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Trends and Advancements in Proteomics

Close up of a protein structure.
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Proteins orchestrate and action practically every cellular process. They drive cell function, or in the case of disease, malfunction. The proteome – the entire catalog of proteins found in a cell, tissue or organism at a specific moment in time – is not constant; it is dynamic and complex. Some proteins are expressed in higher or lower abundance. Some proteins are transient – expressed for one moment and degraded the next, while others are present in cells for longer periods of time.

Proteomics interrogates cellular states at the protein level, exploring protein identification, quantity, structure, localization, function and interactions with other proteins. This data can offer a deeper insight into cellular states than genomics and transcriptomics information that, in isolation, provide indirect measurements and fail to capture data such as the contribution of post-translational modification (PTM) to a cell’s phenotype.

Many analytical tools for studying the proteome have emerged and continue to evolve rapidly. Existing methods include, but are by no means limited to, antibody-based, aptamer-based, imaging-based, mass spectrometry (MS)-based and more recently, sequencing-based approaches. The combination of technologies and workflows used in a lab will be unique to the research question and equipment available.

Proteomics methods are now applied across a variety of scientific disciplines, including health and disease biomarker discovery, drug target discovery, biopharmaceutical development, food and environmental analysis and plant biology, to name just a few examples.

In this article, we explore just some of the recent trends and advancements in high-throughput and single-cell proteomics.

Advances in high-throughput proteomics


MS is undoubtedly the most widely used experimental approach in proteomics. For the potential of the field to be realized, methods that can explore the gargantuan number of proteins expressed in each cell, across a large number of samples and, ideally, at a quick pace, are paramount – particularly for clinical applications. Dr. Georg Kustatscher, research group leader at the Institute for Quantitative Biology, Biochemistry and Biotechnology at the University of Edinburgh, expects the recent development of high-throughput MS-based approaches – particularly those pioneered in the laboratory of Professor Markus Ralser – will have a “massive” impact on biology. “The key point about this development is that it is not just a matter of making sample measurement faster, cheaper and more robust. The improvement is happening to a degree that is game-changing,” he says.

What is high-throughput proteomics?

High throughput proteomics (HTP) refers to the use of automated and high-capacity analytical techniques to conduct large-scale protein analyses, such as protein identification, quantification and characterization.

Recent advances in the speed and sensitivity of mass spectrometers, new methods for sample preparation, data interrogation and analysis are collectively enabling HTP. “With data-dependent acquisition (DDA), proteins were often missing at random from some samples, due to the stochastic nature in which peptides are sequenced by DDA. “That made proteomics rather unsuitable for routine clinical usage, of course,” Kustatscher says.

Data-independent acquisition (DIA), in contrast, fragments all precursor ions that are observed in cycle one of tandem MS (MS1) in the second cycle (MS2), which enables unbiased analyses and greater coverage of the proteome. “Now, with DIA, we can identify the same set of proteins reproducibly across thousands of samples.”

Novel capabilities in chromatography have also been instrumental in turning MS-based proteomics into a technology that is ready for large-scale sample analysis. “Previously, peptides were resolved for liquid chromatography-tandem mass spectrometry (LC-MS/MS) at nano-liter flow rates (300 nl/min) in very small capillary LC columns,” Kustatscher describes. These columns were not totally robust, could be easily clogged and elution times were poorly reproducible. “However, the higher speed of modern mass spectrometers allowed Ralser’s group to switch to micro-liter (5-200 µl/min) and even high-flow LC (800 µl/min),” he says, adding that the outcome is faster, more robust and reproducible chromatography, an “often overlooked but absolutely essential difference to traditional MS.”

A 2023 collaboration between Kustatscher, Ralser and Christoph Messner, published in Cell, showcased the potential of HTP in functional proteomics. Understanding how genetic mutations impact an organism’s phenotype requires comprehension of how protein networks operate and are affected by mutation. Piecing this molecular landscape together is challenging, considering that we still don’t know how many proteins function.

Kustatscher and colleagues used HTP-MS proteomics with DIA to conduct the largest proteomic study to date, analyzing the proteomes of 4,699 Saccharomyces cerevisiae strains, each of which had a different gene deleted.

An average of 2,520 proteins were quantified across the knock-out strains, revealing new insights that could aid the discovery of novel drug targets. “The resulting dataset allowed us to understand how the proteome adapts to such genetic perturbations, and also to make functional predictions on many understudied yeast genes,” Kustatscher describes. A “surprising” finding from the research was that a protein’s response to mutations depends not just on its function, but also on biological properties such as its translation rate and stability.      

“I think [this work] really shows the potential of HTP, combined with in-depth computational analysis, to gain biological insights that could not have been achieved without operating at this scale,” Kustatscher adds.

