Label-free Semiquantitative Peptide Feature Profiling of Human Breast Cancer and Breast Disease Sera via Two-dimensional Liquid Chromatography-Mass Spectrometry

Proteomics and Mass Spectrometry for Cancer Biomarker Discovery

Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.

Overcoming the dynamic range problem in mass spectrometry-based shotgun proteomics

Protein profiling using mass spectrometry technology has emerged as a powerful method for analyzing large-scale protein-expression patterns in cells and tissues. However, a number of challenges are present in proteomics research, one of the greatest being the high degree of protein complexity and huge dynamic range of proteins expressed in the complex biological mixtures, which exceeds six orders of magnitude in cells and ten orders of magnitude in body fluids. Since many important signaling proteins have low expression levels, methods to detect the low-abundance proteins in a complex sample are required. This review will focus on the fundamental fractionation and mass spectrometry techniques currently used for large-scale shotgun proteomics research.

Protein changes in immunodepleted cerebrospinal fluid from a transgenic mouse model of Alexander disease detected using mass spectrometry

Cerebrospinal fluid (CSF) is a low protein content biological fluid with a dynamic range spanning at least 9 orders of magnitude in protein content and is in direct contact with the brain. A modified IgY-14 immunodepletion treatment was performed to enhance analysis of the low volumes of CSF that are obtainable from mice. As a model system in which to test this approach, we utilized transgenic mice that overexpress the intermediate filament glial fibrillary acidic protein (GFAP). These mice are models for Alexander disease (AxD), a severe leukodystrophy in humans. From the CSF of control and transgenic mice we report the identification of 289 proteins, with relative quantification of 103 proteins. Biological and technical triplicates were performed to address animal variability as well as reproducibility in mass spectrometric analysis. Relative quantitation was performed using distributive normalized spectral abundance factor (dNSAF) spectral counting analysis. A panel of biomarker proteins with significant changes in the CSF of GFAP transgenic mice has been identified with validation from enzyme-linked immunosorbent assay (ELISA) and microarray data, demonstrating the utility of our methodology and providing interesting targets for future investigations on the molecular and pathological aspects of AxD.

Analysis of the differentially expressed low molecular weight peptides in human serum via an N-terminal isotope labeling technique combining nano-liquid chromatography/matrix-assisted laser desorption/ionization mass spectrometry

RATIONALE

Peptidomics analysis of human serum is challenging due to the low abundance of serum peptides and interference from the complex matrix. This study analyzed the differentially expressed (DE) low molecular weight peptides in human serum integrating a DMPITC-based N-terminal isotope labeling technique with nano-liquid chromatography and matrix-assisted laser desorption/ionization mass spectrometry (nano-LC/MALDI-MS).

METHODS

The workflow introduced a [d(6)]-4,6-dimethoxypyrimidine-2-isothiocyanate (DMPITC)-labeled mixture of aliquots from test samples as the internal standard. The spiked [d(0)]-DMPITC-labeled samples were separated by nano-LC then spotted on the MALDI target. Both quantitative and qualitative studies for serum peptides were achieved based on the isotope-labeled peaks.

RESULTS

The DMPITC labeling technique combined with nano-LC/MALDI-MS not only minimized the errors in peptide quantitation, but also allowed convenient recognition of the labeled peptides due to the 6 Da mass difference. The data showed that the entire research procedure as well as the subsequent data analysis method were effective, reproducible, and sensitive for the analysis of DE serum peptides.

CONCLUSIONS

This study successfully established a research model for DE serum peptides using DMPITC-based N-terminal isotope labeling and nano-LC/MALDI-MS. Application of the DMPITC-based N-terminal labeling technique is expected to provide a promising tool for the investigation of peptides in vivo, especially for the analysis of DE peptides under different biological conditions.

