The human plasma proteome: history, character, and diagnostic prospects

Subcellular Fractionation for DIGE-Based Proteomics

Mass spectrometry-based protein methodologies have revolutionized the field of analytical biochemistry and enable the identification of hundreds to thousands of proteins in biological fluids, cell lines, and tissue. This methodology requires the initial separation of a protein constellation, and this has been successfully achieved using gel-based techniques, particularly that of fluorescence two-dimensional difference gel electrophoresis (2D-DIGE). However, given the complexity of the proteome, fractionation techniques may be required to optimize the detection of low-abundance proteins, which are often underrepresented but which may represent important players in health and disease. Such subcellular fractionation protocols typically utilize density-gradient centrifugation and have enabled the enrichment of crude microsomes, the cytosol, the plasmalemma, the nuclei, and the mitochondria. In this chapter, we describe the experimental steps involved in the enrichment of crude microsomes from the skeletal muscle using differential centrifugation and subsequent verification of enrichment by gel electrophoresis and immunoblotting, prior to comparative 2D-DIGE analysis.

Changes of tRNA-Derived Fragments by Alzheimer's Disease in Cerebrospinal Fluid and Blood Serum

BACKGROUND

Alzheimer's disease (AD) is the most common type of dementia, affecting individuals over 65. AD is also a multifactorial disease, with disease mechanisms incompletely characterized, and disease-modifying therapies are marginally effective. Biomarker signatures may shed light on the diagnosis, disease mechanisms, and the development of therapeutic targets. tRNA-derived RNA fragments (tRFs), a family of recently discovered small non-coding RNAs, have been found to be significantly enhanced in human AD hippocampus tissues. However, whether tRFs change in body fluids is unknown.

OBJECTIVE

To investigate whether tRFs in body fluids are impacted by AD.

METHODS

We first used T4 polynucleotide kinase-RNA-seq, a modified next-generation sequencing technique, to identify detectable tRFs in human cerebrospinal fluid and serum samples. The detectable tRFs were then compared in these fluids from control, AD, and mild cognitive impairment patients using tRF qRT-PCR. The stability of tRFs in serum was also investigated by checking the change in tRFs in response to protein digestion or exosome lysis.

RESULTS

Among various tRFs, tRF5-ProAGG seemed to be impacted by AD in both cerebrospinal fluid and serum. AD-impacted serum tRF5-ProAGG showed a correlation with the AD stage. Putative targets of tRF5-ProAGG in the hippocampus were also predicted by a computational algorithm, with some targets being validated experimentally and one of them being in a negative correlation with tRF5-ProAGG even using a small size of samples.

CONCLUSIONS

tRF5-ProAGG showed the potential as an AD biomarker and may play a role in disease progression.

Printed Antifouling Electrodes for Biosensing Applications

Biosensors based on miniaturized, functional electrodes are of high potential for various biosensing applications, especially at the point-of-care setting among others. However, the sensor performance of such electrochemical devices is still strongly limited, especially due to surface fouling in complex sample fluids, such as blood serum. Electrode coatings based on conductive nanomaterials embedded in antifouling matrices offer a promising strategy to overcome this limitation. However, known composite coatings require long (typically >24 h) and complex fabrication processes, which pose a strong barrier for cost-effective mass manufacturing and successful commercialization. Here, we describe a novel polymer/carbon nanotube (CNT) composite coating that can be produced from an ink containing a photoreactive and antifouling copolymer as well as conductive CNTs using fast and highly scalable printing processes. Coatings were prepared on screen-printed electrodes and characterized using cyclic voltammetry (CV) and protein fouling experiments. The coatings offered an electroactive surface area (EASA) comparable to uncoated screen-printed electrodes and retained >90% of initial EASA after 1 h of exposure to concentrated bovine serum albumin solution, while uncoated electrodes decreased to <20% of initial EASA after the same treatment. Utilizing the universal crosslinking reaction of the polymer coating, antibodies against the inflammatory biomarker C-reactive protein (CRP) were photochemically immobilized on the electrodes. Functionalized electrodes were fabricated in <2 h and were successfully used to quantify nanogram-range concentrations of CRP spiked in undiluted human blood serum using a sandwich-immunoassay with electrochemical read-out, demonstrating the high potential of the platform for biosensing applications.