Non-MS approaches for HTP in biomarker discovery


Other approaches for HTP are emerging that might complement or act as a potential alternative to MS approaches, specifically in screening and diagnostic settings. Patient samples are finite resources, so tools that can get a lot of data from a small amount of sample are cause for excitement in the clinical space. Multiplex immunoassays, which enable simultaneous measurements of multiple proteins in a sample using a single assay, are one example of non-MS approaches to HTP proteomics.

Dr. Ulf Gyllensten, professor at Uppsala University and platform scientific director at Clinical Genomics Uppsala, part of SciLifeLab, is developing novel methods for screening and diagnosing the three most common forms of gynecological cancer: cervical, ovarian and endometrial. Diagnostic surgery is often required for these patients, which can be distressing and costly for healthcare systems. Plasma-based biomarkers, accessible through simple blood tests, are an attractive alternative.

Gyllensten and colleagues utilized a multiplex immunoassay to search for novel ovarian cancer biomarkers by comparing circulating plasma levels of 593 proteins in 3 patient cohorts. The researchers discovered 11 proteins associated with ovarian cancer, including 2 that hadn’t been previously linked to the disease – MUCIN-16 and WFDC2. The final protein panel was evaluated in a fourth, independent cohort of ovarian cancer patients, demonstrating high positive predictive value, specificity and sensitivity. “We have developed a strategy for the identification of protein cores that resulted in mutually exclusive combinations of protein signatures that can separate between benign tumors and ovarian cancers,” the authors write in Communications Biology.

Gyllensten says, “The fact that you can look at so many proteins with such high analytical sensitivity and such high specificity makes it a really useful and very powerful technology […] we also have samples from the follow up of the patients, so we could see how this proteomics risk score that we developed changed with a prognosis and relapse patterns of patients. It looks very informative for the patients.”

“The drawback, as with many proteomics technologies, is that it’s not a quantitative technology,” Gyllensten adds. “You have to add on additional steps, and you have to be careful when you compare sample-to-sample variation. It is still a screening and validation technology, rather than a clinical technology.”

It is anticipated that the future utility of non-MS approaches as large-scale multi-protein biomarker discovery platforms will be directed both by the evolving requirements for biomarkers, and the sensitivity, speed and coverage capabilities of new MS instruments. While perhaps more user-friendly and lower cost than MS, current immunoassay approaches are not able to cover high percentages of the proteome; thus, it’s likely that MS will remain the predominant tool of choice for HTP.

Single-cell proteomics: Methods and applications

Higher throughput is particularly important for single-cell analyses, one of the most rapidly growing areas in modern proteomics. Historically, proteome profiling has been conducted across populations of thousands or millions of cells.

“This provides an average abundance measurement of each protein across all of the underlying cells, but information about how those proteins are distributed within the samples is lost,” explains Dr. Ryan Kelly, an associate professor in the department of chemistry and biochemistry at Brigham Young University. Kelly’s research centers around the development of novel technological solutions for ultrasensitive biochemical analyses, including single-cell proteomics.

Bulk proteomics fails to distinguish the functional and morphological characteristics of the individual cells that make up a multicellular organism. This context is critical when distinguishing between diseased and healthy cells, or heterogenous cell types such as stem cells. “There are a lot of biological questions for which this cell-to-cell variation is critically important, and single-cell proteomics can fill this gap,” says Kelly. It is no easy feat, however. The human genome contains approximately 20,000 protein-encoding genes. But as a result of variance in primary sequence, PTMs and interactions with other proteins, the diversity of protein isoforms within the human proteome is estimated to be near 1 billion.

A further challenge is the limited amount of starting material. Unfortunately, the polymerase chain reaction, used to amplify genomic material, has no equivalent in proteomics for boosting peptide number. Until recently, these complexities meant that single-cell RNA sequencing (scRNA-seq), a key method in transcriptomics, was the star of the single-cell analysis show.

This started to change in 2018, when the first papers outlining proteome profiling from single mammalian cells using MS were published. Antibody-based methods had been used to quantify proteins within single cells previously but were limited by throughput and accuracy. In Genome Biology, Professor Nikolai Slavov and colleagues at Northeastern University shared “Single Cell ProtEomics by Mass Spectrometry” – or SCoPE-MS. Slavov’s laboratory pioneers methods for single-cell proteomics and has used them to connect protein covariation across cells to the cellular phenotype. His team developed plexDIA, a framework that parallelizes the analysis of both single cells and peptides, which inspired the creation of the Parallel Squared Technology Institute. This is a nonprofit research organization - Slavov is its Founding Director – that seeks to accelerate proteome biology research .

In SCoPE-MS, peptides from isolated cells and a carrier protein are labeled with different mass tags. Upon MS analysis, the tags fragment, releasing reporter ions that enable peptide quantification, revealing which cell the peptide originally came from. In 2021, Slavov and colleagues published SCoPE2 in Genome Biology, an updated version of the method that introduces automation and miniaturized sample preparation to increase quantitative accuracy and throughput.

