Single-Cell Proteomics Enable Advancements in Disease Research

Single-cell technologies are transforming our understanding of human health and disease. Single-cell genomics and transcriptomics are mature disciplines that can be used to study more than a million single cells (1). However, human

biology cannot be understood through the analysis of DNA and RNA alone. It also requires the study of proteins and protein modifications, lipids, and metabolites.

Proteins are biochemically active and also serve as signaling molecules. As a result, it is not surprising that approximately 95% of drugs are targeted at proteins. However, protein molecules cannot be amplified like DNA or RNA to perform single-cell proteomics measurements. Thus, novel and highly sensitive technologies are required that can decipher this complexity at the single-cell level and contribute to our understanding of emergent issues surrounding health and disease (2).

Single-cell omics landscape

Protein function is frequently modulated through post-translational modifications, such as phosphorylation and ubiquitylation, that can change the functional course of the cell with fast kinetics. Processes such as endogenous proteolysis and glycosylation are known to play a role in oncological mechanisms (3,4). In addition, gene expression is affected by so-called bursts in expression, which results in additional variations that would be automatically normalized by post-translational regulatory processes in the case of proteins (5). Furthermore, alternative splicing of RNA transcripts can result in additional protein variants. Single-cell proteomics technologies are now entering the mainstream thanks to the pioneering work of a relatively small group of dedicated scientists and the emergence of extremely sensitive mass spectrometers. Typical estimates of the protein content of individual cells are in the order of 200 picograms (which is one-billionth of a milligram). In a recent study, qualitative and quantitative information for up to 1400 proteins was obtained from single cells using an unbiased single proteomics approach that did not require complex isobaric labeling chemistries to boost the peptide signals (6). Cluster analysis of the data could distinguish cell types and cell cycle stages, despite the technology not specifically targeting known and verified markers.

Technological advances

This microheterogeneity in seemingly homogenous cell populations plays a key role in decisive pathways pursued by biological systems. The underlying microheterogeneity is caused by variations in genes and their expression, and understanding these variations at the single-cell level helps to identify the few cells that act as a seed for cancer development, for example. Studying DNA and RNA molecules within the cell is one of the most common approaches to single-cell biology and has also helped motivate the measurement of proteins at the single-cell level. Exponential advances have been achieved in single-cell DNA and RNA sequencing technologies and, depending on the application, a variety of sequencing techniques can be employed for these studies (2).

With the help of such advanced technologies, studies involving the measurement of the single-cell transcriptomes of more than a million individual cells are now feasible (1) and have revealed previously unseen biology as well as highlighted the heterogeneity of single cells—thereby opening up new areas of biology and medicine. A common enabling factor in all these approaches is the ability to amplify DNA and RNA molecules to virtually any desired amount, bringing these molecules into a detectable or quantifiable range (7).

Unbiased proteomics of single cells have been performed in recent years by specialized research groups involving nano-fluidics that are not yet readily adopted by the general research community. These applications often focus on minimizing the loss during sample preparation and multiplexing samples to boost signal intensity (8,9). Despite these solutions, however, the field still has need of innovations that can boost the sensitivity of the mass spectrometer.

Trapped ion mobility spectrometry

The development of parallel accumulation and serial fragmentation (PASEF) (10) provided a spectroscopic technique used with liquid chromatography coupled to mass spectrometry (LC–MS)-based proteomics to improve sequencing speed and sensitivity. PASEF makes efficient usage of the ion beam and, together with intelligent precursor selection of ions eluting from a trapped ion mobility spectrometry (TIMS) cycle, achieves rapid MS/MS identification speed. In addition, the ions are focused in space and time within the TIMS cell, resulting in a significant boost in the sensitivity. This enables the analysis of low sample amounts, in the range of low nanogram peptide loads.

TIMS measurements also provide collisional cross-section (CCS) values and separation of isomeric species that are mobility offset but mass aligned and alleviate ratio compression in multiplexed quantification approaches. The introduction of these 4D-proteomics capabilities has bridged the gap between the requirements of the most demanding proteomics approaches—such as clinical research proteomics, companion diagnostics research, and personalized medicine research—and the solutions effectively available on the market.

Conclusion

Next-generation sequencing technologies now represent a multibillion-dollar industry (11) that promises to help deliver personalized medicine and precision therapeutics, which will help tackle complex and heterogeneous conditions, such as cancer and Alzheimer’s disease.

Single-cell protein technologies have the potential to transform our understanding of cell biology at the macromolecular level and answer fundamental questions regarding protein dynamics, cell differentiation trajectories, and mechanisms of disease. These processes act at the nano- and microscopic level but fundamentally influence higher-order macroscopic behavior. Hence, it is vital that these processes are understood at the highest possible spatial resolution.

References

1. J. Cao, et al., Nature 566, 496–502 (2019).
2. G. Chen, B. Ning, and T. Shi, Front. Genet., April 5, 2019.
3. L.A. Liotta and E.F. Petricoin, J Clin Invest. 116 (1) 26–30 (2006).
4. M.A. Connelly, et al., J Transl Med 15, 219 (2017).
5. G.K. Marinov, et al., Genome Res. 24, 496–510 (2014).
6. A.D. Brunner, et al., bioRxiv 12.22.423933 (2020).
7. C.F.A. de Bourcy, et al., PLoS ONE 9 (8) e105585 (2014).
8. D.Hartlmay, et al., bioRxiv 04.14.439828 (2021).
9. N. Slavov, Current Opinion in Chemical Biology 60, 1–9 (2021).
10. F. Meier, et al., Mol Cell Proteomics. 17 (12) 2534–2545 (2018).
11. Markets and Markets, Single-cell Analysis Market by Cell Type (Human, Animal, Microbial), Product (Consumables, Instrument), Technique (Flow Cytometry, NGS, PCR, Microscopy, MS), Application (Research, Medical), End User (Pharma, Biotech, Hospitals)—Global Forecast to 2026, marketsandmarkets.com, February 2020.

About the author

Gary Kruppa, PhD, is vice-president of Proteomics, Bruker Daltonics.