Soluble HLA peptidome: A new resource for cancer biomarkers

Using circulating molecular biomarkers to screen for cancer and other debilitating disorders in a high-throughput and low-cost fashion is becoming increasingly attractive in medicine. One major limitation of investigating protein biomarkers in body fluids is that only one-fourth of the entire proteome can be routinely detected in these fluids. In contrast, Human Leukocyte Antigen (HLA) presents peptides from the entire proteome on the cell surface. While peptide-HLA complexes are predominantly membrane-bound, a fraction of HLA molecules is released into body fluids which is referred to as soluble HLAs (sHLAs). As such peptides bound by sHLA molecules represent the entire proteome of their cells/tissues of origin and more importantly, recent advances in mass spectrometry-based technologies have allowed for accurate determination of these peptides. In this perspective, we discuss the current understanding of sHLA-peptide complexes in the context of cancer, and their potential as a novel, relatively untapped repertoire for cancer biomarkers. We also review the currently available tools to detect and quantify these circulating biomarkers, and we discuss the challenges and future perspectives of implementing sHLA biomarkers in a clinical setting.

Integrating age, BMI, and serum N-glycans detected by MALDI mass spectrometry to classify suspicious mammogram findings as benign lesions or breast cancer

While mammograms are the standard tool for breast cancer screening, there remains challenges for mammography to effectively distinguish benign lesions from breast cancers, leading to many unnecessary biopsy procedures. A blood-based biomarker could provide a minimally invasive supplemental assay to increase the specificity of breast cancer screening. Serum N-glycosylation alterations have associations with many cancers and several of the clinical characteristics of breast cancer. The current study utilized a high-throughput mass spectrometry workflow to identify serum N-glycans with differences in intensities between patients that had a benign lesion from patients with breast cancer. The overall N-glycan profiles of the two patient groups had no differences, but there were several individual N-glycans with significant differences in intensities between patients with benign lesions and ductal carcinoma in situ (DCIS). Many N-glycans had strong associations with age and/or body mass index, but there were several of these associations that differed between the patients with benign lesions and breast cancer. Accordingly, the samples were stratified by the patient's age and body mass index, and N-glycans with significant differences between these subsets were identified. For women aged 50-74 with a body mass index of 18.5-24.9, a model including the intensities of two N-glycans, 1850.666 m/z and 2163.743 m/z, age, and BMI were able to clearly distinguish the breast cancer patients from the patients with benign lesions with an AUROC of 0.899 and an optimal cutoff with 82% sensitivity and 84% specificity. This study indicates that serum N-glycan profiling is a promising approach for providing clarity for breast cancer screening, especially within the subset of healthy weight women in the age group recommended for mammograms.

Stable sensing platform for diagnosing electrolyte disturbance using laser-induced breakdown spectroscopy

Electrolyte disturbance is very common and harmful, increasing the mortality of critical patients. Hence, rapid and accurate detection of electrolyte levels is vital in clinical practice. Laser-induced breakdown spectroscopy (LIBS) has the advantage of rapid and simultaneous detection of multiple elements, which meets the needs of clinical electrolyte detection. However, the cracking caused by serum drying and the effect of the coffee-ring led to the unstable spectral signal of LIBS and inaccurate detection results. Herein, we propose the ordered microarray silicon substrates (OMSS) obtained by laser microprocessing, to solve the disturbance caused by cracking and the coffee-ring effect in LIBS detection. Moreover, the area of OMSS is optimized to obtain the optimal LIBS detection effect; only a 10 uL serum sample is required. Compared with the silicon wafer substrates, the relative standard deviation (RSD) of the serum LIBS spectral reduces from above 80.00% to below 15.00% by the optimized OMSS, improving the spectral stability. Furthermore, the OMSS is combined with LIBS to form a sensing platform for electrolyte disturbance detection. A set of electrolyte disturbance simulation samples (80% of the ingredients are human serum) was prepared for this platform evaluation. Finally, the platform can achieve an accurate quantitative detection of Na and K elements (Na: RSD < 6.00%, R² = 0.991; K: RSD < 4.00%, R² = 0.981), and the detection time is within 5 min. The LIBS sensing platform has a good prospect in clinical electrolyte detection and other blood-related clinical diagnoses.

