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From Molecules to Meaning: The Promise of Single-Cell Proteomics at IMP Vienna

  11/08/2025

   Cécile Thion, PhD

   8-minute read

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Upcoming Conference – 6th European Symposium on Single Cell Proteomics – August 2025 – Vienna (ESCP)

The IMP Vienna team is also at the forefront of community-building in the field. They are co-organizing the 6th European Symposium on Single Cell Proteomics (ESCP), to be held at the Vienna Biocenter on August 26–27, 2025. This event will gather leading scientists in single-cell and spatial proteomics, multiomics, and emerging technologies. Highlights include a Deep Visual Proteomics (DVP) workshop on August 25, focusing on workflows for low-input tissue analysis. Registration is free (limited to ~200 participants). More details can be found here.

Single‑cell proteomics (SCP) offers researchers the ability to explore protein expression landscapes at the resolution of individual cells, uncovering crucial molecular heterogeneity that bulk proteomics averages out. The SCP science field is growing exponentially, as increasingly sensitive and sustainable methods are being developed [1]. This approach is transformative for understanding developmental trajectories, disease onset, and cellular responses to therapeutics.

The Proteomics Facility & Technology Hub at IMP Vienna, located within the Vienna Biocenter, is spearheading innovation in SCP by developing highly sensitive workflows and integrating advanced instrumentation into a robust multi‑omics ecosystem.

Meet the Team: The leaders of the SCP Platform at IMP

The SCP Platform at IMP Vienna is driven by pioneering leadership and scientific innovation. Karl Mechtler, founder and Head of the Proteomics Tech Hub (spanning IMP, IMBA, and GMI), has guided the facility since 2002. He earned the Juan Pablo Albar Proteome Pioneer Award in 2022 for outstanding contributions to proteomics and post‑translational modification analysis [2]. His group continues to shape method development, including cross-linking MS and high-sensitivity workflows.

Supporting this legacy of innovation is Dr Manuel Matzinger, Deputy Head of the SCP platform (Figure 1). Since joining IMP Vienna in 2020, he has been instrumental in advancing ultra‑low‑input proteomics and cross-linking MS and is now focused on improving workflows that enable deep, reproducible single‑cell analysis [e.g., 3, 4]. His combined experimental and computational expertise ensures that the platform generates robust datasets and interprets them effectively in a biological context.

Under the leadership of Mechtler and Matzinger, IMP Vienna has become a European leader in method development, creating innovative technologies, which have set new benchmarks for reproducibility and sensitivity.

Figure 1. Manuel Matzinger in the SCP Platform lab. © Ludwig Schedl.

Daily Evolving SCP Methods for Always Higher Sensitivity and Throughput

Single-cell proteomics is evolving at an unprecedented pace, with groundbreaking innovations emerging daily from research teams across the globe, and IMP Vienna stands out as one of the leading hubs driving this revolution.

In this dynamic context, two complementary strategies are currently explored by the scientists of the field: multiplexed and label-free SCP. In multiplexed single-cell proteomics, multiple samples are labelled with chemical tags and analysed together in a single mass spectrometry run, massively increasing throughput. This approach necessitates the introduction of TMT labels, which can be automated by liquid handling instruments, like the cellenONE using the nPOP workflow [5, 6, 7]. In label-free approaches, each sample is analysed individually without tags, reducing analysis throughput, for one cell equals one MS sample, but increasing achievable proteome depth [1, 8]. These two approaches, still being daily improved in parallel, are perceived as complementary [1], one allowing the simultaneous analysis of thousands of cells while the other achieves in-depth coverage of the proteomes of fewer cells.

Whichever the chosen experimental strategy, one of the primary challenges, and research focuses, has been to reduce sample loss during preparation, as this is crucial for improving both sensitivity and efficiency in SCP workflows. Among the solutions developed, the “everything-in-one-pot” approach streamlined SCP sample preparation by consolidating lysis, proteolysis, labelling, and clean-up into a single vessel, i.e., standard 384-well plates, minimizing sample loss [9]. This semi-automatized SCP assay, using cellenONE instrument for liquid dispensing, single cell isolation, single cell morphology imaging and SCP sample prep incubations (Figure 2), significantly reduced manual handling and improved reproducibility.

Figure 2. One pot workflow for label-free single cell proteomics. From Matzinger et al. 2023, Analytical Chemistry [9].

Building on these foundations, IMP Vienna also pioneered a second key innovation, the co-development of the proteoCHIP product line, a microwell-based consumable designed to miniaturize SCP sample prep and minimize sample loss. Over the past five years, this technology has been optimized with Cellenion, enabling seamless SCP sample prep, both multiplexed and label-free, within the cellenONE, that delivers both reagents and individual cells directly into the microwells, while allowing on-chip incubation in a fully integrated workflow [10, 11, 12].

