PhD Thesis Presentation - Method Development and Applications of Low-input Proteomics

Proteomics offers complementary and more direct information to genomics and transcriptomics, essential for the understanding of complex biological processes. Low-input proteomics is performed when the available sample amount is limited, and it requires improvements in the whole workflow, including sample preparation, peptide separation, mass spectrometry (MS) detection, and data analysis, for higher sensitivity and accurate statistical analysis. In this thesis, several advances in low-input proteomics and its applications are described. First, an easy-to-use and scalable device for sample preparation, a 3-frit mixed-mode rare cell proteomic reactor (RCPR) for integrated processing low-input samples with minimized sample loss was developed. Using the 3-frit mixed-mode RCPR, 2 998±106 and 2 595±230 protein groups from 100 human embryonic kidney cells and 500 mouse cochlear hair cells, respectively, were identified, representing the best results so far in the literature for such low-input samples. Second, a fast and robust column fabrication method for narrow-bore capillary columns with negligible dead volume was developed, allowing the identification of an average of 3 043±39 protein groups from 1 ng of protein digest from human cells. Third, the sensitivity of the Q Exactive HF-X and timsTOF Pro mass spectrometers for nanogram-level samples was systematically optimized and improved by optimizing their data-dependent acquisition parameters. In a proof-of-concept application, we developed a two-step machine learning-based T cell subtyping strategy, and successfully extracted unique proteome classifiers for eight T cell subtypes for low-input samples collected from single multiple myeloma patients, despite different input cell numbers and individual differences. Collectively, a series of improvements in the whole workflow of low-input proteomics were made and their applications were demonstrated in two proof-of-concept applications.