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

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data

Background

Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline.

Results

We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data.

Conclusions

The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.

Application of protein expression profiling to screen chemicals for androgenic activity

Protein expression changes can be used for detection of biomarkers that can be applied diagnostically to screen chemicals for endocrine modifying activity. In this study, surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) coupled with a short term fish assay was used to investigate changes in plasma protein expression as a means to screen chemicals for androgenic activity. Adult gravid female sheepshead minnows (Cyprinodon variegatus) were placed into separate aquaria for seawater control, ethanol solvent control, and the following androgen agonist treatments at 5.0μg/L: dihydrotestosterone (DHT), methyldihydrotestosterone (MDHT), testosterone (T), methyltestosterone (MT) and trenbolone (TB). Treatments of 0.6μg/L endosulfan and 40μg/L chlorpyrifos (CP) served as non-androgenic negative stressor controls. Test concentrations were maintained using an intermittent flow-through dosing apparatus supplying exposure water at 20L/h. Fish were sampled at 7 days, the plasma diluted, processed on weak cation exchange CM10 ProteinChip arrays and analyzed. Spectral processing resulted in 249 individual m/z peak clusters for the androgen exposed fish. Partial least squares-discriminant analysis was used to develop an androgen-responsive model using sample spectra from exposures with DHT and unexposed solvent control fish as the training set. The androgen classification model performed with ≥79% specificity (% true negative) and ≥70% sensitivity (% true positive) for non-aromatizable androgens. The aromatizable androgens T and MT were classified as androgenic with specificities of 42 and 79%, respectively. The reduction in sensitivity observed with T is thought to be caused by its metabolic conversion to an estrogen by aromatase. The results of these studies show diagnostic plasma protein expression models can correctly classify chemicals by their androgenic activity using a combination of high throughput mass spectrometry and multivariate approaches.

Proteomic identification and profiling of canine lymphoma patients

This study employed proteomic and bioinformatic approaches to identify serum biomarkers in canine lymphoma patients. Chilled serum samples derived from non-lymphoma (n = 92) and lymphoma (n = 87) patients were shipped from first opinion veterinary practices, subjected to ion exchange chromatography and analysed by surface-enhanced laser desorption ionization mass spectrometry. Nineteen serum protein peaks were identified between the two groups as being significantly different (P < 0.05) based upon their normalized ion intensities. Two biomarkers were identified that were capable of differentiating lymphoma and non-lymphoma patients. Analysis of the test data provided a positive predictive value (PPV) of 82%. A clinical follow-up study was carried out on 96 canine patients suspected of having lymphoma. Evaluation of this data gave a specificity value of 91%, sensitivity of 75%, PPV of 80% and negative predictive value of 88%. In conclusion, the expression pattern of two serum biomarkers has enabled serum samples to be classified into either lymphoma or non-lymphoma categories.

SELDI-TOF as a method for biomarker discovery in the urine of aristolochic-acid-treated mice

Aristolochic acids (AAs) present in Aristolochia plants are substances responsible for Chinese herbs nephropathy. Recently, strong indications have also been presented, which dietary poisoning with AA is responsible for endemic (Balkan) nephropathy (EN), an enigmatic renal disease that affects rural population living in some countries in Southeastern Europe. A mouse model was applied to follow the effects of two forms of AA, AAI and AAII. SDS-PAGE and SELDI-TOF mass spectrometry with normal phase chips were used to evaluate changes in the urine of treated animals. These two methods are demonstrated to be comparable. The use of SELDI-TOF MS for rapid analysis of a large number of samples and the combination of this method with nano-LC-ESI MS/MS for protein identification were demonstrated. Biomarker discovery after analysis of large cohort of EN patients will be the final aim of these investigations.

Proteomic data analysis workflow for discovery of candidate biomarker peaks predictive of clinical outcome for patients with acute myeloid leukemia

Our goal in this paper is to show an analytical workflow for selecting protein biomarker candidates from SELDI-MS data. The clinical question at issue is to enable prediction of the complete remission (CR) duration for acute myeloid leukemia (AML) patients. This would facilitate disease prognosis and make individual therapy possible. SELDI-mass spectrometry proteomics analyses were performed on blast cell samples collected from AML patients pre-chemotherapy. Although the biobank available included approximately 200 samples, only 58 were available for analysis. The presented workflow includes sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. Specific difficulties have been the small number of samples and the skew distribution of the CR duration among the patients. Further, we had to deal with both noisy SELDI-MS data and a diverse patient cohort. This has been handled by sample selection and several methods for data preprocessing and feature detection in the analysis workflow. Four conceptually different methods for peak detection and alignment were considered, as well as two diverse methods for feature selection. The peak detection and alignment methods included the recently developed annotated regions of significance (ARS) method, the SELDI-MS software Ciphergen Express which was regarded as the standard method, segment-wise spectral alignment by a genetic algorithm (PAGA) followed by binning, and, finally, binning of raw data. In the feature selection, the "standard" Mann-Whitney t test was compared with a hierarchical orthogonal partial least-squares (O-PLS) analysis approach. The combined information from all these analyses gave a collection of 21 protein peaks. These were regarded as the most potential and robust biomarker candidates since they were picked out as significant features in several of the models. The chosen peaks will now be our first choice for the continuing work on protein identification and biological validation. The identification will be performed by chromatographic purification and MALDI MS/MS. Thus, we have shown that the use of several data handling methods can improve a protein profiling workflow from experimental optimization to a predictive model. The framework of this methodology should be seen as general and could be used with other one-dimensional spectral omics data than SELDI MS including an adequate number of samples.

Proteomics in clinical prostate research

The incidence of early prostate cancer (PCa) has increased rapidly in recent years. The majority of newly diagnosed PCa are in early tumor phase. Presently, we do not have adequate biomarkers to assess tumor aggressiveness in individual cases. Consequently, too many patients are given curatively intended treatment. An exploration of the human proteome may provide clinically useful markers. 2-DE has been successfully used for analysis of the protein phenotype using clinical samples. Proteins are separated according to size and charge, gels are compared by image analysis, protein spots of interest are excised, and proteins identified by MS. This method is exploratory and allows protein identification. However, low-abundance proteins are difficult to detect and 2-DE is currently too labor-intensive for routine use. In recent years, nongel based techniques, such as LC-MS, SELDI-MS, and protein arrays have emerged. They require smaller sample sizes and can be more automated than 2-DE. In this review, we describe studies of the protein expression of benign prostatic tissue and PCa, which is likely to serve as the first step in prognostic biomarker discovery. The prostate proteome is still far from a complete mapping which would enhance our understanding of PCa biology.