Serum Peptide Profiling using MALDI Mass Spectrometry

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

Effect of Surfactant-Keratin Hydrolysate Interactions on the Hydration Properties of a Stratum Corneum Substitute

A novel stratum corneum substitute (SCS) has been developed, and the fundamental mechanism of the dehydration process has been studied using the SCS. After washing with cleansers which contain surfactants, our skin "feels" dehydrated (or hydrated). Although many studies have focused on the effect of surfactants on the regulation of the water loss by the lipid bilayers in the stratum corneum (SC) for a long timescale or at equilibrium, only few studies have focused on the acute effect of the surfactant interaction on dehydration. In addition, the interaction between the surfactant and keratin has been often underappreciated compared to lipid bilayers although keratin is the major nonaqueous component of the SC. Here, we have developed novel SCS models, nonkeratinized (lipid only) and keratinized, to study the effect of keratin hydrolysates on the dehydration rate. We have confirmed that the lipid organizational structure of the SCS was similar to that of the human SC using X-ray scattering. We have revealed that keratin hydrolysates play a significant role in the dehydration rate, accelerating the rate for the short term. We have also demonstrated that the effect of surfactants on dehydration is more pronounced for keratinized samples than that for the nonkeratinized sample. However, the dehydration rate for the nonkeratinized SCS with the surfactant became faster than the that for the keratinized SCS after the 20 min evaporation process, suggesting that the water binding sites of keratin hydrolysates slowed down evaporation, while the surfactant interacting with the lipids accelerated the water loss. Lastly, the study demonstrated that the SCS model can be a great platform to test macroscopic properties and analyze the underlying mechanism at the molecular level for various chemicals.

Intermediate and low abundant protein analysis of vitamin D deficient obese and non-obese subjects by MALDI-profiling

Obesity is a pathological condition caused by genetic and environmental factors, including vitamin D deficiency, which increases the risk of developing cardiovascular disorders and diabetes. This case-control study was designed to verify whether serum profiles could be identified differentiating obese and non-obese Saudis characterized by vitamin D deficiency and pathological levels of triglycerides, high-density lipoprotein cholesterol and high total cholesterol levels. The serum protein profiles of 64 vitamin D deficient (serum 25(OH)D < 50nmol/L) individuals with metabolic syndrome and with (n = 31; BMI ≥ 30) or without (n = 33; BMI < 30) obesity were analyzed by a quantitative label-free mass spectrometry approach (MALDI-profiling), combined with different serum immunodepletion strategies (Human7 and Human14 immuno-chromatographies), to analyze the intermediate- and low-abundant protein components. The analysis of intermediate-abundant proteins (Human7) in obese vs. non-obese subjects identified 14 changed peaks (p < 0.05) in the m/z range 1500–35000. Furthermore, the Human14 depletion provided new profiles related to obesity (121 changed peaks). Among changed peaks, 11 were identified in the m/z range 1500–4000 Da by high-resolution tandem mass spectrometry, belonging to apolipoprotein CIII, apolipoprotein B100, alpha-1-antichymotrypsin and complement C3. Data herein show that distinct protein profiles identify specific peptides belonging to lipid metabolism and inflammation processes that are associated with obesity and vitamin D deficiency.

MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer

Background

Due to high mortality and lack of efficient screening, new tools for ovarian cancer (OC) diagnosis are urgently needed. To broaden the knowledge on the pathological processes that occur during ovarian cancer tumorigenesis, protein-peptide profiling was proposed.

Methods

Serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF). Eighty nine serum samples (44 ovarian cancer and 45 healthy controls) were pretreated using solid-phase extraction method. Next, a classification model with the most discriminative factors was identified using chemometric algorithms. Finally, the results were verified by external validation on an independent test set of samples.

Results

Main outcome of this study was an identification of potential OC biomarkers by applying liquid chromatography coupled with tandem mass spectrometry. Application of this novel strategy enabled the identification of four potential OC serum biomarkers (complement C3, kininogen-1, inter-alpha-trypsin inhibitor heavy chain H4, and transthyretin). The role of these proteins was discussed in relation to OC pathomechanism.

