analysis of MALDI-MSI of cancer tissues will involve the study of the MCR-ALS results and the posterior use of MCR-ALS scores as dimensionality reduction for image segmentation based on K-means clustering. The resolution method will provide the MS signatures and their distribution maps for each tissue in the sample. Then, the resolved distribution maps for each biological component (MCR scores) will be submitted as initial information to K-means clustering for image segmentation to obtain information on the boundaries of the different tissular regions in the samples studied. MCR-ALS prior to K-means not only provides the desired dimensionality reduction, but additionally resolved non-biological signal contributions are not used and the weight given to the different biological components in the segmentation process can be modulated by suitable preprocessing methods.

The clinical impact of patients with bloodstream infection with different groups of Viridans group streptococci by using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS).

The accuracy of matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for identifying viridans group streptococcus (VGS) was improving. However, the clinical impact of identifying VGS had not been well recognized. Our study had comprehensively studied the clinical manifestations and outcome of VGS blood stream infection by using MALDI-TOF MS for identification.This retrospective study enrolled 312 adult patients with a monomicrobial blood culture positive for VGS. Blood culture was examined through MALDI-TOF MS.The most common VGS species were the Streptococcus anginosus group (38.8%) and Streptococcus mitis group (22.8%). Most species showed resistance to erythromycin (35.6%), followed by clindamycin (25.3%) and penicillin (12.5%). Skin and soft tissue infection and biliary tract infection were significantly related to S. anginosus group bacteremia (P = .001 and P = .005, respectively). S. mitis group bacteremia was related to infective endocarditis and bacteremia with febrile neutropenia (P = .005 and P < .001, respectively). Infective endocarditis was also more likely associated with S. sanguinis group bacteremia (P = .009). S. anginosus group had less resistance rate to ampicillin, erythromycin, clindamycin, and ceftriaxone (P = .019, <.001, .001, and .046, respectively). A more staying in intensive care unit, underlying solid organ malignancy, and a shorter treatment duration were independent risk factors for 30-day mortality. This study comprehensively evaluated different VGS group and their clinical manifestations, infection sources, concomitant diseases, treatments, and outcomes. Categorizing VGS into different groups by MALDI-TOF MS could help clinical physicians well understand their clinical presentations.

Matrix-assisted laser desorption ionization/time-of-flight mass spectrometry for clinical diagnosis.

BACKGROUND: Matrix-assisted laser desorption ionization/time-of-flight (MALDI-TOF) mass spectrometry is known as an extremely sensitive analytical tool for characterizing different types of biological compounds including proteins, peptides and lipids. Since MALDI-TOF analysis requires very simple sample pretreatment, the technique can be used for rapidly detecting biochemical compounds serving as disease biomarkers. RESULTS: This mini-review focuses on the applications of MALDI-TOF in the detection of potential disease biomarkers in various biological samples. CONCLUSIONS: The potential disease biomarkers are mostly abundant proteins, peptides, or lipids including: albumin; hemoglobin; α-defensins; trimethylamine; phospholipids; and glycated α- and ß-globin, which are indicators of albuminuria; fecal occult blood and ischemic stroke; dry eye disease and/or aging; trimethylaniuria; breast cancer; and diabetes, respectively.

Imaging mass spectrometry analysis for follicular lymphoma grading.

Follicular lymphoma (FL) is the second most common non-Hodgkins lymphoma in the United States. While the current diagnosis depends heavily on the review of H&E-stained tissues, additional sources of information such as IHC are occasionally needed. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) can be used to generate protein profiles from localized tissue regions, thus making it possible to relate changes in tissue histology to the changes in the protein signature of the tissue. It may be possible to determine potential biomarkers that can indicate disease state and prognosis based on the protein profile. This research aims to combine two different but related types of data in order to develop a unique diagnosis methodology that can potentially improve the accuracy of diagnosis. Preliminary analysis has shown promising results for distinguishing intrafollicle regions from the mantle and follicle zones in normal tissue.

Feature extraction and dimensionality reduction for mass spectrometry data.

