Recent Advances in Mass Spectrometry-Based Single-Cell Multidimensional Multi-Omics Analysis

As the fundamental structural and functional unit of living organisms, individual cells exhibit substantial differences in phenotype, function, and molecular composition, even under a uniform genomic background. This cellular heterogeneity—encompassing subpopulation differentiation, dynamic developmental processes, and microenvironmental interactions—is often obscured in traditional bulk measurements. To overcome this limitation, single-cell analysis techniques have emerged as powerful tools in systems biology, enabling dissection of cell states and functional transitions at an unprecedented resolution. Although single-cell RNA sequencing and other nucleic acid-based omics approaches have advanced rapidly, single-cell multidimensional multi-omics centered on the proteome continues to face significant challenges due to the intrinsic non-amplifiable nature, low abundance, and wide dynamic range of proteins and metabolites. Mass spectrometry (MS) has become a cornerstone of single-cell multi-omics research, offering high sensitivity, quantitative robustness, and broad molecular coverage. This review provides a systematic overview of recent advances in MS-based single-cell multi-omics analysis, with a focus on strategies for proteomic and integrated multi-omics profiling. Key aspects including sample preparation, data acquisition and processing, and spatially resolved omics are discussed to illustrate the value of MS in elucidating multilayered molecular interaction networks within individual cells. Key advances in sample preparation—including nanoliter-volume microreactors, carrier-assisted quantification strategies, and fully integrated microfluidic platforms—have dramatically enhanced protein recovery and analytical throughput. Likewise, cutting-edge acquisition strategies, such as data-independent acquisition (DIA), plexDIA, and nDIA, have expanded proteome depth while maintaining quantitative accuracy in low-input contexts. Beyond intracellular dimensions, this review also highlights developments in single-cell secretome analysis. Both targeted approaches (e.g., barcoded antibody chips, CITE-seq) and untargeted MS-based methods (e.g., SCSP) are evaluated in terms of sensitivity, scalability, and coverage, which offer crucial insights into intercellular communication, immune regulation, and tumor microenvironmental dynamics. Finally, the review highlights current challenges, including low signal intensity, limited spatial and temporal resolution, and data integration complexity. It outlines prospective directions for the field, such as the development of spatially resolved multi-omics, AI-assisted data interpretation, and standardized pipelines for reproducible high-throughput analysis. Collectively, MS-based single-cell multidimensional multi-omics is poised to reshape biomedical research, providing powerful tools to unravel the molecular mechanisms underlying health and disease at single-cell resolution.