Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Proteomic profiling technology”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4Linked to original sources
PubMed2005

Is Melanoma a stem cell tumor? Identification of neurogenic proteins in trans-differentiated cells.

BACKGROUND: Although several genes and proteins have been implicated in the development of melanomas, the molecular mechanisms involved in the development of these tumors are not well understood. To gain a better understanding of the relationship between the cell growth, tumorigenesis and differentiation, we have studied a highly malignant cat melanoma cell line that trans-differentiates into neuronal cells after exposure to a feline endogenous retrovirus RD114. METHODS: To define the repertoire of proteins responsible for the phenotypic differences between melanoma and its counterpart trans-differentiated neuronal cells we have applied proteomics technology and compared protein profiles of the two cell types and identified differentially expressed proteins by 2D-gel electrophoresis, image analyses and mass spectrometry. RESULTS: The melanoma and trans-differentiated neuronal cells could be distinguished by the presence of distinct sets of proteins in each. Although approximately 60-70% of the expressed proteins were shared between the two cell types, twelve proteins were induced de novo after infection of melanoma cells with RD114 virus in vitro. Expression of these proteins in trans-differentiated cells was significantly associated with concomitant down regulation of growth promoting proteins and up-regulation of neurogenic proteins (p = < 0.001). Based on their physiologic properties, >95% proteins expressed in trans-differentiated cells could be associated with the development, differentiation and regulation of nervous system cells. CONCLUSION: Our results indicate that the cat melanoma cells have the ability to differentiate into distinct neuronal cell types and they express proteins that are essential for self-renewal. Since melanocytes arise from the neural crest of the embryo, we conclude that this melanoma arose from embryonic precursor stem cells. This model system provides a unique opportunity to identify domains of interactions between the expressed proteins that halt the tumorigenic potential of melanoma cells and drive them toward neurogenerative pathways involved in early neurogenesis. A better understanding of these proteins in a well-coordinated signaling network would also help in developing novel approaches for suppression of highly malignant tumors that arise from stem-like embryonic cells.

Journal Article↗
PubMed2002

Molecular markers in prostate cancer: the role in preoperative staging.

Radical prostatectomy as a primary treatment for clinically localized prostate cancer has increased dramatically over the past decade due to prostate-specific antigen (PSA) screening and the awareness of the increased incidence of localized disease. Despite the stage migration to increase clinically localized disease, there are still vast numbers of men who harbor occult extraprostatic extension and develop recurrence after surgery. The study of molecular markers in the blood or tissue of surgical patients prior to treatment, called " molecular staging, " is the focus of this review. The reverse transcriptase- polymerase chain reaction (RT-PCR) test for PSA gene expression in peripheral blood or bone marrow has received considerable attention since its first report in 1992. The test detects messenger RNA species for prostate-specific/abundant genes such as PSA and prostate-specific membrane antigen. These messenger RNAs were not detected in normal blood or bone marrow, but were detected in some prostate cancer patients presumably due to circulating prostatic epithelial cells. These prostate epithelial cells are thought to be occult metastases cells, and early studies correlated a positive RT-PCR test with surgical pathology adverse features such as positive margins. Despite the many studies over the past few years, there have been inconsistent results, and the most recent studies have not been able to confirm clinical utility. Bone marrow RT-PCR has been more promising; however, it is still a research tool that needs further study. The study of molecular markers in tissue material, ie, prostate biopsy samples prior to radical prostatectomy, is problematic due to the sampling error inherent in a multifocal heterogeneous tumor such as prostate cancer. The tumor suppressor proteins p53 and p27, Bcl-2 oncoprotein, Ki-67 proliferation index protein, E-cadherin, and microvessel density have been assessed in preradical prostatectomy needle biopsy. Results have been conflicting, and none are yet accepted as a clinically useful marker. Current and future work is focusing on analysis of multiple gene expressions or proteins simultaneously via gene chip or proteomics technology. While these expression profiles might be of value in whole prostate surgical specimens where tissues are well characterized, it is unclear how this new technology will be applied to the needle biopsy samples. Although molecular staging of radical prostatectomy patients has been under study for a decade, all assays remain research tools. Still, this area holds great promise for improving the accuracy of staging and providing a more accurate prognosis of individual men with clinically localized prostate cancer.

Biomarkers, Tumor↗
PubMed2006

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications.

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.

Biomarkers↗
PubMed2006

Clinical proteomics and mass spectrometry profiling for cancer detection.

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.

Biomarkers, Tumor↗
PubMed2005

Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles.

