Browsing by Author "Adamec, Jiri"

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    A large, consistent plasma proteomics data set from prospectively collected breast cancer patient and healthy volunteer samples
    (BMC, 2011-05-27) Riley, Catherine P; Zhang, Xiang; Nakshatri, Harikrishna; Schneider, Bryan; Regnier, Fred E; Adamec, Jiri; Buck, Charles
    Background Variability of plasma sample collection and of proteomics technology platforms has been detrimental to generation of large proteomic profile datasets from human biospecimens. Methods We carried out a clinical trial-like protocol to standardize collection of plasma from 204 healthy and 216 breast cancer patient volunteers. The breast cancer patients provided follow up samples at 3 month intervals. We generated proteomics profiles from these samples with a stable and reproducible platform for differential proteomics that employs a highly consistent nanofabricated ChipCube™ chromatography system for peptide detection and quantification with fast, single dimension mass spectrometry (LC-MS). Protein identification is achieved with subsequent LC-MS/MS analysis employing the same ChipCube™ chromatography system. Results With this consistent platform, over 800 LC-MS plasma proteomic profiles from prospectively collected samples of 420 individuals were obtained. Using a web-based data analysis pipeline for LC-MS profiling data, analyses of all peptide peaks from these plasma LC-MS profiles reveals an average coefficient of variability of less than 15%. Protein identification of peptide peaks of interest has been achieved with subsequent LC-MS/MS analyses and by referring to a spectral library created from about 150 discrete LC-MS/MS runs. Verification of peptide quantity and identity is demonstrated with several Multiple Reaction Monitoring analyses. These plasma proteomic profiles are publicly available through ProteomeCommons. Conclusion From a large prospective cohort of healthy and breast cancer patient volunteers and using a nano-fabricated chromatography system, a consistent LC-MS proteomics dataset has been generated that includes more than 800 discrete human plasma profiles. This large proteomics dataset provides an important resource in support of breast cancer biomarker discovery and validation efforts.
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    Lipidomic Analysis of Glioblastoma Multiforme Using Mass Spectrometry
    (Bentham Science Publishers, 2014-04-01) Ha, Soo Jung; Showalter, Gordon; Cai, Shanbao; Wang, Haiyan; Liu, Wei Michael; Cohen-Gadol, Aaron A.; Sarkaria, Jann N.; Rickus, Jenna; Springer, John; Adamec, Jiri; Pollok, Karen E.; Clase, Kari L.; Department of Neurological Surgery, IU School of Medicine
    Glioblastoma multiforme (GBM) is the most common and malignant form of primary brain tumors. It is highly invasive and current treatment options have not improved the survival rate over the past twenty years. Novel approaches and technologies from systems biology have the potential to identify biomarkers that could serve as new therapeutic targets for GBM. This study employed lipid profiling technology to investigate lipid biomarkers in ectopic and orthotopic human GBM xenograft models. Primary patient cell lines, GBM10 and GBM43, were injected into the flank and the right cerebral hemisphere of NOD/SCID mice. Tumors were harvested from the brain and flank and proteins, metabolites, and lipids extracted from each sample. Reverse phase based high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry (LC-FTMS) was used to analyze the lipid profiles of tumor samples. Statistical and clustering analyses were performed to detect differences. Over 500 lipids were identified in each tumor model and lipids with the greatest fold effect in the comparison of ectopic versus orthotopic tumor models fell predominantly into four main classes of lipids: glycosphingolipids, glycerophoshpoethanolamines, triradylglycerols, and glycerophosphoserines. Lipidomic analysis revealed differences in glycosphingolipid and triglyceride profiles when the same tumor was propagated in the flank versus the brain. These results underscore the importance of the surrounding physiological environment on tumor development and are consistent with the hypothesis that specific classes of lipids are critical for GBM tumor growth in different anatomical sites.