SELDI-TOF mass spectra: A view on sources of variation

Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva

SARS-CoV-2 has caused a large outbreak since its emergence in December 2019. COVID-19 diagnosis became a priority so as to isolate and treat infected individuals in order to break the contamination chain. Currently, the reference test for COVID-19 diagnosis is the molecular detection (RT-qPCR) of the virus from nasopharyngeal swab (NPS) samples. Although this sensitive and specific test remains the gold standard, it has several limitations, such as the invasive collection method, the relative high cost and the duration of the test. Moreover, the material shortage to perform tests due to the discrepancy between the high demand for tests and the production capacities puts additional constraints on RT-qPCR. Here, we propose a PCR-free method for diagnosing SARS-CoV-2 based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling and machine learning (ML) models from salivary samples. Kinetic saliva samples were collected at enrollment and ten and thirty days later (D0, D10 and D30), to assess the classification performance of the ML models compared to the molecular tests performed on NPS specimens. Spectra were generated using an optimized protocol of saliva collection and successive quality control steps were developed to ensure the reliability of spectra. A total of 360 averaged spectra were included in the study. At D0, the comparison of MS spectra from SARS-CoV-2 positive patients (n = 105) with healthy healthcare controls (n = 51) revealed nine peaks that significantly distinguished the two groups. Among the five ML models tested, support vector machine with linear kernel (SVM-LK) provided the best performance on the training dataset (accuracy = 85.2%, sensitivity = 85.1%, specificity = 85.3%, F1-Score = 85.1%). The application of the SVM-LK model on independent datasets confirmed its performances with 88.9% and 80.8% of correct classification for samples collected at D0 and D30, respectively. Conversely, at D10, the proportion of correct classification had fallen to 64.3%. The analysis of saliva samples by MALDI-TOF MS and ML appears as an interesting supplementary tool for COVID-19 diagnosis, despite the mitigated results obtained for convalescent patients (D10).

A study of the properties of Gaussian mixture model for stable isotope standard quantification in MALDI-TOF MS

The quantification of peptides in Matrix assisted laser desorption/ionization time-of-flight mass spectrum analysis coupled with stable isotope standards has been used to quantify native peptides under many experimental conditions. This approach has difficulties quantifying samples containing peptides with ion currents in overlapping (convolved) spectra. In a previous article we proposed a reparametrized Gaussian mixture model based on the known characteristics of the peptides that could also accommodate overlapping spectra. We demonstrated the application of our model in a series of single and overlapping peptides quantification experiments. Here, we focus solely on studying the properties of our approach and examine the characteristics of the GMM approach in convolved peptides using simulated spectra and provide a method for simulating these spectra.

Serum proteomics for gastric cancer

According to the World Health Organization, 800,000 cancer-related deaths are caused by gastric cancer each year globally, hence making it the second leading cause of cancer-related deaths in the world. Gastric cancer is often either asymptomatic or causing only nonspecific symptoms in its early stages. By the time the symptoms occur, the cancer has usually reached an advanced stage, which is one of the main reasons for its relatively poor prognosis. Therefore, early diagnosis and early treatment are very crucial. The differential analysis of serum protein between cancer patients and healthy controls can be performed using proteomics techniques and can hence be adopted as tumor biomarkers for the early diagnosis of cancer. So far, several serum tumor biomarkers have been identified for gastric cancer. However due to their poor specificity and sensitivity, they have proven to be insufficient for the reliable diagnosis of gastric cancer. Thus, using modern advanced proteomics techniques to find some new and reliable serum tumor biomarkers for earlier and reliable diagnosis of gastric cancer is a must. Nowadays, proteomic-based techniques, such as SELDI and HCLP, are available to discover biomarkers in gastric cancer. Numerous novel serum tumor biomarkers such as SAA, plasminogen and C9c, have been discovered through serological proteomics strategies. This review mainly focuses on the serum proteomics techniques and their application in the research of gastric cancer.

Investigation of serum protein profiles in scrapie infected sheep by means of SELDI-TOF-MS and multivariate data analysis

Background

Classical scrapie in sheep is a fatal neurodegenerative disease associated with the conversion PrP^C to PrP^Sc. Much is known about genetic susceptibility, uptake and dissemination of PrP^Sc in the body, but many aspects of prion diseases are still unknown. Different proteomic techniques have been used during the last decade to investigate differences in protein profiles between affected animals and healthy controls. We have investigated the protein profiles in serum of sheep with scrapie and healthy controls by SELDI-TOF-MS and LC-MS/MS. Latent Variable methods such as Principal Component Analysis, Partial Least Squares-Discriminant Analysis and Target Projection methods were used to describe the MS data.