Proteomic research in psychiatry

Psychiatric disorders such as Alzheimer's disease, schizophrenia and mood disorders are severe and disabling conditions of largely unknown origin and poorly understood pathophysiology. An accurate diagnosis and treatment of these disorders is often complicated by their aetiological and clinical heterogeneity. In recent years proteomic technologies based on mass spectrometry have been increasingly used, especially in the search for diagnostic and prognostic biomarkers in neuropsychiatric disorders. Proteomics enable an automated high-throughput protein determination revealing expression levels, post-translational modifications and complex protein-interaction networks. In contrast to other methods such as molecular genetics, proteomics provide the opportunity to determine modifications at the protein level thereby possibly being more closely related to pathophysiological processes underlying the clinical phenomenology of specific psychiatric conditions. In this article we review the theoretical background of proteomics and its most commonly utilized techniques. Furthermore the current impact of proteomic research on diverse psychiatric diseases, such as Alzheimer's disease, schizophrenia, mood and anxiety disorders, drug abuse and autism, is discussed. Proteomic methods are expected to gain crucial significance in psychiatric research and neuropharmacology over the coming decade.

Development and validation of a liquid chromatography-mass spectrometry metabonomic platform in human plasma of liver failure caused by hepatitis B virus

This paper presents an liquid chromatography (LC)/mass spectrometry (MS)-based metabonomic platform that combined the discovery of differential metabolites through principal component analysis (PCA) with the verification by selective multiple reaction monitoring (MRM). These methods were applied to analyze plasma samples from liver disease patients and healthy donors. LC-MS raw data (about 1000 compounds), from the plasma of liver failure patients (n = 26) and healthy controls (n = 16), were analyzed through the PCA method and a pattern recognition profile that had significant difference between liver failure patients and healthy controls (P < 0.05) was established. The profile was verified in 165 clinical subjects. The specificity and sensitivity of this model in predicting liver failure were 94.3 and 100.0%, respectively. The differential ions with m/z of 414.5, 432.0, 520.5, and 775.0 were verified to be consistent with the results from PCA by MRM mode in 40 clinical samples, and were proved not to be caused by the medicines taken by patients through rat model experiments. The compound with m/z of 520.5 was identified to be 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine through exact mass measurements performed using Ion Trap-Time-of-Flight MS and METLIN Metabolite Database search. In all, it was the first time to integrate metabonomic study and MRM relative quantification of differential peaks in a large number of clinical samples. Thereafter, a rat model was used to exclude drug effects on the abundance of differential ion peaks. 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine, a potential biomarker, was identified. The LC/MS-based metabonomic platform could be a powerful tool for the metabonomic screening of plasma biomarkers.

Identification of serum biomarkers for colorectal cancer metastasis using a differential secretome approach

Lymph node metastasis is the major concern that causes death in colorectal cancers. However, biomarkers for cancer metastasis are still lacking. In this study, we applied an LC-MS/MS-based label-free quantitative proteomics approach to compare the differential secretome of a primary cell line SW480 and its lymph node metastatic cell line SW620 from the same colorectal cancer patient. We identified a total of 910 proteins from the conditioned media and 145 differential proteins between SW480 and SW620 (>1.5-fold change). The differential expression pattern of 6 candidate proteins was validated by Western blot analysis. Among them, trefoil factor 3 and growth/differentiation factor 15, two up-regulated proteins in SW620, were further analyzed in a large cohort of clinical tissue and serum samples. Sandwich ELISA assay showed that the serum levels of both proteins were significantly higher in lymph node metastatic colorectal cancers. Receiver operating characteristic curve analysis confirmed that serum trefoil factor 3 and growth/differentiation factor 15 could provide a discriminatory diagnostic test for predicting colorectal cancer metastasis. Immunohistochemical analysis also showed that the overexpression of trefoil factor 3 or growth/differentiation factor 15 in colorectal cancer was associated with lymph node metastatic behavior. This study showed an accurate, sensitive, and robust label-free quantitation approach for differential analysis of cancer secretome. The comparison of the cancer secretome in vitro is a feasible strategy to obtain valuable biomarkers for potential clinical application. Both trefoil factor 3 and growth/differentiation factor 15 could serve as potential biomarkers for the prediction of colorectal cancer metastasis.

