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Results for “Proteomic profiling technology”

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At least 109 records · Page 6Linked to original sources
PubMed2006

Cytokeratin-20 and seminal vesicle secretory protein VI as possible marker proteins in urinary bladder preneoplastic lesions induced by N-butyl-N-(4-hydroxybutyl) nitrosamine.

BACKGROUND: A simple and non-invasive methods for the diagnosis of transitional cell carcinoma of the bladder are needed for the prevention of invasive tumor. A proteomic technology has recently been developed to facilitate protein profiling of biological mixtures. We tried to detect the marker proteins by proteomic approach during the initiation stages on N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN)-induced bladder carcinogenesis in rats. METHODS: Ten rats in group A were given 0.05% BBN in drinking water for 12 weeks. Other 10 rats in group B with 10 rats were designated as a control group and were not given BBN. Whole urinary bladders of all rats were excised at 12 weeks from the beginning of the experiment. Conventional proteomics was performed with high resolution 2-dimension gel electrophoresis followed by computational image analysis and protein identification using mass spectrometry. RESULTS: A comparison of urinary bladder hyperplasia tissue with control tissue showed that five proteins; actin gamma2 propeptide, cytokeratin-20 (CK-20), proapolipoprotein, alpha2 actin (alpha-cardiac actin) and heat shock 27 kDa protein-1 were over-expressed in hyperplastic tissues. Three proteins; transcription factor myocardin, seminal vesicle secretory protein VI (SVS-VI) precursor and hypothetical protein RMT-7 were under-expressed in hyperplastic tissues. CONCLUSION: In our animal mode, BBN-induced urinary bladder mucosal hyperplasia resulted in an increase in the expression of five proteins and a decrease in the expression of three proteins. Of these, CK-20 and SVS-VI seem to be of particular interest. However other method such as Western blotting seems to be needed for confirmation of these proteins and more information on human bladder tissue is needed for clinical application.

Animals↗
PubMed2005

Proteomic analysis of anti-cancer effects by paclitaxel treatment in cervical cancer cells.

OBJECTIVES: Paclitaxel (Taxol), a potent drug of natural origin isolated from the bark of the Pacific yew, is widely used in the treatment of ovarian, lung and breast cancer. At present, there is little information regarding the anti-cancer mechanism of paclitaxel against cervical carcinoma cells. We thus tried to show the anti-cancer effect of paclitaxel on cervical carcinoma cell line carrying HPV by using a proteomic analysis and to investigate the mechanism of actions. METHODS: We treated paclitaxel to cervical carcinoma cells and then carried out MTT assay to observe the anti-proliferate activity. Using proteomics analysis including two-dimensional (2-DE) gel electrophoresis and MALDI-TOF-MS, we tried to find the anti-proliferate activity-related proteins. Among them, paclitaxel treatment suppressed the expression of the mitotic checkpoint protein BUB3. Functional proteomic analysis by small interfering RNA (siRNA) targeting was tried to illuminate a role of mitotic checkpoint protein BUB3 in cell cycle progression. RESULTS: The cytotoxicity effects of paclitaxel were determined in HPV-16 positive CaSki, HPV-18 positive HeLa and HPV-negative C33A cervical carcinoma cell lines. Using efficient proteomics methods including 2-DE/MALDI-TOF-MS, we identified several cellular proteins that are responsive to paclitaxel treatment in HeLa cells. Paclitaxel treatment elevated mainly apoptosis-related, immune response-related and cell cycle check point-related proteins. On the other hand, paclitaxel treatment diminished growth factor/oncogene-related proteins and transcription regulation-related proteins. Paclitaxel showed anti-proliferate activity through the membrane death receptor (DR)-mediated apoptotic pathway involving activation of caspase-8 with a TRAIL-dependent fashion as well as the mitochondrial-mediated pathway involving down-regulation of bcl-2 by cytochrome c release. Furthermore, we found siRNA-induced BUB3 knock down on cell cycle progression blocked by cell cycle arrest after paclitaxel treatment. CONCLUSIONS: The proteome profiling technique provided a broad-base and effective approach for the identification of protein changes induced by paclitaxel and showed anti-proliferate activity through the membrane death receptor-mediated apoptotic pathway, the mitochondrial-mediated pathway. This study shows the power of proteomic profiling with functional analysis using RNAi technology for the discovery of novel molecular targets and a better understanding of the actions of paclitaxel at the molecular level in cervical carcinoma cells.

