Proteomics: Tools, Techniques and Translational Potential

Credit: iStock Mapping the Plasma Proteome: Unlocking Biomarkers for Precision Medicine Clinical Proteomics: Progress and Future Prospects With Professor Jennifer Van Eyk Unlocking Nature’s Secrets With Metabolome Informed Proteome Imaging PROTEOMICS: Tools, Techniques and Translational Potential SPONSORED BY CONTENTS 4 Mastering the Western Blotting Frontier: Advanced Tips and Tricks for Success 9 Mapping the Plasma Proteome: Unlocking Biomarkers for Precision Medicine 13 Current and Emerging Applications of ELISAs 18 Clinical Proteomics: Progress and Future Prospects With Professor Jennifer Van Eyk 22 Innovating Drug Discovery With Next-Generation Proteomics 26 Perfect Your Protein Prep for Mass Spectrometry 30 Unlocking Nature’s Secrets With Metabolome Informed Proteome Imaging 33 Unveiling Disease-Associated Antigens: The Role of Immunoproteomics 34 Advancing Mass Spectrometry Data Analysis Through Artificial Intelligence and Machine Learning 38 Advances in Biomarker Discovery and Analysis PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 3 TECHNOLOGYNETWORKS.COM FOREWORD Proteins shape cellular behavior, mark disease progression and guide our understanding of health. In today’s rapidly evolving biomedical landscape, the ability to analyze, interpret and apply protein-centric data has never been more crucial. From mastering foundational techniques like western blotting and ELISAs to exploring the vast capabilities of mass spectrometry and AI-enhanced data analysis, this eBook illuminates the workflows and technologies driving proteomics research. Readers will gain insights into high-throughput plasma profiling, the integration of metabolomics with spatial proteome imaging and the emergence of remote sampling as a tool for decentralized diagnostics. Whether you are deepening your expertise or just beginning to explore the proteomic landscape, this eBook offers a valuable resource for understanding where the field stands today and where it’s headed. The Technology Networks editorial team 4 PROTEOMICS If you had to pick a few techniques to use in a laboratory, western blotting would most likely be one of those techniques. If you love puzzles, you probably love western blotting. The technique is like solving a mystery with clues hidden in the gel, antibodies, buffers and the membrane. When you use suitable antibodies, buffers and detection methods, you will reveal the secrets of your protein samples. On June 28, 2024, a PubMed search for "western blot" returned 413,000 results, indicating its immense popularity. This blotting technique's affordability and adaptability have contributed significantly to its use in biological research compared to other methods. Western blotting, or immunoblotting, is simply a multi-step procedure that utilizes highly selective antibodies that bind to a specific protein of interest, allowing investigators to detect a specific protein in a complex mixture of proteins.1 Besides determining if a protein of interest is present in a sample, western blotting can also allow you to determine the amount of protein, the molecular weight of the protein and detect modifications on proteins.1 The most common protein modifications that can be detected include phosphorylation, glycosylation, ubiquitination, acetylation and methylation. The key to detecting protein modifications is having an antibody specific for the modification on the protein of interest. The main steps of a western blotting experiment include sample preparation, applying the sample to a polyacrylamide gel, which separates proteins based on size, protein transfer from the polyacrylamide gel to a membrane (typically nitrocellulose or polyvinylidene fluoride (PVDF)), blocking non-specific binding sites Mastering the Western Blotting Frontier: Advanced Tips and Tricks for Success Aldrin V. Gomes, PhD Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 5 on the membrane, incubation of the membrane with an antibody that is specific for the protein of interest, incubation of the membrane with secondary antibodies that interact with the primary antibody and detection of the signal generated by the secondary antibody. While western blotting is one of my favorite techniques, it has two main downsides: the high dependency of the method on the availability of a specific antibody and the thousands of possible variations of protocols for the technique. To help reduce the variability in procedures for new users, we have a detailed protocol that has been optimized over 15 years — it is available on protols.io.2 While a quick search online can find tips for each step of the western blotting procedure, I will discuss hidden gems that are essential for mastering western blotting skills. 1) The amount of protein loaded into each well is critical In a blotting experiment, while the quantity and characteristics of the antibody play a role in the final results, equally important is the amount of protein loaded into each gel well. Detecting a protein within its signal range relies on its abundance and the total protein amount in each lane. It is essential to optimize the detection of a protein within its linear intensity range for the best outcomes. Some studies from our laboratory suggest that loading 10–20 μg of total protein per lane is ideal for most proteins.3,4 For about 40 different antibodies, our laboratory typically uses 10–20 μg of total protein per lane. While we no longer use more than 20 μg of total protein for western blots, in some experiments, to test the limits of total protein loading, we found that when wells contain more than 80 μg of protein, the pattern of protein bands is often distorted relative to how they run when lower concentrations of total protein are used. 2) Be careful not to overload wells While some scientists try to maximize the volume they load into wells, loading more than 85% of the maximum capacity of a well can lead to non-reproducible results. If a well can hold a maximum of 15 ml of a sample, loading more than 13 ml of a sample often causes spillover into nearby lanes. In our lab, we often observe spillage into wells when loading 15 μl of a sample into a 15 μl well. While this spillover is not always apparent, it could be detected by loading sample buffer without any protein into the lanes next to the well containing the maximum well loading capacity and doing a western blot for a highly abundant protein. 3) All antibodies need validation Every antibody that will be used for western blotting requires validation.5,6 We use VCV (verification, confirmation, validation) for antibodies used in our laboratory. While some antibodies come with validation documentation from suppliers, many lack this validation. VCV plays a role in enhancing the reproducibility of antibodies that are not properly validated. While having a validated antibody specific to your protein of interest does not ensure an excellent western blotting result, using an unvalidated, nonspecific antibody ensures that your result will be inaccurate. VCV enhances the reproducibility of antibodies that haven't undergone validation. It is vital to verify that an antibody can accurately identify the target protein at its expected weight in controls and exhibit no signal at that weight in negative controls. The specificity of an antibody can be confirmed by testing it against an epitope matching peptide used in its development. Specificity refers to the ability of an antibody to bind to an epitope or antigen. This peptide can often be obtained from the manufacturer of the antibody. The reproducibility of western blots using a specific antibody can be validated by doing at least two independent experiments to confirm consistent results within the same laboratory. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 6 4) Use a gel percentage optimized for your target of interest The composition percentage of gels determines pore size, influencing how proteins migrate during electrophoresis. The higher the percentage of acrylamide, the smaller the pore size, resulting in slower mobility or exclusion of larger proteins. Using a single gel percentage is advantageous for separating proteins with similar sizes effectively. It's essential to remember that acrylamide is a neurotoxin; so it's crucial to wear gloves for safety. Table 1: Rough guidelines for selecting a suitable polyacrylamide gel percentage according to the protein size for tris-glycine gels. Protein Size (kDa) Optimal Polyacrylamide Gel Percentage 4–40 20% 12–45 15% 10–70 12.5% 15–100 10% 40–200 8% 60– > 200 4–6% While single-percentage gels are great for separating bands that are close in molecular weight, for unfamiliar or unknown samples, it is best to use a gradient gel, such as 4–20% (separates 10–250 kDa), for evaluation of the sample, and then utilize an appropriate singlepercentage gel once the size range of protein has been determined (see Table 1). 5) Optimize transfer for the target protein of interest Most scientists I asked use the same set of transfer conditions regardless of what protein of interest they are investigating. Various factors such as the composition of the transfer buffer, transfer duration, size of the membrane, type of membrane and thickness of the gel influence how proteins are transferred from gels to membranes. Thinner gel layers enable the transfer of proteins to membranes more easily compared to thicker gels, while a smaller pore size (0.2 μm) is recommended for nitrocellulose membranes when blotting proteins under 15 kDa. Dystrophin, with a molecular weight of 427 kDa, can be effectively transferred to a nitrocellulose membrane under constant mAs (300) and refrigerated at 4 °C for 18 hours.7 On the other hand, certain small proteins may pass through the membrane depending on the conditions under which they are transferred. 6) Some antibodies work best on autoclaved or cross-linked proteins Some proteins, such as ubiquitin, seem to renature after transfer to membranes. Autoclaving the membrane or cross-linking the proteins on the membrane with glutaraldehyde has been shown to improve the sensitivity of western blotting for ubiquitin.6,8,9 In one case, the pre-treatment of the blot with 0.5% glutaraldehyde drastically increased the signal intensity of free ubiquitin and polyubiquitinated proteins.6 7) Use total protein staining instead of housekeeping proteins for normalization Everyone who does western blotting knows the proteins actin, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and tubulin. These are the housekeeping proteins that are most commonly used for the normalization of western blots. However, many studies have disproved that housekeeping proteins don't vary with experimental conditions. Another disadvantage of many housekeeping proteins is that they saturate the blot at lower total protein concentrations than proteins that the user is interested in quantifying.5,10 Some housekeeping proteins were found to be saturated at a total protein concentration of less than 10 mg, a total protein amount that is less than most laboratories use.11 Therefore, normalizing the signal intensity of the protein of interest against a housekeeping protein can sometimes lead to errors. Total protein staining, which measures the protein content in each lane, proves more TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 7 reliable for normalizing western blots as it does not rely on any specific reference protein.10,12 Our data and literature strongly suggest that total protein staining is more accurate and reproducible than housekeeping proteins for normalization of western blots. The most common total protein staining method is Ponceau S. 8) When doing western blotting to check for specific modifications, use inhibitors in the sample preparation buffer to prevent these modifications from being removed The number and quality of antibodies available to detect modifications on proteins continues to increase significantly. Unfortunately, in my experience, I have noticed that some labs that use blotting to detect translational modifications may not be optimizing the procedure effectively. For instance, it's crucial to include phosphatase inhibitors in the samples to prevent protein dephosphorylation during preparation. When studying modifications, like ubiquitination, methylation and acetylation, adding proteasome, demethylase and deacetylase inhibitors respectively is necessary.13 For glycosylation sites, iodoacetamide and sodium fluoride can be used to inhibit O-linked glycosidases, while deoxynojirimycin can be used to inhibit N-linked glycosidases. Conducting all sample preparations on ice or in a cold room is also essential. When researchers use inhibitors, this information should be included in manuscripts to encourage others to use these techniques for better results. 9) The buffer used to wash blots and dilute antibodies matters If you spend a few minutes on PubMed looking at the western blot methods section of manuscripts, you will notice that most researchers use a buffer containing 3–5% non-fat milk for diluting antibodies. However, a buffer containing 1% non-fat milk gives better results. Along with the amount of non-fat milk, the presence of sodium chloride in the buffer plays a significant role. In our lab, we once encountered a two-week period where all our western blots showed a weak signal. After troubleshooting, the poor results were due to a tris buffered saline (TBS) solution obtained from a commercial manufacturer, which contained 500 mM sodium chloride instead of 150 mM, which is commonly used. Final words: Understanding how the western blotting procedure could be optimized will help everyone using this technique obtain more reproducible and visually attractive images! Give it a try and see your efforts rewarded with reliable results!! REFERENCES 1. Sule R, Rivera G, Gomes AV. Western blotting (immunoblotting): History, theory, uses, protocol and problems. Biotechniques. 2023;75:99-114. doi:10.2144/btn-2022-0034 2. Sule R, Rivera G, Gomes AV. Detailed western blotting (immunoblotting) protocol. Protocols.io. https://www.protocols. io/view/detailed-western-blotting-immunoblotting-protocolyxmvmn2rng3p/ v1. Published 2022. Accessed June 25th, 2024. 3. Sule RO, Phinney BS, Salemi MR, Gomes AV. Mitochondrial and proteasome dysfunction occurs in the hearts of mice treated with triazine herbicide prometryn. Int J Mol Sci. 2023;24. doi:10.3390/ ijms242015266 4. Ghosh R, Gilda JE, Gomes AV. The necessity of and strategies for improving confidence in the accuracy of western blots. Expert Rev Proteomics. 