LC–MS-Based Proteomics for Protein Biomarker Quantification for Both Prognosis and Diagnosis in the Clinical Setting

Biomarkers play a significant role in evaluating disease risk and treatment by acting as indicators of biological processes as well as pharmacological reactions to therapy. Candidate protein biomarkers are highly promising, specific biomarkers. These provide more functional information and reflect a more precise physiological cellular state. However, reliable and robust measurement of low-abundance protein biomarkers remains a challenge, primarily because of the presence of an array of post-translational modifications (PTMs). In recent years, advances in protein quantification technologies that provide higher sensitivity and specificity are expected to accelerate protein biomarker discovery and verification. In this column, we discuss the label-free and stable isotope labeling proteomics approaches that help in biomarker discovery. We also discuss the different enrichment techniques, such as stable isotope labeling by amino acid in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ), and tandem mass tags (TMT, that help in measuring low-abundance protein biomarkers.

Biomarkers play a key role in evaluating disease risk and the human body’s response to therapeutic interventions. But what are biomarkers? Biomarkers are measurable indicators of biological processes or states, such as disease states, and can be specific cells, genes, hormones, or gene products, such as proteins. Protein biomarkers specifically provide insights into cellular functions and physiology, making them valuable in both research and clinical settings. But protein biomarkers do not come without their challenges. In particular, low-abundance proteins are difficult to detect because of post-translational modifications (PTMs) (1–3). PTMs are covalent processing events that occur on amino acids after their biosynthesis, changing the proteins properties and, in some cases, function (4). Thus, robust and sensitive measurement techniques are a necessity in detecting these low-abundance proteins (1–3). To that end, we will discuss mass spectrometry techniques using label-free and stable isotope labeling proteomics approaches. This will include different enrichment techniques, such as stable isotope labeling by amino acid in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT), that help in measuring low-abundance protein biomarkers.

Protein Biomarkers

Why protein biomarkers? Along with protein biomarkers, RNA and DNA biomarkers are perhaps the most commonly used in diagnostics. However, RNA and DNA biomarkers provide information at the genetic level, which represents gene expression or the genetic code. Protein biomarkers, in contrast, give a more functional perspective by representing biological processes and functional states. DNA and RNA biomarkers do not give information about the physiological state of a cell, as they do not account for PTMs (or protein interactions), which often represent the true physiological state of a cell (5). That is to say, protein biomarkers provide more detailed functional, physiological, and pathological information about an organism, which makes them more promising as a diagnostic tool and for monitoring the efficacy of therapeutic interventions (5–7).

PTMs, as we mentioned, can influence protein structure and function in addition to potentially causing challenges in the detection and quantification of low-abundance proteins. PTMs, such as phosphorylation, glycosylation, and ubiquitination, which change the molecular mass of proteins, can mask the presence of proteins in a sample and introduce variability in measurements. PTMs also create heterogeneity in protein populations, which complicates their measurement, not to mention that the PTM-modified protein itself may be the protein state of interest in detection. Thus, reliable and sensitive measurement techniques, such as mass spectrometry, are needed to advance protein biomarker discovery (5–7).

Challenges in Measuring Low-Abundance Protein Biomarkers

As we have discussed thus far, detecting and quantifying low-abundance proteins is a challenge due to such things as PTMs. In addition to PTMs, low-abundance proteins may be difficult to detect because they are overshadowed by highly abundant proteins. That is, highly abundant proteins dominate the available signals, which obscures or even masks the presence of low-abundance proteins (8–10). In proteomic approaches, it is particularly problematic because the protein concentrations vary greatly. For example, in a proteomic sample digested with trypsin, there are more tryptic peptides from the high-abundance proteins than the low-abundance proteins, causing low-abundance peptides to be masked. To detect these low-abundance peptides in proteomic approaches, more extensive and sophisticated chromatographic separation is needed. This reduces the dynamic range and biases identification towards the abundant proteins (8).

To overcome the challenges of PTMs and high-abundance proteins, improved sensitivity and specificity in protein quantification methods are needed. There are numerous ways to accomplish this, including selective depletion (high-abundance proteins) or enrichment techniques (low-abundance proteins) (8–10). Antibody arrays, ligand libraries, and chromatographic prefractionation can be used to reduce high-abundance proteins, whereas techniques that exploit the abundance-dependent Michaelis-Menten kinetics of tryptic digestion can be used to selectively digest and deplete abundant proteins (DigDeAPr) (8). In addition, the use of mass spectrometry-based proteomics with novel fractionation, enrichment techniques, and advances in data analysis, can be used to detect low-abundance proteins (8–10).