Antineoplastic Agents, Phytogenic↗
PubMed2026

Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA.

Recent advances in spatial omics technologies have enabled simultaneous profiling of transcriptomic, proteomic, epigenomic, metabolomic and imaging data at high spatial resolution, offering unprecedented opportunities to dissect tissue complexity. However, integrating these diverse and large-scale spatial multimodal datasets remains a major computational challenge. We present SCIGMA, a scalable and generalizable deep learning framework for spatial multiomics integration. SCIGMA introduces an uncertainty-aware contrastive learning objective and multiview graph neural networks to preserve modality-specific signals while learning biologically meaningful joint representations. Unlike previous methods, SCIGMA provides spatially resolved uncertainty estimates, interpretably identifying regions of biological or technical heterogeneity. SCIGMA supports integration of up to five modalities, and its modular framework is extensible to future technologies with even more modalities. It also scales to more than 1 million spatial locations, enabling analysis of high-resolution datasets such as Visium HD and Xenium Prime. We evaluated SCIGMA across 19 datasets spanning 8 modalities, 10 tissues and 9 platforms. On benchmarkable datasets, SCIGMA outperformed other methods in spatial domain detection, modality preservation, feature reconstruction and reproducibility. SCIGMA identifies biologically meaningful structures, refined spatial domains and modality-specific regulatory programs, providing a robust, flexible and future-ready solution for scalable spatial multimodal integration.

Multiomics↗
PubMed2006

Two-dimensional difference gel electrophoresis.

The two-dimensional (2D) polyacrylamide gel-based approach to protein profiling has been successful because it is an accessible, inexpensive, and powerful tool for the analysis of global patterns of protein expression. All protein spots that are resolved and detected within the 104 to 105 dynamic range of gel capacity can be studied qualitatively and quantitatively in relation to each other, and viewed as a single image. Two-dimensional difference gel electrophoresis (DIGE) has strengthened the 2D platform by allowing the detection and quantitation of differences between samples resolved on the same gel, or across multiple gels, when linked by an internal standard. The technology is based on modification of protein extracts with fluorescent cyanine dyes, which have distinct excitation and emission spectra and are migration (charge and size) matched so that the same protein labeled with any of the dyes (Cy2, Cy3, Cy5) will migrate to the same position within a 2D gel, greatly enhancing reproducibility. It is becoming clear that each technology that is currently available for quantifying differential protein expression has its own weaknesses and strengths, and that multiplatform, integrated approaches will be necessary to provide the most complete analysis of any given proteome. We believe that DIGE is, and will remain in the future, a key front-end proteomic tool that will strongly complement other protein-profiling technologies.

Animals↗
PubMed2005

Transcriptomic and proteomic responses of human renal HEK293 cells to uranium toxicity.

The industrial use of uranium, in particular depleted uranium, has pin-pointed the need to review its chemical impact on human health. Global methodologies, applied to the field of toxicology, have demonstrated their applicability to investigation of fine molecular mechanisms. This report illustrate the power of toxicogenomics to evaluate the involvement of certain genes or proteins in response to uranium. We particularly show that 25% of modulated genes concern signal transduction and trafficking, that the calcium pathway is heavily disturbed and that nephroblastomas-related genes are involved (WIT-1, STMN1, and STMN2). A set of 18 genes was deregulated whatever the concentration of toxicant, which could constitute a signature of uranium exposure. Moreover, a group of downregulated genes, with corresponding disappearing proteins (HSP90, 14-3-3 protein, HMGB1) in two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), are good candidates for use as biomarkers of uranium effects. These results reveal a cross-checking between transcriptomic and proteomic technologies. Moreover, our temporal gene expression profiles suggest the existence of a concentration threshold between adaptive response and severe cell deregulation. Our results confirm the involvement of genes already described and also provide new highlights on cellular response to uranium.

Apoptosis↗
PubMed2006

Protein profiling of sickle cell versus control RBC core membrane skeletons by ICAT technology and tandem mass spectrometry.

