Quantitative quality-assessment techniques to compare fractionation and depletion methods in SELDI-TOF mass spectrometry experiments

Search for breast cancer biomarkers in fractionated serum samples by protein profiling with SELDI-TOF MS

BACKGROUND

Many high-abundant acute phase reactants have been previously detected as potential breast cancer biomar-kers. However, they are unlikely to be specific for breast cancer. Cancer-specific biomarkers are thought to be among the lower abundant proteins.

METHODS

We aimed to detect lower abundant discriminating proteins by performing serum fractionation by strong anion exchange chromatography preceding protein profiling with SELDI-TOF MS. In a pilot study, we tested the different fractions resulting from fractionation, on several array types. Fraction 3 on IMAC30 and Fraction 6 on Q10 yielded the most discriminative proteins and were used for serum protein profiling of 73 incident breast cancer cases and 73 matched controls.

RESULTS

Eight peaks showed statistically significantly different intensities between cases and controls (P⧁0.05), and had less than 10% chance to be a false-positive finding. Seven of these were tentatively identified as apolipoprotein C-II (m/z 8,909), oxidized apolipoprotein C-II (m/z 8,925), apolipoprotein C-III (m/z 8,746), fragment of coagulation factor XIIIa (m/z 3,959), heterodimer of apolipoprotein A-I and apolipoprotein A-II (m/z 45,435), hemoglobin B-chain (m/z 15,915), and post-translational modified hemoglobin (m/z 15,346).

CONCLUSION

By extensive serum fractionation, we detected many more proteins than in previous studies without fractionation. However, discriminating proteins were still high abundant. Results indicate that either lower abundant proteins are less distinctive, or more rigorous fractionation and selective protein depletion, or a more sensitive assay, are needed to detect lower abundant discriminative proteins.

Identification of potential prognostic markers for knee osteoarthritis by serum proteomic analysis

BACKGROUND

As osteoarthritis (OA) is a highly heterogeneous disease in terms of progression, establishment of prognostic biomarkers would be highly beneficial for treatment. The present study was performed to identify novel biomarkers capable of predicting the progression of knee OA.

METHODS

A total of 69 plasma samples (OA patients undergoing radiographic progression, n = 25; nonprogression, n = 33; healthy donors, n = 11) were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), and ion peaks of interest were identified by liquid chromatography and matrix-assisted laser desorption/ionization (MALDI)-TOF MS. The identities of these proteins were further validated by immunoprecipitation combined with SELDI-TOF MS analysis.

RESULTS

SELDI-TOF MS analysis indicated that the intensities of 3 ion peaks differed significantly between progressors and nonprogressors. Subsequent analyses indicated that these peaks corresponded to apolipoprotein C-I, C-III, and an N-terminal truncated form of transthyretin, respectively. The identities of these proteins were confirmed by the loss of ion peaks in SELDI-TOF MS spectra by immunoprecipitation using specific antibodies for the respective proteins.

CONCLUSIONS

Three potential biomarkers were identified whose serum levels differed significantly between OA progressors and nonprogressors. These biomarkers are expected to be prognostic biomarkers for knee OA and to facilitate the development of novel disease-modifying treatments for OA.

Systems biology approaches to evaluate arsenic toxicity and carcinogenicity: an overview

Long term exposure to arsenic, either through groundwater, food stuff or occupational sources, results in a plethora of dermatological and non-dermatological health effects including multi-organ cancer and early mortality. Several epidemiological studies, across the globe have reported arsenic-induced health effects and cancerous outcomes; but the prevalence of such diseases varies depending on environmental factors (geographical location, exposure level), and genetic makeup (and variants thereof); which is further modulated by several other factors like ethnicity, age-sex, smoking status, diet, etc. It is also interesting to note that, chronic arsenic exposure to a similar extent, even among the same family members, result in wide inter-individual variations. To understand the adverse effect of this toxic metabolite on biological system (cellular targets), and to unravel the underlying molecular basis (at the level of transcript, proteome, or metabolite), a holistic, systems biology approach was taken. Due to the paradoxical nature and unavailability of any suitable animal model system; the literature review is primarily based on cell line and population based studies. Thus, here we present a comprehensive review on the systems biology approaches to explore the underlying mechanism of arsenic-induced carcinogenicity, along with our own observations and an overview of mitigation strategies and their effectiveness till date.

Solid-phase fractionation strategies applied to proteomics investigations

Methods for protein fractionation in the proteomics investigation field are relatively numerous. They apply to the prefractionation of the sample to obtain less complex protein mixtures for an easier analysis; they are also used as a means to evidence specific proteins or protein classes otherwise impossible to detect. They involve depletion of high-abundance proteins suppressing the signal of dilute species; they are also capable to enhance the detectability of low-abundance species while concomitantly decreasing the concentration of abundant proteins such as albumin in serum and hemoglobin in red blood cell lysates. Fractionation of proteomes is also used for the isolation of targeted species that are selected for their different expression under certain pathological conditions and that are detected by mass spectrometry. Two unconventional methods of large interest in proteomics due to the low level of protein redundancy between fractions are also reported.All these methods are reviewed and detailed method given to allow specialists of proteomics investigation to access selected separation methods generally dispersed on different technical reviews or books.

Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations

We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outcome is observed, that is, the selection probability, is either known by design or estimated under a correctly specified model. In addition, we show that a specific AIPW kernel estimator in our class that employs the fitted values from a model for the conditional mean of the outcome given covariates and auxiliaries is double-robust, that is, it remains consistent if this model is correctly specified even if the selection probabilities are modeled or specified incorrectly. Furthermore, when both models happen to be right, this double-robust estimator attains the smallest possible asymptotic variance of all AIPW kernel estimators and maximally extracts the information in the auxiliary variables. We also describe a simple correction to the AIPW kernel estimating equations that while preserving double-robustness it ensures efficiency improvement over nonaugmented IPW estimation when the selection model is correctly specified regardless of the validity of the second model used in the augmentation term. We perform simulations to evaluate the finite sample performance of the proposed estimators, and apply the methods to the analysis of the AIDS Costs and Services Utilization Survey data. Technical proofs are available online.

Semi-quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high-throughput environment. The untargeted DIMSMS method uses top-down data-dependent fragmentation yielding MS(2) and MS(3) spectra. We have developed software tools to assess the structural homogeneity of the MS(2) and MS(3) spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS(1) bins, 22 ions gave one or more reproducible QTL across the 2 years.

Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

BACKGROUND

Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.

RESULTS

We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.

CONCLUSION

We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.

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.