Translate this page to:
In JoVE (1)
- Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples
Other Publications (3)
Articles by Brian Haab in JoVE
Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples
Chen Lu1, Joshua L. Wonsidler1, Jianwei Li2, Yanming Du1, Timothy Block3, Brian Haab4, Songming Chen5
1Institute for Hepatitis and Virus Research, 2Department of Microbiology and Immunology, Thomas Jefferson University, 3Drexel University College of Medicine, 4Van Andel Research Institute, 5Institute for Hepatitis and Virus Research, Serome Biosciences Inc.
In this study, we describe an improved protocol for a multiplexed high-throughput antibody microarray with lectin detection method that can be used in glycosylation profiling of specific proteins. This protocol features new reliable reagents and significantly reduces the time, cost, and lab equipment requirements as compared to the previous procedure.
Other articles by Brian Haab on PubMed
Overview of the HUPO Plasma Proteome Project: Results from the Pilot Phase with 35 Collaborating Laboratories and Multiple Analytical Groups, Generating a Core Dataset of 3020 Proteins and a Publicly-available Database
Proteomics. Aug, 2005 | Pubmed ID: 16104056
HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.
A Reagent Resource to Identify Proteins and Peptides of Interest for the Cancer Community: a Workshop Report
Molecular & Cellular Proteomics : MCP. Oct, 2006 | Pubmed ID: 16867976
On the basis of discussions with representatives from all sectors of the cancer research community, the National Cancer Institute (NCI) recognizes the immense opportunities to apply proteomics technologies to further cancer research. Validated and well characterized affinity capture reagents (e.g. antibodies, aptamers, and affibodies) will play a key role in proteomics research platforms for the prevention, early detection, treatment, and monitoring of cancer. To discuss ways to develop new resources and optimize current opportunities in this area, the NCI convened the "Proteomic Technologies Reagents Resource Workshop" in Chicago, IL on December 12-13, 2005. The workshop brought together leading scientists in proteomics research to discuss model systems for evaluating and delivering resources for reagents to support MS and affinity capture platforms. Speakers discussed issues and identified action items related to an overall vision for and proposed models for a shared proteomics reagents resource, applications of affinity capture methods in cancer research, quality control and validation of affinity capture reagents, considerations for target selection, and construction of a reagents database. The meeting also featured presentations and discussion from leading private sector investigators on state-of-the-art technologies and capabilities to meet the user community's needs. This workshop was developed as a component of the NCI's Clinical Proteomics Technologies Initiative for Cancer, a coordinated initiative that includes the establishment of reagent resources for the scientific community. This workshop report explores various approaches to develop a framework that will most effectively fulfill the needs of the NCI and the cancer research community.
A Hierarchical Approach Employing Metabolic and Gene Expression Profiles to Identify the Pathways That Confer Cytotoxicity in HepG2 Cells
BMC Systems Biology. 2007 | Pubmed ID: 17498300
Free fatty acids (FFA) and tumor necrosis factor alpha (TNF-alpha) have been implicated in the pathogenesis of many obesity-related metabolic disorders. When human hepatoblastoma cells (HepG2) were exposed to different types of FFA and TNF-alpha, saturated fatty acid was found to be cytotoxic and its toxicity was exacerbated by TNF-alpha. In order to identify the processes associated with the toxicity of saturated FFA and TNF-alpha, the metabolic and gene expression profiles were measured to characterize the cellular states. A computational model was developed to integrate these disparate data to reveal the underlying pathways and mechanisms involved in saturated fatty acid toxicity.