2014;11:549-560. doi:10.1586/14789450.2014.939635 5. Brooks HL, de Castro Brás LE, Brunt KR, et al. Guidelines on antibody use in physiology research. Am J Physiol Renal Physiol. 2024;326:F511-F533. doi:10.1152/ajprenal.00347.2023 6. Gilda JE, Ghosh R, Cheah JX, West TM, Bodine SC, Gomes AV. Western blotting inaccuracies with unverified antibodies: Need for a western blotting minimal reporting standard (WBMRS). PLoS One. 2015;10: e0135392. doi:10.1371/journal.pone.0135392 7. Taylor LE, Kaminoh YJ, Rodesch CK, Flanigan KM. Quantification of dystrophin immunofluorescence in dystrophinopathy muscle specimens. Neuropathol Appl Neurobiol. 2012;38:591-601. doi:10.1111/j.1365-2990.2012.01250.x 8. Swerdlow PS, Finley D, Varshavsky A. Enhancement of immunoblot sensitivity by heating of hydrated filters. Anal Biochem. 1986;156:147-153. doi:10.1016/0003-2697(86)90166-1 9. Emmerich CH, Cohen P. Optimising methods for the preservation, capture and identification of ubiquitin chains and ubiquitylated proteins by immunoblotting. Biochem Biophys Res Commun. 2015;466:1-14. doi:10.1016/j.bbrc.2015.08.109 10. Gilda JE, Gomes AV. Stain-free total protein staining is a superior loading control to beta-actin for western blots. Anal Biochem. 2013;440:186-188. doi:10.1016/j.ab.2013.05.027 11. Moritz CP. Tubulin or not tubulin: Heading toward total protein staining as loading control in Western blots. Proteomics. 2017;17. doi:10.1002/pmic.201600189 12. Eaton SL, Roche SL, Hurtado ML, et al. Total protein analysis as a reliable loading control for quantitative fluorescent western blotting. PLoS One. 2013;8. doi:10.1371/journal.pone.0072457 13. Mishra M, Tiwari S, Gomes AV. Protein purification and analysis: Next generation western blotting techniques. Expert Rev Proteomics. 2017;14:1037-1053. doi:10.1080/14789450.2017.1388 167 Western blotting, resolved —free tips and techniques from our experts.Find out about the latest advances in western blottingThis fun-to-read western blotting guide will bring you up to speed on all the new advances in western blotting, including Stain-Free technology, total protein normalization, 3-minute rapid transfer, and 5-minute rapid universal blocking, as well as state-of-the-art advances in digital imaging. Request a free PDF or hard copy now. bio-rad.com/WesternGuide#ScienceForward 9 PROTEOMICS Advances in high-throughput proteomics now allow researchers to profile circulating plasma proteins at unprecedented depth and scale – accelerating efforts to identify novel biomarkers for the early detection of disease, monitoring and predicting response to treatment. However, the complexity of the plasma proteome poses challenges: it contains a diverse array of proteins that vary considerably in abundance. “Proteins exhibit significant inter-individual variability, particularly in blood, so large-scale analyses are crucial to uncover disease-specific changes and transition these discoveries into the clinic,” says Christoph Messner, assistant professor for precision proteomics at the University of Zurich, Switzerland. Researchers employ a range of next-generation tools for high-throughput analysis of hundreds to thousands of blood samples to identify and validate potential diseaserelated biomarkers. “We rely on mass spectrometry (MS)-based proteomics to detect and quantify the highly abundant proteins, while multiplex affinity-based assays allow us to profile those at lower concentrations,” says Fredrik Edfors Arfwidsson, assistant professor and docent in biotechnology at the KTH Royal Institute of Technology, Sweden. “By combining these complementary technologies, we can capture a more comprehensive view of the plasma proteome.” Mapping the Plasma Proteome: Unlocking Biomarkers for Precision Medicine Alison Halliday, PhD Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 10 Next-generation tools for proteomics MS-based proteomic workflows are well-established in research laboratories and are routinely used for biomarker discovery and profiling. Over the past decade, dramatic advances in technology have led to faster, more sensitive instruments capable of identifying thousands of proteins and peptides in a single run. “You can scan much faster now, which has significantly increased throughput,” says Messner. “The latest instruments also enable unprecedented proteome depth, allowing us to detect a vast number of proteins within remarkably short measurement times.” In recent years, the introduction of data-independent acquisition (DIA) methods has also been a game changer for MS analyses. “This was a really important step forward,” expresses Messner. “DIA enables shorter measurement gradients – reducing analysis times and increasing throughput. The results are also more reproducible than conventional data-dependent acquisition (DDA) methods.” Affinity-based assays that use binding to target proteins to enable the simultaneous detection and quantification of hundreds or even thousands of specific proteins from a single sample, offer a powerful alternative to MS-based proteomics. The number of assays on these highlymultiplexed proteomic platforms has increased ten-fold over the past 15 years, rising from fewer than 1,000 to over 11,000. One of these high-throughput proteomics platforms uses the proximity extension assay (PEA), which combines oligonucleotide-linked antibodies with quantitative real-time PCR. “These multiplex panels have expanded dramatically, thanks to antibody barcoding,” explains Edfors Arfwidsson. “We can now measure up to 5,400 proteins from just a single drop of blood, or less than 10 microliters of plasma.” Another tool for large-scale proteomics employs aptamers – nucleotide-based compounds with high protein-binding specificity and sensitivity – instead of antibodies. The latest version enables the simultaneous measurement of over 11,000 proteins from just 55 microliters of plasma.* Large-scale plasma proteomics As a Scientific Director of the Human Blood Atlas, part of the wider Human Protein Atlas, Edfors Arfwidsson is at the forefront of efforts to use advanced proteomic tools for the large-scale profiling of plasma proteins in health and disease. “We initially focused on profiling circulating proteins in healthy individuals, which revealed that each person has a unique proteomic fingerprint that remains remarkably stable over time,” says Edfors Arfwidsson. “This insight led us to expand our work and start to explore different disease states.” In 2015, the team published a landmark study analyzing the plasma proteome in cancer patients. Using the PEA, they measured 1,463 proteins in minute amounts of blood from more than 1,400 cancer patients across 12 cancer types collected at the time of diagnosis and before treatment. “By applying machine learning, we identified biomarker panels that could effectively differentiate between different cancer types and even stage colorectal cancers,” says Edfors Arfwidsson. “This was achieved from just one drop of blood.” Building on this work, the Disease Blood Atlas was launched at the most recent Human Proteome Organization (HUPO) annual meeting. This openaccess resource contains next-generation blood profiling data across 59 different diseases and 6,121 patients, covering cardiovascular, metabolic, cancer, psychiatric, autoimmune, infectious and pediatric diseases. “It’s fascinating to observe how some well-known blood-based biomarkers behave across different diseases,” says Edfors Arfwidsson. “For example, we found that a cancer biomarker was also upregulated in certain infections.” TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 11 Other large-scale proteomics studies are generating independent datasets that will enable cross-validation of candidate biomarkers across different populations. One such study examined the impact of common genetic variation on circulating blood proteins and their role in disease. The researchers measured the abundance of 2,293 proteins in 54,219 participants, uncovering over 14,000 associations between common genetic variants and plasma proteins, over 80% of which were previously unknown. This vast dataset is now accessible to scientists around the world through the UK Biobank. Another team applied high-throughput MS to map the plasma proteome for sepsis, a life-threatening condition caused by a dysregulated host response to infection leading to organ failure. By analyzing 2,612 samples from 1,611 patients, they identified potential biomarkers that could help pave the way for precision medicine approaches to improve sepsis diagnosis and treatment. Researchers from the UK and China found that loneliness is linked to a higher risk of illnesses such as heart disease, stroke, type 2 diabetes and susceptibliity to infection. They drew this conclusion after studying data from over 42,000 participants across nearly 3,000 plasma proteins in the UK Biobank. The scale of these plasma proteomics studies continues to grow, promising to revolutionize our understanding of diseases and their treatments. The UK Biobank recently launched the world’s most comprehensive study of plasma proteins to date, aiming to measure up to 5,400 proteins across 600,000 samples, including those from half a million participants and 100,000 follow-up samples collected up to 15 years later. This unparalleled large-scale initiative will create a first-ofits- kind database, enabling researchers to investigate how changes in blood protein levels during mid-to-late life influence an individual’s disease risk. Translational challenges While large-scale proteomics studies are very powerful tools for plasma biomarker discovery, significant barriers remain in translating these findings into clinical applications. A big challenge lies in integrating and analyzing the enormous datasets generated to identify panels of protein biomarkers associated with specific disease phenotypes. “We have a core team of four bioinformaticians working solely on these datasets, and we’re only just scratching the surface of their potential,” says Edfors Arfwidsson. “We’ll probably spend another 5 or 10 years integrating these data with other resources.” The adoption of artificial intelligence (AI) and machine learning tools has significantly enhanced the capacity to interpret these complex datasets. For example, in MS-based proteomics, machine learning – particularly deep learning – can now predict experimental peptide measurements from amino acid sequences alone. However, while these tools excel in pattern recognition and predictive modeling, their success relies on access to large, high-quality annotated datasets that are often limited in proteomics. Beyond data handling and analysis, achieving regulatory approval poses additional challenges. Validation of protein biomarkers requires rigorous evidence of their accuracy, specificity, sensitivity and reproducibility across diverse patient populations. However, the lack of standardized experimental protocols, data formats and quality control measures often undermines reproducibility and comparability across studies. “These advanced proteomics technologies, combined with AI and machine learning, are generating a lot of promising data – but we’re still missing the tools we need to validate these findings,” says Edfors Arfwidsson. “We’re identifying promising biomarker panels, but it’s really hard to crossvalidate them because of the lack of standardization.” Addressing these hurdles will require coordinated efforts between researchers, industry and regulatory bodies. Establishing standardized frameworks and robust validation protocols is critical to ensuring biomarkers can transition from the lab to the clinic without compromising patient safety. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 12 An exciting future Next-generation proteomics is enabling researchers to explore the plasma proteome with unprecedented depth and scale to deepen understanding of human health and disease. “It’s an exciting time to work in this field,” says Messner. “As instrumentation and methodologies continue to advance rapidly, proteomics will become increasingly important for clinical applications.” Blood-based protein biomarkers hold the potential to revolutionize healthcare by enabling earlier disease detection, personalized treatment strategies and more effective monitoring of therapeutic responses. “In the near future, I can foresee that we’ll be routinely profiling the plasma proteome to detect early signs of disease or other markers that can provide information about a person’s health trajectory,” predicts Edfors Arfwidsson. “An annual blood test to assess risk factors or monitor specific diseases would be feasible to deploy at scale.” *This article is based on research findings that are yet to be peer-reviewed. Results are therefore regarded as preliminary and should be interpreted as such. Find out about the role of the peer review process in research here. For further information, please contact the cited source. ABOUT THE INTERVIEWEES: Christoph Messner is an assistant professor for precision proteomics at the University of Zurich and head of the Precision Proteomics Center at the Swiss Insititute of Allergy and Asthma Research (SIAF). His research focuses on the development of high-throughput proteomics technologies and their application in biomarker discovery and systems biology. Fredrik Edfors Arfwidsson is an assistant professor and docent in biotechnology with a specialization in precision medicine and diagnostics at KTH-Royal Institute of Technology in Sweden. His research focuses on leveraging cutting-edge proteomics technologies for plasma profiling to deepen understanding of human health and disease. 