BACKGROUND: Proteomic peptide profiling is an emerging technology harbouring great expectations to enable early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels. METHODS: We present a new, efficient, multivariate feature selection strategy that extracts useful feature panels directly from the high-throughput spectra. The strategy takes advantage of the characteristics of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) profiles and enhances widely used univariate feature selection strategies with a heuristic based on multivariate de-correlation filtering. We analyse and compare two versions of the method: one in which all feature pairs must adhere to a maximum allowed correlation (MAC) threshold, and another in which the feature panel is built greedily by deciding among best univariate features at different MAC levels. RESULTS: The analysis and comparison of feature selection strategies was carried out experimentally on the pancreatic cancer dataset with 57 cancers and 59 controls from the University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA. The analysis was conducted in both the whole-profile and peak-only modes. The results clearly show the benefit of the new strategy over univariate feature selection methods in terms of improved classification performance. CONCLUSION: Understanding the characteristics of the spectra allows us to better assess the relative importance of potential features in the diagnosis of cancer. Incorporation of these characteristics into feature selection strategies often leads to a more efficient data analysis as well as improved classification performance.

Journal Article↗
PubMed2001

Applications of display technologies to proteomic analyses.

With the rapid accumulation of genetic information, development of general experimental approach suitable for large scale annotation and profiling of the whole proteome have become one of the major challenges in postgenomic era. Biomolecular display technologies, which allow expressing of a large pool of modularly coded biomolecules, are extremely useful for accessing and analyzing protein diversity and interaction profile on a large scale. Recent advances in protein display technologies and their applications to proteomic analyses have been discussed.

Cloning, Molecular↗
PubMed2024

Chemical proteomics to study metabolism, a reductionist approach applied at the systems level.

Cellular metabolism encompasses a complex array of interconnected biochemical pathways that are required for cellular homeostasis. When dysregulated, metabolism underlies multiple human pathologies. At the heart of metabolic networks are enzymes that have been historically studied through a reductionist lens, and more recently, using high throughput approaches including genomics and proteomics. Merging these two divergent viewpoints are chemical proteomic technologies, including activity-based protein profiling, which combines chemical probes specific to distinct enzyme families or amino acid residues with proteomic analysis. This enables the study of metabolism at the network level with the precision of powerful biochemical approaches. Herein, we provide a primer on how chemical proteomic technologies custom-built for studying metabolism have unearthed fundamental principles in metabolic control. In parallel, these technologies have leap-frogged drug discovery through identification of novel targets and drug specificity. Collectively, chemical proteomics technologies appear to do the impossible: uniting systematic analysis with a reductionist approach.

Humans↗
PubMed2001

Global profiling of gene expression in cancer using genomics and proteomics.

Global profiling of gene expression at the genomic level using DNA microarrays and at the protein level using a variety of technologies have followed separate paths. Studies of gene expression for several types of tumors, using DNA microarrays, have been published recently that are informative with respect to delineating distinct patterns of gene expression among subsets of related tumors. Proteomics-based profiling uniquely allows delineation of global changes in protein expression patterns resulting from transcriptional and post-transcriptional control, post-translational modifications and shifts in proteins between different cellular compartments. Some of the current technologies for proteome profiling and the application of proteomics to the analysis of tumor tissues are reviewed. Given that comprehensive expression profiles obtained using genomics and proteomics are highly complementary, a combined approach to profiling may well uncover expression patterns that could not be predicted using a single approach.

Gene Expression Profiling↗
PubMed2003

The diagnosis and management of pre-invasive breast disease: promise of new technologies in understanding pre-invasive breast lesions.

Array-based comparative genomic hybridization, RNA expression profiling, and proteomic analyses are new molecular technologies used to study breast cancer. Invasive breast cancers were originally evaluated because they provided ample quantities of DNA, RNA, and protein. The application of these technologies to pre-invasive breast lesions is discussed, including methods that facilitate their implementation. Data indicate that atypical ductal hyperplasia and ductal carcinoma in situ are precursor lesions molecularly similar to adjacent invasive breast cancer. It is expected that molecular technologies will identify breast tissue at risk for the development of unfavorable subtypes of invasive breast cancer and reveal strategies for targeted chemoprevention or eradication.

Breast↗
PubMed2004

Use of proteomic analysis to monitor responses to biological therapies.

Proteomics has the potential to revolutionise diagnosis and disease management. Serum protein pattern profiling by surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF) mass spectrometry is emerging as a novel approach to discover protein patterns capable of distinguishing disease and disease-free states with high sensitivity and specificity. This method has shown great promise for early diagnosis of ovarian cancer and is being applied to a range of pathological states. Protein microarray technology is being evaluated as a new means to track biological responses to therapy. Through the measurement of key protein phosphorylation sites at different stages of disease progression or before and after treatment, protein signal pathways can be mapped and thus become the starting point for individualised therapy. Laser capture microdissection (LCM) coupled with immunostaining of protein microarrays allows isolation of pure cell populations and relative quantitation of phosphorylated and non-phosphorylated forms of the cell's key signalling proteins. This technology is currently in use at the National Institutes of Health in Phase II clinical trials of metastatic breast and ovarian cancer. Cell survival and apoptotic protein pathways are monitored as biological markers of disease progression in these clinical trials. Proteomic technologies, such as serum protein pattern profiling, combined with protein microarray technologies, constitute a new paradigm for detecting disease and monitoring disease response to therapy. Ultimately, proteomics and genomics will become integrated into cancer patient management through the design and tracking of individualised therapy.