Overview of quantitative LC-MS techniques for proteomics and activitomics

LC-MS is a useful technique for protein and peptide quantification. In addition, as a powerful tool for systems biology research, LC-MS can also be used to quantify post-translational modifications and metabolites that reflect biochemical pathway activity. This review discusses the different analytical techniques that use LC-MS for the quantification of proteins, their modifications and activities in a multiplex manner.

An integrated LC-ESI-MS platform for quantitation of serum peptide ladders. Application for colon carcinoma study

Mounting evidence indicates that MS analysis of the human blood peptidome allows to distinguish between cancer and non-cancer samples, giving promise for a new MS-based diagnostic tool. However, several aspects of already published work have been criticized and demand for more methodical approach has been formulated. Motivated by this we undertook a systematic study of the plasma and serum peptidome using an integrated ESI-LC-MS-based platform, equipped with new data analysis tools for relative and absolute peptide quantitation. We used a high resolution LC-ESI-MS to analyze well-separated MS signals corresponding to peptides, and measured the variability of >1000 peptide signal amplitudes across a set of plasma and serum samples from healthy individuals. By spiking serum samples with known amounts of isotopically labeled versions of a selected set of peptides we measured the variability of their absolute concentration in this sample set and demonstrated a strong influence of clotting time on the concentration of these peptides in serum. Finally, we used this new LC-ESI-MS analytical platform for the differential analysis of healthy versus colon cancer serum samples and found that it was possible to distinguish the two groups with 89.8% sensitivity and 94.6% specificity.

Mass spectrometry-based proteomic profiling of lung cancer

In an effort to further our understanding of lung cancer biology and to identify new candidate biomarkers to be used in the management of lung cancer, we need to probe these tissues and biological fluids with tools that address the biology of lung cancer directly at the protein level. Proteins are responsible of the function and phenotype of cells. Cancer cells express proteins that distinguish them from normal cells. Proteomics is defined as the study of the proteome, the complete set of proteins produced by a species, using the technologies of large-scale protein separation and identification. As a result, new technologies are being developed to allow the rapid and systematic analysis of thousands of proteins. The analytical advantages of mass spectrometry (MS), including sensitivity and high-throughput, promise to make it a mainstay of novel biomarker discovery to differentiate cancer from normal cells and to predict individuals likely to develop or recur with lung cancer. In this review, we summarize the progress made in clinical proteomics as it applies to the management of lung cancer. We will focus our discussion on how MS approaches may advance the areas of early detection, response to therapy, and prognostic evaluation.

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.

Overview and introduction to clinical proteomics

As the field of clinical proteomics progresses, discovery of disease biomarkers becomes paramount. However, the immediate challenges are to establish standard operating procedures for both clinical specimen handling and reduction of sample complexity and to increase the ability to detect proteins and peptides present in low amounts. The traditional concept of a disease biomarker is shifting toward a new paradigm, namely, that an ensemble of proteins or peptides would be more efficient than a single protein/peptide in the diagnosis of disease. Because clinical proteomics usually requires easy access to well-defined fresh clinical specimens (including morphologically consistent tissue and properly pretreated body fluids of sufficient quantity), biorepository systems need to be established. Here, we address these questions and emphasize the necessity of developing various microdissection techniques for tissue specimens, multidimensional fractionation for body fluids, and other related techniques (including bioinformatics), tools which could become integral parts of clinical proteomics for disease biomarker discovery.

Quantifying raft proteins in neonatal mouse brain by 'tube-gel' protein digestion label-free shotgun proteomics

Background

The low concentration and highly hydrophobic nature of proteins in lipid raft samples present significant challenges for the sensitive and accurate proteomic analyses of lipid raft proteins. Elimination of highly enriched lipids and interfering substances from raft samples is generally required before mass spectrometric analyses can be performed, but these procedures often lead to excessive protein loss and increased sample variability. For accurate analyses of the raft proteome, simplified protocols are needed to avoid excessive sample handling and purification steps.