13 PROTEOMICS Enzyme-linked immunosorbent assays (ELISAs) are a cornerstone of clinical and research laboratories. ELISAs can detect and quantify the presence of antibodies, antigens or proteins in an enormous range of biological and environmental samples. Generally carried out in 96-well plates, ELISAs rely on the immobilization of an antigen or antibody to a solid surface (i.e., the base of the plate) and the subsequent binding of a corresponding antibody or antigen in a specific manner. Although the most basic form of an ELISA simply detects the presence of a particular antigen or antibody, quantification can be achieved by adding an antibody conjugated to a substrate that produces a measurable product, such as fluorescence or a color change. ELISAs were first developed in 1971, by two independent groups. Both of these simultaneously developed assays were based on enzymatic reporters, rather than the radioactive reporters used by previous immunoassay iterations.1 These enzyme-based immunoassays were safer and easier to perform than radioactivity-based immune assays, and quickly became widespread. Since then, ELISAs have been adapted for use in a wide variety of fields for numerous applications, from epidemiological monitoring to environmental pollutant analysis. There are now hundreds of commercially available ELISA kits for the detection and quantification of antibodies and antigens. However, research continues into the development of novel biomarkers and ELISA-based technologies to improve sensitivity and broaden the potential uses. Some of these current and emerging applications and methodologies for ELISAs are discussed below. Current and Emerging Applications of ELISAs Kate Harrison, PhD Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 14 Clinical diagnostics and epidemiology One of the most widespread applications of ELISAs is in clinical diagnostics and epidemiology. ELISAs can be used to detect both antigen markers of a disease and the antibodies against it, depending on the type of assay selected. Therefore, they can be used to diagnose an active infection and track the spread of a disease in an outbreak scenario, and also identify previously infected individuals. To help monitor disease epidemics epidemiologically, a portable field version of an ELISA would be advantageous for onsite diagnosis of infectious disease. However, although several avenues of research have been investigated to this end such as a battery-operated ELISA system, 2 and smartphonelinked ELISA systems nothing is, as yet, commercially available.3,4 In addition to infectious diseases, ELISAs can also be used to indicate the presence of auto-reactive antibodies. Both the general presence of autoantibodies, (using a plate with a variety of self-antigens adsorbed to the surface), or autoantibodies related to specific autoimmune diseases can be analyzed, making ELISAs indispensable in autoimmune disease diagnostics.5 One such example is the identification of rheumatoid factors (RFs). RFs are antibodies that bind to other antibodies and are commonly found in systemic autoimmune diseases. RFs have diagnostic and prognostic value in several autoimmune diseases and are part of the specific diagnostic criteria for rheumatoid arthritis.6 For other autoimmune diseases, like autoimmune connective tissue diseases (CTDs), ELISAs for autoreactive antinuclear antibodies (ANAs) are emerging as a more sensitive, more specific and less time-consuming alternative to the current gold standard: indirect immunofluorescence assays.7 Despite their range and versatility, traditional ELISAs are limited in scenarios requiring extremely high sensitivity for nano-molar levels of sample, making them unsuitable for early diagnosis or screening of diseases with subtle physiological changes such as Alzheimer’s disease (AD). However, the recent development of nano-ELISAs is now opening more avenues for highly sensitive detection. In a nano-ELISA, a detector antibody and a signaling molecule such as horseradish peroxidase (HRP) are coupled with gold nanoparticles. These nanoparticles act as signal amplifiers, significantly increasing the detection limit and allowing the detection of antigens or antibodies even in the picogram range.8 This method has been used to identify the AD biomarker Aβ42 in serum samples at extremely low levels.9 As cerebral spinal fluid is currently collected for detection of AD biomarkers, this suggests future potential for nano-ELISAs as a novel, less invasive method for AD diagnosis.10 Biopharmaceutical and vaccine development Biopharmaceuticals are therapeutics derived from, or created in, biological sources. Demand for these therapeutics continues to grow as they revolutionize the treatment of a wide range of diseases, including cancer and autoimmune diseases. As they are produced from biological sources, they must be purified to an extremely high standard, to remove any host cell proteins (HCPs) or residual manufacturing reagents. ELISAs are one of the standard tools for quantifying the total levels of residual impurities, due to their high-throughput, high sensitivity and high specificity.11 However, they are unable to give any information on the specific, individual HCPs present, so are now often coupled with liquid The recent development of nano-ELISAs is now opening more avenues for highly sensitive detection. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 15 chromatography-mass spectrometry (LC-MS) for more in-depth evaluation of biopharmaceutical purity. One of the simplest uses for ELISAs is the quantification of antibodies in biological samples. This is essential in the development of novel vaccines. Many vaccines protect against infection by inducing the creation of specific antibodies, meaning quantification of antibody levels post-vaccination is needed to assess vaccine efficacy. ELISAs have therefore been used extensively in the clinical development of approved vaccines, including the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2 and the recently approved R21 and RTS,S vaccines against malaria.12,13,14 Forensic investigations One of the most common applications for ELISAs in forensic science is drug testing. Toxicologists can test forensic samples for a wide range of illicit substances, including cocaine, amphetamines, opiates, cannabinoids and benzodiazepines. Both liquid samples, such as blood and urine, and solid samples such as hair can be processed for ELISA testing, for purposes such as monitoring drug abstinence and workplace testing.15,16 ELISAs can also be used to determine if a blood sample is human for forensic investigation by using antibodies targeting specifically human blood components.15 By targeting the assay at highly abundant antigens in the blood, such as human albumin, blood can still be identified as human by ELISA even in ancient, buried samples over 3,000 years old.17 In addition to analyzing bodily fluids, ELISAs can also be used in forensic investigations to help determine a cause of death. Traumatic brain injury (TBI) is one of the leading causes of mortality worldwide and is responsible for approximately 30% of all injury-related deaths.18 Post-mortem identification of TBI can be difficult, as ante-mortem clinical diagnosis usually involves neurological assessment. Recent research has shown elevated levels of cerebral protein biomarkers, such as tau protein and myelin basic protein, in blood and cerebral spinal fluid samples from cases where TBI was the confirmed cause of death. These markers can therefore be used as an indicator of TBI in post-mortem examination, even in the absence of visible central nervous system damage.19,20,21 Food safety ELISAs play a significant role in food safety testing and are used to screen for contaminants and allergens. Food allergies affect approximately 2% of the Western population, and allergic and anaphylactic reactions to food result in 30,000 hospital visits and 150 deaths per year in the United States alone.22 Allergies can be extremely sensitive. For example, even trace amounts of peanuts can trigger an anaphylactic reaction. Therefore, it is very important that foods labeled as allergen-free, are indeed safe for consumption. Cross-contact with allergens can occur at many stages of food processing and production and is often the Both liquid samples, such as blood and urine, and solid samples such as hair can be processed for ELISA testing, for purposes such as monitoring drug abstinence and workplace testing. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 16 result of failure to clean machines appropriately when switching between allergen-containing and allergen-free foods. However, ELISAs are used by manufacturers to detect common allergens in food products, including nuts, meat, milk and milk products, fish and soybeans. The highly sensitive and specific nature of ELISAs can identify cross-contact, preventing the need for food recalls and protecting allergic populations. However, as large numbers of batch samples will typically need to be tested on a regular basis, ELISAs, despite their relative speed, can still result in bottlenecks. Novel, rapid microfluidic-based ELISA platforms are currently being researched to develop a method for more sensitive and far faster allergen identification.23,24 Despite their widespread use in the field, traditional ELISA techniques can lack the sensitivity needed to detect extremely low, but still dangerous, levels of toxic or microbial contaminants. However, the development of nano-ELISAs have enabled the detection of harmful microbes, pesticides, drug residues and pollutants in food.25 Nano-ELISA techniques have now been developed to identify a range of food contaminants, including aflatoxin in corn, pathogenic Listeria monocytogenes in milk and veterinary drug traces in poultry.26,27,28 Environmental analysis Although ELISAs have typically been used for biological sample analysis, their applications in environmental sampling is increasing, particularly for the detection and quantification of agricultural and industrial pollutants in water. The use of pesticides has increased steadily over the last 30 years worldwide, resulting in increased levels of pollution and potentially toxic agricultural runoff.29 Often, analysis of environmental soil and water samples for pollutants uses techniques such as gas and liquid chromatography (GC and LC). However, these techniques can be costly and time-consuming compared to ELISAs, which have been shown to be just as sensitive as chromatography methods.30 Indeed, ELISAs, along with other immune-based assays, have been successfully used in several large-scale water quality surveys in the US.30 Pharmaceutical pollutants, such as hormones and drugs, can also be assessed in both water samples and the tissues of aquatic organisms by ELISA.31 These emerging pollutants can have a significant effect on aquatic ecosystems, damaging animal life, altering microbial populations leading to detrimental algal blooms and perpetuating antibiotic resistance. Monitoring these pollutants is essential for minimizing harm and developing better regulatory guidelines to prevent their release into the environment.32 REFERENCES 1. Deutschmann C, Roggenbuck D, Schierack P, Rödiger S. Autoantibody testing by enzyme-linked immunosorbent assay-a case in which the solid phase decides on success and failure. Heliyon. 2020;6(1). doi: 10.1016/j.heliyon.2020.e03270 2. Singh H, Morioka K, Shimojima M, et al. A handy field-portable ELISA system for rapid onsite diagnosis of infectious diseases. Japan J Infect Dis. 2016;69(5):435-438. doi: 10.7883/yoken. jjid.2015.417 3. Long KD, Yu H, Cunningham BT. Smartphone instrument for portable enzyme- linked immunosorbent assays. Biomed Opt Express. 2014;5(11):3792-3806. doi: 10.1364/boe.5.003792 4. Zhdanov A, Keefe J, Franco-Waite L, Konnaiyan KR, Pyayt A. Mobile phone based ELISA (MELISA). Biosens Bioelectron. 2018;103:138-142. doi: 10.1016/j.bios.2017.12.033 5. Drijvers JM, Awan IM, Perugino CA, Rosenberg IM, Pillai S. The enzyme-linked immunosorbent assay. In: Jalali M, Saldanha FYL, Jalali M, eds. Basic Science Methods for Clinical Researchers. Academic Press;2017:119-133. doi: 10.1016/b978-0-12-803077- 6.00007-2 6. Trier NH, Houen G. (2019). Determination of Rheumatoid Factors by ELISA. In: Houen G, eds. Autoantibodies. Methods in Molecular Biology, vol 1901. New York, NY: Humana Press. doi: 10.1007/978- 1-4939-8949-2_23 7. Alsaed OS, Alamlih LI, Al-Radideh O, Chandra P, Alemadi S, Al-Allaf A-W. Clinical utility of ANA-ELISA vs ANAimmunofluorescence in connective tissue diseases. Sci Rep. 2021;11(1):8229. doi: 10.1038/s41598-021-87366-w 8. Ambrosi A, Castañeda MT, Killard AJ, Smyth MR, Alegret S, Merkoçi A. Double-codified gold nanolabels for enhanced immunoanalysis. Anal Chem. 2007;79(14):5232-5240. doi: 10.1021/ ac070357m 9. Hu T, Lu S, Chen C, Sun J, Yang X. Colorimetric Sandwich Immunosensor for Aβ(1-42) based on dual antibody-modified gold nanoparticles. Sensors Actuat B Chem. 2017;243:792-799. doi: 10.1016/j.snb.2016.12.052 10. Al Abdullah S, Najm L, Ladouceur L, et al. Functional nanomaterials for the diagnosis of Alzheimer’s disease: Recent progress and future perspectives. Adv Funct Mater. 2023;33(37):2302673. doi: 10.1002/adfm.202302673 11. Zhu-Shimoni J, Yu C, Nishihara J, et al. Host cell protein testing by ELISAs and the use of orthogonal methods. Biotechnol Bioeng. 2014;111(12):2367-2379. doi: 10.1002/bit.25327 12. Ramasamy MN, Minassian AM, Ewer KJ, et al. Safety and immunogenicity of ChAdOx1 nCoV-19 vaccine administered in a prime-boost regimen in young and old adults (COV002): A TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 17 single-blind, randomised, controlled, phase 2/3 trial. Lancet. 2020;396(10267):1979-1993. doi: 10.1016/s0140-6736(20)32466-1 13. Genito CJ, Brooks K, Smith A, et al. Protective antibody threshold of RTS,S/AS01 malaria vaccine correlates antigen and adjuvant dose in mouse model. NPJ Vaccines. 2023;8(1):114. doi: 10.1038/ s41541-023-00714-x 14. Datoo MS, Natama HM, Somé A, et al. Efficacy and immunogenicity of R21/Matrix-M vaccine against clinical malaria after 2 years’ follow-up in children in Burkina Faso: A phase 1/2B randomised controlled trial. Lancet Infect Dis. 2022;22(12):1728-1736. doi: 10.1016/s1473-3099(22)00442-x 15. Perrigo BJ, Joynt BP. Use of ELISA for the detection of common drugs of abuse in forensic whole blood samples. Can Soc Forensic Sci J. 1995;28(4):261-269. doi: 10.1080/00085030.1995.10757486 16. Agius R, Nadulski T. Utility of ELISA screening for the monitoring of abstinence from illegal and legal drugs in hair and urine. Drug Test Anal. 2014;6(S1):101-109. doi: 10.1002/dta.1644 17. Cattaneo C, Gelsthorpe K, Phillips P, Sokol RJ. Reliable identification of human albumin in ancient bone using ELISA and monoclonal antibodies. Am J Biol Anthropol. 1992;87(3):365-372. doi: 10.1002/ajpa.1330870311 18. Demlie TA, Alemu MT, Messelu MA, Wagnew F, Mekonen EG. Incidence and predictors of mortality among traumatic brain injury patients admitted to Amhara region Comprehensive Specialized Hospitals, Northwest Ethiopia, 2022. BMC Emerg Med. 2023;23(1):55. doi: 10.1186/s12873-023-00823-9 19. Olczak M, Poniatowski ŁA, Niderla-Bielińska J, et al. Concentration of microtubule associated protein tau (MAPT) in urine and saliva as a potential biomarker of traumatic brain injury in relationship with blood–brain barrier disruption in postmortem examination. Forensic Sci Int. 2019;301:28-36. doi: 10.1016/j.forsciint.2019.05.010 20. Olczak M, Niderla-Bielińska J, Kwiatkowska M, Samojłowicz D, Tarka S, Wierzba-Bobrowicz T. Tau protein (MAPT) as a possible biochemical marker of traumatic brain injury in postmortem examination. Forensic Sci Intl. 2017;280:1-7. doi: 10.1016/j. forsciint.2017.09.008 21. Olczak M, Poniatowski ŁA, Siwińska A, Kwiatkowska M. Postmortem detection of neuronal and astroglial biochemical markers in serum and urine for diagnostics of Traumatic Brain Injury. Int J Legal Med. 2023;137(5):1441-1452. doi: 10.1007/s00414-023-02990- 7 22. Food allergies: The “big 9”. USDA Food Safety and Inspection Service. https://www.fsis.usda.gov/food-safety/safe-foodhandling- and-preparation/food-safety-basics/food-allergies. Published March 21, 2024. Accessed April 2, 2024. 