Conclusions

The study results may contribute to the development of clinically useful multi-component diagnostic tools in OC. In addition, identifying a novel panel of discriminative proteins could provide a new insight into complex signaling and functional networks associated with this multifactorial disease.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3467-2) contains supplementary material, which is available to authorized users.

Identification of Serum Peptidome Signatures of Non-Small Cell Lung Cancer

Due to high mortality rates of lung cancer, there is a need for identification of new, clinically useful markers, which improve detection of this tumor in early stage of disease. In the current study, serum peptide profiling was evaluated as a diagnostic tool for non-small cell lung cancer patients. The combination of the ZipTip technology with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the analysis of peptide pattern of cancer patients (n = 153) and control subjects (n = 63) was presented for the first time. Based on the observed significant differences between cancer patients and control subjects, the classification model was created, which allowed for accurate group discrimination. The model turned out to be robust enough to discriminate a new validation set of samples with satisfactory sensitivity and specificity. Two peptides from the diagnostic pattern for non-small cell lung cancer (NSCLC) were identified as fragments of C3 and fibrinogen α chain. Since ELISA test did not confirm significant differences in the expression of complement component C3, further study will involve a quantitative approach to prove clinical utility of the other proteins from the proposed multi-peptide cancer signature.

The application of fuzzy statistics and linear discriminant analysis as criteria for optimizing the preparation of plasma for matrix-assisted laser desorption/ionization mass spectrometry peptide profiling

An alternative bioinformatics approach based on fuzzy theory statistics and linear discriminant analysis is proposed for the interpretation of MALDI MS spectra in peptide profiling. When applied, the methodology enables the establishment of a reproducible plasma preparation protocol appropriate for the evaluation of small data sets. The samples were collected from pregnant women affected by gestational diabetes mellitus (GDM), n=18 and control group, n=13. The following pre-treatment sets were tested: pipette tips with C18 stationary phase (ZipTip, Millipore and Omix, Agilent) and magnetic bead-based weak cation exchange chromatography kit (MB WCX, Bruker Daltonics). The spectra were recorded using a MALDI TOF mass spectrometer (UltrafleXtreme, Bruker Daltonics) for a mass range of m/z from 1000 to 10,000. The significant features were selected using the wrapper selection method, and two classification systems were tested: discriminant analysis (DA) and fuzzy inference system (FIS). ClinProTools software was employed to compare the usefulness of the proposed methodology. The study showed that the optimum results for MS spectra were obtained after the use of the ZipTip as pre-treatment method in plasma preparation. Chemometric analysis allowed the differentiation of the GDM group from the control with a high degree of accuracy: 0.7333 (DA) and 0.8065 (FIS).

Influence of honeybee sting on peptidome profile in human serum

The aim of this study was to explore the serum peptide profiles from honeybee stung and non-stung individuals. Two groups of serum samples obtained from 27 beekeepers were included in our study. The first group of samples was collected within 3 h after a bee sting (stung beekeepers), and the samples were collected from the same person a second time after at least six weeks after the last bee sting (non-stung beekeepers). Peptide profile spectra were determined using MALDI-TOF mass spectrometry combined with Omix, ZipTips and magnetic beads based on weak-cation exchange (MB-WCX) enrichment strategies in the mass range of 1–10 kDa. The samples were classified, and discriminative models were established by using the quick classifier, genetic algorithm and supervised neural network algorithms. All of the statistical algorithms used in this study allow distinguishing analyzed groups with high statistical significance, which confirms the influence of honeybee sting on the serum peptidome profile. The results of this study may broaden the understanding of the human organism’s response to honeybee venom. Due to the fact that our pilot study was carried out on relatively small datasets, it is necessary to conduct further proteomic research of the response to honeybee sting on a larger group of samples.

MALDI MS profiling of post-DRE urine samples highlights the potential of β-microseminoprotein as a marker for prostatic diseases

BACKGROUND

To use spectra acquired by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) from pre- and post-digital rectal examination (DRE) urine samples to search for discriminating peaks that can adequately distinguish between benign and malignant prostate conditions, and identify the peaks' underlying biomolecules.

