Rajasree Menon

Bio: Rajasree Menon is an academic researcher from University of Michigan. The author has contributed to research in topics: Kidney disease & Alternative splicing. The author has an hindex of 28, co-authored 71 publications receiving 3029 citations.

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.

Abstract: 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.

Systematic comparison of the human saliva and plasma proteomes

Abstract: The proteome of human salivary fluid has the potential to open new doors for disease biomarker discovery. A recent study to comprehensively identify and catalog the human ductal salivary proteome led to the compilation of 1166 proteins. The protein complexity of both saliva and plasma is large, suggesting that a comparison of these two proteomes will provide valuable insight into their physiological significance and an understanding of the unique and overlapping disease diagnostic potential that each fluid provides. To create a more comprehensive catalog of human salivary proteins, we have first compiled an extensive list of proteins from whole saliva (WS) identified through MS experiments. The WS list is thereafter combined with the proteins identified from the ductal parotid, and submandibular and sublingual (parotid/SMSL) salivas. In parallel, a core dataset of the human plasma proteome with 3020 protein identifications was recently released. A total of 1939 nonredundant salivary proteins were compiled from a total of 19 474 unique peptide sequences identified from whole and ductal salivas; 740 out of the total 1939 salivary proteins were identified in both whole and ductal saliva. A total of 597 of the salivary proteins have been observed in plasma. Gene ontology (GO) analysis showed similarities in the distributions of the saliva and plasma proteomes with regard to cellular localization, biological processes, and molecular function, but revealed differences which may be related to the different physiological functions of saliva and plasma. The comprehensive catalog of the salivary proteome and its comparison to the plasma proteome provides insights useful for future study, such as exploration of potential biomarkers for disease diagnostics.

Human Proteinpedia enables sharing of human protein data.

Abstract: Proteomic technologies, such as yeast two-hybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories.

Single-cell analysis of progenitor cell dynamics and lineage specification in the human fetal kidney

Abstract: The mammalian kidney develops through reciprocal interactions between the ureteric bud and the metanephric mesenchyme to give rise to the entire collecting system and the nephrons. Most of our knowledge of the developmental regulators driving this process arises from the study of gene expression and functional genetics in mice and other animal models. In order to shed light on human kidney development, we have used single-cell transcriptomics to characterize gene expression in different cell populations, and to study individual cell dynamics and lineage trajectories during development. Single-cell transcriptome analyses of 6414 cells from five individual specimens identified 11 initial clusters of specific renal cell types as defined by their gene expression profile. Further subclustering identifies progenitors, and mature and intermediate stages of differentiation for several renal lineages. Other lineages identified include mesangium, stroma, endothelial and immune cells. Novel markers for these cell types were revealed in the analysis, as were components of key signaling pathways driving renal development in animal models. Altogether, we provide a comprehensive and dynamic gene expression profile of the developing human kidney at the single-cell level.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Human Protein Reference Database—2009 update

Abstract: Human Protein Reference Database (HPRD--http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system--Human Proteinpedia (http://www.humanproteinpedia.org/)--through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15,000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome.

Towards a knowledge-based Human Protein Atlas

Abstract: 1. Anonymous. Nat. Biotechnol. 28, 987 (2010). 2. Plosila, W.H. Econ. Dev. Q. 18, 113–126 (2004). 3. Stayn, S. BNA Med. Law Pol. Rep. 5, 718–725 (2006). 4. Lomax, G. & Stayn, S. BNA Med. Law Pol. Rep. 7, 695–698 (2008). 5. Levine, A.D. Public Adm. Rev. 68, 681–694 (2008). 6. Levine, A.D. Nat. Biotechnol. 24, 865–866 (2006). 7. McCormick, J.B., Owen-Smith, J. & Scott, C.T. Cell Stem Cell 4, 107–110 (2009). 8. Fossett, J.W., Ouellette, A.R., Philpott, S., Magnus, D. & Mcgee, G. Hastings Cent. Rep. 37, 24–35 (2007). 9. Mintrom, M. Publius 39, 606–631 (2009). 10. Scott, C.T., McCormick, J.B. & Owen-Smith, J. Nat. Biotechnol. 27, 696–697 (2009). 11. Takahashi, K. & Yamanaka, S. Cell 126, 663–676 (2006). Foundation and the Georgia Research Alliance, and Georgia Tech. They thank J. Walsh at Georgia Tech for helpful comments on an earlier version of this manuscript. They also appreciate the assistance they received with data collection from officials in various state stem cell agencies. A.D.L. would also like to thank A. Jakimo, whose comment at a meeting of the Interstate Alliance on Stem Cell Research inspired collection of these data. stem cell programs, as well as similar state programs supporting other areas of science, is uncertain. The analysis here suggests that state stem cell funding programs are sufficiently large and established that simply ending the programs, at least in the absence of substantial investment in the field by other funding sources, could have deleterious effects. Such action would fail to capitalize on the initial efforts of scientists who have been drawn to the field of stem cell research by state programs and leave many stem cell scientists suddenly searching for funding to continue their research. Large-scale state funding for basic research is a relatively new phenomenon, and many questions remain about the impact of these programs on the development of scientific fields and the careers of scientists. The influence of state funding programs on the distribution of research publications, the acquisition of future external funding, the creation of new companies and the translation of basic research into medical practice, for instance, are important unanswered questions. Similarly, comparing state funding programs with federal funding programs as well as foundations could offer new insight into the relative priorities of different funding bodies and the extent to which their funding portfolios overlap or are distinct. We hope the analysis presented here and the public release of the underlying database will inspire additional analysis of state science funding programs generally and state-funded stem cell science in particular.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.