Results

We have devised a simple protocol using a 'tube-gel' protein digestion that, when combined with mass spectrometry, can be used to obtain comprehensive and reproducible identification and quantitation of the lipid raft proteome prepared from neonatal mouse brain. Lipid rafts (detergent-resistant membranes using Triton X-100 extraction) prepared from neonatal mouse brain were directly incorporated into a polyacrylamide tube-gel matrix without prior protein separation. After in-gel digestion of proteins, nanospray LC-MS/MS was used to analyze the extracted peptides, and the resulting spectra were searched to identify the proteins present in the sample. Using the standard 'label-free' proteomics approach, the total number of MS/MS spectra for the identified proteins was used to provide a measure of relative protein abundances. This approach was successfully applied to lipid rafts prepared from neonatal mouse brain. A total of 216 proteins were identified: 127 proteins (58.8%) were predicted to be membrane proteins, or membrane-associated proteins and 175 proteins (~80%) showed less than a 2-fold variation in the relative abundance in replicate samples.

Conclusion

The tube-gel protein digestion protocol coupled with nanospray LC-MS/MS (TubeGeLC-MS/MS) offers a simple and reproducible method for identifying and quantifying the changes of relative abundances in lipid raft proteins from neonatal mouse brain and could become a useful approach for studying lipid raft proteins from various tissues.

Quantitative proteomics for identification of cancer biomarkers

Quantitative proteomics can be used for the identification of cancer biomarkers that could be used for early detection, serve as therapeutic targets, or monitor response to treatment. Several quantitative proteomics tools are currently available to study differential expression of proteins in samples ranging from cancer cell lines to tissues to body fluids. 2-DE, which was classically used for proteomic profiling, has been coupled to fluorescence labeling for differential proteomics. Isotope labeling methods such as stable isotope labeling with amino acids in cell culture (SILAC), isotope-coded affinity tagging (ICAT), isobaric tags for relative and absolute quantitation (iTRAQ), and (18) O labeling have all been used in quantitative approaches for identification of cancer biomarkers. In addition, heavy isotope labeled peptides can be used to obtain absolute quantitative data. Most recently, label-free methods for quantitative proteomics, which have the potential of replacing isotope-labeling strategies, are becoming popular. Other emerging technologies such as protein microarrays have the potential for providing additional opportunities for biomarker identification. This review highlights commonly used methods for quantitative proteomic analysis and their advantages and limitations for cancer biomarker analysis.

Mass spectrometry based proteomics in urine biomarker discovery

All organisms contain 1,000s of proteins and peptides in their body fluids, which undergo disease-specific changes. Advances in the understanding of the functional relevance of these polypeptides under different (patho)physiological conditions and the identification of indicative changes with disease would greatly enhance diagnosis and therapy. The low-molecular-weight proteome, also termed peptidome, provides a rich source of information. Due to its lower molecular weight, the peptidome can be assessed without the need for sample manipulation like tryptic digests. This advantage facilitates comparative analysis but it also raises technical challenges differing from those in proteomics. The first part of this manuscript, is focused on the low-molecular-weight urinary proteome and reviews methodological aspects of sample collection, preparation, analysis, and data evaluation. The second part summarizes the recent progress in the definition and identification of clinically relevant polypeptide markers.

Global proteomic analysis distinguishes biologic differences in head and neck squamous carcinoma

The goal of this study was to establish a method for detecting biologically significant differences in protein expression of head and neck squamous cell carcinoma (HNSCC) obtained from the same samples utilized in gene expression analyses. Proteins from two head and neck tumor cell lines, SCC-25 and FaDu, were isolated from the denatured protein solution remaining from the TRIzol extraction procedure used for isolation of total RNA for microarray analysis. Peptides resulting from chemical and enzymatic digestion of the proteins were first separated by strong cation-exchange chromatography, followed by liquid chromatography-mass spectrometry (LC-MS) analysis on a QqTOF mass spectrometer. Stable isotope-labeled synthetic peptides were added to each ion-exchange fraction as internal standards, for reversed-phase HPLC retention time alignment. Protein extraction and digestion were repeated three times for each cell line and each extract was analyzed three times by LC-MS. To discriminate between technical vs biological variation, the ion-exchange fraction, retention time, normalized mass and signal intensity of these nine data sets were constructed into numerical arrays for statistical analysis. Of the approximately 50,000 signals, 90 peptide ions were found to discriminate the two cell lines with high stringency. Of those, six peptides were derived from vimentin and four peptides were derived from annexin II; both expressed more in SCC-25. Follow-up analysis of some of these signals by LC-MS/MS and RNA expression profiling revealed both concordance and discordance of RNA and protein expression. This study demonstrates that this procedure is highly reliable for identifying peptides that distinguish biological variability among samples, indicating that this method can be applied to study clinical samples, to identify potential prognostic biomarkers for HNSCC.

