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Plasma, the cell-free component of blood, is gaining increasing recognition as a reservoir of biological information relevant for disease studies. It carries a plethora of diverse circulating biomolecules, ranging from proteins and metabolites to lipids and nucleic acids, which is a mirror of the organism's physiological and pathological status. This systemic nature of plasma makes it an ideal biospecimen for investigation into disease pathophysiology, ranging from cancer to metabolic and inflammatory disorders. A major benefit of using plasma as a sample lies in its minimally invasive collection method, allowing for repeated sampling in clinical settings, which is important for observation of disease progression or therapeutic response. However, plasma exhibits an extremely complex proteomic matrix, whereby proteins such as albumins and immunoglobulin levels span more than ten orders of magnitude, masking identification1.
Over the past two decades, plasma proteomics has enabled the identification of clinically relevant biomarkers such as beta-human chorionic gonadotropin (β-hCG) and troponin I, driven by advances in large-scale protein analysis technologies2. These tools accept, as input, highly sensitive information, and with their high-throughput nature, provide large-scale profiling of plasma proteins. With the advent of liquid chromatography-mass spectrometry (LC-MS/MS) platforms, it is now possible to analyze thousands of protein species using as little as 10-50 µL of plasma input, with a dynamic detection range spanning several orders of magnitude3,4. This has increased the use of plasma proteomics in translational research: early disease detection, improved prognostic models, and the discovery of new therapeutic targets5.
An important milestone in this area was the establishment of the Human Plasma Proteome Project (HUPO-HPPP), which systemically catalogued plasma proteins and demonstrated the feasibility of plasma proteomics as an established field6. Afterwards, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) adopted rigorously validated protocols and cross-laboratory studies to address issues of reproducibility that earlier confounded proteomic research7. These initiatives underscored the need for standardization and highlighted key procedures, including uniform plasma handling, depletion of abundant proteins, enzymatic digestion, and consistent LC-MS/MS parameters. Recent studies by Geyer and colleagues showed that plasma proteomic signatures can be reliably generated across many individuals when standardized methodologies are used, providing a basis for population-level studies and biomarker validation processes8.
The progression of plasma proteomics has been shaped considerably by the recognition of quality control (QC) and performance metrics as critical components of experimental design. Conventionally, differences in digestion efficiency, peptide identification rates, and instrument performance have led to inconsistencies across research studies, thereby affecting reproducibility. Many studies have been undertaken to address the issues. Gawor et al. outlined measurable quality indicators for sample preparation and mass spectrometry performance, thereby providing a systematic method for assessing the robustness of the protein profiling pipeline9. Tsantilas et al. presented computerized benchmarking tools that allow researchers to compare datasets across platforms and laboratories, thereby increasing transparency in the presentation of proteomics data10. Moreover, advances in methodology, such as immunodepletion, size-exclusion fractionation, and Data-independent acquisition (DIA), have enhanced proteome coverage and reduced technical bias11,12. Such quality assurance measures ensure that plasma proteomic data are reproducible and serve as a scaffold for future clinical translation studies.
Despite these significant advances, plasma's inherent complexity continues to harbor challenges. It is common for direct analysis of crude plasma to yield suboptimal proteome coverage, as very abundant proteins overwhelm low-abundance analytes, many of which are the most promising biomarker candidates. Moreover, technical variation stemming from variable protein quantitation, incomplete digestion, and inadequate cleanup can contribute to batch artifacts and decreased cohort comparability13. These limitations underscore the need for highly optimized workflows that balance proteome depth, reproducibility, and scalability. This study protocol addresses this need by demonstrating an optimized plasma proteomics workflow that combines immunodepletion, molecular-weight-cutoff filtration, lyophilization, protein normalization and quantitation, enzymatic digestion, and LC-MS/MS profiling. By increasing proteome coverage and reducing technical variation, the methodological framework enables consistent relative quantitation between disease-affected and control groups. By introducing scalability and accommodating downstream analyses, the workflow provides a usable, reproducible method for extracting clinically significant information from plasma proteomics.