23. Weng X, Gaur G, Neethirajan S. Rapid detection of food allergens by microfluidics ELISA-based optical sensor. Biosensors. 2016;6(2):24. doi: 10.3390/bios6020024 24. Uddin MJ, Bhuiyan NH, Shim JS. Fully integrated rapid microfluidic device translated from conventional 96-well ELISA kit. Sci Rep. 2021;11(1):1986. doi: 10.1038/s41598-021-81433-y 25. Wu L, Li G, Xu X, Zhu L, Huang R, Chen X. Application of nano- ELISA in food analysis: Recent advances and challenges. Trends Analyt Chem. 2019;113:140-156. doi: 10.1016/j.trac.2019.02.002 26. Zhan S, Hu J, Li Y, Huang X, Xiong Y. Direct competitive ELISA enhanced by dynamic light scattering for the ultrasensitive detection of aflatoxin B1 in corn samples. Food Chem. 2021;342:128327. doi: 10.1016/j.foodchem.2020.128327 27. Wang W, Liu L, Song S, Xu L, Zhu J, Kuang H. Gold nanoparticlebased paper sensor for multiple detection of 12 Listeria spp. by P60-mediated monoclonal antibody. Food Agr Immunol. 2017;28(2):274-287. doi: 10.1080/09540105.2016.1263986 28. Song M, Xiao Z, Xue Y, Zhang X, Ding S, Li J. Development of an indirect competitive ELISA based on immunomagnetic beads’ clean-up for detection of maduramicin in three chicken tissues. Food Agr Immunol. 2018;29(1):590-599. doi: 10.1080/09540105.2017.1418842 29. Agricultural consumption of pesticides worldwide from 1990 to 2021. Statista. https://www.statista.com/statistics/1263077/ global-pesticide-agricultural-use/. Published July 2023. Accessed April 02, 2024. 30. Aga DS, Thurman EM. Environmental immunoassays: Alternative techniques for soil and water analysis. In: Aga DS, Thurman EM, eds. Immunochemical Technology for Environmental Applications. ACS Sym Ser;1997:1-20. doi: 10.1021/bk-1997-0657.ch001 31. Jaria G, Calisto V, Otero M, Esteves VI. Monitoring pharmaceuticals in the aquatic environment using enzyme-linked immunosorbent assay (ELISA)—a practical overview. Anal Bioanal Chem. 2020;412(17):3983-4008. doi: 10.1007/s00216-020-02509-8 32. Khan AH, Barros R. Pharmaceuticals in water: Risks to aquatic life and remediation strategies. Hydrobiology. 2023;2(2):395-409. doi: 10.3390/hydrobiology2020026 18 PROTEOMICS “As a scientific discipline, proteomics isn’t really that old – it’s like protein biochemistry gone crazy,” Dr. Jennifer Van Eyk, professor of cardiology, director of the Advanced Clinical Biosystems Institute and the Erika Glazer Endowed Chair in Women’s Heart at Cedars-Sinai Medical Center, said. Prof. Van Eyk’s journey in clinical proteomics began when she was a peptide chemist: “I was a minimalist, taking big proteins and breaking them down to their fundamental amino acids.” In peptide chemistry, proteins are typically studied in isolation using techniques such as sodium dodecylsulfate polyacrylamide gel electrophoresis or Edman degradation. In the latter portion of the 20th century, proteomics – the study of the proteome – emerged as its own field. Over recent decades, it has evolved to new heights thanks to projects such as the Human Genome Project, the launch of organizations such as the Human Proteome Organization (HUPO) and increasingly higher-throughput techniques for protein analysis. Now an international leader in clinical proteomics, Prof. Van Eyk is no longer a minimalist and strives to achieve a completely different goal in her work: characterizing as many proteins as possible at once. What is clinical proteomics? Clinical proteomics, as the name suggests, describes the study of the proteome and the application of subsequent Clinical Proteomics: Progress and Future Prospects With Professor Jennifer Van Eyk Molly Coddington Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 19 insights in a clinical context. Such insights might be used to interrogate the biological underpinnings of a disease, identify and validate disease biomarkers, identify novel drug targets, predict disease outcomes or understand drug resistance mechanisms. The proteome is dynamic and incredibly complex. Studying it with the aspiration to translate basic research into clinical insights is far from easy, but it’s a field that Prof. Van Eyk has always been passionate about: “It’s my personal belief that if I am lucky enough to work as a scientist, I want to be able to pay back to society – I want to help change lives. You can do that in a lot of different ways, and I’m choosing to do it through medicine,” she said. Having worked in clinical proteomics for over 25 years, Prof. Van Eyk’s research has helped to shape the field as we know it today. The laboratories at Cedar-Sinai are set up to explore and quantify proteins in all their forms, on small and large scales. Prof. Van Eyk is renowned for her research developing technical pipelines for de novo discovery and larger-scale quantitative mass spectrometry (MS) methods. Primary research in the Van Eyk laboratory has two core goals: • Understand the molecular mechanism underlying acute and chronic disease and treatment therapies, and • Develop clinically robust circulating biomarkers. Technology Networks had the pleasure of interviewing Prof. Van Eyk – who is also the organization’s President – to learn more about the exciting work going on in her laboratory, how she hopes to address bottlenecks in clinical proteomics and what she hopes her legacy will be in this field. Innovations in clinical proteomics Remote sampling devices Prof. Van Eyk highlighted microsampling as a particularly exciting area of research within clinical proteomics. “Not everybody has the money and access [to healthcare] that we have in the Western World, but we all have the right to know the status of our health,” she said. “Microsampling can help science and medicine become more inclusive, reduce costs for healthcare systems and help people access important insights about their health.” Human blood is a rich source of protein biomarkers that can be used to determine our health status at a given time point. Repeated blood samples taken from the same individual can also provide insights into their health over time. The insights from such data cannot be understated – it could facilitate the discovery of novel biomarkers associated with disease progression, provide insights into the effectiveness of lifestyle interventions or monitor an individual’s response to a drug treatment. However, current health assessments require in-clinic venipuncture by a trained phlebotomist. The blood sample is then safely packaged and transported to a laboratory for processing and analysis. There are several issues with this system: first, it’s not always convenient “What we now know, and what we can achieve, is just remarkable. Every year, I see a new level of sophistication, innovation and thinking in my trainees, who are so adept at speaking the language of this evolving field. I want to continue watching their capabilities grow and support them for a little while longer because it’s such a joy.” TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 20 for individuals to access even one clinic appointment, let alone make repeated visits to have blood drawn over time. Second, venipuncture can be uncomfortable, which may discourage individuals from undergoing blood tests. Collectively, these factors limit accessibility to important health insights and make it challenging to conduct large-scale population health studies. Imagine being able to collect a smaller sample of blood at home, using a safe, minimally invasive device, before packaging your sample and posting it for analysis at a location that’s convenient for you. This vision for the future of telehealth is driving the development of remote sampling devices, capable of collecting important protein biomarker data using increasingly smaller samples of blood (microsampling). Prof. Van Eyk has been contributing to this emerging area of research for many years, developing robust MS-based workflows for remote sampling devices and exploring their viability compared to existing approaches for health analysis. Though remote sampling devices remain in development, Prof. Van Eyk is enthusiastic that they can one day democratize health care: “It’s going to be a long road, but I think we have a responsibility to help people access their data and have some level of control over their health.” The Molecular Twin In oncology, Prof. Van Eyk shared a novel precision medicine platform known as the “Molecular Twin”. Precision oncology is an innovative approach that considers a patient’s unique molecular makeup when designing and implementing their treatment plan. Genetic insights provide one layer of detail, but even with the same genetic mutations, some cancer patients respond differently to identical treatment plans. At Cedars-Sinai, Prof. Van Eyk and Prof. Dan Theodorescu and many other colleagues helped to build Molecular Twin by integrating clinical and multiomic features from patients with resected pancreatic ductal adenocarcinoma. Multiomic features were gathered using techniques including next-generation sequencing, full-transcriptome RNA sequencing, paired tumor tissue proteomics, unpaired plasma proteomics, lipidomics and computational pathology. This data was integrated and, with a helping hand from machine-learning models, accurately predicted disease survival rates. “It turned out that plasma proteomics was a very good indicator of disease survival,” Prof. Van Eyk said. By continuing to build Molecular Twins across multiple cancer types, she hopes that clinicians can compare incoming cancer patients with their “twin” data from the platform and better tailor their treatments: “We will know who [from the database] the patient looked like at the beginning of their treatment, and then either follow the same course because it was successful or move to another course.” Understanding drug responsiveness using single-cell analysis “We also have some remarkable work looking at how patients respond to treatment in inflammatory bowel disease (IBD),” Prof. Van Eyk said. IBD is a group of inflammatory conditions that represent a major public health burden. A cure does not exist, but symptoms can be managed using medication. If medications fail to calm inflammation, surgery may be required. IBD patients who require surgery to remove part of the colon will typically be prescribed anti-TNF therapy as a standard of care. “Unfortunately, many of these patients will become unresponsive to that therapy within a few weeks. Then at some point, whether it’s one year, two years or five years after surgery, it will no longer work in all patients and they will have to move onto another therapy,” Prof. Van Eyk explained. Her laboratory is looking to the proteome to identify biomarkers of unresponsiveness and to investigate whether it’s possible to predict which patients might benefit from changing therapies earlier. The lab is also adopting single-cell proteomics techniques to understand how populations of cells respond differently to drugs. “If we think about the heart as an example, and the cells that cause the heart muscle to contract, we now know that there are multiple populations of cells that almost ‘group’. When TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 21 you give these cells a drug, they might not respond equally. If a drug fails, it might be because the individual does not have enough of that specific population of cells for the drug to be effective,” Prof. Van Eyk said. “Which, of course, has important implications for drug development. We’ll see how this research progresses in the long run, but I think these types of technology advancements are letting us answer questions that we never even thought to ask before, which is very cool.” Moving biomarkers to the clinic – overcoming obstacles and carving a legacy Despite incredible advances over recent decades, translating proteomics insights to the clinic is a work in progress. Prof. Van Eyk discussed some of the key challenges in discovering biomarkers, validating them and their route to clinical implementation: “I think the idea that we can identify analytes that closely define pathophysiology within a group of individuals is becoming a closer reality. The challenge of getting this research into clinical implementation is that, often, the middle technologies or expertise in that domain are not as well established in academia. There are funding issues, and there isn’t always a clear route to success. Lots of factors can influence that success, which ultimately has nothing to do with whether you have a good biomarker.” Prof. Van Eyk emphasized that, even if researchers create an assay and gather sufficient data to prove its usefulness, there is still a long way to go for that assay to become a part of standard care. It’s a process that requires input from a group of different communities, and oftentimes, the original researchers working on the discovery aspect are not involved in it. Clinicians also need to be able to inform researchers completing the discovery work – and those responsible for its funding – about the key questions that need to be answered through discovery research. “It has to be a two-way conversation,” Prof. Van Eyk said. While knitting these communities together isn’t easy, it’s going to be critical for the future of the field, she emphasized: “I think a lot of clinical proteomics over recent decades has been about developing tools that ensure our data is reproducible. Now, we’re at the point where we need to engage in conversations about how this data is actually used. Those conversations need to happen globally within the field.” Looking to the future, Prof. Van Eyk is determined to continue her work addressing these bottlenecks. “I think the legacy that I’d like to leave in this field is that I made it easier for researchers to get their biomarkers to the clinic efficiently. That way, they don’t have to go through all the learning processes that I did,” she said. A highly respected mentor in proteomics, Prof. Van Eyk is passionate about using her expertise and experience to support early-career researchers. “When I first started in proteomics, it wasn’t even a word,” she laughed. “What we now know, and what we can achieve, is just remarkable. Every year, I see a new level of sophistication, innovation and thinking in my trainees, who are so adept at speaking the language of this evolving field. I want to continue watching their capabilities grow and support them for a little while longer because it’s such a joy.” MEET THE INTERVIEWEE Jennifer Van Eyk, PhD is an international leader in the area of clinical proteomics. She is professor of cardiology, director of the Advanced Clinical Biosystems Institute and the Erika Glazer Endowed Chair in Women’s Heart at Cedars-Sinai Medical Center. 