Mass-spectrometry-based proteomics: from single cells to clinical applications

**Abstract: Mass-Spectrometry-Based Proteomics: From Single Cells to Clinical Applications** Mass-spectrometry-based proteomics has emerged as a powerful tool for understanding the structure and function of proteins at a molecular level, with applications ranging from single-cell analysis to clinical diagnostics. This field has seen significant advancements in recent years, driven by technological innovations and the integration of artificial intelligence (AI) to enhance data interpretation and biomarker discovery. **Comprehensive Proteome Mapping** A landmark study by Uhlen et al. (2015) presented a tissue-based map of the human proteome, covering 32 different tissues and organs. This map, generated through immunohistochemistry (IHC), provides a detailed visual representation of protein expression patterns in both healthy and diseased states, offering valuable insights into the biological underpinnings of various conditions. Building on this, Aebersold and Mann (2003) provided a foundational overview of mass spectrometry (MS) in proteomics, highlighting its potential for deep and quantitative analysis. Their subsequent work in 2016 further explored the capabilities of MS in elucidating proteome structure and function, emphasizing the importance of high-resolution and high-sensitivity techniques. **Technological Innovations** Several technological advancements have revolutionized the field of proteomics. Fenn et al. (1989) introduced electrospray ionization, a technique that significantly enhanced the ability to analyze large biomolecules using MS. More recently, the development of the Orbitrap mass spectrometer by Makarov (2000) and its subsequent evolution (Eliuk & Makarov, 2015) have provided unprecedented sensitivity and resolution in proteomic studies. Parallel accumulation–serial fragmentation (PASEF) technology, as described by Meier et al. (2021), has further improved the speed and depth of proteome analysis. The integration of the Astral mass analyzer with PASEF (Stewart et al., 2023; Guzman et al., 2024) has enabled ultra-fast and high-throughput proteomics, allowing for the quantification of nearly 10,000 proteins in single-shot analyses of human cell lines. **High-Throughput and Single-Cell Proteomics** High-throughput proteomics has become increasingly important in both research and clinical settings. Hughes et al. (2014) and Batth et al. (2019) introduced methods for ultrasensitive proteome analysis using paramagnetic bead technology and microparticles, respectively, which have expanded the scope of proteomic studies. Single-cell proteomics has also seen significant progress, with techniques like SCoPE-MS (Budnik et al., 2018) and nanodroplet processing (Zhu et al., 2018) enabling the quantification of proteome heterogeneity during cell differentiation and other biological processes. Recent studies (Ye et al., 2024; Brunner et al., 2022) have further refined these methods, achieving comprehensive proteome coverage in minimal cells and single zygotes. **Clinical Applications** The clinical applications of proteomics are vast and growing. Niu et al. (2022) demonstrated the potential of high-throughput plasma proteomics for non-invasive diagnosis and staging of alcohol-related liver disease, outperforming existing clinical tests. Similarly, Geyer et al. (2024) reviewed the technological developments and current challenges in analyzing the circulating proteome, highlighting the need for robust and scalable methods. Urine proteomics, as explored by Bi et al. (2022) and Virreira Winter et al. (2021), has shown promise in monitoring biomarkers for conditions like bronchopulmonary dysplasia and familial Parkinson’s disease. Cerebrospinal fluid (CSF) proteomics, particularly in Alzheimer’s disease, has been advanced by studies such as Tao et al. (2024), which identified early diagnostic and staging biomarkers through large-scale proteomic and metabolomic profiling. **AI and Data Analysis** AI has played a crucial role in enhancing the analysis and interpretation of proteomic data. Mann et al. (2021) discussed the integration of AI in proteomics and biomarker discovery, while Gyori and Vitek (2024) explored how AI can assist in interpreting proteomic investigations in the context of evolving scientific knowledge. The introduction of DIA-NN by Demichev et al. (2020) and AlphaPept by Strauss et al. (2024) has significantly improved peptide identification and quantification, allowing for more comprehensive and precise proteome profiling. **Protein Interactions and Modifications** Understanding protein interactions and modifications is essential for functional proteomics. Michaelis et al. (2023) mapped the near-complete protein-protein interaction network in yeast, providing insights into the social and structural architecture of an entire proteome. Piersimoni et al. (2022) and Zheng et al. (2019) reviewed the use of cross-linking MS and hydrogen/deuterium exchange MS, respectively, for investigating protein conformations and interactions. These techniques have been applied to study various biological processes, including the pathogenesis of viral infections (Shah et al., 2018; Thorne et al., 2022; Hiatt et al., 2022) and the structural diversity of tauopathy strains in Alzheimer’s disease (Arakhamia et al., 2021; Wesseling et al., 2020). **Spatial Proteomics** Spatial proteomics, which combines high-resolution MS with AI-driven image analysis, has opened new avenues for studying protein expression patterns in specific cell types within complex tissues. Mund et al. (2022) introduced Deep Visual Proteomics (DVP), a method that maps protein expression with exceptional spatial resolution. Nordmann et al. (2024) used spatial proteomics to identify Janus kinase inhibitors (JAKi) as a potential treatment for a lethal skin disease, marking a significant step towards spatial medicine. Dong et al. (2024) and Ma et al. (2022) developed techniques for spatial proteomics of single cells and organelles, further enhancing the depth and specificity of proteomic analyses. **Biomarker Discovery and Precision Medicine** Biomarker discovery is a key application of proteomics in clinical settings. Niu et al. (2022) and Cai et al. (2023) used high-throughput proteomics to identify novel biomarkers for liver disease and metabolic syndrome, respectively. Sun et al. (2022) and Jiang et al. (2019) applied proteomics to develop accurate protein-based classification systems for thyroid nodules and early-stage hepatocellular carcinoma, demonstrating the potential of proteomics-driven precision medicine. The use of AI in these studies, as highlighted by Qian et al. (2024) and He et al. (2024), has further enhanced the ability to classify and predict disease states, paving the way for personalized treatment strategies. **Challenges and Future Directions** Despite these advancements, challenges remain in the field of proteomics. The dark proteome, or the part of the proteome that is not well understood, continues to be a focus of research (Perdigao et al., 2015; Kustatscher et al., 2022). Studies such as Bludau et al. (2022) and Skowronek et al. (2023) have addressed issues of peptide interference and data analysis, improving the reliability and depth of proteomic studies. Additionally, the need for standardized and reproducible methods in proteomics, as discussed by Masson et al. (2019) and Viode et al. (2023), remains a critical area for future research. In conclusion, mass-spectrometry-based proteomics has evolved from a research tool to a clinically relevant technology, driven by technological innovations and the integration of AI. These advancements have not only deepened our understanding of protein structure and function but also hold the potential to transform diagnostics and personalized medicine. The field is poised for further growth, with ongoing efforts to address remaining challenges and expand the scope of proteomic applications.