Results

The serum proteomic profiles showed variable differences between the groups both throughout the incubation period and at the clinical end stage of scrapie. At the end stage, the target projection model separated the two groups with a sensitivity of 97.8%, and serum amyloid A was identified as one of the protein peaks that differed significantly between the groups.

Conclusions

At the clinical end stage of classical scrapie, ten SELDI peaks significantly discriminated the scrapie group from the healthy controls. During the non-clinical incubation period, individual SELDI peaks were differently expressed between the groups at different time points. Investigations of differences in -omic profiles can contribute to new insights into the underlying disease processes and pathways, and advance our understanding of prion diseases, but comparison and validation across laboratories is difficult and challenging.

Quality measures of imaging mass spectrometry aids in revealing long-term striatal protein changes induced by neonatal exposure to the cyanobacterial toxin β-N-methylamino-L-alanine (BMAA)

Many pathological processes are not directly correlated to dramatic alterations in protein levels. The changes in local concentrations of important proteins in a subset of cells or at specific loci are likely to play a significant role in disease etiologies, but the precise location might be unknown, or the concentration might be too small to be adequately sampled for traditional proteomic techniques. Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is a unique analytical method that combines analysis of multiple molecular species and of their distribution in a single platform. As reproducibility is essential for successful biomarker discovery, it is important to systematically assess data quality in biologically relevant MALDI IMS experiments. In the present study, we applied four simple tools to study the reproducibility for individual sections, within-group variation, and between-group variation of data acquired from brain sections of 21 animals divided into three treatment groups. We also characterized protein changes in distinct regions of the striatum from six-month-old rats treated neonatally (postnatal days 9–10) with the cyanobacterial toxin β-N-methylamino-l-alanine (BMAA), which has been implicated in neurodegenerative diseases. The results showed that optimized experimental settings can yield high-quality MALDI IMS data with relatively low variation (14% to 15% coefficient of variance) that allow the characterization of subtle changes in protein expression in various subregions of the brain. This was further exemplified by the dose-dependent reduction of myelin basic protein in the caudate putamen and the nucleus accumbens of adult rats neonatally treated with BMAA (150 and 460 mg/kg). The reduction in myelin basic protein was confirmed through immunohistochemistry and indicates that developmental exposure to BMAA may induce structural effects on axonal growth and/or directly on the proliferation of oligodendrocytes and myelination, which might be important for the previously shown BMAA-induced long-term cognitive impairments.

Time-of-flight mass spectrometry in screening serum-specific protein in patients with scrotal Paget's disease

The weak cationic chip (WCX2) and the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) were used to test the serum differential proteins in 20 healthy persons, 20 patients with chronic scrotal eczema, and 30 patients with scrotal Paget's disease and test the specificity and sensitivity of screening scrotal Paget's disease with differential proteins. We found that the differences between the 5 protein peaks of the normal group and the scrotal Paget's disease group in the range of 2000 to 30 000 Da were statistically significant (P < .01) and the difference of 3 protein peaks between the scrotal eczema group and the scrotal Paget's disease group in the range of 2000 to 30 000 Da was statistically significant (P < .05). SELDI-TOS-MS technique has certain application value in the early diagnosis of the scrotal Paget's disease and screening for the specific tumor markers.

Analysis of neuropeptides by MALDI imaging mass spectrometry

Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) is one of the most effective tools for localizing small molecules and compounds directly in thin tissue sections. MALDI IMS should be used when the distribution of molecular species is not known and to localize changes due to a disease process or a treatment. In recent years it has become increasingly clear that many pathological processes are not readily correlated to dramatic changes in protein levels. MALDI IMS can aid the localization of areas where the cellular concentration of proteins may be high enough to play an important biological role, but when the precise location is unknown. Here, we present a MALDI IMS protocol and data analysis of molecular imaging of multiple rat brain sections.

Proteomic biomarkers predicting lymph node involvement in serum of cervical cancer patients. Limitations of SELDI-TOF MS

BACKGROUND

Lymph node status is not part of the staging system for cervical cancer, but provides important information for prognosis and treatment. We investigated whether lymph node status can be predicted with proteomic profiling.