22 PROTEOMICS Significant hurdles are still faced in drug discovery, with most known disease targets being classified as “undruggable.” These lack the structural features ideal for small molecule binding, such as deep, well-defined ligand binding pockets. Instead, undruggable proteins often have flat or highly dynamic surfaces without obvious binding hotspots. Next-generation proteomics is helping to overcome these challenges. For example, the emergence of data-independent acquisition mass spectrometry (DIA-MS) techniques is improving the discovery of drugs for notoriously challenging targets. DIA-MS is an untargeted mass spectrometry approach that offers in-depth coverage of the proteome with high reproducibility, accuracy and throughput. This method not only identifies novel disease-relevant biomarkers and targets but also enables the discovery of novel therapeutic compounds through fragment-based screening. In this approach, chemical probes can be screened against the entire proteome. This MS-based technique precisely maps target-ligand interactions in relevant human cells and tissues. This article discusses how fragment-based screening using DIA-MS drives the discovery of novel drug compounds, including covalent and reversible inhibitors. We also explore how Evotec is expanding the druggable proteome with its next-generation proteomics platforms, including high-throughput activity-based protein profiling (HTABPP), high-throughput photoaffinity labeling mass spectrometry (HT-PALMS) and ScreenPep™. Innovating Drug Discovery With Next-Generation Proteomics Evotec Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 23 Discovering novel covalent fragments using a DIA-MS-based method Covalent fragments hold the potential to drug the undruggable with their ability to bind to shallow, cryptic, allosteric or transient binding sites. By forming irreversible covalent bonds with their targets, these fragments offer high target occupancy, potency and a prolonged duration of action. Novel covalent fragments can be discovered using DIA-MS-based approaches, like activity-based protein profiling (ABPP). In ABPP, labeled chemical probes are screened against the entire proteome, with DIAMS used to map target reactive sites and occupancy. This assesses probe specificity, identifying safe and potent hits, even for challenging or poorly characterized disease targets. The possibilities of covalent fragment discovery are being taken a step further with Evotec’s high throughput ABPP (HT-ABPP) platform. Firstly, workflows are streamlined using a robotic liquid handling platform for peptide enrichment and clean-up. Stable isotope labeling by amino acids in cell culture (SILAC) is used to assess ligand-target binding in vivo, with the ability to analyze many different human cell lines and primary cells. This DIA-MS-based platform has high precision and speed, allowing for the consistent mapping of 70,000+ reactive cysteine sites on 14,000+ unique proteins and 12,000+ distinct reactive lysine sites on 3,500+ unique proteins, with a throughput of up to 180 samples per day. Designing reversible inhibitors using DIA-MS-based photoaffinity labeling DIA-MS approaches are also powerful tools to identify reversible inhibitors. As the name suggests, this type of drug binds reversibly with its target(s), typically via non-covalent bonds. Reversible fragments can bind to challenging targets, and in contrast to covalent drugs, temporary binding to off-targets helps reduce potential toxicity. When combined with photoaffinity labeling, DIA-MS can discover novel reversible inhibitors. In this method, a library of photoreactive probes detect reversible ligand-target interactions. These probes contain a photoreactive diazirine group, a variable fragment and an alkyne handle attached to a reporter or affinity tag. Following probe screening using DIA-MS, identified fragments can be structurally elaborated to optimize binding affinity, specificity or activity. High-throughput photoaffinity labeling mass spectrometry (HT-PALMS) platforms like Evotec’s are accelerating the discovery of safe and potent reversible compounds. Using advanced mass spectrometry, thousands of small molecule fragment-protein interactions can be mapped directly in human cells. Generated hits and their structurally elaborated analogs can be further profiled with tailored, competitive assays to determine structure-activity relationships, identifying the most promising chemical starting point for your target of interest. Enabling large-scale proteomics discovery Recent advances in mass spectrometry-based proteomics are promising to take drug discovery even further, offering higher throughput without compromising on coverage or precision. These advances are being driven by Evotec’s DIA-MS-based compound screening platform “ScreenPep.” The platform integrates fast automation and parallelization with the ability to process up to 1,000 samples per day. ScreenPep has a fully automated workflow that encompasses cell culture, sample preparation, DIAMS data acquisition and data processing – ensuring consistent and reliable results. Additionally, ScreenPep can quantify various post-translational modifications, such as phosphorylation, ubiquitination, acetylation and methylation, enhancing the ability to identify novel ligand-target interactions. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 24 Setting new benchmarks in drug discovery DIA-MS-based techniques are driving the paradigm shift towards next-generation proteomics. These fragment-based approaches are setting new benchmarks in drug discovery, enabling the discovery of innovative drug modalities, including covalent and reversible inhibitors, for challenging disease targets across the proteome. At Evotec, advanced platforms – like HT-ABPP, HT-PALMS and ScreenPep – are combined with a team of cross-disciplinary experts and over 25 years of experience in the field. These capabilities are transforming modern medicine, discovering novel ligand-target interactions with unmatched precision, depth and quality. Figure 1: Overview of the ScreenPep™ platform. Credit: Evotec. Evotec’s cutting-edge proteomics solutions empower your drug discovery pipeline with actionable insights into novel targets, mechanism of action, and biomarker candidates. From breakthrough therapeutics to biomarkers, we deliver tailored proteomics approaches to accelerate your path to clinical success. Collaborate with us to transform proteomic data into innovative medicines. To learn more contact us at info@evotec.comAccelerating Your Drug Discovery with High-Throughput, Quantitative ProteomicsLearn more: 26 PROTEOMICS Mass spectrometry (MS) is a versatile and powerful technique for proteomics. In the last decade, rapid developments in MS instrumentation and methodologies have provided a new level of accuracy and sensitivity in protein analysis, opening up new applications in biological research, biopharmaceuticals and diagnostics.1,2 A well-designed MS experiment can identify and quantify unknown compounds in a biological sample. However, it is important to ensure the quality and reliability of your MS results by taking the time to plan your experiment and prepare a high-quality sample. Biological samples are complex and there is no gold standard for sample preparation. Protocols will differ depending on your sample type and experimental goals, with it important to understand the different options that are available for you to optimize your workflow. This guide provides some essential tips and tricks to help you as you plan your MS experiment; from workflow optimization to practical tips about equipment use, quality control measures and choosing the right analytical method. Guiding principles Top-down or bottom-up? Two fundamental strategies are used in protein identification by MS: top-down and bottom-up. In top-down analysis, intact proteins are separated from complex biological samples and analyzed in their intact form. This can be a useful technique for the analysis of single proteins. The bottom-up, or “shotgun” approach, however, is still considered the mainstay of proteomic analysis and is used to identify unknown protein components in a sample. Proteins are digested into peptides, separated via liquid chromatography (LC) and Perfect Your Protein Prep for Mass Spectrometry Kaja Ritzau-Reid, PhD Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 27 analyzed by MS.3 We will focus on bottom-up strategy in this guide. Key considerations before you start Taking the time to properly plan your experiment before diving into lab work is the simplest way to get on the right track for experimental success. Here are some key questions that you should consider before starting your MS experiment: • What is my biological question and how will MS help with answering it? • How will I acquire the sample and what handling and storage conditions are required? • How abundant is the protein in my sample? • What are the appropriate controls I can use? • What MS instrumentation is available to me? Choose your workflow There is no one-size-fits-all method to prepare your protein sample for MS. The workflow you design for a specific sample will depend on the sample source, its complexity, the protein abundance and subcellular location. Here are some key considerations for a bottom-up workflow: 1. Protein extraction Cell lysis can be achieved through mechanical- or reagent-based methods. Mechanical-based methods (such as grinding, liquid homogenization or sonication) are ideal for use with biological tissue. However, this method is generally not suitable for small sample sizes or high-throughput experiments and reproducibility can vary. Reagent-based methods tend to provide a gentler method for lysing cells, leading to higher protein yields. This method is more suitable for small sample sizes. However, detergents should be used carefully to avoid damaging the cell contents and removed later to avoid sample contamination. It may be necessary to add protease inhibitors to your lysis buffer to prevent protein degradation by endogenous enzymes. 2. Protein digestion and extraction Sample digestion can be performed in two ways: in-gel or in-solution digestion. In-solution digestion is quicker to perform, with a greater high-throughput potential and is typically used for small sample sizes. In-gel digestion uses SDS polyacrylamide gel electrophoresis and individual protein bands can be visualized by staining and excised directly from the gel. The proteins are reduced, alkylated and digested in situ, and peptides are extracted directly from the gel matrix, which simultaneously removes much of the detergents and salts. However, this can also lead to peptide loss. You may want to consider using filter-based strategies such as filter-aided sample preparation, which combine the advantages of in-gel and in-solution digestion and provide efficient protein recovery and depletion of contaminants using a filtration step. This is particularly useful for samples that require the use of detergents and salts during extraction.4 3. Peptide enrichment and cleanup Sample cleanup is essential to remove any detergents or salts used in the peptide isolation process, as these will interfere with the ionization and downstream analysis. Reverse-phase methods using the C18 matrix are commonly used to capture hydrophobic proteins, and there are a variety of commercial kits to choose from. However, these are unable to remove detergents or polyethylene glycol (PEG), and the SP2 method, using carboxylate-modified paramagnetic particles, has recently been described as an effective alternative.5,6 Graphite spin columns are another option, particularly suited to hydrophilic peptides which bind poorly to the C18 resins. Post-digestion, your sample may require peptide enrichment to detect low-abundance proteins or posttranslationally modified peptides. Enrichment kits are available that specifically target certain enzyme classes. This should be carefully selected based on your target peptide and binding mechanism. In recent years several one-pot workflows have emerged, streamlining the sample preparation steps TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 28 for MS proteomic analysis by merging some of the steps. Consider this approach for high-throughput applications.7,8 Keeping it compatible Make sure that the buffers and reagents you use are compatible with MS. Here are some tips to keep in mind as you plan your workflow: • Use high-quality LC-MS grade water. • Avoid the use of strong acids (like trifluoroacetic acid) which can mask your protein signals. • Avoid the use of PEG-based detergents such as TritonX, tween and NP-40 as these can interfere with the peptide signals. • Avoid the use of non-volatile organic solvents such as DMF or DMSO, as these are highly viscous and can damage instrument parts. • Avoid the use of salts and non-volatile buffers in the final purification steps of your sample prep. Using the right tools for the job Before starting your experiment, run through the equipment that you will require. Here are some tips for choosing the right tools for the job: • It is important to eliminate any risk of contamination, so using a laminar air flow hood and protective clothes is generally recommended during sample preparation. • Keratin is one of the most common contaminants in proteomics. The protein comes from your skin, so unless you are specifically working with skin cells – wear gloves! • Use low-binding tubes and pipette tips. Proteins and peptides bind to untreated plasticware and using low-binding tubes can significantly reduce protein and peptide loss. • Use filter pipette tips to prevent contamination. • Don’t use autoclaved plastic material. The heat and pressure in the autoclaving process can degrade the plastic leaving residues that can contaminate your samples. • Avoid washing any glassware that you plan to use with detergents as this can contaminate your sample. Use hot water and an organic solvent instead. Quality control Take the time to monitor each step of your sample preparation. This