Abstract: As the direct executors of life activities, proteins play an irreplaceable role in regulating cellular processes such as proliferation, differentiation, and signal transduction. Their expression levels and post-translational modification patterns directly reflect cellular functional heterogeneity, which is a key feature associated with tissue homeostasis, disease progression and stimulus response. Conventional bulk proteomics analyzes mixed cell populations, which has an obvious averaging effect that masks biologically significant intercellular differences, hindering the exploration of rare cell subsets, heterogeneous disease mechanisms, and precise therapeutic targets. In contrast, single-cell resolution proteomic analysis has emerged as a pivotal driving force for the advancement of precision medicine and modern cell biology, enabling the decoding of cellular diversity at the molecular level. In this study, cell printing technology was innovatively employed to establish a high-efficiency sample preparation workflow specifically designed for single-cell proteomics, addressing the long-standing challenges of low throughput, high sample loss, and prolonged processing time in traditional methods. Specifically, cell lysis buffer was precisely printed into 96-well plates using a high-precision printing system to ensure uniform reaction conditions; this was followed by the accurate sorting of A549 and HeLa single cells into the pretreated wells, and subsequent in-well enzymatic hydrolysis and acidification steps to complete sample processing. For proteomic detection, data-independent acquisition (DIA) was conducted on a Vanquish Neo-Orbitrap Astral mass spectrometer, which delivers excellent sensitivity and reproducibility. Data analysis was systematically completed using Spectronaut software to ensure reliable identification and quantification of proteins. The results demonstrated that a total of 6 072 protein groups and 41 577 peptides were successfully identified in the two cell lines at the single-cell level, achieving comprehensive coverage of the cellular proteomes. Further analysis revealed that the protein abundances in single cells showed excellent correlation, while maintaining distinct heterogeneity. Gene Ontology (GO) analysis further indicated that the identified proteins displayed an unbiased spatial distribution across subcellular compartments such as the nucleus, cytoplasm, and cell membrane, ensuring the integrity of proteomic profiling. Notably, this integrated approach minimizes cellular viability loss and protein degradation by shortening the time window between cell sorting and lysis, significantly reduces the overall experimental duration compared to conventional workflows, and improves detection throughput through 96-well plate parallel processing. Collectively, this method reduces cell viability loss and protein degradation, shortens experiment time, and enhances throughput, thus promoting mass spectrometry-based single-cell proteomics research for deciphering complex biological systems.