Cell-Derived Vesicles for Nanoparticles' Coating: Biomimetic Approaches for Enhanced Blood Circulation and Cancer Therapy

Cancer nanomedicines are designed to encapsulate different therapeutic agents, prevent their premature release, and deliver them specifically to cancer cells, due to their ability to preferentially accumulate in tumor tissue. However, after intravenous administration, nanoparticles immediately interact with biological components that facilitate their recognition by the immune system, being rapidly removed from circulation. Reports show that less than 1% of the administered nanoparticles effectively reach the tumor site. This suboptimal pharmacokinetic profile is pointed out as one of the main factors for the nanoparticles' suboptimal therapeutic effectiveness and poor translation to the clinic. Therefore, an extended blood circulation time may be crucial to increase the nanoparticles' chances of being accumulated in the tumor and promote a site-specific delivery of therapeutic agents. For that purpose, the understanding of the forces that govern the nanoparticles' interaction with biological components and the impact of the physicochemical properties on the in vivo fate will allow the development of novel and more effective nanomedicines. Therefore, in this review, the nano-bio interactions are summarized. Moreover, the application of cell-derived vesicles for extending the blood circulation time and tumor accumulation is reviewed, focusing on the advantages and shortcomings of each cell source.

Discovering new peripheral plasma biomarkers to identify cognitive decline in type 2 diabetes

Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD), and thus identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl, n = 30), and the age-/sex-matched T2DM patients with mild cognitive impairment (T2DM-MCI, n = 30) or T2DM without MCI (T2DM-nMCI, n = 25). The candidate biomarkers identified by proteomics and bioinformatics analyses were verified by ELISA, and their diagnostic capabilities were evaluated with machine learning. A total of 53 differentially expressed proteins (DEPs) were identified in T2DM-MCI compared with T2DM-nMCI patients. These DEPs were significantly enriched in multiple biological processes, such as amyloid neuropathies, CNS disorders, and metabolic acidosis. Among the DEPs, alpha-1-antitrypsin (SERPINA1), major viral protein (PRNP), and valosin-containing protein (VCP) showed strong correlation with AD high-risk genes APP, MAPT, APOE, PSEN1, and PSEN2. Also, the levels of PP2A cancer inhibitor (CIP2A), PRNP, corticotropin-releasing factor-binding protein (CRHBP) were significantly increased, while the level of VCP was decreased in T2DM-MCI patients compared with that of the T2DM-nMCI, and these changes were correlated with the Mini-Mental State Examination (MMSE) score. Further machine learning data showed that increases in PRNP, CRHBP, VCP, and rGSK-3β(T/S9) (ratio of total to serine-9-phosphorylated glycogen synthase kinase-3β) had the greatest power to identify mild cognitive decline in T2DM patients.

