Plasma prefractionation methods for proteomic analysis and perspectives in clinical applications

Placenta-Derived Extracellular Vesicles in Pregnancy Complications and Prospects on a Liquid Biopsy for Hemoglobin Bart's Disease

Extracellular vesicles (EVs) are nano-scaled vesicles released from all cell types into extracellular fluids and specifically contain signature molecules of the original cells and tissues, including the placenta. Placenta-derived EVs can be detected in maternal circulation at as early as six weeks of gestation, and their release can be triggered by the oxygen level and glucose concentration. Placental-associated complications such as preeclampsia, fetal growth restriction, and gestational diabetes have alterations in placenta-derived EVs in maternal plasma, and this can be used as a liquid biopsy for the diagnosis, prediction, and monitoring of such pregnancy complications. Alpha-thalassemia major ("homozygous alpha-thalassemia-1") or hemoglobin Bart's disease is the most severe form of thalassemia disease, and this condition is lethal for the fetus. Women with Bart's hydrops fetalis demonstrate signs of placental hypoxia and placentomegaly, thereby placenta-derived EVs provide an opportunity for a non-invasive liquid biopsy of this lethal condition. In this article, we introduced clinical features and current diagnostic markers of Bart's hydrops fetalis, extensively summarize the characteristics and biology of placenta-derived EVs, and discuss the challenges and opportunities of placenta-derived EVs as part of diagnostic tests for placental complications focusing on Bart's hydrop fetalis.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Advancements in automation for plasma proteomics sample preparation

Automation is necessary to increase sample processing throughput for large-scale clinical analyses. Replacement of manual pipettes with robotic liquid handler systems is especially helpful in processing blood-based samples, such as plasma and serum. These samples are very heterogenous, and protein expression can vary greatly from sample-to-sample, even for healthy controls. Detection of true biological changes requires that variation from sample preparation steps and downstream analytical detection methods, such as mass spectrometry, remains low. In this mini-review, we discuss plasma proteomics protocols and the benefits of automation towards enabling detection of low abundant proteins and providing low sample error and increased sample throughput. This discussion includes considerations for automation of major sample depletion and/or enrichment strategies for plasma toward mass spectrometry detection.

Translational Proteomic Approach for Cholangiocarcinoma Biomarker Discovery, Validation, and Multiplex Assay Development: A Pilot Study

Cholangiocarcinoma (CCA) is a highly lethal disease because most patients are asymptomatic until they progress to advanced stages. Current CCA diagnosis relies on clinical imaging tests and tissue biopsy, while specific CCA biomarkers are still lacking. This study employed a translational proteomic approach for the discovery, validation, and development of a multiplex CCA biomarker assay. In the discovery phase, label-free proteomic quantitation was performed on nine pooled plasma specimens derived from nine CCA patients, nine disease controls (DC), and nine normal individuals. Seven proteins (S100A9, AACT, AFM, and TAOK3 from proteomic analysis, and NGAL, PSMA3, and AMBP from previous literature) were selected as the biomarker candidates. In the validation phase, enzyme-linked immunosorbent assays (ELISAs) were applied to measure the plasma levels of the seven candidate proteins from 63 participants: 26 CCA patients, 17 DC, and 20 normal individuals. Four proteins, S100A9, AACT, NGAL, and PSMA3, were significantly increased in the CCA group. To generate the multiplex biomarker assays, nine machine learning models were trained on the plasma dynamics of all seven candidates (All-7 panel) or the four significant markers (Sig-4 panel) from 45 of the 63 participants (70%). The best-performing models were tested on the unseen values from the remaining 18 (30%) of the 63 participants. Very strong predictive performances for CCA diagnosis were obtained from the All-7 panel using a support vector machine with linear classification (AUC = 0.96; 95% CI 0.88–1.00) and the Sig-4 panel using partial least square analysis (AUC = 0.94; 95% CI 0.82–1.00). This study supports the use of the composite plasma biomarkers measured by clinically compatible ELISAs coupled with machine learning models to identify individuals at risk of CCA. The All-7 and Sig-4 assays for CCA diagnosis should be further validated in an independent prospective blinded clinical study.

Mass Spectrometric-Based Proteomics for Biomarker Discovery in Osteosarcoma: Current Status and Future Direction

Due to a lack of novel therapies and biomarkers, the clinical outcomes of osteosarcoma patients have not significantly improved for decades. The advancement of mass spectrometry (MS), peptide quantification, and downstream pathway analysis enables the investigation of protein profiles across a wide range of input materials, from cell culture to long-term archived clinical specimens. This can provide insight into osteosarcoma biology and identify candidate biomarkers for diagnosis, prognosis, and stratification of chemotherapy response. In this review, we provide an overview of proteomics studies of osteosarcoma, indicate potential biomarkers that might be promising therapeutic targets, and discuss the challenges and opportunities of mass spectrometric-based proteomics in future osteosarcoma research.

Human Milk Extracellular Vesicles: A Biological System with Clinical Implications

The consumption of human milk by a breastfeeding infant is associated with positive health outcomes, including lower risk of diarrheal disease, respiratory disease, otitis media, and in later life, less risk of chronic disease. These benefits may be mediated by antibodies, glycoproteins, glycolipids, oligosaccharides, and leukocytes. More recently, human milk extracellular vesicles (hMEVs) have been identified. HMEVs contain functional cargos, i.e., miRNAs and proteins, that may transmit information from the mother to promote infant growth and development. Maternal health conditions can influence hMEV composition. This review summarizes hMEV biogenesis and functional contents, reviews the functional evidence of hMEVs in the maternal–infant health relationship, and discusses challenges and opportunities in hMEV research.