A proteomic approach using a cleavable ICAT reagent and nano-LC ESI tandem mass spectrometry was used to perform protein profiling of core RBC membrane skeleton proteins between sickle cell patients (SS) and controls (AA), and determine the efficacy of this technology. The data was validated through Peptide/Protein Prophet and protein ratios were calculated through ASAPratio. Through an ANOVA test, it was determined that there is no significant difference in the mean ratios from control populations (AA1/AA2) and sickle cell versus control populations (AA/SS). The mean ratios were not significantly different from 1.0 in either comparison for the core skeleton proteins (alpha spectrin, beta spectrin, band 4.1 and actin). On the natural-log scale, the variation (standard deviation) of the method was determined to be 14.1% and the variation contributed by the samples was 13.8% which together give a total variation of 19.7% in the ratios.

Anemia, Sickle Cell↗
PubMed2006

Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry.

Recently, the focus of cancer research has expanded from genetic information in the human genome to protein expression analyses. Because this 'proteome' reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumour markers for disease diagnosis and therapy monitoring. Some current proteomic technologies are particularly suitable for protein profiling in the search for new biomarkers. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry has been used frequently, highlighting many new proteins as biomarkers (e.g. for ovarian, breast, prostate and colorectal cancer). However, it is becoming increasingly recognized that reproducibility and validation of these biomarkers should be addressed carefully, as should their origin and identity. If these efforts are made, protein profiling can contribute to the better diagnosis of patients and the optimization of their treatment.

Biomarkers, Tumor↗
PubMed2026

Machine learning-assisted plasma PEA proteomics enables differential diagnosis of melancholic depression and bipolar disorder.

Differentiating bipolar disorder (BD) from major depressive disorder (MDD) remains a critical unmet need in psychiatry due to overlapping clinical presentations and the absence of reliable biological markers. In this study, we assessed the capacity of multivariate machine learning models to accurately differentiate BD from MDD with melancholic features using plasma proteomic profiles obtained via Proximity Extension Assay (PEA) technology. A total of 67 participants were included (23 BD, 20 MDD, and 24 HC), and plasma protein expression was assessed using the Olink Target 96 Neurology panel. Differential proteomic analysis revealed distinct disorder-specific expression patterns, identifying 21 differentially expressed proteins in BD versus MDD, 18 in BD versus healthy controls, and 7 in MDD versus healthy controls. Using a stepwise feature reduction strategy, machine learning models were trained on three feature sets comprising all proteins, the top 20 most informative proteins, and the top 5 most beneficial proteins, and evaluated across BD-MDD, BD-HC, and MDD-HC classification tasks using five algorithms. For BD-MDD discrimination, the Random Forest model achieved the highest performance when trained on the top 5 protein set (LXN, HAGH, MATN3, PLXNB1, and CTSC), yielding an AUC of 0.905, with similarly strong performance observed using the top 20 protein set. Feature importance analysis highlighted proteins involved in neurodevelopmental processes, immune regulation, and extracellular matrix organization. Overall, these findings demonstrate that integrating plasma proteomics with machine learning enables robust differentiation between BD and MDD with melancholic features, supporting the development of scalable and biologically informed diagnostic tools for precision psychiatry.

Bipolar disorder↗
PubMed2005

Micro-separation toward systems biology.

Current biology is experiencing transformation in logic or philosophy that forces us to reevaluate the concept of cell, tissue or entire organism as a collection of individual components. Systems biology that aims at understanding biological system at the systems level is an emerging research area, which involves interdisciplinary collaborations of life sciences, computational and mathematical sciences, systems engineering, and analytical technology, etc. For analytical chemistry, developing innovative methods to meet the requirement of systems biology represents new challenges as also opportunities and responsibility. In this review, systems biology-oriented micro-separation technologies are introduced for comprehensive profiling of genome, proteome and metabolome, characterization of biomolecules interaction and single cell analysis such as capillary electrophoresis, ultra-thin layer gel electrophoresis, micro-column liquid chromatography, and their multidimensional combinations, parallel integrations, microfabricated formats, and nano technology involvement. Future challenges and directions are also suggested.

Chromatography, Liquid↗
PubMed2002

Proteomics: technologies and applications.