Crowding modulates the glycation of plasma proteins: In vitro analysis of structural modifications to albumin and transferrin and identification of sites of modification

Protein modification occurs in biological milieus that are characterized by high concentrations of (macro)molecules (i.e. heterogeneous and packed environments). Recent data indicate that crowding can modulate the extent and rate of protein oxidation, however its effect on other post-translational modifications remains to be explored. In this work we hypothesized that crowding would affect the glycation of plasma proteins. Physiologically-relevant concentrations of albumin (35 mg mL^-1) and transferrin (2 mg mL^-1) were incubated with methylglyoxal and glyoxal (5 μM-5 mM), two α-oxoaldehyde metabolites that are elevated in the plasma of people with diabetes. Crowding was induced by adding dextran or ficoll polymers. Electrophoresis, electron microscopy, fluorescence spectroscopy and mass spectrometry were employed to investigate the structural consequences of glycation under crowded conditions. Our data demonstrate that crowding modulates the extent of formation of transferrin cross-links, and also the modification pathways in both albumin and transferrin. Arginine was the most susceptible residue to modification, with lysine and cysteine also affected. Loss of 0.48 and 7.28 arginine residues per protein molecule were determined on incubation with 500 μM methylglyoxal for albumin and transferrin, respectively. Crowding did not influence the extent of loss of arginine and lysine for either protein, but the sites of modification, detected by LC-MS, were different between dilute and crowded conditions. These data confirm the relevance of studying modification processes under conditions that closely mimic biological milieus. These data unveil additional factors that influence the pattern and extent of protein modification, and their structural consequences, in biological systems.

The Surface Charge of Polymer-Coated Upconversion Nanoparticles Determines Protein Corona Properties and Cell Recognition in Serum Solutions

Applications of nanoparticles (NPs) in the life sciences require control over their properties in protein-rich biological fluids, as an NP quickly acquires a layer of proteins on the surface, forming the so-called "protein corona" (PC). Understanding the composition and kinetics of the PC at the molecular level is of considerable importance for controlling NP interaction with cells. Here, we present a systematic study of hard PC formation on the surface of upconversion nanoparticles (UCNPs) coated with positively-charged polyethyleneimine (PEI) and negatively-charged poly (acrylic acid) (PAA) polymers in serum-supplemented cell culture medium. The rationale behind the choice of UCNP is two-fold: UCNP represents a convenient model of NP with a size ranging from 5 nm to >200 nm, while the unique photoluminescent properties of UCNP enable direct observation of the PC formation, which may provide new insight into this complex process. The non-linear optical properties of UCNP were utilised for direct observation of PC formation by means of fluorescence correlation spectroscopy. Our findings indicated that the charge of the surface polymer coating was the key factor for the formation of PC on UCNPs, with an ensuing effect on the NP-cell interactions.

The human host response to monkeypox infection: a proteomic case series study

The rapid rise of monkeypox (MPX) cases outside previously endemic areas prompts for a better understanding of the disease. We studied the plasma proteome of a group of MPX patients with a similar infection history and clinical manifestation typical for the current outbreak. We report that MPX in this case series is associated with a strong plasma proteomic response among nutritional and acute phase response proteins. Moreover, we report a correlation between plasma proteins and disease severity. Contrasting the MPX host response with that of COVID-19, we find a range of similarities, but also important differences. For instance, CFHR1 is induced in COVID-19, but suppressed in MPX, reflecting the different roles of the complement system in the two infectious diseases. Of note, the spatial overlap in response proteins suggested that a COVID-19 biomarker panel assay could be repurposed for MPX. Applying a targeted protein panel assay provided encouraging results and distinguished MPX cases from healthy controls. Hence, our results provide a first proteomic characterization of the MPX human host response and encourage further research on protein-panel assays in emerging infectious diseases.

Integrative Metallomics Studies of Toxic Metal(loid) Substances at the Blood Plasma–Red Blood Cell–Organ/Tumor Nexus

