CiteULike: jyuh's Uhlen

The epitope space of the human proteome

Lisa Berglund — 2008-04-26T08:47:46-00:00

Protein Sci, Vol. 17, No. 4. (1 April 2008), pp. 606-613.

In the post-genome era, there is a great need for protein-specific affinity reagents to explore the human proteome. Antibodies are suitable as reagents, but generation of antibodies with low cross-reactivity to other human proteins requires careful selection of antigens. Here we show the results from a proteome-wide effort to map linear epitopes based on uniqueness relative to the entire human proteome. The analysis was based on a sliding window sequence similarity search using short windows (8, 10, and 12 amino acid residues). A comparison of exact string matching (Hamming distance) and a heuristic method (BLAST) was performed, showing that the heuristic method combined with a grid strategy allows for whole proteome analysis with high accuracy and feasible run times. The analysis shows that it is possible to find unique antigens for a majority of the human proteins, with relatively strict rules involving low sequence identity of the possible linear epitopes. The implications for human antibody-based proteomics efforts are discussed. 10.1110/ps.073347208

Antibody-based tissue profiling as a tool for clinical proteomics

Caroline Kampf — 2008-04-26T08:45:19-00:00

Clinical Proteomics, Vol. 1, No. 3. (2004), pp. 285-299.

Abstract Here, we show a strategy for high-throughput antibody-based tissue profiling with the aim to create an atlas of protein expression patterns in normal human tissues and cancer tissues representing the 20 most prevalent cancer types. A set of standardized tissue microarrays (TMAs) was produced to allow for rapid screening of a multitude of different cells and tissues using immunohistochemistry. Eight TMA blocks were produced containing 48 different normal human tissues in triplicate and cancer tissue from 216 individually different tumors in duplicate. Sections from these blocks were immunohistochemically stained using five commercial and five in-house generated antibodies. Digital images for annotation of expression profiles were generated using a semiautomated approach. Five hundred seventy-six images and annotation data corresponding to a total of 30 Gbytes of data were collected for each antibody. The data presented here suggest that antibody-based profiling of protein expression in tissues can be used as a valuable tool in clinical proteomics.

Human Proteinpedia enables sharing of human protein data

Suresh Mathivanan — 2008-02-08T00:40:35-00:00

Nature Biotechnology, Vol. 26, No. 2., pp. 164-167.

A reagent resource to identify proteins and peptides of interest for the cancer community: a workshop report.

BB Haab — 2008-01-18T02:56:33-00:00

Mol Cell Proteomics, Vol. 5, No. 10. (October 2006), pp. 1996-2007.

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 high-throughput strategy for protein profiling in cell microarrays using automated image analysis.

S Strömberg — 2008-01-18T02:11:25-00:00

Proteomics, Vol. 7, No. 13. (June 2007), pp. 2142-2150.

Advances in antibody production render a growing supply of affinity reagents for immunohistochemistry (IHC), and tissue microarray (TMA) technologies facilitate simultaneous analysis of protein expression in a multitude of tissues. However, collecting validated IHC data remains a bottleneck problem, as the standard method is manual microscopical analysis. Here we present a high-throughput strategy combining IHC on a recently developed cell microarray with a novel, automated image-analysis application (TMAx). The software was evaluated on 200 digital images of IHC-stained cell spots, by comparing TMAx annotation with manual annotation performed by seven human experts. A high concordance between automated and manual annotation of staining intensity and fraction of IHC-positive cells was found. In a limited study, we also investigated the possibility to assess the correlation between mRNA and protein levels, by using TMAx output results for relative protein quantification and quantitative real-time PCR for the quantification of corresponding transcript levels. In conclusion, automated analysis of immunohistochemically stained in vitro-cultured cells in a microarray format can be used for high-throughput protein profiling, and extraction of RNA from the same cell lines provides a basis for comparing transcription and protein expression on a global scale.

Mapping the human proteome using antibodies.

M Uhlen — 2008-01-01T03:14:18-00:00

Mol Cell Proteomics, Vol. 6, No. 8. (August 2007), pp. 1455-1456.

A web-based tool for in silico biomarker discovery based on tissue-specific protein profiles in normal and cancer tissues.

Erik Björling — 2007-10-11T02:22:15-00:00

Mol Cell Proteomics (3 October 2007)

Here we report the development of a publicly available web-based analysis tool for exploring proteins expressed in a tissue- or cancer-specific manner. The search queries are based on the human tissue profiles in normal and cancer cells in the Human Protein Atlas portal and rely on the individual annotation performed by pathologists of images representing immunohistochemically stained tissue sections. Approximately 1.8 million images representing more than 3000 antibodies directed towards human proteins were used in the study. The search tool allows for the systematic exploration of the protein atlas, to discover potential protein biomarkers. Such biomarkers include tissue specific markers, cell type specific markers, tumor type specific markers, markers of malignancy and prognostic or predictive markers of cancers. Here we show examples of database queries to generate sets of candidate biomarker proteins for several of these different categories. Expression profiles of candidate proteins can then subsequently be validated by examination of the underlying high-resolution images. The present study shows examples of search strategies revealing several potential protein biomarkers, including proteins specifically expressed in normal cells and in cancer cells from specified tumor types. The lists of candidate proteins can be used as a starting point for further validation in larger patient cohorts using both immunological approaches and technologies employing more classical proteomics tools.

Multivariate analysis of 2-DE protein patterns--practical approaches.

S Jacobsen — 2007-07-11T07:01:14-00:00

Electrophoresis, Vol. 28, No. 8. (April 2007), pp. 1289-1299.

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.