MATERIAL & METHODS

Serum samples of 60 cervical cancer patients (FIGO I/II) were obtained before primary treatment. Samples were run through a HPLC depletion column, eliminating the 14 most abundant proteins ubiquitously present in serum. Unbound fractions were concentrated with spin filters. Fractions were spotted onto CM10 and IMAC30 surfaces and analyzed with surface-enhanced laser desorption time of flight (SELDI-TOF) mass spectrometry (MS). Unsupervised peak detection and peak clustering was performed using MASDA software. Leave-one-out (LOO) validation for weighted Least Squares Support Vector Machines (LSSVM) was used for prediction of lymph node involvement. Other outcomes were histological type, lymphvascular space involvement (LVSI) and recurrent disease.

RESULTS

LSSVM models were able to determine LN status with a LOO area under the receiver operating characteristics curve (AUC) of 0.95, based on peaks with m/z values 2,698.9, 3,953.2, and 15,254.8. Furthermore, we were able to predict LVSI (AUC 0.81), to predict recurrence (AUC 0.92), and to differentiate between squamous carcinomas and adenocarcinomas (AUC 0.88), between squamous and adenosquamous carcinomas (AUC 0.85), and between adenocarcinomas and adenosquamous carcinomas (AUC 0.94).

CONCLUSIONS

Potential markers related with lymph node involvement were detected, and protein/peptide profiling support differentiation between various subtypes of cervical cancer. However, identification of the potential biomarkers was hampered by the technical limitations of SELDI-TOF MS.

An estrogen-responsive plasma protein expression signature in Atlantic cod (Gadus morhua) revealed by SELDI-TOF MS

Compound-specific protein expression signatures (PESs) can be revealed by proteomic techniques. The SELDI-TOF MS approach is advantageous due to its simplicity and high-throughput capacity, however, there are concerns regarding the reproducibility of this method. The aim of this study was to define an estrogen-responsive PES in plasma of Atlantic cod (Gadus morhua) using the SELDI-TOF MS technique. Protein expression analysis of male cod exposed to 17β-estradiol (E₂) showed that 27 plasma peaks were differentially expressed following exposure. The reproducibility of this result was evaluated by reanalyzing the samples six months later, and a significant change in expression was confirmed for 13 of the 27 peaks detected in the first analysis. The performance of the reproducible E₂-responsive PES, constituting these 13 peaks, was then tested on samples from juvenile cod exposed to 4-nonylphenol, North Sea oil, or North Sea oil spiked with alkylphenols. Principal component analysis revealed that nonylphenol-exposed cod could be separated from unexposed cod based on the E₂-responsive PES, indicating that the PES can be used to assess estrogenic exposure of both juvenile and adult specimens of cod. A targeted antibody-assisted SELDI-TOF MS approach was carried out in an attempt to identify the E₂-responsive peaks. Results indicated that 2 peaks were fragments of the well-known biomarkers VTG and/or ZRP. In this study, the SELDI-TOF MS technology has shown its potential for defining compound-specific PESs in fish. Nevertheless, thorough validation of reproducibility, specificity and sensitivity of a PES is required before it can be applied in environmental monitoring.

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

A protein profile study to discriminate CIN lesions from normal cervical epithelium

Background

Cervical intraepithelial neoplasia (CIN), a frequently encountered disease caused by Human Papilloma Virus (HPV) is often diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. Since it is known that this procedure strongly affects the water-soluble proteins contained in the cervical tissue we decided to investigate whether a water-soluble protein-saving biopsy processing method can be used to support the diagnosis of normal and CIN.

Methods

Cervical punch biopsies from 55 women were incubated for 24 h at 4°C in RPMI1640 medium for protein analysis prior to usual FFPE processing and p16 and Ki67-supported histologic consensus diagnosis was assessed. The biopsy supernatants were subjected to surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF MS) for identifying differentially expressed proteins. Binary logistic regression and classification and regression trees (CART) were used to develop a classification model.

Results

The age of the patients ranged from 26 to 40 years (median 29.7). The consensus diagnoses were normal cervical tissue (n = 10) and CIN2-3 (n = 45). The mean protein concentration was 1.00 and 1.09 mg/ml in the normal and CIN2-3 group, respectively. The peak detection and clustering process resulted in 40 protein peaks. Many of these peaks differed between the two groups, but only three had independent discriminating power. The overall classification results were 88%.