Animals↗
PubMed2005

Genetics and proteomics in scleroderma.

The pathogenesis of systemic sclerosis (SSc; scleroderma) is still enigmatic. Genetic, familial, and twin studies suggest that SSc occurs in genetically susceptible individuals. Recent high-throughput technologies, including gene expression profiling and proteomics, have accelerated the rate of information acquired on possible mechanisms involved in SSc pathogenesis. The potential of the data obtained lies in their use for identifying new disease markers, monitoring disease activity, and developing tailored therapies. The result is an era of unprecedented advance that will benefit SSc and many other diseases.

Alleles↗
PubMed2004

The development and application of methods for activity-based protein profiling.

Recent advances in genomic and proteomic technologies have begun to address the challenge of assigning molecular and cellular functions to the numerous protein products encoded by prokaryotic and eukaryotic genomes. In particular, chemical strategies for proteome analysis have emerged that enable profiling of protein activity on a global scale. Herein, we highlight these chemical proteomic methods and their application to the discovery and characterization of disease-related enzyme activities.

Animals↗
PubMed2007

A combined dataset of human cerebrospinal fluid proteins identified by multi-dimensional chromatography and tandem mass spectrometry.

Human cerebrospinal fluid (CSF) is an important source for studying protein biomarkers of age-related neurodegenerative diseases. Before characterizing biomarkers unique to each disease, it is necessary to categorize CSF proteins systematically and extensively. However, the enormous complexity, great dynamic range of protein concentrations, and tremendous protein heterogeneity due to post-translational modification of CSF create significant challenges to the existing proteomics technologies for an in-depth, nonbiased profiling of the human CSF proteome. To circumvent these difficulties, in the last few years, we have utilized several different separation methodologies and mass spectrometric platforms that greatly enhanced the identification coverage and the depth of protein profiling of CSF to characterize CSF proteome. In total, 2594 proteins were identified in well-characterized pooled human CSF samples using stringent proteomics criteria. This report summarizes our efforts to comprehensively characterize the human CSF proteome to date.

Cerebrospinal Fluid Proteins↗
PubMedDate not supplied

Nanotechnologies and microchips in genetic diseases.

Microarrays or microchips represent a new area of high technology, which will completely change the methodological approach to basic research and clinical diagnostics. This technology can be used for genotyping, expression profiling and proteome analysis. Genetics and molecular medicine have an expanding need for rapid genotyping, mutational analysis and DNA re-sequencing technologies, i.e. microarrays that have a clear potential for miniaturization, parallelization, and automation and enable high-throughput screening. Expression profiling technology is a new tool for investigating expression patterns, identifying new disease genes either for monogenic disorders or for complex traits, identifying new functional and cellular relationships and identifying new pathways and possible related drugs. This technology has been successfully applied to the study of complex traits, i.e. cardiovascular diseases, cancer and type II diabetes, providing new insights into possible pathogenetic mechanisms and new therapeutical approaches. Finally, microarray can further improve proteome analysis. This review discusses these points.

Gene Expression Profiling↗
PubMedDate not supplied

Diagnosis of pancreatic cancer using serum proteomic profiling.

In the United States, mortality rates from pancreatic cancer (PCa) have not changed significantly over the past 50 years. This is due, in part, to the lack of early detection methods for this particularly aggressive form of cancer. The objective of this study was to use high-throughput protein profiling technology to identify biomarkers in the serum proteome for the early detection of resectable PCa. Using surface-enhanced laser desorption/ionization mass spectrometry, protein profiles were generated from sera of 49 PCa patients and 54 unaffected individuals after fractionation on an anion exchange resin. The samples were randomly divided into a training set (69 samples) and test set (34 samples), and two multivariate analysis procedures, classification and regression tree and logistic regression, were used to develop classification models from these spectral data that could distinguish PCa from control serum samples. In the test set, both models correctly classified all of the PCa patient serum samples (100% sensitivity). Using the decision tree algorithm, a specificity of 93.5% was obtained, whereas the logistic regression model produced a specificity of 100%. These results suggest that high-throughput proteomics profiling has the capacity to provide new biomarkers for the early detection and diagnosis of PCa.

Biomarkers, Tumor↗
PubMed2004

Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml.