Proteomic analysis reveals plasma haptoglobin, interferon-γ, and interleukin-1β as potential biomarkers of pediatric refractory epilepsy

BACKGROUND

Children with refractory epilepsy (RE) are associated with increased mortality rate, nonfatal injuries, disability, and diminished quality of life. Biomarkers for the early prediction of RE is still an unmet need.

METHODS

Eighteen children with RE and six age-matched unrelated controls were included in this study. Plasma samples were prefractionated by the optimized thermal treatment before proteomic analysis using 2DE-LC-MS/MS. Bioinformatic analysis was carried out using STRING protein network. Immunoassay of unprocessed plasma was applied to confirm changes of proteins of interest. P-value < 0.05 was considered statistically significant.

RESULTS

Proteomic analysis (n = 6 each group) revealed nine differentially expressed proteins, i.e., haptoglobin, S100A9, serpin B1, apolipoprotein A-I, apolipoprotein A-IV, apolipoprotein C-II, alpha-1-acid glycoprotein 1 and 2, and transthyretin. Western immunoblotting confirmed haptoglobin upregulation in the RE group. STRING protein network predicted the inflammatory cytokines, i.e., interferon gamma (IFN-γ), interleukin-1 beta (IL-1β) and tumor necrosis factor alpha (TNF-α), play roles in pathophysiology in RE patients. Cytokine immunoassay (n = 24, 18 RE vs. 6 controls) exhibited plasma IFN-γ was upregulated in RE patients as compared to the healthy individuals (median [IQR]; 2.9 [2.9, 4.9] vs. 1.32 [0.8, 1.5] pg/mL, p = 0.0013), and plasma IL-1β was significantly downregulated in patients (1.0 [0.2, 1.9] vs. 4.5 [1.9, 11.0] pg/mL, p = 0.01). TNF-α had no difference between groups. The results suggest that haptoglobin may be associated with oxidative brain damage, while IFN-γ and IL-1β may be involved with neuroinflammation.

CONCLUSIONS

Alterations in plasma haptoglobin, IFN-γ, and IL-1β were associated with RE patients. Future studies using a combination of these candidate biomarkers may help predict the intractability of epilepsy in pediatric populations.

Evaluation of Aptamers as Affinity Reagents for an Enhancement of SRM-Based Detection of Low-Abundance Proteins in Blood Plasma

Selected reaction monitoring (SRM) is a mass spectrometric technique characterized by the exceptionally high selectivity and sensitivity of protein detection. However, even with this technique, the quantitative detection of low- and ultralow-abundance proteins in blood plasma, which is of great importance for the search and verification of novel protein disease markers, is a challenging task due to the immense dynamic range of protein abundance levels. One approach used to overcome this problem is the immunoaffinity enrichment of target proteins for SRM analysis, employing monoclonal antibodies. Aptamers appear as a promising alternative to antibodies for affinity enrichment. Here, using recombinant protein SMAD4 as a model target added at known concentrations to human blood plasma and SRM as a detection method, we investigated a relationship between the initial amount of the target protein and its amount in the fraction enriched with SMAD4 by an anti-SMAD4 DNA-aptamer immobilized on magnetic beads. It was found that the aptamer-based enrichment provided a 30-fold increase in the sensitivity of SRM detection of SMAD4. These results indicate that the aptamer-based affinity enrichment of target proteins can be successfully employed to improve quantitative detection of low-abundance proteins by SRM in undepleted human blood plasma.

Optimization and Standardization of Thermal Treatment as a Plasma Prefractionation Method for Proteomic Analysis

Prefractionation is a prerequisite step for deep plasma proteomics. Highly abundant proteins, particularly human serum albumin (HSA) and immunoglobulin G (IgG), typically interfere with investigation of proteins with lower abundance. A relatively simple preparation method based on high temperature can precipitate thermolabile proteins, providing a strategic window to access the thermostable plasma subproteome. This study aimed to optimize thermal treatment as a reliable prefractionation method and to compare it with two commercial kits, including HSA and IgG immunodepletion (IMDP) and combinatorial peptide ligand libraries (CPLL), using untreated plasma as a control condition. By varying the temperature and the incubation period, the optimal condition was found as treatment at 95°C for 20 min, which maintained about 1% recovery yield of soluble proteins. Consistency and reproducibility of thermal treatment-derived plasma subproteome were checked by two-dimensional electrophoresis. The coefficient of variation regarding protein spot numbers was less than 10% among three independent specimens. Highly abundant protein depletion of the thermal treatment was evaluated by immunoblotting against HSA and IgG as compared to the untreated plasma, IMDP, and CPLL. Multidimensional comparison based on 489 unique peptides derived from the label-free quantitative mass spectrometry revealed that the thermal treatment, IMDP, and CPLL provided distinct sets of plasma subproteome compared to untreated plasma, and these appeared to be complementary to each other. Comparing the characteristics of the three procedures suggested that thermal treatment was more cost-effective and less time-consuming than IMDP and CPLL. This study proposes the use of thermal treatment as a reliable and cost-effective method for plasma prefractionation which provides benefits to large-scale proteomic projects and biomarker studies.

Pre-fractionation of Noncirculating Biological Fluids to Improve Discovery of Clinically Relevant Protein Biomarkers

Nowadays, significant difficulties remain in the diagnosis and/or prognosis of many diseases, leading to an unsatisfactory patient management and a counterproductive increase in time and costs. It is therefore crucial to bridge the gap between basic and applied research by complying with clinical requirements, notably from the design stage of the experimental workflow. In this chapter we provide key suggestions for selecting appropriate biological samples and reducing pre-analytical and analytical variabilities to improve the discovery of clinically relevant protein biomarkers.