Computational Framework for Data-Independent Acquisition Proteomics.

Mass spectrometry (MS) is one of the main techniques for high throughput discovery- and targeted-based proteomics experiments. The most popular method for MS data acquisition has been data dependent acquisition (DDA) strategy which primarily selects high abundance peptides for MS/MS sequencing. DDA incorporates stochastic data acquisitions to avoid repetitive sequencing of same peptide, resulting in relatively irreproducible results for low abundance peptides between experiments. Data independent acquisition (DIA), in which peptide fragment signals are systematically acquired, is emerging as a promising alternative to address the DDA's stochasticity. DIA results in more complex signals, posing computational challenges for complex sample and high-throughput analysis. As a result, targeted extraction which requires pre-existing spectral libraries has been the most commonly used approach for automated DIA data analysis. However, building spectral libraries requires additional amount of analysis time and sample materials which are the major barriers for most research groups. In my dissertation, I develop a computational tool called DIA-Umpire, which includes computational and signal processing algorithms to enable untargeted DIA identification and quantification analysis without any prior spectral library. In the first study, a signal feature detection algorithm is developed to extract and assemble peptide precursor and fragment signals into pseudo MS/MS spectra which can be analyzed by the existing DDA untargeted analysis tools. This novel step enables direct and untargeted (spectral library-free) DIA identification analysis and we show the performance using complex samples including human cell lysate and glycoproteomics datasets. In the second study, a hybrid approach is developed to further improve the DIA quantification sensitivity and reproducibility. The performance of DIA-Umpire quantification approach is demonstrated using an affinity-purification mass spectrometry experiment for protein-protein interaction analysis. Lastly, in the third study, I improve the DIA-Umpire pipeline for data obtained from the Orbitrap family of mass spectrometers. Using public datasets, I show that the improved version of DIA-Umpire is capable of highly sensitive, untargeted analysis of DIA data for the data generated using Orbitrap family of mass spectrometers. The dissertation work addresses the barriers of DIA analysis and should facilitate the adoption of DIA strategy for a broad range of discovery proteomics applications.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120699/1/tsouc_1.pd

Untargeted, spectral libraryâ free analysis of dataâ independent acquisition proteomics data generated using Orbitrap mass spectrometers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/1/pmic12370_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/2/pmic12370.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/3/pmic12370-sup-0001-SupplementaryInfo.pd

Label-free quantitative proteomics of CD133-positive liver cancer stem cells

Abstract Background CD133-positive liver cancer stem cells, which are characterized by their resistance to conventional chemotherapy and their tumor initiation ability at limited dilutions, have been recognized as a critical target in liver cancer therapeutics. In the current work, we developed a label-free quantitative method to investigate the proteome of CD133-positive liver cancer stem cells for the purpose of identifying unique biomarkers that can be utilized for targeting liver cancer stem cells. Label-free quantitation was performed in combination with ID-based Elution time Alignment by Linear regression Quantitation (IDEAL-Q) and MaxQuant. Results Initially, IDEAL-Q analysis revealed that 151 proteins were differentially expressed in the CD133-positive hepatoma cells when compared with CD133-negative cells. We then analyzed these 151 differentially expressed proteins by MaxQuant software and identified 10 significantly up-regulated proteins. The results were further validated by RT-PCR, western blot, flow cytometry or immunofluorescent staining which revealed that prominin-1, annexin A1, annexin A3, transgelin, creatine kinase B, vimentin, and EpCAM were indeed highly expressed in the CD133-positive hepatoma cells. Conclusions These findings confirmed that mass spectrometry-based label-free quantitative proteomics can be used to gain insights into liver cancer stem cells.http://deepblue.lib.umich.edu/bitstream/2027.42/113089/1/12953_2012_Article_407.pd

Tumorâ Selective Altered Glycosylation and Functional Attenuation of CD73 in Human Hepatocellular Carcinoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151913/1/hep41410_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151913/2/hep41410.pd

IDEAL-Q: An automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation

[[sponsorship]]資訊科學研究所,人文社會科學研究中心[[note]]出版中(accepted);[SCI];有審查制度;具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=1535-9476&DestApp=JCR&RQ=IF_CAT_BOXPLO

IDEAL-Q: An automated tool for high-performance label-free quantitative analysis

[[sponsorship]]資訊科學研究所,資訊科技創新研究中心[[note]]已出版;有審查制度;具代表

Postoperative 30-Day Comparative Complications of Multilevel Anterior Cervical Discectomy and Fusion and Laminoplasty for Cervical Spondylotic Myelopathy: An Evidence in Reaching Consensus

Although a few large-scale studies have investigated multilevel anterior cervical discectomy and fusion (ACDF) and laminoplasty (LAMP) and their related complications for cervical spondylotic myelopathy (CSM), the optimal surgical intervention remains controversial. Therefore, we compared their 30 days of postoperative complications. Through the 2010–2019 ACS NSQIP Participant Use Data Files, we estimated the risk of serious morbidity, reoperation, readmission, mortality, and other postoperative complications. Initially, propensity score matching (PSM) of the preoperative characteristics of both groups was performed for further analysis. Multivariable logistic regression analysis provided OR and 95% CI for comparative complications. After PSM, 621 pairs of cohorts were generated for both groups. Increased frequency of postoperative complications was observed in the LAMP group, especially for surgical wound infection, no matter whether superficial (ACDF/LAMP = 0%/1.13%, p = 0.0154) or deep wound infection (ACDF/LAMP = 0%/0.97%, p = 0.0309). The mean length of total hospital stays (ACDF/LAMP = 2.25/3.11, p p p = 0.0429) and unplanned reoperation (ACDF/LAMP = 6.12%/9.34%, p = 0.0336) were higher in LAMP. Results also indicated congestive heart failure as a risk factor (adjusted OR = 123.402, p = 0.0002). Conclusively, multilevel ACDF may be a safer surgical approach than LAMP for CSM in terms of perioperative morbidities, including surgical wound infection, prolonged hospitalization, and unplanned reoperation. However, these approaches showed no significant differences in systemic complications and perioperative mortality