The Power of Single-Cell Proteomics

Single-cell proteomics is transforming medical research by improving our understanding of the distinct molecular and biological profiles of individual cells and their proteins that drive vital cellular functions. Though traditional lab technologies offer a generalized view of protein interactions based on average cellular populations, single-cell proteomics offers much more granular insights – allowing researchers to observe distinct cellular reactions and unravel pathways and processes triggered at the molecular level (1). This precision is vital for the study of heterogeneous tissues, which is particularly challenging because protein responses often vary from cell to cell.

Proteomics advances have the potential to be transformative for the treatment of many diseases – not least in cancer, where significant heterogeneity can exist in a single tumor.   Single-cell proteomics allows researchers to probe tumor heterogeneity at the protein level, revealing details of distinct but historically elusive cellular subsets that might drive tumor growth, metastasis, or resistance to therapy.

Single-cell proteomics is becoming particularly valuable in drug development; after all, proteins are the primary target of up to 95 percent of drugs (2,3). Traditionally, researchers screening potential new drugs and analyzing their mode of action used methods such as high-throughput screening (HTS), which typically assumes uniform behavior across all cells. In contrast, single-cell proteomics can identify responses at the individual cell level, giving a clearer picture of off-target effects, dose responses, and more (4). Such work can also accelerate the discovery of novel biomarkers, enhancing the ability to detect cancer early with diagnostics and further guiding therapeutic strategies that are specific to individual patients’ tumors (5).

At Johns Hopkins University School of Medicine, USA, researchers are using single-cell proteomics to explore drug activity and toxicity in cells; for example, one recent study focused on human pancreatic cancer cells treated with a KRAS G12D inhibitor. The team was able to evaluate isolated drug responses in the cell cycle using proteomic markers. And cell-to-cell variability was observed in ways that would not have been possible with traditional proteomics tools (6).

The most commonly used technology in single-cell proteomics is mass spectrometry, particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS). Combining the separating capabilities of LC with the analytical strengths of MS offers the most unbiased approach for detailed protein identification and quantification (7). Single-cell mass spectrometry experiments can quantify as many as 1,000 proteins per cell and up to 2,500 proteins across all cells analyzed.

MS can be tailored in a multitude of ways depending on the specific goals of the experiment. Single-cell proteomics experiments can be label-free, for example, which means the relevant proteins must be identified by their mass-to-charge ratios. Or researchers can employ isobaric labeling, in which the intensities of tagged proteins are compared with those of the unlabeled species. Once labeled, the protein of interest can be tracked by other techniques, such as immunofluorescent (IF) microscopy, fluorescence-activated cell sorting (FACS), or cytometry by time of flight (CyTOF) (8). Furthermore, researchers can amplify the scope of their proteomic analyses with multiplexing, which enables the study of multiple proteomes across various samples.

Although single-cell proteomics has been a game-changer for understanding cellular processes, it still faces several challenges that must be overcome to fully harness its potential in drug discovery and personalized medicine.

One inherent challenge is the variability of protein expression across cells – even those derived from the same population, where protein expression levels can vary depending on the cycle stage or external stimuli (1). Moreover, proteins undergo post-translational modifications, which makes the analysis more complex and requires a deep understanding of cellular biology.

There are also several technological challenges in the field of single-cell proteomics (SCP). One such challenge is increasing the signal-to-noise ratio for mass spectrometry-based SCP to increase the identification of low-abundant proteins. Currently, most methods are only able to identify a small portion the cell's wide dynamic range and unfortunately miss low-abundant proteins. However, by increasing the signal-to-noise ratio within the mass spectrometry system, we can further facilitate the identification of low-abundant proteins that have not yet been identified. As a result, there has been significant effort from the proteomics community and technology providers to develop advanced technology that enable researchers the ability to increase the signal of the target protein(s) while reducing the signals from the background and interfering noise elements.

Furthermore, with the current state of SCP research, integration of multiple workflows and technologies is necessary to broaden our understanding. For example, mass spectrometry-based SCP enables the identification and quantitation of proteins at a single-cell level, however, protein subcellular localization and its microenvironment have a direct impact on its biological function. Thus, spatial context of the cell by imaging technologies is also important to integrate in SCP research. However, the integration of multiple workflows can be complex to handle simultaneously. To mitigate this, technological advances such as cell sorting technologies and sample handling robotics are employed. These technologies enhance analytical capabilities and streamline single-cell workflows. 

As a result, the in-depth and thorough analysis of proteins within single cells require the handling and storage of vast amounts of sample and data. The continuous development of advanced analytics provides the computational power and sophisticated algorithms needed to process and interpret this data. 

Advances in single-cell proteomics will ultimately benefit the healthcare system as a whole. Recent estimates suggest 90 percent of drugs are effective in only half of the patients, leading to annual losses of $350 billion in the United States alone (8). The use of single-cell proteomics in medical research promises to improve diagnostic and treatment capabilities in cancer and beyond – helping deliver the right treatments to the right patients exactly when they need them.

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