will ultimately save you time by highlighting any problems with your sample before you proceed with your MS analysis. Here are some key quality control measures to consider during your sample preparation: • Monitor the protein expression of your input sample, preferably at each step of your sample preparation, using standard protein assays such as Coomassie staining or bicinchoninic acid. • Always check that the pH of your sample is in the correct range. • Use blank samples (solvents without the analyte) to help identify any contaminants. • Use internal standards to help correct for any variations in your sample preparation and allow for more precise quantification during data analysis. Next steps Selecting the right approach for your MS analysis is highly dependent on your sample, experiment goals and what is available to you. The technology in MS instrumentation has rapidly developed in the last decade and there are many different types of MS instruments to choose from.9 The first step in MS analysis involves the ionization of your analyte. The two most common ionization methods are matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI).2 MALDI is typically used for larger biomolecules, and the peptides are mixed into a matrix for ionization by a UV-emitting laser. ESI requires a liquid solution free from detergents and salts and is commonly coupled with liquid chromatography to separate the peptide components before they enter the mass spectrometer. This is a popular approach, TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 29 ideal if you are dealing with complex biological samples requiring quantitative analysis. It is commonly referred to as a “shotgun” approach owing to its non-targeted, broad-spectrum analysis. Final considerations Remember, there is no standard method for preparing your samples for MS experiments and your workflow will be highly dependent on your individual sample and experiment requirements. Simplify your sample as best you can, by removing contaminants that may suppress or mask your peptides of interest. If possible, use a core facility to assist you and provide advice. Careful planning will always pay off, avoiding costly and timeconsuming mistakes in the long run. REFERENCES 1. Yates JR, Ruse CI, Nakorchevsky A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng. 2009;11:49-79. doi: 10.1146/annurevbioeng- 061008-124934 2. Rozanova S, Barkovits K, Nikolov M, Schmidt C, Urlaub H, Marcus K. Quantitative mass spectrometry-based proteomics: an overview. In: Marcus K, Eisenacher M, Sitek B, eds. Quantitative Methods in Proteomics. Vol 2228. Springer US; 2021:85-116. doi: 10.1007/978-1-0716-1024-4_8 3. Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113(4):2343- 2394. doi: 10.1021/cr3003533 4. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6(5):359-362. doi: 10.1038/nmeth.1322 5. Waas M, Pereckas M, Jones Lipinski RA, Ashwood C, Gundry RL. SP2: rapid and automatable contaminant removal from peptide samples for proteomic analyses. J Proteome Res. 2019;18(4):1644- 1656. doi: 10.1021/acs.jproteome.8b00916 6. Wojtkiewicz M, Berg Luecke L, Kelly MI, Gundry RL. Facile preparation of peptides for mass spectrometry analysis in bottom‐up proteomics workflows. Curr Protoc. 2021;1(3):e85. doi: 10.1002/cpz1.85 7. Ye Z, Sabatier P, Martin-Gonzalez J, et al. One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes. Nat Commun. 2024;15:2474. doi: 10.1038/s41467-024- 46777-9 8. Chen W, Adhikari S, Chen L, et al. 3D-SISPROT: A simple and integrated spintip-based protein digestion and threedimensional peptide fractionation technology for deep proteome profiling. J Chromatogr A. 2017;1498:207-214. doi: 10.1016/j. chroma.2017.01.033 9. Li C, Chu S, Tan S, et al. Towards higher sensitivity of mass spectrometry: a perspective from the mass analyzers. Front Chem. 2021;9:813359. doi: 10.3389/fchem.2021.813359 30 PROTEOMICS We developed and demonstrated a new metabolomeinformed proteome imaging (MIPI) workflow for studying microscale microhabitats in complex ecosystems. Our workflow combines state-of-the art analytical instrumentation that generates spatial metabolome and proteome-rich data to build biological pathways. In our roles as Pacific Northwest National Laboratory (PNNL) scientists, we led this multi-institutional study, which was published in Nature Chemical Biology. Harnessing fungal communities for sustainable plant degradation and everyday products A major research focus at PNNL is to understand how natural systems efficiently perform complex tasks, such as the breakdown of plants into high-value molecules. The leaf-cutter ant fungal garden ecosystem serves as a naturally evolved model for efficient plant biomass degradation. Characterizing the degradation processes mediated by the symbiotic fungus, Leucoagaricus gongylophorus (L. gongylophorus), is challenging due to the system’s dynamic metabolisms and spatial complexity. In this study, we performed microscale imaging across sections of the Atta cephalotes fungal garden and used MIPI to map lignin degradation. We focused on natural plant degradation because plants are vital to the global economy, creating products like fuels, medicines, insulation and food-safe packaging. Unlocking Nature’s Secrets With Metabolome Informed Proteome Imaging Kristin Burnum-Johnson, PhD, and Marija Velickovic Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 31 Scientists seek cleaner, cheaper methods to break down tough plant materials, but current methods often leave behind waste, such as lignin. Natural fungal communities efficiently decompose these materials into usable nutrients. Understanding these processes at a molecular level can help us sustainably create everyday products. Overcoming traditional limitations: MIPI unveils key biochemical pathways in fungal garden ecosystems Traditional methods measure metabolites, enzymes and other molecules in bulk, providing average data that mask detailed information. MIPI offers detailed insights into biochemical pathways within complex biological matrices. Utilizing matrix-assisted laser desorption/ ionization mass spectrometry imaging (MALDI-MSI), MIPI visualizes metabolite locations and identifies microscale activity hotspots. These hotspots are then processed using microdroplet processing in one pot for trace samples (microPOTS), followed by sensitive liquid chromatography-tandem mass spectrometry (LC-MS/ MS) proteomic analyses. This approach thoroughly examines the whereabouts, timing and molecular participants of biochemical reactions, providing a comprehensive view of intricate biological pathways. Using our new imaging technique, MIPI, we examined 12 μm-thick sections of the leaf-cutter ant fungal garden ecosystem to map lignin degradation and visualize colocalized metabolites and proteins. Untargeted MALDI-MSI analysis was used to look at molecular distributions across fungal garden sections. Using the METASPACE annotation platform and KEGG database, we identified metabolites and mapped distinct microscale lignin and primary metabolite microhabitats. These microscale microhabitats underwent detailed proteomic analysis using laser-capture microdissection and microPOTS followed by LC-MS/MS. Spatial metabolome and proteome data were then integrated. The key findings of the paper were: • Over 7,000 unique and taxon-specific peptides were identified from microscale samples. A diverse array of enzymes actively participating in the breakdown of lignocellulose and aromatic compounds were also revealed. • A comprehensive view of metabolic pathways within microscale regions of this complex ecosystem was obtained from the integrated metabolome and proteome data. • Critical metabolites and enzymes that drive essential biochemical reactions in plant degradation were identified using our MIPI method. • Fungi were found to dominate and lignin breakdown pathways were identified. Our findings highlighted that fungi are the primary degraders of plant material in the system and provided detailed pathway-level resolution of lignin breakdown by the fungus. MIPI reveals fungal dominance in plant material degradation and detailed pathways for lignin breakdown Characterizing biochemical pathways in complex, heterogeneous samples has been challenging due to the limitations of current methods, which focus on bulk averages and obscure finer details. Our MIPI technique overcomes these limitations by visualizing individual molecular components, revealing specific biochemical reactions in plant degradation. Using MIPI, we unveiled key metabolites and enzymes essential for these reactions within the leaf-cutter ant fungal garden ecosystem. We identified significant fungal involvement in breaking down plant material, particularly lignin. MIPI allowed us to observe colocalized metabolites and proteins, providing detailed pathway-level insights into lignin degradation by fungi. Untargeted MALDI-MSI analysis highlighted heterogeneous spatial distributions of molecular features and identified distinct lignin and primary TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 32 metabolite microhabitats. Detailed proteomic analysis using laser-capture microdissection and microPOTS, followed by LC-MS/MS, revealed thousands of unique and taxon-specific peptides. These results emphasized fungi’s prominent role in lignocellulose degradation through various enzymes involved in carbohydrate and aromatic compound metabolism. Through MIPI, we discovered that the fungus, L. gongylophorus, is the primary degrader of plant material and provided detailed pathways for lignin breakdown. These insights enhance our understanding of complex metabolic pathways, paving the way for advancements in both environmental research and biofuel and bioproduct development. We now have a detailed microscale view of how plant degradation naturally occurs within this fungal system. To develop new methods for producing biofuels and bioproducts, we must start with the most basic components. By identifying which metabolites, enzymes and other chemicals initiate reactions at specific times and locations, we can replicate these processes in the lab. This knowledge can inform new strategies for creating better bioproducts and biofuels. We have successfully pinpointed the necessary molecules and biochemical reactions at a molecular level. With this natural blueprint, we can now apply the same techniques in the laboratory. A current limitation of our MIPI workflow is the need for multiple adjacent tissue sections, as different modalities require specific sample slides. To address this, future efforts will focus on enabling MIPI to perform multi-omics profiling from a single tissue section while maintaining or even improving the sensitivity of all modalities. Expanding horizons: Applying MIPI to future research and broader scientific endeavors With our molecular-level understanding of these processes, we can now apply this method to a range of future experiments and broader scientific endeavors. We aim to study how fungal communities respond and protect themselves amid various disturbances, including those caused by extreme weather. Temperature fluctuations, shifts in precipitation patterns and increased exposure to pathogenic microbes can have devastating environmental impacts. Using this method, we can investigate how these communities respond to such stresses. We are thrilled to have a tool that allows us to examine these processes in unprecedented detail, opening a whole new world of exploration. Beyond environmental research, MIPI holds significant promise in clinical applications, offering valuable insights into cellular diversity and disease progression. Future research will leverage MIPI’s capability to map biological systems intricately, investigating fungal responses to environmental stresses like extreme weather. This method not only deepens our understanding of these processes but also paves the way for new explorations and applications in both environmental and clinical settings. REFERENCE Veličković M, Wu R, Gao Y, et al. Mapping microhabitats of lignocellulose decomposition by a microbial consortium. Nat Chem Biol. 2024;20(8):1033-1043. doi:10.1038/s41589-023-01536-7 There are many potential applications of immunoproteomics, offering valuable insights into disease mechanisms. While a few key examples are outlined below, the possibilities extend far beyond these, providing a glimpse into the areas of diagnostic medicine that can benefit from immunoproteomics. Applications of immunoproteomics Click here to view the full infographic 1. Vaccine efficiency and development 4. Early-stage cancer detection 3. Diagnosis and prognosis of autoimmune diseases 2. Monitoring antigenicity of therapeutic drugs Vaccines protect against bacterial and viral diseases by stimulating the production of neutralizing antibodies against specific pathogens. While vaccination has significantly reduced the incidence of many serious diseases, challenges remain for pathogens without vaccines or for individuals with weakened immune systems who may not respond effectively. Immunoproteomics plays a crucial role in vaccine development and efficiency by identifying highly effective immunogens and monitoring immune responses. Many cancer patients produce antibodies against antigens that are specifically expressed by malignant cells. This immune response is a valuable source of diagnostic and prognostic information. Immunoproteomics can aid as a diagnostic approach for the early detection and monitoring of different types of cancer. However, the timing of antibody responses during carcinogenesis remains poorly understood, necessitating careful evaluation to minimize the risk of false-negative diagnoses. Autoimmune diseases arise from erroneous activation of the immune system, resulting in the attack of self-proteins within the human body. Examples of autoimmune diseases include rheumatoid arthritis, psoriasis and systemic lupus erythematosus (SLE). In all these conditions, immunoproteomics holds great promise to aid in the diagnosis, prognosis and monitoring of autoimmune diseases. Immunogenicity is a significant problem associated with protein therapeutics. Due to artificial production processes, recombinant therapeutics are often not completely identical to their human native counterparts. Sometimes, these therapeutics elicit an immune response that interferes with drug affectivity. In principle, immunoproteomic approaches could be used to define the patient’s immune response against a panel of related (protein) drugs to aid in selective drug application to optimize individualized treatment. Click here to view the full infographic 34 PROTEOMICS Since