Identification of the SELDI ProteinChip human serum retentate by microcapillary liquid chromatography-tandem mass spectrometry

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Human body-fluid proteome: quantitative profiling and computational prediction

Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

The Peptidome Comes of Age: Mass Spectrometry-Based Characterization of the Circulating Cancer Peptidome

Peptides play a seminal role in most physiological processes acting as neurotransmitters, hormones, antibiotics, and immune regulation. In the context of tumor biology, it is hypothesized that endogenous peptides, hormones, cytokines, growth factors, and aberrant degradation of select protein networks (e.g., enzymatic activities, protein shedding, and extracellular matrix remodeling) are fundamental in mediating cancer progression. Analysis of peptides in biological fluids by mass spectrometry holds promise of providing sensitive and specific diagnostic and prognostic information for cancer and other diseases. The identification of circulating peptides in the context of disease constitutes a hitherto source of new clinical biomarkers. The field of peptidomics can be defined as the identification and comprehensive analysis of physiological and pathological peptides. Like proteomics, peptidomics has been advanced by the development of new separation strategies, analytical detection methods such as mass spectrometry, and bioinformatic technologies. Unlike proteomics, peptidomics is targeted toward identifying endogenous protein and peptide fragments, defining proteolytic enzyme substrate specificity, as well as protease cleavage recognition (degradome). Peptidomics employs "top-down proteomics" strategies where mass spectrometry is applied at the proteoform level to analyze intact proteins and large endogenous peptide fragments. With recent advances in prefractionation workflows for separating peptides, mass spectrometry instrumentation, and informatics, peptidomics is an important field that promises to impact on translational medicine. This review covers the current advances in peptidomics, including top-down and imaging mass spectrometry, comprehensive quantitative peptidome analyses (developments in reproducibility and coverage), peptide prefractionation and enrichment workflows, peptidomic data analyses, and informatic tools. The application of peptidomics in cancer biomarker discovery will be discussed.

The Role of Mass Spectrometry in the "Omics" Era

Mass spectrometry (MS) is one of the key analytical technology on which the emerging ''-omics'' approaches are based. It may provide detection and quantization of thousands of proteins and biologically active metabolites from a tissue, body fluid or cell culture working in a ''global'' or ''targeted'' manner, down to ultra-trace levels. It can be expected that the high performance of MS technology, coupled to routine data handling, will soon bring fruit in the request for a better understanding of human diseases, leading to new molecular biomarkers, hence affecting drug targets and therapies. In this review, we focus on the main advances in the MS technologies, influencing genomics, transcriptomics, proteomics, lipidomics and metabolomics fields, up to the most recent MS applications to meta-omic studies.

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.

The application of SELDI-TOF-MS in clinical diagnosis of cancers

Cancer diagnosis is important, and the early diagnosis of cancers could predict a more successful treatment. The proteomic studies emerged to be useful in combined analyses of samples from patients and provide more accurate diagnosis when compared to the single-factor-based diagnosis. In recent years, cancer detection with surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF MS) is flourishing and brought significant progress in this area. This paper summarizes some recent results with this technique for cancer diagnosis.

Association of serum amyloid A protein and peptide fragments with prognosis in renal cancer

BACKGROUND

In renal cell carcinoma (RCC), the discovery of biomarkers for clinical use is a priority. This study aimed to identify and validate diagnostic and prognostic serum markers using proteomic profiling.

METHODS

Pre-operative sera from 119 patients with clear cell RCC and 69 healthy controls was analysed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry with stringent in-house quality control and analysis routines. Following identification of one prognostic peak as a fragment of serum amyloid A (SAA), total serum SAA and CRP were also determined by immunoassay for further validation.

RESULTS

Several peptides were identified as having independent prognostic but not diagnostic significance on multivariable analysis. One was subsequently identified as a 1525 Da fragment of SAA (hazard ratio (HR)=0.26, 95% CI 0.08-0.85, P=0.026). This was weakly negatively correlated with total SAA, which was also of independent prognostic significance (HR=2.46, 95% CI 1.17-5.15, P=0.017). Both potentially strengthened prognostic models based solely on pre-operative variables.

CONCLUSIONS

This is the first description of the prognostic value of this peptide in RCC and demonstrates proof of principle of the approach. The subsequent examination of SAA protein considerably extends previous studies, being the first study to focus solely on pre-operative samples and describing potential clinical utility in pre-operative prognostic models.

