Proteomic technologies for tissue engineering investigation and post-translational modification discovery

Author / Creator

Li, Qiyao, 1990-, dissertant

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Summary

This dissertation is separated into two main sections. Section I includes Chapters 1 to 4, presenting the work in developing and applying a proteomics method to help understand the multiple stages ...

This dissertation is separated into two main sections. Section I includes Chapters 1 to 4, presenting the work in developing and applying a proteomics method to help understand the multiple stages of tissue engineering. The tissue engineering technique based on recellularization of decellularized biological scaffolds has shown great promise in regenerating and restoring functions of several tissues/organs, and has the potential to replace orthotopic transplantation as the treatment for severe tissue damage or end-stage organ failure. However, the system-wide changes that happen at the molecular level still remain largely unknown. Chapter 1 introduces the background of this tissue engineering strategy and the motivation of introducing proteomics into the toolbox for tissue engineering investigation. Chapters 2 to 4 first briefly discuss the optimization of a proteomics method and then focus on its applications in addressing some of the key questions regarding protein changes during decellularization and recellularization. Section II includes Chapter 5, which is about developing a bioinformatics tool for global post-translational modification (PTM) discovery. Traditional PTM identification tools either require efficient pre-enrichment strategies and a relatively large amount of starting material, study just one or a few PTM types at a time, or result in high false discovery rate (FDR). Chapter 5 presents a G-PTM-D strategy that identifies a rich variety of peptide modifications with high confidence in complex unenriched biological samples. Finally, Chapter 6 presents a brief conclusion of Sections I and II, describes two ongoing projects with some preliminary data, and points out a few future directions.

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