Genomic and proteomic microglial profiling: pathways for neuroprotective inflammatory responses following nerve fragment clearance and activation

Microglia, a primary immune effector cell of the central nervous system (CNS) affects homeostatic, neuroprotective, regenerative and degenerative outcomes in health and disease. Despite these broad neuroimmune activities linked to specific environmental cues, a precise cellular genetic profile for microglia in the context of disease and repair has not been elucidated. To this end we used nucleic acid microarrays, proteomics, immunochemical and histochemical tests to profile microglia in neuroprotective immune responses. Optic and sciatic nerve (ON and SN) fragments were used to stimulate microglia in order to reflect immune consequences of nervous system injury. Lipopolysaccharide and latex beads-induced microglial activation served as positive controls. Cytosolic and secreted proteins were profiled by surface enhanced laser desorption ionization-time of flight (SELDI-TOF) ProteinChip, 1D and 2D difference gel electrophoresis. Proteins were identified by peptide sequencing with tandem mass spectrometry, ELISA and western blot tests. Temporal expression of pro-inflammatory cytokines, antioxidants, neurotrophins, and lysosomal enzyme expression provided, for the first time, a unique profile of secreted microglia proteins with neuroregulatory functions. Most importantly, this molecular and biochemical signature supports a broad range of microglial functions for debris clearance and promotion of neural repair after injury.

Proteomics in human cancer research

Proteomics is now widely employed in the study of cancer. Many laboratories are applying the rapidly emerging technologies to elucidate the underlying mechanisms associated with cancer development, progression, and severity in addition to developing drugs and identifying patients who will benefit most from molecular targeted compounds. Various proteomic approaches are now available for protein separation and identification, and for characterization of the function and structure of candidate proteins. In spite of significant challenges that still exist, proteomics has rapidly expanded to include the discovery of novel biomarkers for early detection, diagnosis and prognostication (clinical application), and for the identification of novel drug targets (pharmaceutical application). To achieve these goals, several innovative technologies including 2-D-difference gel electrophoresis, SELDI, multidimensional protein identification technology, isotope-coded affinity tag, solid-state and suspension protein array technologies, X-ray crystallography, NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulation have evolved, and are being used in different combinations. This review provides an overview of the field of proteomics and discusses the key proteomic technologies available to researchers. It also describes some of the important challenges and highlights the current pharmaceutical and clinical applications of proteomics in human cancer research.

Comparative proteomic analysis of extracellular matrix proteins secreted by two types of skin fibroblasts

The hair follicle dermal papilla is composed primarily of extracellular matrix (ECM) proteins secreted by resident fibroblasts. Dermal papilla is endowed with hair morphogenic properties, yet its composition is poorly characterized. In an attempt to understand its specificity better, we compared the protein composition of ECM secreted by cultured dermal papilla fibroblasts with that of dermal fibroblasts. ECM proteins are generally large, difficult to solubilize, and abundantly post-translationally modified. We thus implemented an original protocol for analyzing them: ECM samples were enzymatically digested directly in the culture flasks and analyzed by LC-MS/MS. Sequencing of proteolytic peptides by MS/MS yielded protein identification. The relative abundance of a given protein in dermal fibroblast versus dermal papilla samples was estimated by comparing proteolytic peptide intensities detected by MS. Using this approach, several matrix proteins were found to be present at markedly different levels in each ECM type; in particular, thrombospondin 1 and fibronectin appeared to be overrepresented in the dermal papilla fibroblast ECM. MS results were supported by Western blot and immunostaining experiments. In addition, peptide intensities were processed in two ways, which proved to favor either the quantification accuracy or the information precision at the sequence level.