METHODS

Twenty-five participants with prostate cancer (PCa) and 27 participants with a variety of benign prostatic conditions as confirmed by a 10-core tissue biopsy were included. Pre- and post-DRE urine samples were prepared for MALDI MS profiling using an automated clean-up procedure. Following mass spectra collection and processing, peak mass and intensity were extracted and subjected to statistical analysis to identify peaks capable of distinguishing between benign and cancer. Logistic regression was used to combine markers to create a sensitive and specific test.

RESULTS

A peak at m/z 10,760 was identified as β-microseminoprotein (β-MSMB) and found to be statistically lower in urine from PCa participants using the peak's average areas. By combining serum prostate-specific antigen (PSA) levels with MALDI MS-measured β-MSMB levels, optimum threshold values obtained from Receiver Operator characteristics curves gave an increased sensitivity of 96% at a specificity of 26%.

CONCLUSIONS

These results demonstrate that with a simple sample clean-up followed by MALDI MS profiling, significant differences of MSMB abundance were found in post-DRE urine samples. In combination with PSA serum levels, obtained from a classic clinical assay led to high classification accuracy for PCa in the studied sample set. Our results need to be validated in a larger multicenter prospective randomized clinical trial.

MALDI mass spectrometry in prostate cancer biomarker discovery

Matrix-assisted laser desorption/ionisation (MALDI) mass spectrometry (MS) is a highly versatile and sensitive analytical technique, which is known for its soft ionisation of biomolecules such as peptides and proteins. Generally, MALDI MS analysis requires little sample preparation, and in some cases like MS profiling it can be automated through the use of robotic liquid-handling systems. For more than a decade now, MALDI MS has been extensively utilised in the search for biomarkers that could aid clinicians in diagnosis, prognosis, and treatment decision making. This review examines the various MALDI-based MS techniques like MS imaging, MS profiling and proteomics in-depth analysis where MALDI MS follows fractionation and separation methods such as gel electrophoresis, and how these have contributed to prostate cancer biomarker research. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Proteomics applied to transfusion plasma: the beginning of the story

'Safe blood' is and has always been the major concern in transfusion medicine. Plasma can undergo virus inactivation treatments based on physicochemical, photochemical or thermal methodologies for pathogen inactivation. The validation of these treatments is essentially based on clottability assays and clotting factors' titration; however, their impact on plasma proteins at the molecular level has not yet been evaluated. Proteomics appears as particularly adapted to identify, to localize and, consequently, to correlate these modifications to the biological activity change. At the crossroads of biology and analytical sciences, proteomics is the large-scale study of proteins in tissues, physiological fluids or cells at a given moment and in a precise environment. The proteomic strategy is based on a set of methodologies involving separative techniques like mono- and bidimensional gel electrophoresis and chromatography, analytical techniques, especially mass spectrometry, and bioinformatics. Even if plasma has been extensively studied since the very beginning of proteomics, its application to transfusion medicine has just begun. In the first part of this review, we present the principles of proteomics analysis. Then, we propose a state of the art of proteomics applied to plasma analysis. Finally, the use of proteomics for the evaluation of the impact of storage conditions and pathogen inactivation treatments applied to transfusion plasma and for the evaluation of therapeutic protein fractionated is discussed.

[Variability of healthy human proteome]

The purpose of this review is to analyze investigations devoted to characteristic of protein variability and diversity of their posttranslational modifications in healthy humans. The numerous researches have demonstrated that proteomic profile has a considerable both intra- and inter-individual variability, and quite often normal variability of some proteins can be comparable to changes observed in pathological processes. Results obtained by our research group have confirmed high intra-individual variability of serum low-molecular subproteome of healthy volunteers, certified by a special medial committee. Proteins characterized by high variability in normal conditions (e.g. haptoglobin--0-40 mg/ml; lysozyme--0,01-0,1 mg/ml; C-reactive protein--0,01-0,3 mg/ml) should be excluded from the list of potential biomarkers. On the contrary, proteins and peptides characterized by insignificant dispersion in healthy population (such as albumin--coefficient of variation (CV) 9%; transferrin--CV14%; C3c complement--CV 17%, alpha-1 acid glycoprotein--CV 21%, alpha2-macroglobulin--CV 20%; transthyretin fragment--CV 28,3% and beta-chain alpha2-HS-glycoprotein--CV 29,7%) can provide us with important information about state of health. Thus investigations of plasticity in proteomic profiles of healthy humans will help to correct reference intervals used in clinical proteomics.