mass spectrographs and spectrometers were introduced in the early 1900s,1 mass spectrometry (MS) has undergone tremendous technological improvements. Once a methodology primarily used by chemists, MS is now an incredibly versatile analytical technique with several applications in research including structural biology, clinical diagnostics, environmental analysis, forensics, food and beverage analysis, omics and beyond. MS produces a vast quantity of data that needs to be analyzed. Managing, processing and interpreting these large data outputs is computationally intensive and often prone to errors, particularly when manual or semi-automated processes are used. Consequently, artificial intelligence (AI) and machine learning (ML) have become immensely popular for processing MSgenerated data and statistical analysis as they can be applied to various biological disciplines,2 limit errors and enhance data analysis. This article delves into what MS data analysis entails and its associated challenges, how AI/ML can aid analyses and exciting potential future developments in the field, with specific applications to proteomics and metabolomics research. Advancing Mass Spectrometry Data Analysis Through Artificial Intelligence and Machine Learning Isabel Ely, PhD Credit: iStock TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 35 What does MS data analysis entail? “Data analysis in proteomics and metabolomics is a complex, multi-step process that begins with the collection of biological samples and culminates in the extraction of meaningful biological insights,” Dr. Wout Bittremieux, assistant professor in the Adrem Data Laboratory at the University of Antwerp, said. After the laborious sample preparation of extracting proteins, peptides or metabolites of interest,3 they are ionized and introduced into a mass spectrometer where they are detected based on their mass-to-charge (m/z) ratio, producing a mass spectrum. The coupling of MS with other analytical tools, such as gas chromatography and liquid chromatography, allows for the further separation and identification of such analytes. “One of the key challenges in MS is the accurate annotation of MS spectra to their corresponding molecules,” Dr. Bittremieux said. “In proteomics, the dominant method for this task is sequence database searching. This relies on comparing experimental to theoretical spectra simulated from peptides assumed to be present. However, these theoretical spectra are often oversimplified and do not capture detailed fragment ion intensity information, which can lead to significant ambiguities and false identifications.” Once data are quantified, either relatively or absolutely, statistical analyses can take place to facilitate biological interpretation. “To contextualize the results, pathway analysis tools can be used to map the identified proteins or metabolites onto known biological pathways to help in understanding the functional implications of the changes observed in the data. Alternatively, biomarker candidates can be identified based on their ability to distinguish between different biological conditions or groups,” Bittremieux explained. Applying AI/ML to MS data analysis Although some scientists still have concerns about large-scale AI implementation, AI and ML have become indispensable tools for MS data analysis; aiding clinical decisions, guiding metabolic engineering and stimulating fundamental biological discoveries. Applying AI/ML in MS research attempts to minimize errors associated with data analysis –including high noise levels, batch effects during measurements and missing values4 – enhance usability and maximize data outputs.5 Further, training ML models on large datasets of empirical MS spectra allows the generation of highly accurate predicted spectra that closely match the experimental data.6 This overcomes the limitations of traditional sequence database searching, which relies on crude, theoretical spectra. Developments in AI/ML have led to more accurate, efficient and comprehensive interpretations of biological data, including de novo peptide sequencing.7 “De novo peptide sequencing, which involves determining the peptide sequence directly from tandem (MS/ MS) spectra without relying on a reference database, is a challenging problem. ML approaches are starting to impact this area significantly by learning patterns from known spectra and using them to predict peptide sequences from unknown spectra, making it feasible to analyze complex proteomes without relying solely on existing protein databases,” Dr. Bittremieux said. Developments in AI/ ML have led to more accurate, efficient and comprehensive interpretations of biological data, including de novo peptide sequencing. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 36 Another area AI/ML has been applied to in MS data analysis is repository-scale data analysis.8 Public data repositories have continued to expand, now containing millions to billions of MS spectra. Despite existing data providing ample opportunity to extract new biological insights, the sheer volume of data presents significant challenges in terms of data processing and analysis. “We have developed AI algorithms capable of performing large-scale analyses across these repositories, identifying patterns across experiments and detecting novel peptides and proteins that were previously missed. This has led to discoveries that would have been impossible with manual or traditional computational methods.” Recent developments in AI/ML Although advancements in AI have been fruitful, applying these technological developments to MS data is challenging due to its unique nature, making a direct translation of AI advancements to MS data non-trivial. “One of the most significant recent advancements in AI relevant to MS data analysis is the development of more sophisticated deep learning models capable of handling high-dimensional data and extracting intricate patterns,” said Bittremieux. “For example, transformer neural networks, which were originally developed for natural language processing, are now effectively used to ‘translate’ between sequences of peaks in tandem MS spectra to sequences of amino acids during de novo peptide sequencing. These models can learn from vast amounts of empirical MS data, identifying subtle features that traditional methods might overlook.” “Despite such advancements, the successful application of AI to MS data still requires deep expertise in both AI and MS. This multidisciplinary skill set remains relatively rare, which has slowed the broader adoption of AI in the field. However, as more researchers receive training in both areas and as AI tools become more accessible, we are beginning to see a new generation of scientists capable of bridging this gap.” Looking towards the future Although significant advancements in AI and ML have aided the continual development of MS data analysis, there is still room for improvement. “One of the key areas where I believe future developments should be focused is on the generation and curation of high-quality, large-scale datasets. While advancements in AI model architectures have been impressive, these models are only as good as the data they are trained on,” discussed Bittremieux. Greater availability of diverse MS data sets would ultimately enable the development of AI tools suitable for use across multiple experimental conditions in differing biological topics.9 “These datasets should include comprehensive annotations, such as accurate peptide and metabolite identifications, quantification data and metadata related to sample preparation and instrument settings. This diversity will enable AI models to learn more generalizable patterns, improving their performance across different applications.” Researchers are sometimes at fault for testing their models on cherry-picked datasets. This contributes to a lack of standardization evaluations assessing Greater availability of diverse MS data sets would ultimately enable the development of AI tools suitable for use across multiple experimental conditions in differing biological topics. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 37 the performance of different models. Dr. Bittremieux detailed that “the development of benchmarking suites would allow for a fair comparison of different algorithms, fostering transparency and driving genuine progress in the field.” “As AI tools become more accessible and interpretable, we will likely see a surge in innovative applications, from personalized medicine to environmental monitoring.” REFERENCES 1. Wilkinson DJ. Historical and contemporary stable isotope tracer approaches to studying mammalian protein metabolism. Mass Spectrom. Rev. 2018;37(1):57-80. doi:10.1002/mas.21507 2. Neagu AN, Jayathirtha M, Baxter E, Donnelly M, Petre BA, Darie CC. Applications of tandem mass spectrometry (MS/MS) in protein analysis for biomedical research. Molecules. 2022;27(8):2411. doi:10.3390/molecules27082411 3. Luque-Garcia JL, Neubert TA. Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry. J. Chromatogr. A. 2007;1153(1):259-276. doi:10.1016/j.chroma.2006.11.054 4. Liebal UW, Phan ANT, Sudhakar M, Raman K, Blank LM. Machine learning applications for mass spectrometry-based metabolomics. Metabolites. 2020;10(6):243. doi:10.3390/ metabo10060243 5. Beck AG, Muhoberac M, Randolph CE, et al. Recent developments in machine learning for mass spectrometry. ACS Meas Sci Au. 2024;4(3):233-246. doi:10.1021/acsmeasuresciau.3c00060 6. Adams C, Gabriel W, Laukens K, et al. Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF. Nat Commun. 2024;15(1):3956. doi:10.1038/s41467-024- 48322-0 7. Yilmaz M, Fondrie WE, Bittremieux W, et al. Sequence-tosequence translation from mass spectra to peptides with a transformer model. Nat Commun. 2024;15(1):6427. doi:10.1038/ s41467-024-49731-x 8. Bittremieux W, May DH, Bilmes J, Noble WS. A learned embedding for efficient joint analysis of millions of mass spectra. Nat Methods. 2022;19(6):675-678. doi:10.1038/s41592-022-01496-1 9. Dens C, Adams C, Laukens K, Bittremieux W. Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics. J Am Soc Mass Spectrom. 2024;35(9):2143-2155. doi:10.1021/jasms.4c00180 ABOUT THE INTERVIEWEE Dr. Wout Bittremieux is an assistant professor in the Adrem Data Lab at the University of Antwerp, Belgium and a worldleading expert in computational mass spectrometry. He leads a research team that develops advanced artificial intelligence and bioinformatics tools to analyze mass spectrometry-based proteomics and metabolomics data. 38 PROTEOMICS Biomarkers can offer critical information about an individual’s health status, disease prognosis or how they might respond to a treatment. Developments in high-throughput technologies that enable the efficient, sensitive and large-scale analysis of biological samples have significantly advanced biomarker research over recent years. In this article, we highlight just some of the research trends impacting biomarker discovery and analysis, including advances in proteomics research, artificial intelligence (AI) and microsampling. Proteomics powers biomarker discovery Looking to the future of biomarker research, Dr. Irene De Biase, professor of clinical pathology at the University of Utah, expects to see an exponential growth in studies that use omic approaches to evaluate large datasets to identify correlations between biomarkers and clinical outcomes: “For instance, three months into 2025, and there are already almost 300 publications using proteomics in various cohorts of patients with diabetes mellitus,” she told Technology Networks.* Advances in Biomarker Discovery and Analysis Molly Coddington Credit: iStock What is the proteome? The proteome refers to the entire set of proteins found in a cell, tissue or organism at a specific point in time. While an individual’s genome is relatively static throughout their lifetime, the proteome is dynamic and actively responds to both external and internal signals. TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 39 Proteins drive cellular functions, earning their nickname as the “workhorses” of the cell. Proteomics, the largescale study of the proteome, has emerged as a powerful field for biomarker discovery in the post-genomic era. Liquid chromatography-mass spectrometry (LCMS) is the primary method used for protein biomarker discovery due to its high level of sensitivity and specificity. LC-MS has enabled researchers to detect disease-specific protein signatures, analyze posttranslational modifications (PTMs) that affect disease mechanisms and discover novel biomarkers for conditions such as cancer and neurodegenerative diseases.1,2,3 As the demand for higher throughput and scalability in biomarker research grows, recent trends in LCMS proteomics analysis are focused on improving and automating workflows, as well as refining sample preparation and analytical processes.4 Kverneland et al. recently published a fully automated workflow that combines sample digestion, cleanup and loading, and demonstrated its use for processing 192 HeLa cell samples in 6 hours.5 “The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts,” the researchers said. While blood is the most commonly analyzed biofluid due to its accessibility, valuable insights can also be gained from other fluids such as urine, cerebrospinal fluid (CSF), serum and breast milk. A recent paper by Zhang et al. outlined an automated, scalable workflow for preparing hundreds to thousands of samples, including milk and urine, with only minor modifications to the initial steps of a workflow originally designed for blood plasma, serum and CSF.6 This approach expands the scope of biomarker discovery by enabling the automated analysis of a broader range of biological samples. Advances in protein enrichment and depletion methods are also enhancing MS-based proteomics. High abundance proteins in samples such as plasma can mask the signal of low-abundance – but biologically interesting – proteins. A variety of enrichment methods are being implemented to support the discovery and identification of low-abundance proteins that carry biomarker potential.7 For example, Palstrøm et al. recently performed a MS-based proteomic analysis of plasma samples that were enriched using magnetic p-aminobenzamidine (ABA) affinity probes.8 The samples were gathered from 45 patients with abdominal aortic aneurysm (AAA) – a life-threatening disorder that sees improved outcomes with a fast diagnosis – and 45 matched controls. The researchers identified plasma proteome alterations linked to AAA and, using machine learning, developed a potential biomarker panel for earlier and more accurate