Revolutionizing Proteomics: How Labs Can Transform Protein Characterization Workflows

In the field of proteomics, one area of rapid technological innovation is the analysis of proteoforms – the range of protein structures that can arise from a single gene. Proteoforms are generated by a variety of processes, including RNA splicing variance, genetic variation and protein folding or post-translational modifications like glycosylation and phosphorylation.

Characterizing proteoforms is vital to drug discovery because even the smallest modification in a protein can alter its biophysical and/or biochemical properties. This changes protein hydrophilicity in ways that can affect protein purification conditions. It can also affect in vivo targeting by changing a protein’s substrate/antigen binding properties.

Characterizing the processes that give rise to proteoforms raises many challenges, however, especially in the discovery and development phases of research. Analyzing multiple genetic variations, for example, can cause bottlenecks in sample testing, lowering throughput and slowing down the candidate identification process.

Automation can greatly streamline proteomics research, easing the process of detecting and characterizing proteoforms and accelerating the development of new therapies and diagnostics.

New tools are rapidly emerging for automating sample preparation, data collection and more. These technologies not only improve protein characterization, they also reduce the need for manual procedures. This not only minimizes the risk of human error, but also enables researchers to spend more time analyzing their findings so they can gain useable insights to further advance their research. 

More insights, faster

Automation improves researchers’ ability to study proteins in the context of cells. Say, for example, the goal is to determine how a dose of a small molecule changes the aggregation properties of a protein inside a cell. Researchers typically measure this using a cellular thermal shift assay (CETSA). To truly understand how the molecule affects proteins in a variety of conditions, a researcher would need to run the assay using several different drug dilutions and temperatures. With automated workflows, researchers can program all of those parameters into the CETSA assay and run it with little to no hands-on involvement.

The ability to image or identify a protein inside a cell or tissue fluid or lysate is also enhanced by automation. One popular assay involves linking two different antibodies to the same antigen followed by PCR-based signal amplification to see how much of the target protein is expressed. This type of proximity ligation or proximity enhancing assay has given rise to an entire field of research and testing modalities. With automation, researchers can easily examine different combinations of targets and modifications.

Automation simplifies the process of detecting and analyzing proteins in multiple blood samples simultaneously, which can greatly streamline research. Without automation, testing blood samples one at a time used to take two to three days per sample, as enrichment and digestion processes typically had to be performed overnight. With fully automated sample preparation workflows, the process can be optimized to run up to 576 samples at the same time. Six plates can be digested in as little as three hours.

In one study of automated liquid handling and sample preparation of 576 plasma samples, researchers identified between 4,900 and 5,500 proteins per sample, with high and reproducible digestion efficiency and no inter-plate variation. To demonstrate the efficiency of an automated workflow for deep plasma proteomics, they enriched 40 samples, in which they were able to identify 500 proteins using a traditional 200 SPD process. When they used an automated enrichment protocol, they achieved a five-fold increase in the number of proteins identified.

Combining high-throughput automated sample preparation with advanced mass spectrometry, including sophisticated data acquisition and analysis software, can greatly streamline protein characterization workflows. This was demonstrated in a 2023 study in which three applications of capillary electrophoresis for the characterization of biopharmaceuticals were fully automated. In all three cases, hands-free sample preparation for each 96-well plate could be done in under three hours. The trays were then transferred to a multi-capillary electrophoresis system, which facilitated rapid data analysis. The workflow eliminated the need for labor-intensive bench work, and results were highly consistent and reproducible. 

Integrating acoustics

The integration of acoustic technology into proteomics research is enhancing workflows. For example, in sample preparation, adaptive focused acoustics (AFA) technology can be used to improve robustness and reproducibility in the lysis of cells and tissues. A study published in 2024 showed that integrating AFA into an automated sample preparation method for protein digestion facilitated the identification of more than 10,000 proteins with a 24-minute active gradient. The researchers used heart, lung, liver and intestine cells and tissues, concluding that the workflow offered a quick and reproducible way to identify large sections of the proteome.

Acoustic ejection mass spectrometry (AEMS) is another technology enabling fast quantification and characterization of proteins. AEMS harnesses acoustic waves to propel droplets from samples into mass spectrometers, facilitating the rapid analysis of large samples.

In a Nature Communications study published in 2024, scientists led by Ghent University in Belgium and Cedars-Sinai Medical Center in Los Angeles demonstrated the use of this technology in analyzing SARS-CoV-2, the virus that causes COVID-19. They applied AEMS assays to SARS-CoV-2 protein markers from blood and peptides from nose swabs. Using AEMS, they quantified proteins from 267 plasma samples in just under five hours, and peptides from 145 swabs in 10 minutes – 15 times faster than what can be achieved with standard LC-MS workflows, they reported. 

Future innovation

Even more technological innovations are coming that will continue to enhance protein characterization. One active area of development is the use of nanoparticles to enrich proteins of interest before running them through mass spectrometers. With automation, it will be possible to analyze multiple different mixtures of nanoparticles and quickly determine which proteins should be enriched.

Improving protein characterization enhances drug development and manufacturing. In process development, high-throughput sample preparation and analysis helps to detect and quickly eliminate problems, such as unwanted proteoforms. Antibody-drug conjugates, nanobodies and single-chain antibodies are all different therapies derived from the proteins, which can also benefit from automating and streamlining this research.

The exposure risk to any toxicity that can be associated with protein characterization can also be removed by using automation solutions to do the heavy lifting. In manufacturing, it aids quality control by enabling a high-throughput process to ensure there are no inactive or harmful proteoforms in the final product. It’s yet another example of how innovations in protein characterization offer a win-win for researchers.