Multidimensional protein identification technology for clinical proteomic analysis

Proteomics technologies demonstrate enormous data-gathering capabilities to discover disease-specific biomarkers in serum, plasma, urine, tissue and other biological samples. The traditional way to achieve this consists of protein separation performed using two-dimensional polyacrylamide gel electrophoresis (2DE). However, the 2DE approach has its drawbacks with respect to automation, sensitivity, and throughput. Considerable efforts have been devoted to the development of non-gel-based proteome separation technologies able to resolve complex protein and peptide mixtures prior to mass spectrometric (MS) analysis. This review discusses some of the most recent advances in multidimensional peptide separation techniques compatible with MS analysis, including gel electrophoresis, isoelectric focusing, capillary electrophoresis and liquid chromatography techniques. Based on future perspectives and our experiences, special attention is given to the application of two-dimensional chromatographic separation coupled to MS, the so-called multidimensional protein identification technology approach.

Using the protein chip interface with quadrupole time-of-flight mass spectrometry to directly identify peaks in SELDI profiles--initial evaluation using low molecular weight serum peaks

Mass spectrometric profiling, particularly in the form of SELDI, has been used in many studies, particularly in attempts to generate diagnostic serum profiles. Several studies have generated promising results but one of the limitations is the inability to identify easily potential discriminatory peaks. This may enable specific assays to be developed and increased biological insight. We describe the first systematic technical evaluation of the ProteinChip interface coupled to a tandem mass spectrometer which allows direct sequencing of peptides <6000 Da, and describe the direct sequence identification of 21 peaks commonly observed in serum samples. Additionally we describe for the first time the use of on-chip acetylation to assist in the validation of sequence identification.

Sys-BodyFluid: a systematical database for human body fluid proteome research

Recently, body fluids have widely become an important target for proteomic research and proteomic study has produced more and more body fluid related protein data. A database is needed to collect and analyze these proteome data. Thus, we developed this web-based body fluid proteome database Sys-BodyFluid. It contains eleven kinds of body fluid proteomes, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, seminal fluid, human milk and amniotic fluid. Over 10,000 proteins are presented in the Sys-BodyFluid. Sys-BodyFluid provides the detailed protein annotations, including protein description, Gene Ontology, domain information, protein sequence and involved pathways. These proteome data can be retrieved by using protein name, protein accession number and sequence similarity. In addition, users can query between these different body fluids to get the different proteins identification information. Sys-BodyFluid database can facilitate the body fluid proteomics and disease proteomics research as a reference database. It is available at http://www.biosino.org/bodyfluid/.

Use of multidimensional separation protocols for the purification of trace components in complex biological samples for proteomics analysis

The routine detection of low abundance components in complex samples for detailed proteomics analysis continues to be a challenge. Whilst the potential of multidimensional chromatographic fractionation for this purpose has been proposed for some years, and was used effectively for the purification to homogeneity of trace components in bulk biological samples for N-terminal sequence analysis, its practical application in the proteomics arena is still limited. This article reviews some of the recent data using these approaches, including the use of microaffinity purification as part of multidimensional protocols for downstream proteomics analysis.

Serum biomarkers to differentiate benign and malignant mammographic lesions

BACKGROUND

Mammographic screening has increased detection of earlier-stage breast cancers and has decreased mortality from breast cancer. A Breast Imaging-Reporting and Data System (BIRADS) 4 classification prompts a biopsy that most often reveals benign disease. Our objective was to determine if serum protein-expression profiles could be used to differentiate between benign and malignant mammographic lesions.

STUDY DESIGN

After IRB approval, women undergoing an image-guided biopsy for a BIRADS category 4 lesion were recruited. Serum was collected prebiopsy and labeled retrospectively after final pathology was reviewed. Serum was incubated with weak cation exchange magnetic beads and assayed in duplicate for analysis on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) instrumentation (Bruker Daltonics). Spectra were analyzed using ClinProTools 2.0 software (Bruker Daltonics), and classifications determined using a genetic-clustering algorithm.

RESULTS

In a 14-month period, 260 subjects were recruited into this study. Sera from 92 subjects were randomly selected to form benign (n = 46) and cancer (n = 46) cohorts. The MALDI-TOF spectra analysis yielded 273 peaks, with 14 peaks expressed differentially (p < 0.05) between the cancer and benign cohorts. A genetic algorithm model was generated, yielding a sensitivity of 88.3% and specificity of 85.8%.

CONCLUSIONS

MALDI-TOF protein-expression profiles generated from BIRADS 4 sera could be used to distinguish between benign and malignant mammographic lesions.

Application of proteomic technology to neural stem cell science and neurology

There is widespread recognition of the potential that stem cells hold for the treatment and repair of a large number of disorders affecting the human CNS. Therefore, stem cell research will go hand in hand with progress in specific areas of neuroscience. Proteomics has great potential to make important contributions to the basic understanding of neurological processes, and to deliver much needed cellular biomarkers in both of these fields. This review focuses on the importance of proteomic research in neuroscience, in particular the application of biomarker discovery in stem cells and degenerative diseases of the CNS.