Advances in Protein Quantification Technologies

Over the last several years, there have been significant advances in protein quantification technologies that have positively impacted the detection of low-abundance proteins, and thus allowed for advances in protein biomarker discovery. We will briefly discuss two: label-free proteomic approaches and stable isotope labeling approaches.

Label-free proteomic approaches, as their name suggests, do not require the use of any labeling, but instead rely on the mass spectrometric signal’s intensity or the number of spectra matching each protein (spectral count). Label-free methods allow for the analysis of any number of samples without the limitations of the number of available labels. They also enable large-scale comparisons, which makes them ideal for high-throughput studies, including high-throughput biomarker discovery studies. Because of the lack of labeling, these studies tend to be simpler (due to easier sample prep, as just one example) and more cost-effective. That said, label-free approaches do have their limits. They tend to be less reproducible and less sensitive than other techniques. In addition, variability in instrument performance and sample processing can introduce errors, thus affecting the accuracy of results, including biomarker identification (11–13).

Stable isotope labeling approaches, as you might have guessed by now, require the labeling of samples using stable isotopes. There are several different stable isotope labeling techniques used, including stable isotope labeling by SILAC, iTRAQ, and TMT. These techniques tend to offer more controlled and precise quantification of proteins and peptides by incorporating heavy isotopes into them. Briefly, SILAC works by growing cells in media containing isotopically labeled amino acids, resulting in near complete labeling of proteins. iTRAQ and TMT, on the other hand, utilize in vitro chemical labeling after proteolysis and allow for multiplexing, where many samples can be labeled and analyzed at the same time (higher throughput) (11–13).

Enrichment Techniques for Low-Abundance Proteins

Stable isotope labeling approaches, compared to their label-free counterparts, allow for enhanced sensitivity, specificity, and reproducibility, in particular when analyzing complex biological samples. This, however, comes at an increased cost, and is more time-consuming when compared to label-free methods. That said, the enhanced precision and reliability of these isotope-labeled methods make them ideal for biomarker discovery, specifically biomarker discovery of low-abundant proteins (11–13). Thus, let us take a closer look at each of these isotopic labeling techniques.

SILACis an in vivo metabolic labeling technique that incorporates isotopically labeled amino acids (13C or 15N) into proteins during cell growth. In this method, cells are grown in media containing either light or heavy forms of amino acids and are spiked together before analysis. SILAC is often used to quantify protein expression levels across different conditions and is effective at studying dynamic biological processes, such as signaling pathways, due to its ability to detect changes in protein abundance in an accurate and reproducible way. In addition, SILAC can quantify low-abundance protein biomarkers because it minimizes variability due to sample processing. SILAC allows for direct comparison of protein expression across different cell states, making it an effective technique to monitor dynamic biological and disease states. As the name implies, labeling happens in cell culture, which can be a limitation. Not all cells are capable of growing in the necessary media, thus limiting its applicability. Also, it is not useful for tissue, organs, or other in vivo studies (14,15).

iTRAQ is an in vitro chemical labeling technique that enables the simultaneous quantification of proteins from up to eight different samples in one experiment. In iTRAQ, isobaric tags are attached to peptides at the N-terminus and lysine residues, which then release reporter ions during mass spectrometric analysis. The major advantage of iTRAQ is its ability to multiplex, allowing for the comparison of multiple samples aimed at assessing biological variability. A disadvantage of iTRAQ, which could have a significant impact on protein biomarkers, is reduced sensitivity due to sample complexity and interference from high-abundance proteins (14,15).

TMT is a similar technique to iTRAQ in that it allows for multiplexing, but instead of only 8 different samples, TMT allows for the analysis of sixteen samples in one experiment. Like iTRAQ, TMT labels peptides at their N-terminus and lysine residues and releases reporter ions during tandem mass spectrometry. As you might expect, the major advantage of TMT is its throughput, or its ability to analyze sixteen samples at once. TMT is also advantageous in characterizing complex biological samples, such as plasma, as it allows for the comparison of different conditions at the same time. This is particularly useful in analysis of clinical studies or biomarker discovery. The major disadvantage to TMT is the potential for ratio compression, where signal intensities from different samples may interfere with each other, thus providing less accurate quantification (14,15).

