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Institute for Systems Biology

NonprofitSeattle, Washington, United States
About: Institute for Systems Biology is a nonprofit organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Population & Proteomics. The organization has 1277 authors who have published 2777 publications receiving 353165 citations.


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Journal ArticleDOI
TL;DR: It is demonstrated that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types.
Abstract: Differentiation is a key cellular process in normal tissue development that is significantly altered in cancer. Although molecular signatures characterising pluripotency and multipotency exist, there is, as yet, no single quantitative mark of a cellular sample's position in the global differentiation hierarchy. Here we adopt a systems view and consider the sample's network entropy, a measure of signaling pathway promiscuity, computable from a sample's genome-wide expression profile. We demonstrate that network entropy provides a quantitative, in-silico, readout of the average undifferentiated state of the profiled cells, recapitulating the known hierarchy of pluripotent, multipotent and differentiated cell types. Network entropy further exhibits dynamic changes in time course differentiation data, and in line with a sample's differentiation stage. In disease, network entropy predicts a higher level of cellular plasticity in cancer stem cell populations compared to ordinary cancer cells. Importantly, network entropy also allows identification of key differentiation pathways. Our results are consistent with the view that pluripotency is a statistical property defined at the cellular population level, correlating with intra-sample heterogeneity, and driven by the degree of signaling promiscuity in cells. In summary, network entropy provides a quantitative measure of a cell's undifferentiated state, defining its elevation in Waddington's landscape.

102 citations

Journal ArticleDOI
TL;DR: T2-high severe asthma can be predicted to some extent from raised levels of FeNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors and better bedside biomarkers are needed to detect T2- high.
Abstract: Type-2 (T2) immune responses in airway epithelial cells (AECs) classifies mild-moderate asthma into a T2-high phenotype. We examined whether currently available clinical biomarkers can predict AEC-defined T2-high phenotype within the U-BIOPRED cohort.The transcriptomic profile of AECs obtained from brushings of 103 patients with asthma and 44 healthy controls was obtained and gene set variation analysis used to determine the relative expression score of T2 asthma using a signature from interleukin (IL)-13-exposed AECs.37% of asthmatics (45% nonsmoking severe asthma, n=49; 33% of smoking or ex-smoking severe asthma, n=18; and 28% mild-moderate asthma, n=36) were T2-high using AEC gene expression. They were more symptomatic with higher exhaled nitric oxide fraction (FeNO) and blood and sputum eosinophils, but not serum IgE or periostin. Sputum eosinophilia correlated best with the T2-high signature. FeNO (≥30 ppb) and blood eosinophils (≥300 cells·µL-1) gave a moderate prediction of T2-high asthma. Sputum IL-4, IL-5 and IL-13 protein levels did not correlate with gene expression.T2-high severe asthma can be predicted to some extent from raised levels of FeNO, blood and sputum eosinophil counts, but serum IgE or serum periostin were poor predictors. Better bedside biomarkers are needed to detect T2-high.

102 citations

Journal ArticleDOI
TL;DR: The approach used in this study allowed for an automated and improved data validation process for shotgun proteomics projects producing MS/MS peptide identification results of very high confidence.
Abstract: A very popular approach in proteomics is the so-called “shotgun LC−MS/MS” strategy. In its mostly used form, a total protein digest is separated by ion exchange fractionation in the first dimension followed by off- or on-line RP LC−MS/MS. We replaced the first dimension by isoelectric focusing in the liquid phase using the Off-Gel device producing 15 fractions. As peptides are separated by their isoelectric point in the first dimension and hydrophobicity in the second, those experimentally derived parameters (pI and RT) can be used for the validation of potentially identified peptides. We applied this strategy to a cellular extract of Drosophila Kc167 cells and identified peptides with two different database search engines, namely PHENYX and SEQUEST, with PeptideProphet validation of the SEQUEST results. PHENYX returned 7582 potential peptide identifications and SEQUEST 7629. The SEQUEST results were reduced to 2006 identifications by validation with PeptideProphet. Validation of the PeptideProphet, SEQUE...

102 citations

Journal ArticleDOI
TL;DR: A combined micropre-column-microLC-ESI device that is sensitive, rugged and modular in design allowing facile construction and troubleshooting and of high enough quality to identify the peptide sequence by a database search against a complex database using SEQUEST is described.
Abstract: Reversed-phase microcapillary chromatography (RP-μLC) combined with electrospray ionization tandem mass spectrometry (ESI-MS/MS) is one of two prevailing techniques in proteomic analysis, the other being matrix-assisted laser desorption/ionization (MALDI). Despite the arguably better dynamic range obtainable with ESI, MALDI is increasingly popular due to ease of use, ruggedness and the ability to decouple separation from ionization. By contrast, in order to take advantage of the sensitivity and dynamic range afforded by the concentration-dependent nature of ESI, it is directly coupled to separations that take place in small i.d. RP-μLC columns. This gain in sensitivity often comes at a loss of ruggedness due to clogging of the small i.d. RP-μLC columns, one result of which is limited sample throughput. Here we describe a combined μpre-column-μLC-ESI device that is sensitive, rugged and modular in design allowing facile construction and troubleshooting. Due to low signal-to-noise as little as 1 attomole of a peptide can be selected by data-dependent methods for collision-induced dissociation. Importantly, the resulting tandem mass spectrum is of high enough quality to identify the peptide sequence by a database search against a complex database using SEQUEST. Finally, the device is demonstrated to be rugged as judged by >60 consecutive reversed-phase μLC separations on complex peptide mixtures before chromatographic resolution is degraded. Copyright © 2003 John Wiley & Sons, Ltd.

102 citations

Journal ArticleDOI
22 Oct 2009-PLOS ONE
TL;DR: It is shown that whole cell area detection results using the projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and concluded that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification.
Abstract: Background Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity. Methodology We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells. Significance The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining

102 citations


Authors

Showing all 1292 results

NameH-indexPapersCitations
Younan Xia216943175757
Ruedi Aebersold182879141881
David Haussler172488224960
Steven P. Gygi172704129173
Nahum Sonenberg167647104053
Leroy Hood158853128452
Mark H. Ellisman11763755289
Wei Zhang112118993641
John Ralph10944239238
Eric H. Davidson10645447058
James R. Heath10342558548
Alan Aderem9924646682
Anne-Claude Gingras9733640714
Trey Ideker9730672276
Michael H. Gelb9450634714
Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20233
202260
2021216
2020204
2019188
2018168