Mass spectrometry is being used to generate protein profiles from human serum, and proteomic data obtained from mass spectrometry have attracted great interest for the detection of early stage cancer. However, high dimensional mass spectrometry data cause considerable challenges. In this paper we propose a feature extraction algorithm based on wavelet analysis for high dimensional mass spectrometry data. A set of wavelet detail coefficients at different scale is used to detect the transient changes of mass spectrometry data. The experiments are performed on 2 datasets. A highly competitive accuracy, compared with the best performance of other kinds of classification models, is achieved. Experimental results show that the wavelet detail coefficients are efficient way to characterize features of high dimensional mass spectra and reduce the dimensionality of high dimensional mass spectra.

Peak detection in mass spectrometry by Gabor filters and envelope analysis.

Mass Spectrometry (MS) is increasingly being used to discover diseases-related proteomic patterns. The peak detection step is one of the most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false discovery rate in peak detection. Most of them follow two approaches: one is the denoising approach and the other is the decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose two novel methods, named GaborLocal and GaborEnvelop, both of which can detect more true peaks with a lower false discovery rate than previous methods. We employ the method of Gaussian local maxima to detect peaks, because it is robust to noise in signals. A new approach, peak rank, is defined for the first time to identify peaks instead of using the signal-to-noise ratio. Meanwhile, the Gabor filter is used to amplify important information and compress noise in the raw MS signal. Moreover, we also propose the envelope analysis to improve the quantification of peaks and remove more false peaks. The proposed methods have been performed on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate that our methods outperform other commonly used methods in the Receiver Operating Characteristic (ROC) curve.

An insight into high-resolution mass-spectrometry data.

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Identification of alphaB-crystallin, a biomarker of renal cell carcinoma by SELDI-TOF MS.

Spectrometric-based surface-enhanced laser desorption/ionization ProteinChip (SELDI-TOF) facilitates rapid and easy analysis of protein mixtures and is often exploited to define potential diagnostic markers from sera. However, SELDI- TOF is a relatively insensitive technique and unable to detect circulating proteins at low levels even if they are differentially expressed in cancer patients. Therefore, we applied this technology to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that different biomarkers are identified from tissues than those previously identified in serum, and that serum markers are often not produced by the tumors themselves at detectable levels, reflecting the nonspecific nature of many circulating biomarkers. We detected and characterized áB-crystallin as an overexpressed protein in RCC tissues and showed differential expression by immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of biomarkers that are synthesized by diseased tissues than for the identification of serum biomarkers and identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques can then be applied to determine their validity as biomarkers in biological fluids.

Support vector machine approach to separate control and breast cancer serum samples.

The paper presents two analyzes of the MALDI-TOF mass spectrometry dataset. Both analyzes use the support vector machine as a tool to build a prediction model. The first analysis which is our contribution to the competition uses the given spectra data without further processing. In the second analysis, we employed an additional preprocessing step consisting of peak detection, peak alignment and feature selection based on statistical tests. The experimental results suggest that the preprocessing step with feature selection improves prediction accuracy.

Application of the random forest classification method to peaks detected from mass spectrometric proteomic profiles of cancer patients and controls.

The random forest classification method was applied to classify samples from 76 breast cancer patients and 77 controls whose proteomic profile had been obtained using mass spectrometry. The analysis consisted of two stages, the detection of peaks from the profiles and the construction of a classification rule using random forests. Using a peak detection method based on finding common local maxima in the smoothed sample spectra, 444 peaks were detected, reducing to 365 robust peaks found in at least 7 out of 10 random subsets of samples. Subjects were classified as cases or controls using the random forest algorithm applied to the 365 peaks. Based on the prediction of the status of out-of-bag samples, the total error rate was 16.3%, with a sensitivity of 81.6% and a specificity of 85.7%. Measures of importance of each of the peaks were calculated to identify regions of the spectrum influencing the classification, and the four most important peaks were identified as mz3863_13, mz2943_12, mz3193_44 and mz8925_94. Combining initial peak detection with the random forest algorithm provides a high-performance classification system for proteomic data, with unbiased estimates of future performance.

Developing a discrimination rule between breast cancer patients and controls using proteomics mass spectrometric data: a three-step approach.