Mass spectrometric analysis of gingival crevicular fluid biomarkers can predict periodontal disease progression

BACKGROUND AND OBJECTIVE

Gingival crevicular fluid has been suggested as a possible source of biomarkers for periodontal disease progression. This paper describes a technique for the analysis of gingival crevicular fluid from individual sites using mass spectrometry. It explores the novel use of mass spectrometry to examine the relationship between the relative amounts of proteins and peptides in gingival crevicular fluid and their relationship with clinical indices and periodontal attachment loss in periodontal maintenance patients. The aim of this paper was to assess whether the mass spectrometric analysis of gingival crevicular fluid may allow for the site-specific prediction of periodontal disease progression.

MATERIAL AND METHODS

Forty-one periodontal maintenance subjects were followed over 12 mo, with clinical measurements taken at baseline and every 3 mo thereafter. Gingival crevicular fluid was collected from subjects at each visit and was analysed using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. Samples were classified based upon pocket depth, modified gingival index (MGI), plaque index and attachment loss, and were analysed within these groups. A genetic algorithm was used to create a model based on pattern analysis to predict sites undergoing attachment loss.

RESULTS

Three hundred and eighty-five gingival crevicular fluid samples were analysed. Twenty-five sites under observation in 14 patients exhibited attachment loss of > 2 mm over the 12-mo period. The clinical indices pocket depth, MGI, plaque levels and bleeding on probing served as poor discriminators of gingival crevicular fluid mass spectra. Models generated from the gingival crevicular fluid mass spectra could predict attachment loss at a site with a high specificity (97% recognition capability and 67% cross-validation).

CONCLUSIONS

Gingival crevicular fluid mass spectra could be used to predict sites with attachment loss. The use of algorithm-generated models based on gingival crevicular fluid mass spectra may provide utility in the diagnosis of periodontal disease.

Quantitative analysis of S-nitrosylated proteins

Protein S-nitrosylation is the covalent binding of nitric oxide to specific cysteine residues in proteins. This modification influences a large number of cellular events and signaling processes. As this process is finely regulated in vivo, the level of nitrosylation changes in response to different stimuli. Since its introduction, the biotin-switch technique (BST) is the most used indirect method for the study of S-nitrosylation both in vivo and in vitro and its coupling with mass spectrometry-based proteomics lead to the identification of the S-nitroso proteome in different organisms. However, this method does not give any information about the posttranslational modification level on the same residue in different biological conditions. Quantitative proteomic methods can assess the relative change in S-nitrosylation for hundreds sites in a single experiment. Stable isotope labeling by aminoacids in cell culture (SILAC) is one of the most used and accurate quantitative techniques in MS-based proteomics. Here we present a SILAC-based method for the quantification of endogenously S-nitrosylated proteins in RAW 264.7 cells.

Selective enrichment and sensitive detection of peptide and protein biomarkers in human serum using polymeric reverse micelles and MALDI-MS

Reverse-micelle forming amphiphilic homopolymers with carboxylic acid and quaternary amine substituents are used to selectively enrich biomarker peptides and protein fragments from human serum prior to matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS) analysis. After depletion of human serum albumin (HSA) and immunoglobulin G (IgG), low abundance peptide biomarkers can be selectively enriched and detected by MALDI-MS at clinically relevant concentrations by using the appropriate homopolymer(s) and extraction pH value(s). Three breast cancer peptide biomarkers, bradykinin, C4a, and ITIH(4), were chosen to test this new approach, and detection limits of 0.5 ng mL(-1), 0.08 ng mL(-1), and 0.2 ng mL(-1), respectively, were obtained. In addition, the amphiphilic homopolymers were used to detect prostate specific antigen (PSA) at concentrations as low as 0.5 ng mL(-1) by targeting a surrogate peptide fragment of this protein biomarker. Selective enrichment and sensitive MS detection of low abundance peptide/protein biomarkers by these polymeric reverse micelles should be a sensitive and straightforward approach for biomarker screening in human serum.

