Five Common Preanalytical Challenges in Plasma Proteomics

1 Five Common Preanalytical Challenges in Plasma Proteomics Alison Halliday, PhD Next-generation proteomics is revolutionizing our ability to study the proteome with unprecedented depth and resolution, offering powerful insights into human health and disease. Recent advances in technology, particularly in mass spectrometry (MS)-based techniques and multiplex affinity-based assays, now enable researchers to profile proteins in complex biological samples with remarkable precision.1,2 These powerful tools are accelerating research into identifying novel biomarkers for early disease detection and for predicting and monitoring treatment responses. Plasma, the cell-free liquid part of blood, is a widely used and readily accessible biofluid in biomarker research. It is minimally invasive to collect and contains a rich array of circulating proteins (or protein fragments) that may be associated with disease susceptibility, activity or treatment response. Additionally, plasma samples can be cryopreserved for extended periods, supporting large-scale retrospective studies aimed at discovering and validating clinically relevant biomarkers. However, obtaining sufficient sample numbers often requires plasma collection across multiple sites, introducing the challenge of preanalytical variability. Standard operating procedures for blood collection and processing may not be harmonized between laboratories. Even subtle inconsistencies in sample collection, handling, processing and storage have the potential to alter plasma protein profiles, which can compromise data quality, reproducibility and comparability across different studies. This issue is particularly problematic in case-control studies, where systematic differences between groups may introduce bias and undermine the validity of findings.3 Alarmingly, a recent literature review estimated that around half of all published plasma proteomics studies may be affected by preanalytical limitations, highlighting the urgent need for adherence to standardized protocols and rigorous quality control measures.3 Recognizing and minimizing sources of potential variability is essential for generating robust, reproducible data and accelerating the translation of blood-based biomarkers into clinical practice. In this listicle, we explore some of the most common preanalytical challenges in plasma proteomics. 1. Blood collection The process of collecting blood samples from patients is a critical first step in plasma proteomics, which could introduce variability if not standardized. Key factors to consider include the phlebotomy technique, needle gauge and the number of venipunctures.4 Improper technique or repeated sampling could cause hemolysis or activate platelets, both of which could alter the plasma protein profile. To minimize these risks, it is important to implement consistent blood collection protocols across all sites and personnel. Listicle FIVE COMMON PREANALYTICAL CHALLENGES IN PLASMA PROTEOMICS 2 2. Anticoagulant type Plasma is typically isolated from whole blood samples by centrifugation in the presence of an anticoagulant (commonly EDTA, heparin or sodium citrate) to prevent clot formation. This process separates the cellular components from the liquid fraction, leaving a cell-free plasma matrix rich in circulating proteins ready for in-depth proteomic analysis. However, the choice of anticoagulant can influence plasma proteome profiles, as each agent acts differently on the coagulation cascade. For example, EDTA and sodium citrate work by chelating calcium ions, which are essential for clot formation, while heparin inhibits specific enzymes involved in coagulation. A recent study reported notable differences in the abundances of multiple proteins between EDTA- and heparin-derived plasma, with most of these proteins showing reduced levels in heparin samples.5 Such anticoagulant-dependent shifts can introduce unwanted variability, potentially confounding the identification and validation of biomarkers. To minimize this risk, the same type of anticoagulant should be used consistently across all samples in a study. 3. Centrifugation conditions Variability in centrifugation conditions such as speed, duration, number of spins and the application of the centrifuge break may influence the quality and composition of plasma samples. Inadequate or inconsistent centrifugation may result in incomplete separation of cellular components, leaving residual blood cells that could release intracellular proteins into the plasma. While some studies have found that single- and double-spin protocols, centrifugation speeds and the use of the centrifuge brake do not significantly affect overall plasma protein composition, these findings do not eliminate the risk of variability.5,6 Therefore, adopting and adhering to a standardized centrifugation protocol in which force, duration and handling procedures are clearly defined is essential to minimize preanalytical variation and maintain the reliability and reproducibility of proteomic data. 4. Time and temperature before processing The time and temperature between blood collection and plasma separation are critical preanalytical factors that can influence the quality of proteomic data.5 Delays in processing allow ongoing cellular metabolism and enzymatic activity, which may lead to protein degradation or the leakage of intracellular proteins into the plasma. Similarly, storing whole blood at room temperature – or fluctuating temperatures – before centrifugation can activate endogenous proteases and phosphatases, potentially resulting in additional proteolytic degradation or unwanted