Enabling technologies for proteomics (studies examining the protein complement of the genome) have been in development for over 20 years. However, more recently, the field has become formalized by combining the techniques for large-scale protein separation (two-dimensional electrophoresis) with very precise, high fidelity approaches to the analysis and characterisation of the separated proteins (mass spectrometry). These approaches bring to reality the powerful scope of proteomics, enabling the interpretation of cell physiology through profiling the molecular biodescriptors of gene expression, the proteins. Applications of proteomics are wide ranging and can be found within many disciplines, although one of the more developed fields that is examined here covers the arena of toxicological and pharmacological profiling. In this paper, the current technologies that make proteomics possible will be looked at. Some areas where proteomics has delivered valuable biological insight will also be considered, and there will be a look towards future areas of development.

Electrophoresis, Gel, Two-Dimensional↗
PubMedDate not supplied

Proteomics for the identification of new prostate cancer biomarkers.

Molecular profiling studies of human prostate cancer provide great opportunities to identify new prostate cancer biomarkers to improve prostate cancer detection and treatment. Proteomics has distinct advantages over genomic and ribonucleic acid expression studies because it is the proteins that are ultimately responsible for the malignant phenotype. The goal of traditional proteomic studies is to identify disease-specific biomarkers. Two-dimensional (2-D) gel electrophoresis (polyacrylamide gel electrophoresis; PAGE) coupled with mass spectrometry is the most widely used experimental strategy and, to date, has yielded several potentially relevant prostate cancer biomarkers. A promising prostate cancer biomarker identified by 2-D PAGE and mass spectrometry is annexin I. Studies have already confirmed that annexin I is underexpressed in a majority of early stage prostate cancers. Other non-gel-based proteomic technologies that may have improved sensitivity as compared to 2-D PAGE have recently been developed. An example of this is the ProteomeLab PF 2-D (Beckman Coulter, Inc., Fullerton, CA). The goal of most proteomic studies is to identify biomarkers that can be measured by enzyme-linked immunosorbent assay or immunohistochemistry. Improvements in proteomic technology may be changing this paradigm because there are now efforts to develop proteomic technologies directly into clinical diagnostic tests. An example of this technology is surface-enhanced laser desorption ionization time-of-flight mass spectrometry. Using this technology combined with a pattern recognition based bioinformatics tool, discriminatory spectrum proteomic profiles were generated that could help discriminate men with prostate cancer from those with benign prostates. If several technologic hurdles can be overcome, it is possible that methodology will improve the specificity and sensitivity of prostate cancer detection.

Biomarkers, Tumor↗
PubMed2006

[Research progress in SELDI-TOF MS and its clinical applications].

Proteinchip profiling is a powerful and innovative proteomic technology for biomarker discovery and diagnostic/prognostic assay development. Based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen's proteinchip system offers a single, unified, high-throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell, the study of proteomics provides mankind with a better understanding of disease and life. The remarkable findings in disease biomarkers have shed light to the early diagnosis, monitoring and predicting prognosis of various diseases, especially for cancer. In this article, the development and technology of SELDI-TOF MS are introduced. Some research progress and encouraging research results in oncoproteomics, infectious diseases, neurological diseases and diabetes mellitus using SELDI-TOF MS are also reviewed. The paper is closed by the appraisals on its pros and cons, as well as the future prospective is also expounded.

Animals↗
PubMed2001

Assessment of the food safety issues related to genetically modified foods.

International consensus has been reached on the principles regarding evaluation of the food safety of genetically modified plants. The concept of substantial equivalence has been developed as part of a safety evaluation framework, based on the idea that existing foods can serve as a basis for comparing the properties of genetically modified foods with the appropriate counterpart. Application of the concept is not a safety assessment per se, but helps to identify similarities and differences between the existing food and the new product, which are then subject to further toxicological investigation. Substantial equivalence is a starting point in the safety evaluation, rather than an endpoint of the assessment. Consensus on practical application of the principle should be further elaborated. Experiences with the safety testing of newly inserted proteins and of whole genetically modified foods are reviewed, and limitations of current test methodologies are discussed. The development and validation of new profiling methods such as DNA microarray technology, proteomics, and metabolomics for the identification and characterization of unintended effects, which may occur as a result of the genetic modification, is recommended. The assessment of the allergenicity of newly inserted proteins and of marker genes is discussed. An issue that will gain importance in the near future is that of post-marketing surveillance of the foods derived from genetically modified crops. It is concluded, among others that, that application of the principle of substantial equivalence has proven adequate, and that no alternative adequate safety assessment strategies are available.