“The first application of SCoPE-MS was differentiation of embryonic stem cells into different lineages. We found that proteins from certain lineages covaried together, so we could identify which proteins define different lineages,” Slavov explains. “We also found proteins that form subunits of protein complexes are much more strongly correlated to each other, so we could make a hypothesis regarding the relevance of protein complexes to the differentiation of stem cells. There are many applications of SCoPE-MS.” At the recent sixth Single-Cell Proteomics Conference, Slavov shared details of many more biological applications of SCOPE-MS.


Methods for single-cell proteomics using MS, such as SCOPE-MS, have been further developed in laboratories across the globe, and the landscape continues to evolve as new levels of depth and coverage are achieved. In 2022, Professor Matthias Mann and colleagues at the Max Planck Institute of Biochemistry published a workflow removing the need for a carrier proteome by injecting “nanopackages” into a very low-flow rate chromatography system, which increased sensitivity by tenfold. The same year, Slavov introduced a new method – prioritized Single Cell ProtEomics (pSCoPE) – a freely available tool that allows scientists to analyze thousands of peptides of interest across single cells while increasing proteome depth. “We have used pSCoPE to study primary macrophages and there we identified proteins that covary with surprising variability in the phagocytic activity. I say surprising because we found that non-stimulated cells were already heterogenous and after stimulation with lipopolysaccharides, instead of forming one homogenous group of macrophages, we could identify gradients within those activated cells. The gradient correlated with different phagocytic activity that we could verify, and we could measure regulatory aspects of that process.”

Single-cell MS-based proteomics is also being used alongside antibody-based imaging approaches to add spatial context to proteome investigations. The ability to visualize single-cell heterogeneity and tissue organization helps scientists understand systems within individual cells and how they contribute to the cell’s function as a system. In 2022, researchers including Dr. Emma Lundberg, professor in cell biology proteomics at the KTH Royal Institute of Technology, Sweden, and director of the Human Cell Atlas, published Deep Visual Proteomics.

“Here, we do highly multiplex imaging first to visualize all the cell types, then we use a laser microdissection microscope to cut out the different cell types before we do ultra-high mass spectrometry. The end result is the ability to project deep, unbiased measurements onto the original image,” says Lundberg. “It’s not straightforward, but it’s super fun.”

Nanoscale sample handling, improved sample preparation methods to overcome sample loss, DIA and sophisticated algorithms capable of handling large amounts of data – among other advances – are all enabling single-cell MS-based proteomics. These expanded capabilities have yielded insights in fields such as cardiology, plant biology and immunology, to name just a few examples.

But MS-based single-cell proteomics is not without its limitations; its sensitivity could still be improved, and throughput is still much lower than scRNA-seq, albeit recent methods have enabled analysis of thousands of single cells per day. In Nature Methods, Mann, alongside Dr. Florian Rosenberger and Dr. Marvin Thielert, argues that, “while most of the building blocks for single-cell proteomics are in place, applications to date have mostly been technological proofs of principle.” In the authors’ opinion, this is largely due to the “limited depth of quantitative depth of coverage achieved so far” and issues relating to throughput.

Kelly predicts that single-cell proteomics “could become the default mode of proteome profiling” if it continues to advance in terms of throughput and sensitivity. “A similar trend is already happening with transcriptomics. Measuring populations of cells one at a time provides both that single-cell granularity and the population-level information at the same time,” he says.

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Novel sequencing technologies and the future of the field

Looking to the future of the proteomics field, a growing area of interest has been building around non-MS-based single molecule protein sequencing approaches, which measure individual copies of peptides. Still at an early stage of development, these approaches have not yet been utilized to measure peptides or proteins in single cells.

“There are different categories of single molecule methods,” describes Slavov. “One example is nanopores, where different groups have been able to distinguish between a couple of synthetic peptides. A recent and exciting development in this field was the demonstration that a whole protein could pass through the pore. It did not result in sequencing the protein, rather it was a development of the technology itself.”

A second approach is the use of protein degradation to determine a peptide sequence. As amino acids are removed, they are labeled with fluorescent markers such that the pattern of fluorescence can be observed and offer a fingerprint for the peptide sequence. “The third category uses degenerate affinity reagents that bind proteins at multiple places. The combination of these degenerate affinity reagents is used to deduce the protein identity,” says Slavov.

As far as Kelly is concerned, single-cell MS proteomics is “ready for prime time”, whereas single-molecule sequencing is at a proof-of-concept stage. He anticipates that these fields will converge at some point, but they haven’t just yet. When they do, Kelly believes they can be both complementary to one another as well as competitors. Single-molecule sequencing technologies can laser in at a more granular level than MS, which requires thousands of copies to make a measurement. Here, sequencing technologies could fill the data gap for ultra-trace-level proteins. “Particularly if those low-abundance species can be separated from the higher abundance ones,” says Kelly.

Lundberg adds, “We have a long way to go in terms of understanding how proteoforms modify cell function. This is an ongoing challenge that I think novel technologies, such as protein sequencing and advanced MS, can help with.”