Proteins are the workhorses of cells, providing cellular structure and catalyzing almost every cellular reaction. The study of proteins can provide crucial information about diseases that are driven by abnormal protein activity, and also about an experimental drug’s impact on patients’ protein profiles—essential information for researchers developing new therapeutics.
Typical proteomics methods provide a snapshot of a sample’s proteins but fall short of picking up on nuances that can be crucial to understanding diseases and drug responses. Scientists can then struggle to determine, for example, why drug responses vary from patient to patient, but also across individual organs, cell types, and across different cells in the same sample. When researchers work with multiple populations of cells, typical proteomics approaches only pick up on the dominant population.
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The burgeoning field of single-cell proteomics (SCP) is allowing researchers to probe the complexities of heterologous cell populations in ways that weren’t possible before. By analyzing the proteome of individual cells instead of bulk populations, researchers can unearth deeper, more nuanced information. SCP provides a window into how individual cells respond to environmental shifts, from drug exposure to disease manifestation. This technology is not only instrumental in basic biology, but it can also drive the development of personalized therapies by giving researchers the ability to study rare cell populations, such as cancer stem cells.
Johns Hopkins researchers are using SCP to explore pivotal questions in personalized medicine, including how drug reactions vary between patients under identical conditions, and how cell-to-cell variability in a single patient influences reactions to different therapeutics. Recently, they have used single-cell proteomics to investigate post-translational modifications of proteins, untangling why certain patients are resistant to chemotherapies.1
The ability to multiplex samples has enhanced SCP protocols. Traditional SCP systems typically analyze only one sample at a time, a method that is time- and resource-intensive, making it hard to scale. Multiplexing allows multiple cells to be processed together by labeling them, most commonly with isobaric tags. When fragmented, each tag releases a unique reporter ion, allowing the sample to be identified. Multiplexing can increase throughput, enhance data robustness, and reduce time and costs.2
Boosting SCP with carrier proteomes
Another advance in SCP research—the addition of carrier proteomes—is helping scientists deal with a major challenge: protein content from a single cell is typically quite low and cannot be amplified. Managing such small sample volumes requires robust statistical methods to interpret the data.3 It also heightens the need to minimize sample losses throughout preparation and experimental procedures.4 Lastly, this challenge means that protein dynamic range stretches across seven orders of magnitude, with proteins present in quantities ranging from one to 10 million copies per cell.5 Present methodologies often capture only a fragment of this expansive range, overlooking low-abundance proteins referred to as the “dark proteome”.6
A carrier proteome can be added to the mass spectrometer at much higher quantities than the single cells to be measured.7 This carrier boosts the sensitivity of the mass spectrometric analysis, reduces protein losses due to nonspecific adsorption and serves as a quantitative reference by normalizing sample variations. The number of proteins identified in a sample will scale with carrier levels to some degree.8
Historically, researchers have limited the number of cells used in the carrier channel, because once they surpass a certain threshold, carrier effects can occur that make analysis challenging. When high numbers of cells are added to the carrier channel, the signal can bleed over into neighboring experimental channels, leading to quantitative distortion.9
Mass spectrometers vary in their ability to manage higher ranges of sample quantities. This capacity is determined by the machine’s intrascan linear dynamic range (ILDR). The ILDR refers to the range over which a detector can accurately measure and report ion intensity within a single scan.10 In the context of mass spectrometry, it is crucial because it dictates how well a device can differentiate between low-abundance and high-abundance ions.
New mass spectrometers offer a high ILDR to provide the sensitivity needed for SCP experiments. A wider dynamic range allows the instrument to detect both low- and high-abundance species in a sample—without saturating the detector or missing low-abundance ions.
Fueling precision medicine with next-generation SCP
In partnership with Ben Orsburn’s lab at Johns Hopkins, we have found that the quantitative distortion tracks well with the ILDR using the SCIEX ZenoTOF 7600. The mass spectrometer’s ILDR of nearly four orders, in addition to its high scan speeds, makes it a valuable tool for answering complex SCP questions. The lab has also applied SCP to identify cell cycle statuses, studying the effects of a novel treatment for pancreatic cancer on human pancreatic cells. By using a robust microflow system, we can gain a more comprehensive understanding of drug effectiveness.
As we transition to an era where precision and personalized medicine are commonplace, tools that provide a granular understanding of cellular heterogeneity will be indispensable, providing crucial information about drug reactions and the mechanisms underpinning disease resilience and susceptibility. Partnering in extraordinary science, as coined by Thomas Kuhn, will include these next-generation mass spectrometry tools to lead to discoveries beyond current knowledge. SCP's capacity to spotlight the “dark proteome” and to discern the nuanced effects of therapeutic interventions herald a new frontier in biomedical research.
References
1. Orsburn B., et al. (2022). Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry. Nat. Communications. 13, 7246.
2. Slavov, N. (2022). Scaling Up Single-Cell Proteomics. Molecular & Cellular Proteomics. Volume 21, Issue 1.
3. Ahmad, R., et al. (2023). A review of the current state of single-cell proteomics and future perspective. Anal. Bioanal. Chem.
4. Single-cell proteomics: challenges and prospects. 2023. Nat. Methods. 20, 317–318
5. Ibid.
6. Ibid.
7. Cheung, T.K., et al. 2021. Defining the carrier proteome limit for single-cell proteomics. Nat Methods 18, 76–83.
8. Ye, Z., et al. (2022). A deeper look at carrier proteome effects for single-cell proteomics. Commun. Biol. 5, 150.
9. Pankaj D. et al. (2022). Understanding the effect of carrier proteomes in single cell proteomic studies - key lessons. Expert Rev. Proteomics. 19:1, 5-15.
10. Kaufmann A. et al. (2017). Comparison of linear intrascan and interscan dynamic ranges of Orbitrap and ion-mobility time-of-flight mass spectrometers. Rapid Communications in Mass Spectrometry. Volume 31, issue 22.
Katherine Tran is Senior Manager, Global Strategic Marketing, Life Sciences Research, SCIEX