PURPOSE: Artificial intelligence based pattern recognition algorithms have been developed and successfully used to analyze complex serum proteomic data streams generated by surface enhanced, laser desorption ionization time-of-flight mass spectroscopy. In the current study we used a high performance, hybrid quadrupole time-of-flight mass spectrometer to generate discriminatory serum proteomic profiles to determine if this technology could be used to determine the need for prostate biopsy in men with elevated prostate specific antigen (PSA). MATERIALS AND METHODS: Serum samples were collected from 154 men with serum PSA 2.5 to 15.0 ng/ml and/or abnormal digital rectal examination prior to transrectal ultrasound guided biopsy. Serum samples were applied to WCX2 (weak cation exchange protein chip) Protein Arrays (Ciphergen Biosystems, Fremont, California) by a Biomek 2000 robotic liquid handler (Beckman-Coulter, Chaska, Minnesota) and low molecular weight (less than 20 kDa) proteomic patterns were generated with an API QSTAR Pulsar i LC/MS/MS System (Applied Biosystems, Framingham, Massachusetts). High resolution mass spectra were analyzed with a pattern recognition bioinformatics tool, that is Proteome Quest beta version 1.0 (Correlogic Systems, Inc., Bethesda, Maryland), in an attempt to identify and discover key discriminating ion signatures. Serum samples from 63 men (2 or more negative prostate biopsies in 23, 1 negative biopsy in 10 and biopsy detected prostate cancer [CaP] in 30) were used to train the diagnostic algorithm. The remaining 91 samples, including 28 of prostate cancer and 63 of 1 or more negative biopsies, were analyzed in blinded fashion. RESULTS: The most discriminatory model was found using the WCX2 chip. Testing the remaining 91 men with this model yielded 100% sensitivity and 67% specificity. In other words, if the proteomic pattern had been used to determine the need for prostate biopsy in this cohort of men with PSA between 2.5 and 15.0 ng/ml, 67% (42 of 63) with negative biopsies would have avoided unnecessary biopsy, while no cancers would have been missed. CONCLUSIONS: Our data demonstrate that high resolution mass spectroscopy can generate serum proteomic patterns that discriminate men with elevated PSA due to benign processes from men with CaP even when PSA is within the diagnostic gray zone. We are currently expanding the testing set to determine the reliability of this new technology to decrease unnecessary prostate biopsies without compromising the detection of curable CaP.

Algorithms↗
PubMed2005

Proteomics in nutrition research: principles, technologies and applications.

The global profiling of the whole protein complement of the genome expressed in a particular cell or organ, or in plasma or serum, makes it possible to identify biomarkers that respond to alterations in diet or to treatment, and that may have predictive value for the modelling of biological processes. Proteomics has not yet been applied on a large scale in nutritional studies, yet it has advantages over transcriptome profiling techniques in that it directly assesses the entities that carry out the biological functions. The present review summarizes the different approaches in proteomics research, with special emphasis on the current technical 'workhorses': two-dimensional (2D)-PAGE with immobilized pH gradients and protein identification by MS. Using a work-flow approach, we provide information and advice on sample handling and preparation, protein solubilization and pre-fractionation, protein separation by 2D-PAGE, detection and quantification via computer-assisted analysis of gels, and protein identification and characterization techniques by means of MS. Examples from nutritional studies employing proteomics are provided to demonstrate not only the advantages but also the limitations of current proteome analysis platforms.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2001

Profiling the specific reactivity of the proteome with non-directed activity-based probes.

BACKGROUND: The field of proteomics aims to characterize dynamics in protein function on a global level. However, several classes of proteins, in particular low abundance proteins, remain difficult to characterize using standard proteomics technologies. Recently, chemical strategies have emerged that profile classes of proteins based on activity rather than quantity, thereby greatly facilitating the analysis of low abundance constituents of the proteome. RESULTS: In order to expand the classes of proteins susceptible to analysis by activity-based methods, we have synthesized a library of biotinylated sulfonate esters and applied its members to complex proteomes under conditions that distinguish patterns of specific protein reactivity. Individual sulfonates exhibited unique profiles of proteome reactivity that in extreme cases appeared nearly orthogonal to one another. A robustly labeled protein was identified as a class I aldehyde dehydrogenase and shown to be irreversibly inhibited by members of the sulfonate library. CONCLUSIONS: Through screening the proteome with a non-directed library of chemical probes, diverse patterns of protein reactivity were uncovered. These probes labeled protein targets based on properties other than abundance, circumventing one of the major challenges facing contemporary proteomics research. Considering further that the probes were found to inhibit a target enzyme's catalytic activity, the methods described herein should facilitate the identification of compounds possessing both selective proteome reactivities and novel bioactivities.

Aldehyde Dehydrogenase↗