Abstract

Single-cell proteomics provides a novel research strategy for decoding cellular heterogeneity, revealing the molecular mechanisms of rare cell subpopulations, and studying the dynamic responses of cells to external stimuli. However, compared to transcriptomics, single-cell proteomics faces even more challenging technical challenges: extremely low protein amount in single cells, wide dynamic range, significant adsorptive losses during sample preparation, and the complex matrix and trace sample injection demanding extremely high stability and sensitivity from the separation and detection systems. Mass spectrometry-based bottom-up proteomics has become an important tool for analyzing complex biological systems, and nano-flow liquid chromatography-mass spectrometry (nanoLC-MS) exhibits unique advantages in trace sample and single-cell analysis due to its high ionization efficiency and high detection sensitivity. However, traditional ultra-low flow rate gradient elution relies on high-pressure binary pumps and splitting systems, which not only have high hardware costs but also introduce flow rate fluctuations and gradient instability. Simultaneously, narrow-bore chromatographic columns are prone to clogging, limiting the long-term stable operation and analytical throughput of the system. These problems collectively obstruct the further development of single-cell proteomics towards low cost and high throughput. This dissertation focuses the application requirements of nanoLC-MS in single-cell proteomics. By introducing a low-flow rate gradient generation method based on Taylor dispersion effects, stable gradient elution was achieved with simplified hardware, reducing system cost. Simultaneously, a systematic evaluation of different trap column architectures and loading conditions was conducted around the trap-and-elute working mode. This demonstrated that the introduction of a trap column significantly improves system robustness and analytical reproducibility under large-volume injection conditions, while maintaining good separation performance and protein identification depth over a wide range of parameters. Building upon these foundations, this study further integrated these methods into the construction of a high- throughput and low-cost proteomic analysis system. It was verified that high protein coverage and good quantitative reproducibility could still be achieved under short gradient and low sample input conditions. By synergistically optimizing the nanoLC strategies and MS acquisition parameters, an effective balance between sensitivity, stability, and analytical throughput can be achieved, providing a feasible solution for large-scale proteomic analysis of single cells and trace samples.

Degree

PhD

College and Department

Computational, Mathematical, and Physical Sciences; Chemistry and Biochemistry

Rights

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