Trend AnalysisBiology & Life Sciences

Mass Spectrometry Proteomics: From Single Cells to Clinical Diagnostics

Genomics tells us what a cell *could* do; proteomics tells us what it *is* doing. Proteins are the functional molecules of life — enzymes, receptors, structural components, signalling molecules — and ...

By Sean K.S. Shin
This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.

The Question

Genomics tells us what a cell could do; proteomics tells us what it is doing. Proteins are the functional molecules of life — enzymes, receptors, structural components, signalling molecules — and their abundance, modifications, and interactions determine cell behaviour. Yet proteomics has lagged behind genomics in clinical adoption: mass spectrometry (MS) instruments are expensive, sample preparation is complex, and data analysis requires specialised expertise. A landmark 2025 review in Nature by Mann and colleagues declared that proteomics has now reached a tipping point. Is the field ready for routine clinical deployment?

Landscape

Guo, Steen & Mann (2025) within months of publication, provided a definitive review of MS-based proteomics spanning single-cell analysis to clinical applications. Their key argument: three converging advances have brought clinical proteomics within reach — (1) data-independent acquisition (DIA) methods that enable comprehensive, reproducible proteome measurement; (2) trapped ion mobility spectrometry (TIMS) that adds a separation dimension, increasing depth and throughput; and (3) machine learning for peptide identification and quantification that automates previously manual data interpretation. They highlighted plasma proteomics as the most clinically relevant frontier, where thousands of proteins can now be quantified from a single blood draw.

Kirsher et al. (2025) reviewed the plasma proteomics landscape, comparing MS-based approaches with affinity-based platforms (SomaScan, Olink). MS offers unbiased discovery but lower throughput; affinity platforms offer higher throughput but are limited to pre-selected protein panels. Their finding: the two approaches are complementary, and the two approaches have complementary strengths, and systematic comparison reveals key trade-offs in coverage for biomarker discovery.

Zhang et al. (2024) developed a microfluidic flow cytometry system (µCytoMS) combining laser-induced fluorescence with ICP-MS to simultaneously measure a protein biomarker (PTK7) and platinum-based drug uptake at single-cell resolution in breast cancer clinical samples. Joshi et al. (2024) reviewed urinary proteomics, highlighting urine as a non-invasive alternative to blood for biomarker discovery.

Key Claims & Evidence

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ClaimEvidenceVerdict
Clinical proteomics has reached a tipping pointDIA + TIMS + ML convergence enables routine, reproducible proteome measurement (Guo et al. 2025)Supported; instrumentation and computation are ready; clinical validation is the bottleneck
Plasma proteomics can identify disease biomarkersThousands of proteins quantifiable from single blood draw (Kirsher et al. 2025)Supported; regulatory pathway for MS-based diagnostics unclear
Single-cell proteomics is achievable by MSµCytoMS profiles proteins and drug uptake per cell (Zhang et al. 2024)Demonstrated; throughput still low vs. scRNA-seq
Urine is a viable biomarker sourceNon-invasive collection; multiple disease biomarkers identified (Joshi et al. 2024)Supported; standardisation of collection and processing needed

Open Questions

  • Standardisation: Can MS-based clinical proteomics achieve the inter-laboratory reproducibility required for diagnostic use? Reference standards and quality control protocols are still maturing.
  • Single-cell depth: Current single-cell MS detects ~1,000–3,000 proteins per cell. Can this approach the ~6,000+ achieved by single-cell RNA-seq in gene coverage?
  • Data integration: How should proteomic data be integrated with genomic, transcriptomic, and metabolomic layers for multi-omic clinical diagnostics?
  • Cost trajectory: MS instruments cost $500K–1M. Will miniaturisation and competition bring prices to clinical laboratory budgets?
  • Referenced Papers

    • [1] Guo, T., Steen, J.A. & Mann, M. (2025). Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature. DOI: 10.1038/s41586-025-08584-0
    • [2] Joshi, N. et al. (2024). Recent progress in mass spectrometry-based urinary proteomics. Clinical Proteomics, 21, 14. DOI: 10.1186/s12014-024-09462-z
    • [3] Zhang, X. et al. (2024). Multiplex Profiling of Biomarker and Drug Uptake in Single Cells Using µCytoMS. ACS Nano. DOI: 10.1021/acsnano.3c12803
    • [4] Kirsher, D.Y. et al. (2025). Current landscape of plasma proteomics. Communications Chemistry. DOI: 10.1038/s42004-025-01665-1
    • [5] Bartha, Á. et al. (2025). Melanoma Proteomics Unveiled: MEL-PLOT for Biomarker Discovery. J. Proteome Research. DOI: 10.1021/acs.jproteome.4c00749

    References (5)

    Guo, T., Steen, J. A., & Mann, M. (2025). Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature, 638(8052), 901-911.
    Joshi, N., Garapati, K., Ghose, V., Kandasamy, R. K., & Pandey, A. (2024). Recent progress in mass spectrometry-based urinary proteomics. Clinical Proteomics, 21(1).
    Zhang, X., Wei, X., Wu, C., Men, X., Wang, J., Bai, J., et al. (2024). Multiplex Profiling of Biomarker and Drug Uptake in Single Cells Using Microfluidic Flow Cytometry and Mass Spectrometry. ACS Nano, 18(8), 6612-6622.
    Kirsher, D. Y., Chand, S., Phong, A., Nguyen, B., Szoke, B. G., & Ahadi, S. (2025). Current landscape of plasma proteomics from technical innovations to biological insights and biomarker discovery. Communications Chemistry, 8(1).
    Bartha, Á., Weltz, B., Betancourt, L. H., Gil, J., Pinto de Almeida, N., Bianchini, G., et al. (2025). Melanoma Proteomics Unveiled: Harmonizing Diverse Data Sets for Biomarker Discovery and Clinical Insights via MEL-PLOT. Journal of Proteome Research, 24(6), 3117-3128.

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