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

INTRODUCTION

Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.

METHOD

Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.

RESULTS

Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.

CONCLUSION

The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Background

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

Results

We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.

Conclusions

The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Proteomic serum biomarkers and their potential application in cancer screening programs

Early diagnosis of cancer is of pivotal importance to reduce disease-related mortality. There is great need for non-invasive screening methods, yet current screening protocols have limited sensitivity and specificity. The use of serum biomarkers to discriminate cancer patients from healthy persons might be a tool to improve screening programs. Mass spectrometry based proteomics is widely applied as a technology for mapping and identifying peptides and proteins in body fluids. One commonly used approach in proteomics is peptide and protein profiling. Here, we present an overview of profiling methods that have the potential for implementation in a clinical setting and in national screening programs.

Use of SELDI MS to discover and identify potential biomarkers of toxicity in InnoMed PredTox: a multi-site, multi-compound study

A serious bottleneck in the drug development pipeline is the inability of current pre-clinical toxicology evaluation methods to predict early on, and with good accuracy, that a drug candidate will have to be removed from development due to toxicology/safety issues. The InnoMed PredTox consortium attempted to address this issue by assessing the value of using molecular profiling techniques (proteomics, transcriptomics, and metabonomics), in combination with conventional toxicology measurements, on decision making earlier in pre-clinical safety evaluation. In this study, we report on the SELDI-TOF-MS proteomics component of the InnoMed PredTox project. In this large scale, multi-site, multi-compound study, tissue and plasma samples from 14-day in vivo rat experiments conducted for 16 hepato- and nephro-toxicants with known toxicology endpoints (including 14 proprietary compounds and 2 reference compounds) were analyzed by SELDI-TOF-MS. We have identified seven plasma proteins and four liver proteins which were shown to be modulated by treatment, and correlated with histopathological evaluations and can be considered potential biomarker candidates for the given toxicology endpoints. In addition, we report on the intra- and inter-site variations observed based on measurements from a reference sample, and steps that can be taken to minimize this variation.

Proteomics of brain extracellular fluid (ECF) and cerebrospinal fluid (CSF)

Mass spectrometry has become the gold standard for the identification of proteins in proteomics. In this review, I will discuss the available literature on proteomic experiments that analyze human cerebrospinal fluid (CSF) and brain extracellular fluid (ECF), mostly obtained by cerebral microdialysis. Both materials are of high diagnostic value in clinical neurology, for example, in cerebrovascular disorders like stroke, neurodegenerative diseases like Alzheimer's Disease, Parkinson's Disease, amyotrophic lateral sclerosis (ALS), traumatic brain injury and cerebral infectious and inflammatory disease, such as multiple sclerosis. Moreover, there are standard procedures for sampling. In a number of studies in recent years, biomarkers have been proposed in CSF and ECF for improved diagnosis or to control therapy, based on proteomics and mass spectrometry. I will also discuss the needs for a transition of research-based experimental screening with mass spectrometry to fast and reliable diagnostic instrumentation for clinical use.

Targeted metabolomics and mass spectrometry

While a great emphasis has been placed on global metabolomic analysis in recent years, the application of metabolomic style analyses to specific subsets of compounds (targeted metabolomics) also has merits in addressing biological questions in a more hypothesis-driven manner. These analyses are designed to selectively extract information regarding a group of related metabolites from the complex mixture of biomolecules present in most metabolomic samples. Furthermore, targeted metabolomics can also be applied to metabolism within macromolecules, hence furthering the systems biology impact of the analysis. This chapter describes the difference between the global metabolomics approach and the undertaking of metabolomics in a targeted manner and describes the application of this type of analysis in a number of biologically and medically relevant fields.