Globally, an estimated 9 million deaths per year are caused by human exposure to environmental pollutants, including toxic metal(loid) species. Since pollution is underestimated in calculations of the global burden of disease, the actual number of pollution-related deaths per year is likely to be substantially greater. Conversely, anticancer metallodrugs are deliberately administered to cancer patients, but their often dose-limiting severe adverse side-effects necessitate the urgent development of more effective metallodrugs that offer fewer off-target effects. What these seemingly unrelated events have in common is our limited understanding of what happens when each of these toxic metal(loid) substances enter the human bloodstream. However, the bioinorganic chemistry that unfolds at the plasma/red blood cell interface is directly implicated in mediating organ/tumor damage and, therefore, is of immediate toxicological and pharmacological relevance. This perspective will provide a brief synopsis of the bioinorganic chemistry of AsIII, Cd2+, Hg2+, CH3Hg+ and the anticancer metallodrug cisplatin in the bloodstream. Probing these processes at near-physiological conditions and integrating the results with biochemical events within organs and/or tumors has the potential to causally link chronic human exposure to toxic metal(loid) species with disease etiology and to translate more novel anticancer metal complexes to clinical studies, which will significantly improve human health in the 21st century.

Measurements of heterogeneity in proteomics analysis of the nanoparticle protein corona across core facilities

Robust characterization of the protein corona-the layer of proteins that spontaneously forms on the surface of nanoparticles immersed in biological fluids-is vital for prediction of the safety, biodistribution, and diagnostic/therapeutic efficacy of nanomedicines. Protein corona identity and abundance characterization is entirely dependent on liquid chromatography coupled to mass spectroscopy (LC-MS/MS), though the variability of this technique for the purpose of protein corona characterization remains poorly understood. Here we investigate the variability of LC-MS/MS workflows in analysis of identical aliquots of protein coronas by sending them to different proteomics core-facilities and analyzing the retrieved datasets. While the shared data between the cores correlate well, there is considerable heterogeneity in the data retrieved from different cores. Specifically, out of 4022 identified unique proteins, only 73 (1.8%) are shared across the core facilities providing semiquantitative analysis. These findings suggest that protein corona datasets cannot be easily compared across independent studies and more broadly compromise the interpretation of protein corona research, with implications in biomarker discovery as well as the safety and efficacy of our nanoscale biotechnologies.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

Placental proteins with predicted roles in fetal development decrease in premature infants

BACKGROUND

Emerging evidence from animal experiments indicate that factors secreted by the placenta are critical for normal fetal organ development. Our objective was to characterize the umbilical vein and artery proteome in preterm infants and identify proteins that decrease in the neonatal circulation following delivery.

METHODS

Cord blood at delivery and neonatal blood at 48-72 h of life was collected in 25 preterm infants. Plasma protein abundance was determined using the SomaLogic platform.

RESULTS

When comparing protein levels of umbilical venous to arterial cord blood, 434 proteins were significantly higher indicating placental secretion into the fetal circulation. Moreover, when comparing neonatal blood to umbilical vein levels, 142 proteins were significantly lower. These proteins included Endoplasmic reticulum resident protein 29, CD59, Fibroblast growth factor 2 and Dynactin subunit 2, which are involved in brain development and prevention of brain damage as well as Fibroblast growth factor 1 which prevents lung fibrosis.

CONCLUSIONS

The late second trimester human placenta secretes proteins into the fetal circulation which decrease following delivery. Many of these proteins are predicted to be important in the development of fetal organs. Further studies are needed to directly link placental proteins to organ development and poor outcomes in preterm infants.

IMPACT

Prematurity remains a leading cause of morbidity and mortality requiring the development of novel treatments. Emerging evidence from animal studies suggest that factors secreted from the placenta may be critical in the development of the fetus. We report that the preterm human placenta secretes an array of proteins into the fetal circulation. Some of these proteins are predicted to be involved in the development of the brain and the lung. When born prematurely, infants are deprived of these placental proteins, which may contribute to their poor outcomes.

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.

