In this interview, News-Medical speaks with Nikolai Slavov, COE Distinguished Professor of Bioengineering at Northeastern University and Founding Director of the Parallel Squared Technology Institute, about advances in single-cell proteomics and how high-throughput mass spectrometry is revealing proteome diversity, cellular regulation, and non-canonical protein sequences.
Can you please introduce yourself and your role at Northeastern University and the Parallel Squared Technology Institute?
My name is Nikolai Slavov. I am a COE Distinguished Professor of Bioengineering at Northeastern University, where I am also the Director of the Single-Cell Proteomics Center. I am the Founding Director of the Parallel Squared Technology Institute. My research focuses on developing technologies for measuring proteins in individual cells and using these measurements to understand biological regulation and proteome diversity.
What are some of the key advances that are making single-cell proteomics increasingly accessible?
Proteomics is incredibly complex, and for a long time it has been difficult to study the myriad protein modifications and interactions. However, novel technologies are making this increasingly accessible. Depending on the method, we can now prepare anywhere from hundreds to tens of thousands of single cells per day. With highly parallelized approaches, we can analyze thousands of single cells and, depending on the instrument and the size of the cells, quantify from hundreds to many thousands of proteins and modifications.
This progress is particularly important because we are not simply measuring proteins in bulk. We can study protein modifications, transport, synthesis, and degradation at increasingly high throughput and quantitative accuracy. These measurements are beginning to reveal biology that cannot be observed by averaging across populations of cells.
How does your approach to sample preparation help improve the sensitivity and reproducibility of single-cell proteomics?
We have developed multiple generations of technologies for sample preparation. The latest approach that we most frequently use performs sample preparation on glass slides in individual droplets. Each droplet serves as a reaction vessel in which a single cell is isolated, labeled, and processed for analysis.
The method is highly automated, which allows us to minimize sample losses and contamination. This is particularly important when working with single cells because the amount of material is extremely small. The goal is to preserve as much of the material as possible and to process many cells consistently before mass spectrometry analysis.
How are multiplexing strategies helping to increase the throughput of single-cell proteomics?
We have been very excited by the possibility of creating multiplexing regimes that increase throughput without reducing depth of coverage or quantitative accuracy. We developed reagents not only to increase the number of cells that can be multiplexed, but also to increase confidence in sequence identification and enable the discovery of peptide modifications.
One example is the PSMtag for plexDIA. Labeling peptides allows us to identify more fragment ions compared with label-free peptides. Across peptides, this increases our confidence in sequence identification and sensitivity. It also enables multiplexing and the analysis of more samples simultaneously.
In one implementation with a dual-column setup and nine-plex PSM reagents, we were able to analyze well over a thousand single-cell-sized samples per day without significantly reducing the depth of coverage achieved by the coresponding label-free control under similar conditions.
What challenges arise when you combine multiplexing in the mass and time domains?
We have also started multiplexing in the time domain by overlapping the elution gradients of multiple samples. Because the time dimension is orthogonal to the mass dimension, these approaches are highly complementary. When we combine multiplexing in both domains, the number of single cells or data points analyzed per day can increase very rapidly.
Of course, there are challenges associated with having very dense data points, particularly interference and quantitative accuracy. This is why computational methods are so important. In this context, we have developed methods such as JMod, an open-source software approach from the Parallel Squared Technology Institute, for interpreting these highly parallelized and highly multiplexed experiments.
In benchmarking experiments, JMod maintained accurate quantification across a 32-fold dynamic range while quantifying more than 700,000 precursors in a single mass spectrometry run.
What have single-cell proteomics measurements revealed about protein synthesis, degradation, and cell proliferation?
We used metabolic pulse experiments in mice, feeding them food containing heavy isotopes and combining this with multiplexed measurements to determine rates of protein synthesis and degradation. We also analyzed the transcriptome, and both approaches could independently define the different cell types.
More importantly, the mass spectrometry measurements allowed us to measure protein synthesis and degradation rates. Because histones are not cleared or degraded to a significant degree during the experiment, we could use their heavy and light forms to determine how many times an individual cell had divided.
Cells that had not divided contained almost exclusively light histones, whereas cells that had divided showed a higher proportion of heavy histones. This allowed us to quantify proliferation rates across multiple cell types in mice.
The measurements also highlighted the importance of protein clearance; the rate of protein clearance and degradation explained about 50% of the total variation in protein abundance. This contribution decreased more rapidly in cells with higher growth rates because proteins can also be cleared through dilution.