Genetic Engineering↗
PubMed2005

Proteomic cancer classification with mass spectrometry data.

The ultimate goal of cancer proteomics is to adapt proteomic technologies for routine use in clinical laboratories for the purpose of diagnostic and prognostic classification of disease states, as well as in evaluating drug toxicity and efficacy. Analysis of tumor-specific proteomic profiles may also allow better understanding of tumor development and the identification of novel targets for cancer therapy. The biological variability among patient samples as well as the huge dynamic range of biomarker concentrations are currently the main challenges facing efforts to deduce diagnostic patterns that are unique to specific disease states. While several strategies exist to address this problem, we focus here on cancer classification using mass spectrometry (MS) for proteomic profiling and biomarker identification. Recent advances in MS technology are starting to enable high-throughput profiling of the protein content of complex samples. For cancer classification, the protein samples from cancer patients and noncancer patients or from different cancer stages are analyzed through MS instruments and the MS patterns are used to build a diagnostic classifier. To illustrate the importance of feature selection in cancer classification, we present a method based on support vector machine-recursive feature elimination (SVM-RFE), demonstrated on two cancer datasets from ovarian and lung cancer.

Biomarkers, Tumor↗
PubMedDate not supplied

[Clinical proteomics: towards early detection of cancers].

A key challenge in clinical proteomic of cancer is the identification of biomarkers that would allow early detection, diagnosis and monitor progression of the disease to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and surfaced-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is one of the most interesting new approaches for cancer diagnostic using proteomic profiling. This review aims to summarize the results of studies that have used this new technology method 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 standardised and validated.

Biomarkers, Tumor↗
PubMed2003

New molecular research technologies in the study of muscle disease.

PURPOSE OF REVIEW: To describe the current technologies and progress in DNA polymorphism association studies, mRNA expression profiling (microarrays), and proteomics with respect to muscle disease, and the increasing impact of public-access databases of genome-wide information. RECENT FINDINGS: mRNA expression profiling is becoming the most mature of the highly parallel molecular technologies, with microarrays now able to query the large majority of all genes using 1 million oligonucleotide probes built on 1.2-cm2 glass substrates. Applications of microarrays to normal muscle physiology and muscle disease are discussed. Single nucleotide polymorphism association studies promise to determine the predisposition of individuals to acquired muscle disease, including sarcopenia and atrophy, although such studies are in their infancy. Proteomics technologies do not enjoy the sensitivity and specificity of hybridization, and must instead rely on mass spectrometers. Mass spectrometry technology is advancing rapidly, although the sensitivity and throughput is far behind that of mRNA expression profiling. SUMMARY: As the gene mutations responsible for many types of muscular dystrophy and myopathy have been discovered, protein and gene testing has been integrated into the standard patient diagnostic workup. Future developments will include simpler and less expensive molecular diagnostics, advances in the understanding of downstream consequences of these defects, and the genetic predispositions underlying acquired muscle disease.

Female↗
PubMed2002

Contributions of proteome profiling to the molecular analysis of cancer.

The proteome is the most functional compartment encoded for in the genome. Technologies for protein separation and quantitation, coupled with mass spectrometry for protein identification, have provided the means for proteome profiling of tumor cell lines and tissues that complement genomic and transcriptomic profiling. The application of established and novel proteomic technologies to the molecular analysis of cancer is reviewed.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2002

Identification of end points relevant to detection of potentially adverse drug reactions.

Expectations are high that the use of proteomics, gene arrays and metabonomics will improve risk assessment and enable prediction of toxicity early in drug development. These molecular profiling techniques may be used to classify compounds and to identify predictive markers that can be used to screen large numbers of chemicals. One of the challenges for the scientific community is to discriminate between changes in gene/protein expression and metabolic profiles reflecting physiological/adaptive responses, and changes related to pathology and toxicology. In these proceedings we provide a brief overview of the technologies with focus on proteomics and the possible applications to mechanistic and predictive toxicology. The discussion also includes strengths and limitations of molecular profiling technologies.

Animals↗