Pre-analytical factors in clinical proteomics investigations: impact of ex vivo protein modifications for multiple sclerosis biomarker discovery

Serum proteome investigations have raised an incredible interest in the research of novel molecular biomarker, nevertheless few of the proposed evidences have been translated to the clinical practice. One of the limiting factors has been the lack of generally accepted guidelines for clinical proteomics studies and the lack of a robust analytical and pre-analytical ground for the proposed classification models. Pre-analytical issues may results in a deep impact for biomarker discovery campaign. In this study we present a systematic evaluation of sample storage and sampling conditions for clinical proteomics investigations. We have developed and validated a linear MALDI-TOF-MS protein profiling method to explore the low protein molecular weight region (5-20 kDa) of serum samples. Data normalization and processing was performed using optimise peak detection routine (LIMPIC) able to describe each group under investigation. Data were acquired either from healthy volunteers and from multiple sclerosis patients in order to highlight ex vivo protein profile alteration related to different physio-pathological conditions. Our data showed critical conditions for serum protein profiles depending on storage times and temperatures: 23 degrees C, 4 degrees C, -20 degrees C and -80 degrees C. We demonstrated that upon a -20 degrees C short term storage, characteristic degradation profiles are associated with different clinical groups. Protein signals were further identified after preparative HPLC separation by peptide sequencing on a nanoLC-Q-TOF TANDEM mass spectrometer. Apolipoprotein A-IV and complement C3 protein fragments, transthyretin and the oxidized isoforms in different apolipoprotein species represent the major molecular features of such a degradation pattern.

Proteomic analysis for process development and control of therapeutic protein separation from human plasma

The use of proteomics technology during the development of a new process for plasma protein separation was demonstrated. In a two-step process, the two most abundant proteins, HSA and IgG, were removed in a first step of anion-exchange chromatography using a gel with very high capacity. Subsequently, two fractions containing medium and low abundance proteins were re-chromatographed on a smaller column with the same type of gel. Collected fractions were separated by SDS-PAGE and 2-D electrophoresis, and excised proteins were digested with trypsin and identified by LC-ESI-MS/MS. This proteomic analysis proved to be a useful method for detection of low abundance therapeutic proteins and potential harmful contaminants during process development. Based on this method, low abundance therapeutic proteins, such as vitamin-K-dependent clotting factors and inhibitors, could be identified as present in target fractions after chromatographic separation. In addition, the tracking of potentially dangerous impurities and designing proper steps for their removal are important outcomes when developing, refining or controlling a new fractionation schema. For the purpose of in-process control, in-solution digestion of complete fractions followed by protein identification with LC-ESI-MS/MS was demonstrated as a rapid and simple alternative to the entire analysis including 1-D or 2-D electrophoretic steps.

An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies

Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

Rapid metabolite identification with sub parts-per-million mass accuracy from biological matrices by direct infusion nanoelectrospray ionization after clean-up on a ZipTip and LTQ/Orbitrap mass spectrometry

Metabolite identification studies remain an integral part of pre-clinical and clinical drug development programs. Analysis of biological matrices, such as plasma, urine, feces and bile, pose challenges due to the large amounts of endogenous components that can mask a drug and its metabolites. Although direct infusion nanoelectrospray using capillaries has been used routinely for proteomic studies, metabolite identification has traditionally employed liquid chromatographic (LC) separation prior to analysis. A method is described here for rapid metabolite profiling in biological fluids that involves initial sample clean-up using pipette tips packed with reversed-phase material (i.e. ZipTips) to remove matrix components followed by direct infusion nanoelectrospray on an LTQ/Orbitrap mass spectrometer using a protonated polydimethylcyclosiloxane cluster ion for internal calibration. We re-examined samples collected from a prazosin metabolism study in the rat. Results are presented that demonstrate that sub parts-per-million accuracies can be achieved on molecular ions, facilitating identification of metabolites, and on product ions, facilitating structural assignments. The data also show that the high-resolution measurements (R = 100,000 at m/z 400) enable metabolites of interest to be resolved from endogenous components. The extended analysis times available with nanospray enables signal averaging for 1 min or more that is valuable when metabolites are present in low concentrations as encountered here in plasma and brain. Using this approach, the metabolic fate of a drug can be quickly obtained. A limitation of this approach is that metabolites that are structural isomers cannot be distinguished, although such information can be collected by LC/MS during follow-on experiments.