08/09/2023 - Journal Club

Exploring Cellular Complexity: Unveiling Single-Cell Proteomics

by Pinar Altiner and Mostafa Kalhor

There are enormous amounts of biological cascades in every cell – the smallest functional compartment of our body [1]. Understanding the mechanisms underlying the vast array of phenomena is not only the key element to finding any clues about fatal diseases such as Alzheimer’s and cancer but also to progress in developing treatment for such diseases. To address this need, scientists across diverse biological disciplines have embraced a multi-omic analysis approach, deciphering meaningful codes enciphered by cells, such as the genome, transcriptome, and proteome [1,2]. Recently, the discovery of new developments in the omics approaches allows us to conduct these analyses at single-cell resolution [3,4].

In this month of the journal club, we would like to touch upon single-cell technologies from the mass spectrometry-based proteomics perspective. The title of the selected article is “Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation” published by Andreas-David Brunner et al [4]. Before going into details about the article, we would like to give brief information about single-cell technology.

One of the most well-known analogies for the single-cell method is the smoothie example [3]. Imagine that you take a sip from a smoothie, and you will sense different flavors of fruits. However, this feeling will vary depending on the amount and characteristic tastes of fruits. For example, if there are lots of oranges in it, the high acidity due to the number of oranges might mask a less noticeable taste such as blueberry. Things become more complicated when you want to predict the ratio of each fruit that was blended for the smoothie. What if you have the opportunity to taste each fruit separately without making a smoothie? In the context of this analogy, the conventional bottom-up proteomic approach refers to analyzing protein levels by mixing all cells like a smoothie. Thus, the measurement of proteins would be the average of all cells that were involved in the study. Distinguishing the taste of blueberries in this case low-abundant species, rare cell types, and sub-populations becomes more complicated. However, single-cell proteomics enables us comprehensive understanding of cellular heterogeneity such as the immune system and cancer formation phases [2]. Since the combination of single-cell methods with MS-based proteomic studies was implemented less than 10 years ago, some challenges still need to be solved in terms of depth of coverage, sensitivity, robustness, and cost [1]. In this article, they proposed a novel true single-cell proteome (T-SCP) pipeline by enhancing sensitivity and optimizing the mass spectrometry (MS) setup.

They introduced the PASEF acquisition scheme for noise-reduced quantitative mass spectra, enabling highly sensitive and complete proteome measurements. Through a dilution series of HeLa cell lysate, they identified over 550 proteins from low amounts and achieved excellent quantitative reproducibility. To enable true single-cell proteomics, they significantly increased MS sensitivity and adapted their workflow. The researchers sorted and analyzed individual single HeLa cells, achieving protein identification and quantification. Sensitivity improvements allowed the identification of more than 1,890 proteins from as few as six single cells. They established a «core-proteome» subset of stable proteins that could serve as normalization factors and identified distinct protein regulation mechanisms.

Further sensitivity enhancements, including reduced flow rates, enabled a ten-fold increase in sensitivity, leading to the identification and quantification of over 3,900 HeLa proteins from just 1 ng of material. They adopted the diaPASEF acquisition mode for increased reproducibility.

Applying this technology, the study explored the cell cycle’s impact on single-cell proteomes. They treated HeLa cells with thymidine and nocodazole, quantifying up to 2,501 proteins per single cell across different cell cycle stages. The data revealed high quantitative precision and allowed the differentiation of cell cycle stages based on proteomic profiles. Using marker proteins, the researchers successfully assigned cellular states and distinguished cell cycle phases.

The single-cell proteome analysis also revealed differential expression of known cell cycle regulators and highlighted novel proteins associated with the G2/M transition.

Figure 1. A novel mass spectrometer allows the analysis of true single-cell proteomes. (Second figure in the article). A: Raw signal increase from standard versus modified TIMS-qTOF instrument (left) and at the evidence level (quantified peptide features in MaxQuant) (right). B: Proteins quantified from one to six single HeLa cells, either with MBR in MaxQuant (orange) or without MBR (blue). The outlier in the three-cell measurement in gray (no MBR) or white (with MBR) is likely due to failure of FACS sorting as it identified a similar number of proteins as blank runs (Horizontal lines within each respective cell count indicate median values). C: Quantitative reproducibility in a rank order plot of a six-cell replicate experiment. D: Same as C for two independent single cells. E: Rank order of protein signals in the six-cell experiment (blue) with proteins quantified in a single cell colored in orange. F: Raw MS1-level spectrum of one precursor isotope pattern of the indicated sequence and shared between the single-cell (top) and six-cell experiments (bottom).

SC proteomes compared to transcriptomes:

The study evaluated over 430 single-cell proteomes and compared them with Drop-seq and SMART-Seq2 scRNA-seq data to gain technology-independent insights. Proteome measurements exhibited higher average correlations among cells compared to scRNA-seq methods. Protein completeness per cell followed a normal distribution, with proteomic 

capturing about 49% of observed proteins, whereas SMART-Seq2 captured only 27% and Drop-seq captured 8%. Detecting limitations in protein measurements, bimodality in lower protein abundance range indicated potential benefits of imputation or tailored parameter estimation methods. While bulk transcript and protein levels showed moderate correlation, single-cell levels diverged, underscoring distinct regulatory mechanisms.

Examining shared gene CVs, single-cell transcriptomes displayed consistent quantitative variation, unlike proteomes. This emphasized different single-cell regulation for protein and RNA abundance. The research concluded that protein and RNA measurements offer complementary information, uncovering unique regulatory mechanisms. A stable core proteome subset of top 200 proteins with low CVs was identified, representing normalization factors and essential cellular processes. This subset’s distribution across the proteome’s dynamic range indicated stability even during remodeling. Overall, the study deepens insights into single-cell proteome dynamics and its interplay with gene expression.


[1]       K. Vandereyken, A. Sifrim, B. Thienpont, and T. Voet, “Methods and applications for single-cell and spatial multi-omics,” Nat Rev Genet, p. 1, Aug. 2023, doi: 10.1038/S41576-023-00580-2.

[2]       E. Flynn, A. Almonte-Loya, and G. K. Fragiadakis, “Single-Cell Multiomics,” https://doi.org/10.1146/annurev-biodatasci-020422-050645, vol. 6, no. 1, Aug. 2023, doi: 10.1146/ANNUREV-BIODATASCI-020422-050645.

[3]       “What is single-cell sequencing? – Single Cell Discoveries.” https://www.scdiscoveries.com/blog/knowledge/what-is-single-cell-sequencing/ (accessed Aug. 21, 2023).

[4]       A. Brunner et al., “Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation,” Mol Syst Biol, vol. 18, no. 3, Mar. 2022, doi: 10.15252/MSB.202110798.

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