Quantifying protein regulation in single cells | Webinar Highlight with Nikolai Slavov
What does single-cell proteomics reveal about variation between cells of the same type?
We find very strong variation in protein abundance across single cells from the same cell type. This variation can be quite distinct and is not necessarily predicted by the transcriptome.
When we examined pairwise correlations among proteins within cells, the protein correlations were much stronger and more coordinated by functional groups than the corresponding RNA correlations. This is due both to regulated protein synthsis and degradation (which we measured directly) and to the higher reliability of protein measurements, as MS measures higher copy numbers for proteins in individual cells. Protein degradation plays a major role: The more strongly a protein is regulated by degradation, the more its correlations can differ from those of the corresponding transcripts.
This is one of the things that single-cell proteomics uniquely allows us to investigate: variation in protein abundance among individual cells that belong to the same cell type.
How does single-cell proteomics compare with single-cell RNA sequencing in your studies of peripheral blood mononuclear cells?
We analyzed peripheral blood mononuclear cells in a paired fashion using sensitive single-cell RNA sequencing and single-cell proteomics using a timsTOF instrument. We could readily identify cell types from both modalities.
What was particularly interesting was that we could identify and quantify a larger number of gene products at the protein level than at the RNA level in individual cells. We obtained higher depth, with more proteins quantified in individual cells than transcripts, and the protein data also had much higher data completeness. In other words, proteins quantified in one single cell were more likely to be quantified in other single cells.
This has important consequences for quantitative analysis because it allows us to examine coordinated variation in protein and RNA abundances. Within B cells, for example, we found well-defined clusters of functionally related proteins that were not detected in the messenger RNA data, even with very deep and sensitive sequencing.
What does your research suggest about proteins whose sequences are not fully predicted by the genetic code?
For the last six decades, we have largely used protein sequences predicted from nucleic acid sequences based on the genetic code. We can now ask a more direct question: which amino acid is actually incorporated by the ribosome?
In many cases, we find that the amino acid sequence predicted by the genetic code is less abundant than another protein sequence that we measure in the samples. These changes in sequence are widespread across proteins with different functions, including signaling proteins and transcription factors.
What is particularly exciting is that we can increasingly discover these changes at much greater depth. Using timsTOF instruments with multiple proteases, we can detect around 400,000 precursors in a single sample. Hybrid fragmentation enabled by TimsOmni can then be used to confirm and validate many of these modified peptides.
How could advances in single-cell proteomics help researchers understand biological complexity in the future?
The ability to measure proteins directly in individual cells gives us access to biological variation that cannot always be inferred from RNA measurements. Advances may enable us to examine protein abundance, protein synthesis and degradation, modifications, and coordinated protein behavior across individual cells.
At the same time, increasing throughput is allowing us to analyze more cells while maintaining quantitative depth. This combination of scale, sensitivity, and quantitative accuracy is important for connecting molecular measurements to biological phenotypes.
We are also very interested in making these measurements broadly accessible to the community. We have worked with other researchers to establish initial guidelines for single-cell proteomics experiments, and an initiative is establishing a single-cell proteomics portal at PRIDE to facilitate the sharing of proteomics data.
About Nikolai Slavov
Nikolai Slavov is a COE Distinguished Professor of Bioengineering at Northeastern University, Director of the Single-Cell Proteomics Center, and Founding Director of theParallel Squared Technology Institute. His research focuses on single-cell proteomics, quantitative systems biology, mass spectrometry, protein synthesis and degradation,translational regulation, and RNA decoding.
Slavov received his BS in Biology from the Massachusetts Institute of Technology in 2004 and completed his PhD in the Botstein Laboratory at Princeton University in 2010. His doctoral research focused on understanding how cells coordinate growth, gene expression, and metabolism. Following his PhD, he conducted postdoctoral research in the van Oudenaarden Laboratory at MIT, investigating cellular metabolism and the Warburg effect.
His laboratory subsequently pioneered experimental and computational approaches for single-cell proteomics by mass spectrometry, including SCoPE-MS and SCoPE2. These technologies have enabled researchers to investigate proteome heterogeneity across individual cells and connect protein variation with biological phenotypes.
Slavov’s current research encompasses single-cell proteomics, immunology, cancer drug resistance, quantitative systems biology, protein synthesis and degradation, and alternate RNA decoding. His work has also contributed to the development of resources and guidelines intended to support the broader single-cell proteomics community.
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