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Innovations in Single-Cell Proteomics Drive Advances in Disease Research

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Single-cell technologies are transforming our knowledge of human health and disease. Rapid advances in single-cell proteomics (SCP) are enabling more parameters to be measured in individual cells. In principle, with more measurements from each single cell, we should be able to gain a more comprehensive understanding of this heterogeneous system.

The underlying microheterogeneity in cell populations is caused by variations in genes and their expression, and gaining an understanding of these variations at the single-cell level helps identify any cells, for example, that act as a seed for the development of diseases, such as cancer. Studying disease diagnostics and the response to drugs requires proteomics – and, although, in its early stages, SCP is making new inroads in our understanding of the field.

A new era of mass spectrometry

Studying DNA and RNA molecules within the cell is one of the most common approaches to single-cell biology. Major leaps have been made in single-cell DNA and RNAsequencing, especially the transcriptome, which represents all the expressed genes in a cell. Depending on the application, a variety of sequencing techniques can be employed for these studies.1

Extremely sensitive mass spectrometers, however, are now bringing SCP to the mainstream. The protein content of an individual cell is approx. 200 picograms (one billionth of a milligram), but a recent study obtained qualitative and quantitative information for 1400 proteins from single cells using an unbiased single proteomics approach.2 Cluster analysis of the data was able to distinguish cell types and cell cycle stages.

Proteins are biochemically active and 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 SCP measurements. Highly sensitive technologies must be used to decipher the complexity of protein molecules at the single-cell level to contribute to our deeper understanding of human health and disease.2 

Single cell omics landscape

Protein function is frequently modulated through post-translational modifications (PTM) that can change the functional course of the cell. 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, which result in additional variations that would be automatically normalized by post-translational regulatory processes in proteins.5

With the help of advanced technologies, studies on single-cell transcriptomes of more than a million individual cell measurements are now feasible6 and have highlighted the heterogeneity of single cells, opening up new areas of biological research and medicine. A common enabling factor in all these approaches is the ability to amplify DNA and RNA molecules to the amount required, bringing them into a range that is detectable and even quantifiable.7

Unbiased proteomics of single cells has been performed in recent years by specialized research groups, utilizing 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 However, despite these solutions, the field has needed innovations that can increase the sensitivity of the mass spectrometer.

Trapped ion mobility spectrometry (TIMS)

The introduction of parallel accumulation and serial fragmentation (PASEF)10 technology combines liquid chromatography with mass spectrometry (LC-MS) based proteomics to improve sequencing speed and sensitivity. PASEF optimizes the usage of ion beam and, together with intelligent precursor selection of ions eluting from a trapped ion mobility spectrometry (TIMS) cycle, it achieves a rapid MS/MS identification. In addition, the ions are focused in space and time within the TIMS cell, significantly boosting 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 (MOMA) and alleviates ratio compression in multiplexed quantification approaches.

These 4D-proteomics capabilities bridge the gap between the most demanding proteomics approaches, such as clinical research proteomics and personalized medicine research, and the solutions that are already available.

TIMS for proteomics applications

The discovery of quadrupole time-of-flight mass spectrometry (QTOF-MS) coupled with TIMS for proteomics applications (2017) resulted in two paradigm shifts in the proteomics community:

  • The amount of peptide required for standard proteomics analysis was reduced by an order of magnitude.
  • The time required for standard proteomic analysis was reduced by a factor of four.


SCP technologies, still in their infancy, are enabling advancements in personalized medicine and precision therapeutics to help tackle complex and heterogeneous conditions such as cancer and Alzheimer’s disease.

Large-scale single-cell analyses are of fundamental importance in capturing biological heterogeneity, but until very recently have largely been limited to RNA-based technologies. This new depth of SCP analysis has the potential to transform our under­standing of cell biology at the macromolecular level and answer fundamental questions on protein dynamics and the mechanisms of disease.


1.    Brunner AD, Thielert M, Vasilopoulou CG, et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. bioRxiv. 2021:2020.12.22.423933. doi: 10.1101/2020.12.22.423933.

Chen G, Ning B, Shi T. Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front. in Gen. 2019;10:317. doi: 10.3389/fgene.2019.00317.

Liotta LA, Petricoin EF. Serum peptidome for cancer detection: spinning biologic trash into diagnostic gold. J Clin Invest. 2006;116(1):26-30. doi: 10.1172/JCI27467.

Connelly MA, Otvos JD, Shalaurova I, Playford MP, Mehta NN. GlycA, a novel biomarker of systemic inflammation and cardiovascular disease risk. Journal of Translational Medicine. 2017;15(1):219. doi: 10.1186/s12967-017-1321-6.

Marinov GK, Williams BA, McCue K, et al. From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing. Gen Res. 2014;24(3):496-510. doi: 10.1101/gr.161034.113

Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496-502. doi: 10.1038/s41586-019-0969-x.

de Bourcy CFA, De Vlaminck I, Kanbar JN, Wang J, Gawad C, Quake SR. A quantitative comparison of single-cell whole genome amplification methods. PLOS ONE. 2014;9(8):e105585. doi: 10.1371/journal.pone.0105585.

Hartlmayr D, Ctortecka C, Seth A, Mendjan S, Tourniaire G, Mechtler K. An automated workflow for label-free and multiplexed single cell proteomics sample preparation at unprecedented sensitivity. bioRxiv. 2021:2021.04.14.439828. doi: 10.1101/2021.04.14.439828.

Slavov N. Single-cell protein analysis by mass spectrometry. Curr Opin Chem Biol. 2021;60:1-9. doi: 10.1016/j.cbpa.2020.04.018.

Meier F, Brunner AD, Koch S, et al. Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol Cell Proteomics. 2018;17(12):2534-2545. doi: 10.1074/mcp.TIR118.000900.