Researchers from the University of Copenhagen and Uppsala University have developed SC-pSILAC: a single-cell proteomics method capable of simultaneously measuring protein abundance and turnover in individual cells. Combining stable isotope labeling with mass spectrometry and improved sample preparation, the method marks the first time large-scale, two-dimensional proteomic data has been captured at single-cell resolution.
We spoke with the study’s co-author, Pierre Sabatier, to learn about the team’s developmental journey, as well as the potential implications their work could have on fields such as regenerative medicine and cancer biology.
Could you please describe, in a nutshell, how your single-cell approach works?
Our approach relies on a combination of the latest advancements in single-cell proteomics analysis and well-established proteomics techniques.
The core of the method is based on stable isotope labeling by amino acids in cell culture (SILAC), a technique introduced by Ong et al. in 2002. In SILAC, arginine and lysine in the cell culture medium are replaced with their stable isotope-labeled counterparts. When the medium is changed, newly synthesized proteins incorporate isotopically labeled amino acids, while older proteins retain those that are non-labeled. Determining the ratio of old to newly synthesized proteins allows us to measure protein turnover. Traditionally, this is performed using bulk samples over a time series to record decay curves.
In our single-cell approach, we measure protein turnover at a single time-point, aiming for approximately 50 percent total isotope incorporation. This enables us to calculate the ratios of non-labeled to labeled proteins, which reflects relative protein turnover. Simultaneously, the sum of non-labeled and labeled protein intensities provides a measure of protein abundance, facilitating two-dimensional proteomic analysis at the single-cell level.
For our analysis, we integrated the cellenONE with the Evo96 proteochip coupled to the Evosep One and the Orbitrap Astral. This setup achieves the sensitivity required to measure up to 4,000 proteins – including both labels and turnover values – in individual HeLa cells.
Why has two-dimensional proteomics at single-cell resolution eluded researchers up until this point?
This breakthrough could not have been possible without multiple technological advancements, most notably within sample preparation and sensitive analysis. Examples of this include the aforementioned cellenONE instrument and Evo96 proteochip.
Importantly, new mass spectrometers have provided us the necessary sensitivity to achieve sufficient proteome coverage. Additional advancements in software – such as Spectronaut and DIA-NN – have also been instrumental; with them we can now perform multiplexed/plex-DIA analyses. The combination of these technological and software innovations has been essential in overcoming the challenges to achieve large-scale, 2D proteomic insights at single-cell resolution.
Were there any major hurdles you had to overcome during development? Any "eureka" moments?
The main hurdle was ensuring that every component of the pipeline remained optimized at all times. While this was relatively straightforward for small-scale analyses, on the larger scale it became more challenging. This was the case when analyzing stem cell differentiation, for example: the project required the entire workflow to operate without any issues for two months uninterrupted – any delay once differentiation had begun could compromise the cells. Some elements – the Evo96 proteochip for single-cell preparation, for one – had only been recently introduced. Moreover, we had to perform some tests in parallel with the start of cell differentiation; if any of these implementations failed, we’d have no choice but to either discard the cells and start over, or sample at fewer intervals.
We didn’t experience a singular "eureka" moment as such, but as soon as we obtained data with the Orbitrap Astral, we knew that this type of analysis was possible. The instrument was key to validating our approach and confirming that large-scale, two-dimensional proteomic analysis in single cells was achievable.
What makes this such an important breakthrough?
Our breakthrough is significant for several reasons. Firstly, it represents the first global two-dimensional proteomic analysis, providing a comprehensive view of both protein abundance and turnover at the single-cell level. Such analysis is only feasible with mass spectrometry-based proteomics, positioning single-cell proteomics as a powerful tool in the field of single-cell analysis.
Protein turnover offers orthogonal information to abundance measurements, which can be exploited to discover new protein and cellular features. This can enhance our understanding of cellular dynamics and enables a range of applications. For example, it allows the study of protein associations in complexes from one cell to another, as well as improving cell classification.
Our SC-pSILAC approach enables the analysis of specific drug treatments, aiding the study of cellular response heterogeneity. Additionally, protein turnover provides functional insights into cell type differences, which further expands the utility of single-cell analysis in biology. Lastly, we demonstrated that histone turnover can be used to distinguish dividing cells from non-dividing or slowly dividing cells.
What are some of the broader implications and potential impact your research?
Currently, our method is primarily applicable to cultured cells or a few costly animal models. In order to make it suitable for clinical samples – such as patient-derived cell cultures – further implementation will be required. In cancer biology, the approach can be used to study cancer stem cells or dormant cells, which are often resistant to chemotherapies. Identifying and studying these cells will enable the development of more effective treatments.
In regenerative medicine, SC-pSILAC can be employed to study the activation of tissue-resident stem cells upon treatment. This includes processes such as dedifferentiation, re-entry into the cell cycle, and proliferation – all of which are crucial for tissue regeneration. SC-pSILAC can also provide fundamental clues on the heterogeneity of cell response to differentiation protocols, which can lead to the presence of undesirable cell types in a tissue.
While we believe the advantages of using the method for diagnostics may be limited, it could prove valuable for studying the activation of specific pathways upon drug treatment. For example, we have demonstrated its effectiveness in studying the activation of Wnt signaling using a GSK3 inhibitor.
What are the next steps for your team in scaling SC-pSILAC up to broader biomedical applications?
An obvious barrier to widespread adoption is that the method currently cannot be applied directly to patients; it requires a cell culture step, which could alter cell phenotypes. This challenge can be mitigated, however, as day-by-day patient-derived cultures become more robust and available for analysis .
Our immediate goal is to apply SC-pSILAC to patient-derived cells. This will allow us to validate the method's applicability for specific diseases in settings that are more clinically relevant. Finally, SC-pSILAC faces similar hurdles to label-free single-cell proteomics, particularly low throughput. Addressing this issue will be crucial for broader adoption, and we anticipate that ongoing technological advancements will significantly enhance the method's efficiency and scalability.
Pierre Sabatier is a Postdoctoral Researcher in the Department of Surgical Sciences, Uppsala University, and an Affiliated Researcher at the Novo Nordisk Foundation Center for Protein Research, University of Copenhagen.
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