In parallel, ongoing refinements to downstream processes have further boosted performance. IMP Vienna achieved substantial improvements in both the depth of proteome coverage and the accuracy and precision of quantifying ultra-low input samples, by improving liquid chromatography and mass spectrometry, the final wet-lab steps of SCP [3, 4]. State-of-the-art mass spectrometers, operated under stringent QC pipelines, enable IMP Vienna to routinely achieve quantitative profiles of thousands of proteins per single cell, unlocking true SCP-based biology.

As these proteomic workflows mature, a natural next step is to integrate them with complementary molecular layers to capture a fuller picture of cellular states. While RNA levels do not always reflect protein abundance due to translational and post-translational regulation, single-cell multiomics offers deep insights into cellular identity and dynamics, integrating proteomic data alongside genomic and/or transcriptomic profiles. For instance, PNNL’s nanoSPLITS platform addresses this by enabling parallel, unbiased quantification of over 5,000 transcripts and 2,000 proteins from the same single cell, revealing cell-type-specific markers across both molecular layers [14].

Together, these methodological advances and integrative strategies lay the foundation for applying single-cell proteomics across a wide range of biological systems, from early development to complex disease.

From Stem Cells to Precision Oncology: the translational potential of SCP

Although its integration into diagnostics and clinical research is still in early stages, single-cell proteomics emerge as a crucial tool in fields where cellular heterogeneity drives biological outcomes [15].

Mapping protein expression brings crucial insights developmental biology, with implications in disease modeling, tissue engineering, and may ultimately guide strategies to rebuild or replace damaged mammalian tissues. For instance, single cell proteomics helped reconstructing protein expression dynamics during hematopoietic stem cell differentiation [16, 17]. This is clinically relevant, as the hematopoietic hierarchy describes how multipotent stem cells progressively commit to specific blood cell lineages. Disruptions in this hierarchy underlie many hematological diseases, such as leukemia, anemia, and bone marrow failure syndromes. Similarly, multiomics profiling provides detailed insight into how cells diversify and self-organize into complex tissue patterns. The SCP platform at the IMP Vienna therefore starts to perform multiomic investigations in neural tube and brain organoids among other sample types to eventually improve our understanding of human brain aging. Single-cell proteomics is thereby shedding light on both ends of the human life cycle, from the earliest stages of embryonic development to the complex biology of aging and neurodegeneration, incl. Alzheimer’s.

In cancer research, single cell omics and spatial omics applications range from spatial proteomics of tumour microenvironments (TMEs), key determinants of therapeutic efficacy, to peptide mass fingerprinting for cancer subtype classification. Multi-omics have for instance been applied across 25 cancer types to dissect tumor heterogeneity and microenvironmental interactions, identifying predictive molecular features that correlate with resistance to targeted treatments [18]. Many cancer therapies are designed to block overactive proteins that fuel tumor growth, yet the cellular responses to these drugs often remain poorly understood. Zecha et al. developed a proteomics method that captures time- and dose-dependent protein responses to small molecules and antibodies. Applied to 31 drugs, it generated millions of data points, revealing drug-response fingerprints and mechanisms of action, especially in blood cancers [19]. Besides, Several studies highlighted the value of single-cell proteomics (SCP) in advancing precision oncology, an approach that allow targeted therapies to the specific molecular features of both the patient and their tumor [20]. By enabling detailed molecular profiling, including proteomic data, SCP helps identify mutations, biomarkers, and dysregulated signaling pathways that drive tumor growth.

While regulatory compatibility for SCP-based diagnostics and therapeutic decision-making remains a long-term goal, ongoing advances in throughput, standardization, and robustness are steadily paving the way. Institutions like the IMP Vienna are actively contributing to this transformation, helping to accelerate the integration of SCP into clinical research and practice.

Looking Ahead

The trajectory of single-cell proteomics is clear: technologies are becoming faster, more sensitive, and more integrative [21]. Through deeper coverage and cross-omic analyses thanks to technologies like cellenONE and proteoCHIP, researchers can begin to view cellular systems comprehensively, tracking development, disease, and treatment response at single-cell resolution.

As SCP transitions from a novel research method to a standard tool, its applications are set to expand into drug discovery, precision medicine, and clinical diagnostics, making it indispensable in basic and translational science. The Proteomics Facility & Tech Hub at IMP Vienna, under the guidance of Karl Mechtler and the technical leadership of Manuel Matzinger, is fully committed to leading that transition. With ongoing European initiatives like the Austrian Single-Cell Proteomics Platform (AT‑SCP), recently funded with an additional €2.5M from the Austrian Research Promotion Agency to invest in state of the art infrastructure which is now used to build robotics-enabled workflows and deploy high-end mass spectrometry. The group aims to offer SCP as a robust service to Europe’s scientific community [22]. This collaborative ethos, by fostering partnerships, refining methods, and pushing innovation, is ensuring that single-cell proteomics not only reveals complexity but also empowers research to improve human health.