diagnosis. Beyond MS-based approaches, affinity-based techniques are increasingly being used to analyze the proteome and uncover clinically relevant biomarkers. These techniques rely on specific interactions between proteins and binding agents, including aptamers and/or antibodies. The UK Biobank recently announced the launch of the world’s “most comprehensive study of the proteins circulating in our bodies”. The project will harness an antibody-based platform to measure up to 5,400 proteins in 600,000 samples, half a million of which will be obtained from UK Biobank participants, while 100,000 samples will be taken from the same volunteers up to 15 years later. “Adding proteomic data for the full UK Biobank cohort will be an absolute game changer for prediction of disease onset and prognosis, particularly for the many neglected diseases for which good prospective data are lacking,” Professor Claudia Langenberg, director of the Precision Healthcare University Research Institute at Queen Mary University of London, said. “These include debilitating and life-threating diseases, such as polycystic ovary syndrome and motor neurone disease. Just imagine if we could detect these and many other conditions much earlier than is currently possible.” A recent pre-print study by Kirsher et al. compared different plasma proteomics platforms – including aptamer-, antibody- and MS-based approaches by applying these methods to the same cohort of samples covering 13,000 proteins.9** Though the study is yet to TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 40 be peer-reviewed, it suggests that trade-offs in coverage across these platforms have implications for the future of biomarker discovery and translational research. Beyond proteomics, other omics fields – such as genomics, transcriptomics and metabolomics – continue to drive biomarker discovery, with growing efforts now focused on integrating these data through multiomics approaches to gain a more comprehensive view of disease biology. AI and machine learning enhances biomarker research AI-based approaches, such as deep learning and machine learning, are finding a variety of applications in biomarker discovery, development and validation. Such technologies can process and integrate vast, complex multiomics datasets more efficiently and at a higher capacity than traditional analytical tools. In 2022, researchers published a novel pan-cancer proteomic map of 949 human cell lines across over 40 types of cancer.10 They used a deep learning-based computational pipeline – Deep DeeProM – to integrate large volumes of data. “DeeProM enabled the full integration of proteomic data with drug responses and CRISPR-Cas9 gene essentiality screens to build a comprehensive map of protein-specific biomarkers of cancer vulnerabilities that are essential for cancer cell survival and growth,” co-author Associate Professor Qing Zhong of the Children’s Medical Research Institute, University of Sydney, told Technology Networks. The analyses identified biomarkers detectable only at the proteomic level, showing enhanced predictive accuracy compared to models that rely solely on gene expression data. Deep learning has also shown success in analyzing histopathology slides to facilitate the discovery of novel biomarkers. A recent preprint by Shulman et al. presents Path2Space, a deep learning model that predicts spatial gene expression from histopathology slides of breast cancer tumors.11** The researchers aimed to identify microenvironment characteristics and other spatial biomarkers associated with treatment response. When applied to two large cohorts of breast cancer patients treated with trastuzumab and chemotherapy, Path2Space was able to identify spatial biomarkers and predicted therapy response with high accuracy. While these examples showcase AI’s utility in oncology biomarker research, such technologies are also facilitating new biomarker discoveries across diseases including Alzheimer’s, diabetes and cardiovascular disease, among others.12,13,14 Remote sampling devices: The future of biomarker analysis? Measuring blood biomarkers in clinical and research settings presents challenges, including geographical limitations, the cost and inconvenience of in-clinic venipuncture and infrequent sampling. Microsampling is an emerging, minimally invasive alternative approach that enables the collection of samples in a remote location for subsequent laboratory analysis using remote sampling devices, of which there are now several types.15 Dr. Michael Snyder, Stanford W. Ascherman Professor of Genetics at Stanford Medicine, summarized why microsampling is an attractive approach to profiling changes in health: “It lets you measure hundreds to thousands of molecules with good accuracy, it’s convenient and the samples are collected in a natural setting – i.e., a person’s home – which should give a better indication of their true biochemical state rather than using samples collected in a clinic. “Microsampling can help science and medicine become more inclusive, reduce costs for healthcare systems and help people access important insights about their health,” Dr. Jennifer Van Eyk, professor of cardiology, director of the Advanced Clinical Biosystems Institute and the Erika Glazer Endowed Chair in Women’s Heart at Cedars-Sinai Medical Center, said. Van Eyk has been developing MS-based workflows for remote sampling devices, and comparing their viability to existing clinical sampling methods, for several years.16 TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 41 Snyder and colleagues recently published a strategy for regularly capturing and analyzing thousands of metabolites, lipids, cytokines and proteins from 10 μl of blood.17 “In two proof-of-principle studies, we first demonstrate the profiling of a dynamic response to ingestion of a mixed meal shake and discover high heterogeneity in individual metabolic and immune responses, and second, we perform high-resolution profiling of an individual over one week enabling the identification and quantification of thousands of molecular changes and associations across ‘omes’ at a personal level,” the authors said. Snyder said that his team runs most of his studies using a microsampling approach now. “I think many, if not most, studies will be run this way in the future. It will also be used for home health monitoring,” Snyder said. A recent review by Protti et al. emphasized how microsampling technologies hold the potential to democratize access to high-quality analytical testing.15 “However, for the very same reasons, to obtain a transformative effect on a wide range of analytical applications for both clinical and non-clinical purposes, these innovative microsampling techniques must first achieve wide acknowledgment and adoption, which in turn can only be obtained through consistently effected commercial availability, as well as regulatory approval for the intended use, in most countries and regions,” they said. “It’s going to be a long road, but I think we have a responsibility to help people access their data and have some level of control over their health,” Van Eyk said. The next era of biomarker innovation Biomarker research is evolving rapidly. While the trends and innovations highlighted in this article are by no means an exhaustive list, they represent increasingly precise, scalable and accessible approaches to understanding human health and disease. As multiomics integration and decentralized data collection continue to advance, the future of biomarker discovery holds great potential for transforming diagnostics, monitoring wellness and personalizing treatments across a wide range of conditions. *Interview completed in March 2025. ** This article is based on research findings that are yet to be peerreviewed. Results are therefore regarded as preliminary and should be interpreted as such. Find out about the role of the peer review process in research here. For further information, please contact the cited source. REFERENCES: 1. Gajula SNR, Khairnar AS, Jock P, et al. LC-MS/MS: A sensitive and selective analytical technique to detect COVID-19 protein biomarkers in the early disease stage. Expert Rev Proteomics. 2023;20(1-3):5-18. doi: 10.1080/14789450.2023.2191845 2. Jang HN, Moon SJ, Jung KC, et al. Mass spectrometry-based proteomic discovery of prognostic biomarkers in adrenal cortical carcinoma. Cancers. 2021;13(15). doi: 10.3390/cancers13153890 3. Tao QQ, Cai X, Xue YY, et al. Alzheimer’s disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling. The Innovation. 2024;5(1):100544. doi: 10.1016/j.xinn.2023.100544 4. Ye X, Cui X, Zhang L, et al. Combination of automated sample preparation and micro-flow LC–MS for high-throughput plasma proteomics. Clin Proteom. 2023;20(1):3. doi: 10.1186/s12014-022- 09390-w 5. Kverneland AH, Harking F, Vej-Nielsen JM, et al. Fully automated workflow for integrated sample digestion and evotip loading enabling high-throughput clinical proteomics. Mol Cell Proteom. 2024;23(7). doi: 10.1016/j.mcpro.2024.100790 6. Pedersen AL, Ernest M, Affolter M, Dayon L. Analyzing various biological fluids with a single automated proteomic workflow for biomarker discovery. In: Islam Williams T, ed. Tissue Proteomics: Methods and Protocols. Springer US; 2025:157-178. doi: 10.1007/978-1-0716-4298-6_11 7. Boschetti E, Righetti PG. Low-abundance protein enrichment for medical applications: the involvement of combinatorial peptide library technique. Int J Mol Sci. 2023;24(12):10329. 2023. doi: 10.3390/ijms241210329 8. Palstrøm NB, Nielsen KB, Campbell AJ, et al. Affinity-enriched plasma proteomics for biomarker discovery in abdominal aortic aneurysms. Proteomes. 2024;12(4). doi: 10.3390/ proteomes12040037 9. Kirsher DY, Chand S, Phong A, Nguyen B, Szoke BG, Ahadi S. The current landscape of plasma proteomics: technical advances, biological insights, and biomarker discovery. bioRxiv. 2025. doi: 10.1101/2025.02.14.638375 10. Gonçalves E, Poulos RC, Cai Z, et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell. 2022;40(8):835-849.e8. doi: 10.1016/j.ccell.2022.06.010 11. Shulman ED, Campagnolo EM, Lodha R, et al. Path2space: An ai approach for cancer biomarker discovery via histopathology inferred spatial transcriptomics. bioRxiv. 2024. doi: 10.1101/2024.10.16.618609 12. Winchester LM, Harshfield EL, Shi L, et al. Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia. Alzheimers Dement. 2023;19(12):5860-5871. doi: 10.1002/alz.13390 13. Khan S, Mohsen F, Shah Z. Genetic biomarkers and machine learning techniques for predicting diabetes: systematic TECHNOLOGYNETWORKS.COM PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 42 review. Artif Intell Rev. 2024;58(2):41. doi: 10.1007/s10462-024- 11020-w 14. 14. DeGroat W, Abdelhalim H, Patel K, Mendhe D, Zeeshan S, Ahmed Z. Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Sci Rep. 2024;14(1):1. doi: 10.1038/s41598-023-50600-8 15. 15. Protti M, Milandri E, Di Lecce R, Mercolini L, Mandrioli R. New trends in bioanalysis sampling and pretreatment: How modern microsampling is revolutionising the field. Adv Sample Prep. 2025;13:100161. doi: 10.1016/j.sampre.2025.100161 16. 16. Whelan SA, Hendricks N, Dwight ZL, et al. Assessment of a 60-biomarker health surveillance panel (hsp) on whole blood from remote sampling devices by targeted LC/MRM-MS and discovery DIA-MS analysis. Anal Chem. 2023;95(29):11007-11018. doi: 10.1021/acs.analchem.3c01189 17. 17. Shen X, Kellogg R, Panyard DJ, et al. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat Biomed Eng. 2024;8(1):11-29. doi: 10.1038/s41551-022- 00999-8 MEET THE INTERVIEWEES: Irene De Biase, MD,PhD is a professor of pathology at the University of Utah School of Medicine. Jennifer Van Eyk, PhD is an international leader in the area of clinical proteomics. She is professor of cardiology, director of the Advanced Clinical Biosystems Institute and the Erika Glazer Endowed Chair in Women’s Heart at Cedars-Sinai Medical Center. Qing Zhong, PhD is an associate professor at Children’s Medical Research Institute (CMRI), The University of Sydney. Prof. Michael Snyder As a pioneer of Precision Medicine, Snyder has invented many technologies enabling the 21st century of healthcare including systems biology, RNA sequencing, and protein chip. PROTEOMICS: TOOLS, TECHNIQUES AND TRANSLATIONAL POTENTIAL 43 TECHNOLOGYNETWORKS.COM CONTRIBUTORS Aldrin V. Gomes, PhD Aldrin is a professor and vice-chair of the Department of Neurobiology, Physiology and Behavior at the University of California, Davis. He has a PhD in biochemistry and has authored over 200 publications, including several articles on the problems and solutions for western blotting. Alison Halliday, PhD Alison holds a PhD in molecular genetics from the University of Newcastle. As an award-winning freelance science communications specialist, she has 20+ years of experience across academia and industry. Isabel Ely, PhD Isabel is a Science Writer and Editor at Technology Networks. She holds a BSc in exercise and sport science from the University of Exeter, a MRes in medicine and health and a PhD in medicine from the University of Nottingham. Her doctoral research explored the role of dietary protein and exercise in optimizing muscle health as we age. Kaja Ritzau-Reid, PhD Kaja Ritzau-Reid is a freelance science writer. She holds a Masters in neurotechnology and a PhD in neural tissue engineering. She previously worked as a post-doctoral research associate at Imperial College London, Department of Materials. Kate Harrison, PhD Kate Harrison is a senior science writer and is responsible for the creation of custom-written projects. She holds a PhD in virology from the University of Edinburgh. Before working at Technology Networks, she was involved in developing vaccines for neglected tropical diseases and held a lectureship position teaching immunology. Kristin Burnum-Johnson, PhD Kristin Burnum-Johnson is a science group leader for functional and systems biology at Pacific Northwest National Laboratory (PNNL Her research is dedicated to achieving transformative molecular-level insights into environmental and biomedical systems by implementing advanced mass spectrometry (MS) instrumentation. Marija Velickovic Marija Velickovic is a chemist at the Environmental Molecular Sciences Laboratory (EMSL) on the Pacific Northwest National Laboratory (PNNL) campus. Her focus is mass spectrometry-based omics in biological, chemical and environmental research. Molly Coddington Molly Coddington is a Senior Writer and Newsroom Team Lead at Technology Networks. She holds a first-class honors degree in neuroscience. In 2021 Molly was shortlisted for the Women in Journalism Georgina Henry Award.