{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79929"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79929","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Development and Biological Application of Reliable Quantitative Proteomics Technique (IonStar) on Cancer and Cardiovascular Disease Therapeutics","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Wang, Xue"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Qu, Jun","Roswell Park"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:11:11Z","date_published":"2019-07-30T15:11:11Z","updated_at":"2026-07-27T19:05:21Z","subjects":["biophysics","cancer sciences"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79929","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Qu, Jun","Roswell Park"]},{"key":"dc:creator","label":"Author","values":["Wang, Xue"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-07-30T15:11:11Z","2019","2019-05-13 16:06:17"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biophysics","cancer sciences"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79929"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","The rapidly advancing fields of pharmaceutical and clinical research call for systematic, molecular-level characterization of complex biological systems and diseases. To this end, the quantitative proteomics based on liquid chromatography-mass spectrometry (LC-MS) is highly versatile as a powerful tool, and becoming an optimal solution for proteomic analysis in pharmaceutical/clinical research. However, large-cohort proteomic analysis remains challenging because of the suboptimal quantitative quality, high rates of missing data and false-positive discovery of altered proteins, especially when sample size increases. MS1 ion-current-based quantification methods have emerged as an important class of label-free quantification techniques in past several years, showing considerable potential to achieve reproducible protein measurements in large set of samples with high quantitative accuracy/precision. To fully unleash this potential, several critical prerequisites should be met in terms of experimental strategies and optimal data processing methods. Based on these requirements, we have devised a unique MS1-based quantitative strategy, termed as IonStar, which enables comprehensive and reliable proteome-wide quantification with high data quality in large sample cohorts. In this work, we extensively compared the quantitative performances of ultra-high-resolution-MS1 IonStar with Spectronaut, a MS2-DIA quantification as a state-of-the-art tool in clinical/pharmaceutical investigations, in several well-designed large-cohort sets. We demonstrated that our ultra-high-resolution-MS1 IonStar provided more accurate, precise protein measurements with higher sensitivity/selectivity, especially for quantification of low-abundance proteins, in sets containing large numbers of technical or biological replicates (Chapter 2). With this progress, the high-resolution IonStar was applied to three proof-of-concept and large-scale proteomic studies: i) investigation of temporal effects of combined Birinapant and Paclitaxel on pancreatic cancer cells via large-scale, ion-current-based quantitative proteomics (IonStar) (Chapter 3); ii) investigation of tumor-stroma interactions of combined Gemcitabine and Paclitaxel on pancreatic cancer patient-derived xenograft (PDX) model through species-specific proteomics (Chapter 4); iii) quantitative phosphoproteomic and proteomic analysis of swine hearts with myocardial stunning (Chapter 5). Overall, the high-resolution-MS1 IonStar has shown excellent ability for comprehensive and large-scale proteomics characterization. This unique experimental/informatics pipeline could be applied widely to the studies of cancer therapeutics and cardiovascular diseases.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development and Biological Application of Reliable Quantitative Proteomics Technique (IonStar) on Cancer and Cardiovascular Disease Therapeutics"]}]}],"canonical_facts":{"dc:contributor":["Qu, Jun","Roswell Park"],"dc:creator":["Wang, Xue"],"dc:date":["2019-07-30T15:11:11Z","2019","2019-05-13 16:06:17"],"dc:description":["Ph.D.","The rapidly advancing fields of pharmaceutical and clinical research call for systematic, molecular-level characterization of complex biological systems and diseases. To this end, the quantitative proteomics based on liquid chromatography-mass spectrometry (LC-MS) is highly versatile as a powerful tool, and becoming an optimal solution for proteomic analysis in pharmaceutical/clinical research. However, large-cohort proteomic analysis remains challenging because of the suboptimal quantitative quality, high rates of missing data and false-positive discovery of altered proteins, especially when sample size increases. MS1 ion-current-based quantification methods have emerged as an important class of label-free quantification techniques in past several years, showing considerable potential to achieve reproducible protein measurements in large set of samples with high quantitative accuracy/precision. To fully unleash this potential, several critical prerequisites should be met in terms of experimental strategies and optimal data processing methods. Based on these requirements, we have devised a unique MS1-based quantitative strategy, termed as IonStar, which enables comprehensive and reliable proteome-wide quantification with high data quality in large sample cohorts. In this work, we extensively compared the quantitative performances of ultra-high-resolution-MS1 IonStar with Spectronaut, a MS2-DIA quantification as a state-of-the-art tool in clinical/pharmaceutical investigations, in several well-designed large-cohort sets. We demonstrated that our ultra-high-resolution-MS1 IonStar provided more accurate, precise protein measurements with higher sensitivity/selectivity, especially for quantification of low-abundance proteins, in sets containing large numbers of technical or biological replicates (Chapter 2). With this progress, the high-resolution IonStar was applied to three proof-of-concept and large-scale proteomic studies: i) investigation of temporal effects of combined Birinapant and Paclitaxel on pancreatic cancer cells via large-scale, ion-current-based quantitative proteomics (IonStar) (Chapter 3); ii) investigation of tumor-stroma interactions of combined Gemcitabine and Paclitaxel on pancreatic cancer patient-derived xenograft (PDX) model through species-specific proteomics (Chapter 4); iii) quantitative phosphoproteomic and proteomic analysis of swine hearts with myocardial stunning (Chapter 5). Overall, the high-resolution-MS1 IonStar has shown excellent ability for comprehensive and large-scale proteomics characterization. This unique experimental/informatics pipeline could be applied widely to the studies of cancer therapeutics and cardiovascular diseases.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79929"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["biophysics","cancer sciences"],"dc:title":["Development and Biological Application of Reliable Quantitative Proteomics Technique (IonStar) on Cancer and Cardiovascular Disease Therapeutics"],"dc:type":["Dissertation","Text"]},"updated_at":"2026-07-27T19:05:21Z"}