References

  1. Matzinger, M., Mayer, R. L., & Mechtler, K. (2023). Label‐free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing.Proteomics, 23(13-14), 2200162.
  2. https://www.oeaw.ac.at/gmi/detail/news/proteome-pioneer-award-for-karl-mechtler
  3. Zheng, R., Matzinger, M., Mayer, R. L., Valenta, A., Sun, X., & Mechtler, K. (2023). A high-sensitivity low-nanoflow LC-MS configuration for high-throughput sample-limited proteomics. Analytical Chemistry, 95(51), 18673-18678.
  4. Bubis, J. A., Arrey, T. N., Damoc, E., Delanghe, B., Slovakova, J., Sommer, T. M., … & Matzinger, M. (2025). Challenging the Astral mass analyzer to quantify up to 5,300 proteins per single cell at unseen accuracy to uncover cellular heterogeneity. Nature Methods, 22(3), 510-519.
  5. Derks, J., Leduc, A., Wallmann, G., Huffman, R. G., Willetts, M., Khan, S., … & Slavov, N. (2023). Increasing the throughput of sensitive proteomics by plexDIA. Nature biotechnology, 41(1), 50-59.
  6. Leduc, A., Khoury, L., Cantlon, J., Khan, S., & Slavov, N. (2024). Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. Nature protocols, 19(12), 3750-3776.
  7. Ctortecka, C., Stejskal, K., Krssakova, G., Mendjan, S., & Mechtler, K. (2021). Quantitative accuracy and precision in multiplexed single-cell proteomics. Analytical chemistry, 94(5), 2434-2443.
  8. Matzinger, M., Mayer, R. L., & Mechtler, K. (2023). Label‐free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing.Proteomics, 23(13-14), 2200162.
  9. Matzinger, M., Müller, E., Dürnberger, G., Pichler, P., & Mechtler, K. (2023). Robust and easy-to-use one-pot workflow for label-free single-cell proteomics. Analytical Chemistry, 95(9), 4435-4445.
  10. Ctortecka, C., Hartlmayr, D., Seth, A., Mendjan, S., Tourniaire, G., Udeshi, N. D., … & Mechtler, K. (2023). An automated nanowell-array workflow for quantitative multiplexed single-cell proteomics sample preparation at high sensitivity. Molecular & Cellular Proteomics, 22(12), 100665.
  11. Hartlmayr, D., Ctortecka, C., Mayer, R., Mechtler, K., & Seth, A. (2024). Label-Free Sample Preparation for Single-Cell Proteomics. In Mass Spectrometry Based Single Cell Proteomics(pp. 1-7). New York, NY: Springer US.
  12. Zheng, R., Matzinger, M., Mayer, R. L., Valenta, A., Sun, X., & Mechtler, K. (2023). A high-sensitivity low-nanoflow LC-MS configuration for high-throughput sample-limited proteomics. Analytical Chemistry, 95(51), 18673-18678.
  13. Ctortecka, C., Clark, N. M., Boyle, B. W., Seth, A., Mani, D. R., Udeshi, N. D., & Carr, S. A. (2024). Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nature Communications, 15(1), 5707.
  14. Fulcher, J. M., Markillie, L. M., Mitchell, H. D., Williams, S. M., Engbrecht, K. M., Degnan, D. J., … & Zhu, Y. (2024). Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting. Nature Communications, 15(1), 10614.
  15. Guo, T., Steen, J. A., & Mann, M. (2025). Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature, 638(8052), 901-911.
  16. Wallmann, G., Skowronek, P., Brennsteiner, V., Lebedev, M., Thielert, M., Steigerwald, S., … & Mann, M. (2024). AlphaDIA enables end-to-end transfer learning for feature-free proteomics. bioRxiv, 2024-05.
  17. Furtwängler, B., Üresin, N., Richter, S., Schuster, M. B., Barmpouri, D., Holze, H., … & Porse, B. T. (2024). Mapping the human hematopoietic stem and progenitor cell hierarchy through integrated single-cell proteomics and transcriptomics. bioRxiv, 2024-07.
  18. Cheng, X., Peng, T., Chu, T., Yang, Y., Liu, J., Gao, Q., … & Wei, J. (2025). Application of single-cell and spatial omics in deciphering cellular hallmarks of cancer drug response and resistance. Journal of Hematology & Oncology, 18, 70.
  19. Zecha, J., Bayer, F. P., Wiechmann, S., Woortman, J., Berner, N., Müller, J., … & Kuster, B. (2023). Decrypting drug actions and protein modifications by dose-and time-resolved proteomics. Science, 380(6640), 93-101.
  20. Gillette, M. A., Satpathy, S., Cao, S., Dhanasekaran, S. M., Vasaikar, S. V., Krug, K., … & Thangudu, R. R. (2020). Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell, 182(1), 200-225.
  21. Sanchez-Avila, X., de Oliveira, R. M., Huang, S., Wang, C., & Kelly, R. T. (2025). Trends in Mass Spectrometry-Based Single-Cell Proteomics. Analytical Chemistry, 97(11), 5893-5907.
  22. https://www.viennabiocenter.org/about/news/25m-euros-funding-for-single-cell-proteomics/
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