Conclusions

Water-soluble proteins sampled from punch biopsies are promising to assist the diagnosis of normal and CIN2-3.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

Proteomics and biomarkers for ovarian cancer diagnosis

Ovarian cancer remains a leading cause of death from gynecological malignancy. Early diagnosis is the most important determinant of survival. Current diagnostic tools have had very limited success in early detection. In recent years, the advancing techniques for proteomics have accelerated the discovery of ovarian cancer biomarkers. Numerous proteomics-based molecular biomarkers/panels have been identified and hold great potential for diagnostic applications, but they need further development and validation. This article reviews recently published data on the diagnosis of ovarian cancer with proteomics, including the major proteomics technologies and promising strategies for biomarker discovery and development.

A magnetic bead-based serum proteomic fingerprinting method for parallel analytical analysis and micropreparative purification

ProteinChip surface-enhanced laser desorption/ionization technology and magnetic beads-based ClinProt system are commonly used for semi-quantitative profiling of plasma proteome in biomarker discovery. Unfortunately, the proteins/peptides detected by MS are non-recoverable. To obtain the protein identity of a MS peak, additional time-consuming and material-consuming purification steps have to be done. In this study, we developed a magnetic beads-based proteomic fingerprinting method that allowed semi-quantitative proteomic profiling and micropreparative purification of the profiled proteins in parallel. The use of different chromatographic magnetic beads allowed us to obtain different proteomic profiles, which were comparable to those obtained by the ProteinChip surface-enhanced laser desorption/ionization technology. Our assays were semi-quantitative. The normalized peak intensity was proportional to concentration measured by immunoassay. Both intra-assay and inter-assay coefficients of variation of the normalized peak intensities were in the range of 4-30%. Our method only required 2 microL of serum or plasma for generating enough proteins for semi-quantitative profiling by MALDI-TOF-MS as well as for gel electrophoresis and subsequent protein identification. The protein peaks and corresponding gel spots could be easily matched by comparing their intensities and masses. Because of its high efficiency and reproducibility, our method has great potentials in clinical research, especially in biomarker discovery.

ReSASC: a resampling-based algorithm to determine differential protein expression from spectral count data

Label-free methods for MS/MS quantification of protein expression are becoming more prevalent as instrument sensitivity increases. Spectral counts (SCs) are commonly used, readily obtained, and increase linearly with protein abundance; however, a statistical framework has been lacking. To accommodate the highly non-normal distribution of SCs, we developed ReSASC (resampling-based significance analysis for spectral counts), which evaluates differential expression between two conditions by pooling similarly expressed proteins and sampling from this pool to create permutation-based synthetic sets of SCs for each protein. At a set confidence level and corresponding p-value cutoff, ReSASC defines a new p-value, p', as the number of synthetic SC sets with p>p(cutoff) divided by the total number of sets. We have applied ReSASC to two published SC data sets and found that ReSASC compares favorably with existing methods while being easy to operate and requiring only standard computing resources.

Challenges for biomarker discovery in body fluids using SELDI-TOF-MS

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

Fiber evanescent wave spectroscopy using the mid-infrared provides useful fingerprints for metabolic profiling in humans

Fiber evanescent wave spectroscopy (FEWS) explores the mid-infrared domain, providing information on functional chemical groups represented in the sample. Our goal is to evaluate whether spectral fingerprints obtained by FEWS might orientate clinical diagnosis. Serum samples from normal volunteers and from four groups of patients with metabolic abnormalities are analyzed by FEWS. These groups consist of iron overloaded genetic hemochromatosis (GH), iron depleted GH, cirrhosis, and dysmetabolic hepatosiderosis (DYSH). A partial least squares (PLS) logistic method is used in a training group to create a classification algorithm, thereafter applied to a test group. Patients with cirrhosis or DYSH, two groups exhibiting important metabolic disturbances, are clearly discriminated from control groups with AUROC values of 0.94+/-0.05 and 0.90+/-0.06, and sensibility/specificity of 8684% and 8787%, respectively. When pooling all groups, the PLS method contributes to discriminate controls, cirrhotic, and dysmetabolic patients. Our data demonstrate that metabolic profiling using infrared FEWS is a possible way to investigate metabolic alterations in patients.

Analysis of mass spectrometry data using sub-spectra

BACKGROUND

Spectra resulting from Surface-Enhanced Laser Desorption/Ionisation (SELDI) mass spectrometry measurements are constructed by combining sub-spectra, each of which are the result of a single firing of the laser responsible for the process of desorption/ionisation. These firings are performed at different locations of the spot on which the sample is analysed. The final spectrum is then constructed by summing over all these sub-spectra. This process is sub-optimal in that it can average out peaks from peptides that are present in low abundance or are unevenly distributed across the spot, particularly because the amount of noise varies considerably between sub-spectra. This argues for analysing sub-spectra separately and combining results afterwards.

