Abstract

Innovation is the vehicle that drives scientific research forward in virtually every field. Advances in genome sequencing propelled the human genome project to completion in 2003 and set the stage for the era of proteomics. Now decades of innovation in liquid chromatography (LC) and mass spectrometry (MS) have improved sensitivity sufficiently to enable single-cell proteomics and other sample-limited studies. Single-cell proteomics reveal patterns of gene expression that are often obscured by bulk-scale proteomics, leading to the identification of new protein biomarker candidates. Sensitive targeted proteomics is the key to successful translation of low-abundance protein assays into clinical applications. With a rapidly growing interest in sample-limited proteomics, there is a need to increase accessibility of nanoLC-MS workflows. The work presented in this dissertation focuses on the development of accessible systems to automate sample preparation and LC separation for single-cell and targeted proteomic workflows. It outlines our efforts to adapt a low-cost pipetting robot to emulate the sample preparation process outlined in nanoPOTS method for single-cell proteomics. Using this system, we compared the standard nanoPOTS sample preparation method with a simplified method for faster sample preparation. The simplified method reduced the preparation time from ~18 down to 6 h while maintaining comparable proteome coverage (~2000 proteins identified from 70 HeLa cells). To enable autosampling for nanowell chis and other custom workflows, we developed an open-source software package capable of integrating a variety of modular LC components. Using this software, we modified a commercial pipetting robot for nanoscale autosampling and LC applications. We used the modified system to identify ~800 proteins from 250 pg injections of HeLa digest. We also applied our experience with custom LC configurations to automate enrichment of affinity enrichment for targeted measurements of low-abundance biomarkers. We packed an affinity enrichment trap column in house and performed online enrichment of 100-fmol injections of synthetic peptides corresponding to HER2 (a known breast cancer biomarker).

Degree

PhD

College and Department

Computational, Mathematical, and Physical Sciences; Chemistry and Biochemistry

Rights

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