Abstract

The overarching goal of biologists is to characterize biological systems. When the workings of a system are understood, they can be fixed, broken, preserved, mimicked, and even replicated. The field of proteomics seeks to characterize systems through the lens of proteins. Proteins are referred to as the 'workhorse' of the cell to illustrate that they perform most functional tasks within the cell. These tasks are highly varied, including enzymatic activity, structural support, transport, storage, cell signaling, gene regulation, immune response, and more. Proteomics has been instrumental in new biological discoveries by characterizing the functional state of cells. A new area of study within proteomics is single-cell proteomics, which samples individual cells to preserve the heterogeneity that exists between cells. With single-cell measurements we can use new methods to make biological discoveries that are not possible via traditional, bulk experiments. For example, single-cell measurements can be used to build time course trajectories allowing us to track how proteins change as cells undergo some biological process, such as developing from a stem cell to muscle cell, or as they progress through a disease. Much of the research in single-cell proteomics has been focused on for sample preparation and instrumental developments. Computational research has lagged behind, but to utilize all the information available in this new data type, new computational methods must be developed Single-cell data has several features that are absent in traditional, bulk proteomics data. These new features are what make new biological discoveries possible, but only if new algorithms and statistical methods are developed to incorporate these features. In this dissertation we address these computational needs. Firstly, we review challenges and present possible solutions for algorithms that process raw spectral data into protein quantitation tables, including peptide and protein identification and quantitation. Secondly, we address one of the main challenges which is characterizing how features that are important for protein identification and quantification algorithms differ between single-cell data and bulk data, and suggest solutions and optimization based on these differences. Thirdly, we provide guidance on sample size requirements for time course trajectories. Finally, we develop a group comparison method that incorporates all features of single-cell data, improving sensitivity and enabling detection of novel differential proteins, that are obscured in bulk experiments.

Degree

PhD

College and Department

Life Sciences; Biology

Rights

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