RESULTS

Here, we propose to analyse these sub-spectra one-by-one and combine the results using a framework which includes a significance test. This allows one to, for the first time, attach a confidence measure to detected peaks, based on the signal strength of a peak across sub-spectra. In a comparison with three other approaches the sub-spectral approach achieves a higher sensitivity and a low FDR. We further introduce the notion of peak-bags, which provide rich information about the sub-spectral contributions to a given peak.

CONCLUSION

The proposed procedure offers better control over the process of distinguishing signal from noise, resulting in an improved performance over other available methods. Moreover, our method provides an implicit deconvolution of peaks, yielding insight in the actual shape of a peak, potentially aiding in a deeper understanding of peak distribution.

AVAILABILITY

Implementations of the algorithm in R are available upon request.

Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Cervical mucous, produced in the region where cervical neoplasia occurs, is thought to be a good choice for discovery of biomarkers to improve cervical cancer screening. In this study, SELDI-TOF MS analysis was used to evaluate parameters for protein profiling of mucous. Proteins were extracted from mucous collected with Weck-Cel sponges. Several parameters like extraction reagent, loading protein concentration, matrix type, bind/wash conditions and sample fractionation, on different protein chip surfaces were evaluated. SELDI peak number and consistency in the resulting spectra were used to evaluate each condition. Analysis of spectra generated by different protein chips revealed an average of 30 peaks in the 2.5-30 kDa mass range using sinnapinic acid in the unfractionated sample. Sample concentration and buffer conditions evaluated did not lead to large alterations in the profiles. Quality control spectra were reproducible with intra- and inter-assay intensity CV for CM10, H50 and Q10 arrays being less than 20% and 30% respectively. IMAC30-Cu chips had higher intra- and inter-assay CV's at 25% and 35%. Current data showed that optimizing pre-analytical parameters can help in standardization and reproducibility of protein profiles produced by cervical mucous, and thus can be used for protein biomarker discovery with the SELDI platform.

Proteomics of human neurodegenerative diseases

The technology, experimental approaches, and bioinformatics that support proteomic research are evolving rapidly. The application of these new capabilities to the study of neurodegenerative diseases is providing insight into the biochemical pathogenesis of neurodegeneration as well as fueling major efforts in biomarker discovery. Here, we review the fundamentals of commonly used proteomic approaches and the outcomes of these investigations with autopsy and cerebrospinal fluid samples from patients with neurodegenerative diseases.

Proteomic studies of early-stage and advanced ovarian cancer patients

OBJECTIVES

The objectives of this study were to evaluate the diagnostic value for ovarian cancer using proteomic pattern established by surface-enhanced laser desorption/ionization (SELDI-TOF-MS) profiling of plasma proteins coupled with support vector machine (SVM) data analysis, and to investigate whether the proteomic pattern established by advanced ovarian cancer could be used for diagnosis of early-stage ovarian cancer patients.

METHODS

The study included 44 ovarian cancer patients (11 early-stage and 33 advanced ovarian cancer patients) and 31 age-matched non-cancer controls. SELDI-TOF-MS coupled with SVM analysis was performed to establish a proteomic pattern to discriminate 33 advanced ovarian cancer patients from 31 non-cancer controls. A blind test, including 11 early-stage ovarian cancer cases, was performed to investigate whether proteomic pattern established by advanced ovarian cancer could be used for diagnosis of early-stage ovarian cancer patients.

RESULTS

A seven-peak proteomic pattern was established which discriminated 33 advanced ovarian cancer patients from 31 non-cancer controls effectively. A sensitivity of 93.94% (31/33) and a specificity of 93.55% (29/31) were yielded from the proteomic pattern. Among the 7 protein peaks, 5 with mass charge ratio (m/z) 4099 Da, 5488 Da, 4144 Da, 4479 Da and 3940 Da were up-regulated, while 2 peaks, with m/z 13 783 Da and 8588 Da were down-regulated in the advanced ovarian cancer group compared with non-cancer control group. After blind test, 9 of 11 early-stage ovarian cancer patients were successfully diagnosed with the accuracy of 81.82% (9/11).

CONCLUSIONS

This study demonstrated that SELDI-TOF-MS coupled with SVM is effective in distinguishing protein expression between ovarian cancer and non-cancer plasma and it may be feasible to diagnose early-stage ovarian cancer using proteomic pattern established by advanced ovarian cancer. The gained and lost protein peaks in plasma may exist in both early-stage and advanced ovarian cancer plasma. Further studies should be performed using larger sample numbers.

Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model

Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.

Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data

Background

Mass spectrometry for biological data analysis is an active field of research, providing an efficient way of high-throughput proteome screening. A popular variant of mass spectrometry is SELDI, which is often used to measure sample populations with the goal of developing (clinical) classifiers. Unfortunately, not only is the data resulting from such measurements quite noisy, variance between replicate measurements of the same sample can be high as well. Normalisation of spectra can greatly reduce the effect of this technical variance and further improve the quality and interpretability of the data. However, it is unclear which normalisation method yields the most informative result.

Results

In this paper, we describe the first systematic comparison of a wide range of normalisation methods, using two objectives that should be met by a good method. These objectives are minimisation of inter-spectra variance and maximisation of signal with respect to class separation. The former is assessed using an estimation of the coefficient of variation, the latter using the classification performance of three types of classifiers on real-world datasets representing two-class diagnostic problems. To obtain a maximally robust evaluation of a normalisation method, both objectives are evaluated over multiple datasets and multiple configurations of baseline correction and peak detection methods. Results are assessed for statistical significance and visualised to reveal the performance of each normalisation method, in particular with respect to using no normalisation. The normalisation methods described have been implemented in the freely available MASDA R-package.

Conclusion

In the general case, normalisation of mass spectra is beneficial to the quality of data. The majority of methods we compared performed significantly better than the case in which no normalisation was used. We have shown that normalisation methods that scale spectra by a factor based on the dispersion (e.g., standard deviation) of the data clearly outperform those where a factor based on the central location (e.g., mean) is used. Additional improvements in performance are obtained when these factors are estimated locally, using a sliding window within spectra, instead of globally, over full spectra. The underperforming category of methods using a globally estimated factor based on the central location of the data includes the method used by the majority of SELDI users.

Discovery and validation of serum biomarkers expressed over the first twelve weeks of Fasciola hepatica infection in sheep

Serum biomarkers associated with Fasciola hepatica infection of Corriedale sheep were analysed during the first 12 weeks of infection using surface-enhanced laser desorption ionisation time of flight mass spectrometry (SELDI-TOF MS). In the discovery phase of analysis, pooled sera collected at week 0 and at each week p.i. to week 12 were fractionated by anion-exchange chromatography and the protein mass fingerprints obtained in individual fractions were in the M/z range 1.5-150 kDa. A total of 2302 protein clusters (peaks) were identified that varied between time-points following infection with peaks increasing or decreasing in intensity, or showing transient variation in intensity, during the 12 weeks of parasite challenge. In the validation phase, candidate biomarkers in sera of individual sheep at weeks 3 and 9 p.i. were analysed, identifying 100 protein peaks, many of which are small peptides <10 kDa in size: 54% of these peaks were up-regulated in intensity at week 3 or 9 p.i. Twenty-six biomarkers were chosen for further study, ranging in size from 1832 to 89,823 Da: six biomarkers were up-regulated at weeks 3 and 9 p.i., 16 biomarkers were up-regulated only at week 9 p.i. and four biomarkers were down-regulated at week 9 p.i. Two biomarkers up-regulated at week 9 were identified as transferrin (77.2 kDa) and Apolipoprotein A-IV (44.3 kDa), respectively. The results show that the interaction between the host and F. hepatica is complex, with changes in biomarker patterns beginning within 3 weeks of infection and either persisting to weeks 9-12 or showing transient changes during infection. Identification of biomarkers expressed during ovine fasciolosis may provide insights into mechanisms of pathogenesis and immunity to Fasciola and may assist in the rational development and delivery of vaccines.

MetaNetwork: a computational protocol for the genetic study of metabolic networks

We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a two-part model to account for the often observed spike in the distribution of metabolite abundance data. MetaNetwork predicts and visualizes potential associations between metabolites using correlations of mQTL profiles, rather than of abundance profiles. Simulation and permutation procedures are used to assess statistical significance. Analysis of about 20 metabolite mass peaks from a mass spectrometer takes a few minutes on a desktop computer. Analysis of 2,000 mass peaks will take up to 4 days. In addition, MetaNetwork is able to integrate high-throughput data from subsequent metabolomics, transcriptomics and proteomics experiments in conjunction with traditional phenotypic data. This way MetaNetwork will contribute to a better integration of such data into systems biology.