To discriminate between breast cancer patients and controls, we used a three-step approach to obtain our decision rule. First, we ranked the mass/charge values using random forests, because it generates importance indices that take possible interactions into account. We observed that the top ranked variables consisted of highly correlated contiguous mass/charge values, which were grouped in the second step into new variables. Finally, these newly created variables were used as predictors to find a suitable discrimination rule. In this last step, we compared three different methods, namely Classification and Regression Tree (CART), logistic regression and penalized logistic regression. Logistic regression and penalized logistic regression performed equally well and both had a higher classification accuracy than CART. The model obtained with penalized logistic regression was chosen as we hypothesized that this model would provide a better classification accuracy in the validation set. The solution had a good performance on the training set with a classification accuracy of 86.3%, and a sensitivity and specificity of 86.8% and 85.7%, respectively.

Classification of breast cancer versus normal samples from mass spectrometry profiles using linear discriminant analysis of important features selected by random forest.

We present our approach to classifying the processed proteomic data that were made available to the participants of the classification competition. Although classification of the spectra was the goal of the competition we feel that proteomic applications to cancer biomarker studies make certain additional demands. For example, one such requirement should be identification of certain features which collectively could differentiate the two groups of samples. Also ideally, the size of the feature set should be small. To that end we propose a linear discriminant classifier based on nine m/z intensity values. Construction and performance of this classifier are discussed.

A classification model for the Leiden proteomics competition.

A strategy is presented to build a discrimination model in proteomics studies. The model is built using cross-validation. This cross-validation step can simply be combined with a variable selection method, called rank products. The strategy is especially suitable for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, Principal Component Discriminant Analysis is used; however, the methodology can be used with any classifier. A data set containing serum samples from breast cancer patients and healthy controls is analysed. Double cross-validation shows that the sensitivity of the model is 82% and the specificity 86%. Potential putative biomarkers are identified using the variable selection method. In each cross-validation loop a classification model is built. The final classification uses a majority voting scheme from the ensemble classifier.

Combined experimental and statistical strategy for mass spectrometry based serum protein profiling for diagnosis of breast cancer: a case-control study.

Serum protein profiling by mass spectrometry is a promising method for early detection of cancer. We have implemented a combined strategy based on matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) and statistical data analysis for serum protein profiling and applied it in a well-described breast cancer case-control study. A rigorous sample collection protocol ensured high quality specimen and reduced bias from preanalytical factors. Preoperative serum samples obtained from 48 breast cancer patients and 28 controls were used to generate MALDI MS protein profiles. A total of nine mass spectrometric protein profiles were obtained for each serum sample. A total of 533 common peaks were defined and represented a 'reference protein profile'. Among these 533 common peaks, we identified 72 peaks exhibiting statistically significant intensity differences ( p < 0.01) between cases and controls. A diagnostic rule based on these 72 mass values was constructed and exhibited a cross-validated sensitivity and specificity of approximately 85% for the detection of breast cancer. With this method, it was possible to distinguish early stage cancers from controls without major loss of sensitivity and specificity. We conclude that optimized serum sample handling and mass spectrometry data acquisition strategies in combination with statistical analysis provide a viable platform for serum protein profiling in cancer diagnosis.

MALDI/SELDI protein profiling of serum for the identification of cancer biomarkers.

The ability to visualize the full depth of the serum proteome in a high-throughput manner is a major goal of clinical proteomics. Methodologies, which combine higher throughput with the ability to observe differential protein expression levels, have been applied to this goal. An example of such a system is the coupling of robotic sample processing to matrix-assisted laser desorption time of flight mass spectrometry (MALDI-TOF-MS). Within this paradigm is a modification of MALDI-TOF termed surface-enhanced laser desorption/ionization-TOF (SELDI-TOF). Both conventional MALDI and SELDI have been used to generate protein expression profiles reflective of potential peptide changes in serum. This information can be used to identify proteins, which may enable new diagnostic and therapeutic strategies.

Evaluating reproducibility and similarity of mass and intensity data in complex spectra--applications to tubulin.