post-translational modifications. These alterations can distort the plasma proteome, masking biologically meaningful signals and compromising downstream analyses. This concern is supported by two recent studies, which found that both the duration and temperature of storage before centrifugation can significantly contribute to plasma proteomic variability, including elevated levels of intracellular proteins.5,6 To mitigate these effects, the Early Detection Research Network (EDRN), an initiative of the US National Cancer Institute (NCI), has published a detailed protocol for plasma collection and processing.7 According to these guidelines, blood samples should ideally be centrifuged immediately following collection or stored at 4 °C for no more than four hours before processing. Adhering to these guidelines will help to preserve protein integrity and minimize preanalytical variability. Listicle FIVE COMMON PREANALYTICAL CHALLENGES IN PLASMA PROTEOMICS 3 5. Storage conditions and freeze-thaw cycles Long-term storage at -80 °C is widely regarded as the standard practice for preserving plasma samples.7 However, several studies have indicated that repeated freeze-thaw cycles can negatively affect protein stability, potentially introducing artefacts that compromise downstream proteomic analysis.7 To mitigate this risk, it is recommended that plasma samples are aliquoted into smaller volumes during the initial processing phase. This approach avoids the need to thaw and refreeze the same sample, reducing the likelihood of protein degradation or aggregation. Additionally, thawing should be performed rapidly and uniformly to minimize the risk of detrimental effects. Beyond proper handling, maintaining consistent storage histories – such as duration, temperature conditions and number of freeze-thaw cycles – is essential. This is particularly important in large-scale biomarker discovery studies, where batch effects arising from inconsistent storage protocols could lead to inaccurate findings. 6. Conclusion Plasma proteomics is increasingly recognized as a powerful platform for clinical translation, offering immense potential to identify minimally invasive, protein-based biomarkers for early disease detection, patient stratification and therapeutic monitoring. Unlike tissue biopsies, plasma samples can be collected with minimal discomfort and at multiple time points, making them well-suited for tracking disease progression and evaluating treatment response over time. Advances in high-throughput proteomic technologies, including MS- and affinity-based assays, have opened new avenues for identifying complex protein signatures associated with a wide range of illnesses, including cancer, cardiovascular diseases, neurodegenerative disorders and inflammatory conditions. Despite its promise, translating plasma proteomics into clinical practice requires overcoming several key challenges. These include standardizing workflows, validating potential biomarkers in large and diverse patient populations, and demonstrating clinical utility. For a proteomic biomarker to be integrated into clinical decision-making, it must deliver actionable insights that improve diagnosis, prognosis or therapeutic outcomes beyond existing methods. Furthermore, regulatory approval and clinical adoption require robust reproducibility, scalability and cost-effectiveness. Collaborative efforts, such as the Human Plasma Proteome Project and other large-scale biomarker initiatives, are playing a critical role in bridging the gap between discovery and clinical application by promoting harmonized protocols, multi-center validation and open data sharing. However, the clinical utility of plasma proteomics is critically dependent on the quality of the samples analyzed. Preanalytical variables – introduced during blood collection, processing, handling or storage – can potentially alter the plasma proteome, compromising data integrity and reproducibility. To ensure meaningful and reliable results, it is essential to proactively identify, control and minimize these sources of variability. Ultimately, such efforts will accelerate the successful translation of plasma proteomics into clinically impactful tools, driving forward the implementation of precision medicine. Sponsored by Listicle FIVE COMMON PREANALYTICAL CHALLENGES IN PLASMA PROTEOMICS 4 References 1. Shuken SR. An introduction to mass spectrometry-based proteomics. J Proteome Res. 2023;22(7):2151-2171. doi: 10.1021/ acs.jproteome.2c00838 2. Smith JG, Gerszten RE. Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease. Circulation. 2017;135(17):1651-1664. doi: 10.1161/CIRCULATIONAHA.116.025446 3. Geyer PE, Voytik E, Treit PV, et al. Plasma proteome profiling to detect and avoid sample-related biases in biomarker studies. EMBO Mol Med. 2019;11(11):e10427. doi: 10.15252/emmm.201910427 4. Ignjatovic V, Geyer PE, Palaniappan KK, et al. Mass spectrometry-based plasma proteomics: Considerations from sample collection to achieving translational data. J Proteome Res. 2019;18(12):4085-4097. doi: 10.1021/acs.jproteome.9b00503 5. Halvey P, Farutin V, Koppes L, et al. Variable blood processing procedures contribute to plasma proteomic variability. Clin Proteomics. 2021;18(1):5. doi: 10.1186/s12014-021-09311-3 6. Hassis ME, Niles RK, Braten MN, et al. Evaluating the effects of preanalytical variables on the stability of the human plasma proteome. Anal Biochem. 2015;478:14-22. doi: 10.1016/j.ab.2015.03.003 7. Tuck MK, Chan DW, Chia D, et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res. 2009;8(1):113-7. doi: 10.1021/pr800545q About the author: Alison Halliday 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. Listicle