Future Directions in Protein Biomarker Identification

Several emerging technologies will have an impact on protein biomarker discovery. Next-generation proteomics techniques, such as single-cell proteomics, will allow for the analysis of proteins at an individual cell level. This will allow for a greater understanding of cellular heterogeneity, which is valuable in monitoring tumors or immune responses. Single-cell proteomics allows for the unique protein expression in each cell to be monitored and not lost in the averaging of expression across multiple cells (16–18). In addition to single-cell proteomics, miniaturization and automation will likely impact protein biomarker identification. As both suggest by their names, miniaturization of instrumentation will allow for point-of-care like protein biomarker characterization, and automation will potentially allow for more accurate less complicated assays (19).

In addition to new techniques like single-cell analysis, machine learning likely will have an impact on protein biomarker discovery. Machine learning will allow for the analysis and better management of large experimental datasets. This analysis will enable patterns and correlations to be identified that were not possible before. These new patterns and correlations can improve the sensitivity and specificity of biomarker detection, leading to more accurate diagnostics (16–18).

These emerging technologies will have a significant impact on accelerating the transition from discovery to clinical validation and application. For example, single-cell proteomics provides a more nuanced understanding of disease at the cellular level. This could lead to the identification of novel biomarkers that are more precise in monitoring disease progression or therapeutic response. In addition, improvement in sensitivity in mass spectrometry techniques could lead to the discovery of low-abundance biomarkers, permitting early diagnosis, intervention, and personalized treatments. Machine learning and other advanced computational techniques allow for the rapid analysis of datasets, enabling the identification of biomarkers with greater precision and speed. In both cases, the pipeline from discovery to clinical application could be streamlined, allowing for faster development of diagnostics and therapeutic targets, ultimately improving patient outcomes. Thus, the integration of novel techniques and novel computational tools likely will bridge the gap between research and clinical application (16–18).

When considering emerging technologies, however, it is important to consider potential limitations and challenges. Analyzing the vast amount of data from single-cell proteomics, even with machine learning, is a challenge. The complexity of proteome-wide studies and variability introduced by PTMs require novel computational approaches to ensure accuracy and reproducibility. Improving the sensitivity of mass-spectrometry-based proteomics is a necessity in detecting low-abundance proteins that are often the most informative biomarkers. Machine learning tools also need continued refinement to handle complex datasets and reduce noise without losing critical information. These and other emerging technologies hold great promise for clinical biomarkers; however, further development and refinement are needed to realize their use in a clinical setting (16–18).

Conclusion

Protein biomarkers are instrumental in being able to detect and diagnose diseases as well as to detect and monitor the efficiency of therapeutics. This is in no small part due to their ability to monitor complex biological processes and cellular states. The detection of low-abundance proteins, which are critically important in disease states, is a significant challenge in biomarker discovery due to post-translation modifications and high-abundance proteins. Highly sensitive and specific measurement techniques are needed to overcome these challenges and allow for the detection of low-abundance proteins.

Label-free and stable isotope labeling approaches play a role in addressing these challenges. Label-free methods are simple and cheap, whereas stable isotope labeling techniques are more precise and reproducible. In addition, isotopic labeling allows for multiplexing, which is an advantage when characterizing complex biological samples. In both cases, there are strengths and weaknesses, but the strengths have allowed for improved measurements of low-abundance proteins, improving reliable biomarker discovery.

Biomarker discovery and clinical diagnostics will be positively impacted by innovations in enrichment and quantification. Single-cell proteomics and machine learning will enhance the sensitivity of proteomic studies, allowing for more precise analysis of cellular heterogeneity and complex biological processes. New advances like these will accelerate the translation of biomarkers into clinical applications, which will lead to more personalized and effective medical interventions, improving patient outcomes.

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About the Authors

Anantdeep Kaur is a Postdoctoral Research Associate at the Biopharmaceutical Analysis Training Laboratory for the Department of Chemistry and Chemical Biology at Northeastern University, in Boston, Massachusetts.

About the Editors

Jared R. Auclair is Interim Dean at the College of Professional Studies, Vice Provost Research Economic Development and Director of Bioinnovation at Northeastern University, in Boston, Massachusetts. He is also the Director of Biotechnology and Informatics, as well as the Director of the Biopharmaceutical Analysis Training Laboratory.

Anurag S. Rathore is a professor in the Department of Chemical Engineering at the Indian Institute of Technology in Delhi, India.