We present a data processing approach based on the spectral dot product for evaluating spectral similarity and reproducibility. The method introduces 95% confidence intervals on the spectral dot product to evaluate the strength of spectral correlation; it is the only calculation described to date that accounts for both the non-normal sampling distribution of the dot product and the number of peaks the spectra have in common. These measures of spectral similarity allow for the recursive generation of a consensus spectrum, which incorporates the most consistent features from statistically similar replicate spectra. Taking the spectral dot product and 95% confidence intervals between consensus spectra from different samples yields the similarity between these samples. Applying the data analysis scheme to replicates of brain tubulin CNBr peptides enables a robust comparison of tubulin isotype expression and post-translational modification patterns in rat and cow brains.

Analysis of MALDI-TOF mass spectrometry data for detection of glycan biomarkers.

We present a computational framework for analysis of MALDI-TOF mass spectrometry data to enable quantitative comparison of glycans in serum. The proposed framework enables a systematic selection of glycan structures that have good generalization capability in distinguishing subjects from two pre-labeled groups. We applied the proposed method for a biomarker discovery study that involves 203 participants from Cairo, Egypt; 73 hepatocellular carcinoma (HCC) cases, 52 patients with chronic liver disease (CLD), and 78 healthy individuals. Glycans were enzymatically released from proteins in serum and permethylated prior to mass spectrometric quantification. A subset of the participants (35 HCC and 35 CLD cases) was used as a training set to select global and subgroup-specific peaks. The peak selection step is preceded by peak screening, where we eliminate peaks that seem to have association with covariates such as age, gender, and viral infection based on the 78 spectra from healthy individuals. To ensure that the global peaks have good generalization capability, we subjected the entire spectral preprocessing and peak selection step to a cross-validation; a randomly selected subset of the training set was used for spectral preprocessing and peak selection in multiple runs with resubstitution. In addition to global peak identification method, we describe a new approach that allows the selection of subgroup-specific glycans by searching for glycans that display differential abundance in a subgroup of patients only. The performance of the global and subgroup-specific peaks is evaluated via a blinded independent set that comprises of 38 HCC and 17 CLD cases. Further evaluation of the potential clinical utility of the selected global and subgroup-specific candidate markers is needed.

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.

Isotopologue distributions of peptide product ions by tandem mass spectrometry: quantitation of low levels of deuterium incorporation.

Protonated molecular peptide ions and their product ions generated by tandem mass spectrometry appear as isotopologue clusters due to the natural isotopic variations of carbon, hydrogen, nitrogen, oxygen, and sulfur. Quantitation of the isotopic composition of peptides can be employed in experiments involving isotope effects, isotope exchange, and isotopic labeling by chemical reactions and in studies of metabolism by stable isotope incorporation. Both ion trap and quadrupole-time of flight mass spectrometry are shown to be capable of determining the isotopic composition of peptide product ions obtained by tandem mass spectrometry with both precision and accuracy. Tandem mass spectra of clusters of isotopologue ions obtained in profile mode are fit by nonlinear least squares to a series of Gaussian peaks which quantify the Mn/M0 values which define the isotopologue distribution (ID). To determine the isotopic composition of product ions from their ID, a new algorithm that predicts the Mn/M0 ratios and obviates the need to determine the intensity of all of the ions of an ID is developed. Consequently a precise and accurate determination of the isotopic composition of a product ion may be obtained from only the initial values of the ID, however, the entire isotopologue cluster must be isolated prior to fragmentation. Following optimization of the molecular ion isolation width, fragmentation energy, and detector sensitivity, the presence of isotopic excess (2H, 13C, 15N, 18O) is readily determined within 1%. The ability to determine the isotopic composition of sequential product ions permits the isotopic composition of individual amino acid residues in the precursor ion to be determined.

Mass spectrometry proteomic diagnosis: enacting the double cross-validatory paradigm.

This paper presents an approach to the evaluation and validation of the diagnostic potential of mass spectrometry data in an application on the construction of an "early warning" diagnostic procedure. Our approach is based on a full implementation and application of double cross-validatory calibration and evaluation. It is a key feature of this methodology that we can jointly optimize the classifiers for prediction while simultaneously calculating validated error rates. The methodology leaves the size of the training data nearly intact. We present application to data from a designed experiment in a colon-cancer study. Subsequent to presentation of results from the double cross-validatory analysis, we explore a post-hoc analysis of the calibrated classifiers to identify the markers that drive the classification.