Multiplex quadruple bioluminescent assay system

Bioluminescence (BL) is unique cold body radiation of light, generated by luciferin-luciferase reactions and commonly used in various bioassays and molecular imaging. However, most of the peak emissions of BL populate the blue-yellow region and have broad spectral bandwidths and thus superimpose each other, causing optical cross-leakages in multiplex assays. This study synthesized a new series of coelenterazine (CTZ) analogues, named K-series, that selectively illuminates marine luciferases with unique, blue-shifted spectral properties. The optical property and specificity of the K-series CTZ analogues were characterized by marine luciferases, with K2 and K5 found to specifically luminesce with ALuc- and RLuc-series marine luciferases, respectively. The results confirmed that the luciferase specificity and color variation of the CTZ analogues minimize the cross-leakages of BL signals and enable high-throughput screening of specific ligands in the mixture. The specificity and color variation of the substrates were further tailored to marine luciferases (or single-chain bioluminescent probes) to create a multiplex quadruple assay system with four integrated, single-chain bioluminescent probes, with each probe designed to selectively luminesce only with its specific ligand (first authentication) and a specific CTZ analogue (second authentication). This unique multiplex quadruple bioluminescent assay system is an efficient optical platform for specific and high-throughput imaging of multiple optical markers in bioassays without optical cross-leakages.

Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device

Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.

Immunoglobulin G glycans - Biomarkers and molecular effectors of aging

Immunoglobulin G (IgG) antibodies are post-translationally modified by the addition of complex carbohydrate molecules - glycans, which have profound effects on the IgG function, most significantly as modulators of its inflammatory capacity. Therefore, it is not surprising that the changes in IgG glycosylation pattern are associated with various physiological states and diseases, including aging and age-related diseases. Importantly, within the inflammaging concept, IgG glycans are considered not only biomarkers but one of the molecular effectors of the aging process. The exact mechanism by which they exert their function, however, remains unknown. In this review, we list and comment on, to our knowledge, all studies that examined changes in IgG glycosylation during aging in humans. We focus on the information obtained from studies on general population, but we also cover the insights obtained from studies of long-lived individuals and people with age-related diseases. We summarize the current knowledge on how levels of different IgG glycans change with age (i.e., the extent and direction of the change with age) and discuss the potential mechanisms and possible functional roles of changes in IgG glycopattern that accompany aging.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Polymer-tethered glycosylated gold nanoparticles recruit sialylated glycoproteins into their protein corona, leading to off-target lectin binding

Upon exposure to biological fluids, the fouling of nanomaterial surfaces results in non-specific capture of proteins, which is particularly important when in contact with blood for in vivo and ex vivo applications. It is crucial to evaluate not just the protein components but also the glycans attached to those proteins. Polymer-tethered glycosylated gold nanoparticles have shown promise for use in biosensing/diagnostics, but the impact of the glycoprotein corona has not been established. Here we investigate how polymer-tethered glycosylated gold nanoparticles interact with serum proteins and demonstrate that the protein corona introduces new glycans and hence off-specific targeting capability. Using a panel of RAFT-derived polymers grafted to the gold surface, we show that the extent of corona formation is not dependent on the type of polymer. In lectin-binding assays, a glycan (galactose) installed on the chain-end of the polymer was available for binding even after protein corona formation. However, using sialic-acid binding lectins, it was found that there was significant off-target binding due to the large density of sialic acids introduced in the corona, confirmed by western blotting. To demonstrate the importance, we show that the nanoparticles can bind Siglec-2, an immune-relevant lectin post-corona formation. Pre-coating with (non-glycosylated) bovine serum albumin led to a significant reduction in the total glycoprotein corona. However, sufficient sialic acids were still present in the residual corona to lead to off-target binding. These results demonstrate the importance of the glycans when considering the protein corona and how 'retention of the desired function' does not rule out 'installation of undesired function' when considering the performance of glyco-nanomaterials.

Construction of 2DE Patterns of Plasma Proteins: Aspect of Potential Tumor Markers

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

Parallelization with Dual-Trap Single-Column Configuration Maximizes Throughput of Proteomic Analysis

Proteomic analysis on the scale that captures population and biological heterogeneity over hundreds to thousands of samples requires rapid mass spectrometry methods, which maximize instrument utilization (IU) and proteome coverage while maintaining precise and reproducible quantification. To achieve this, a short liquid chromatography gradient paired to rapid mass spectrometry data acquisition can be used to reproducibly quantify a moderate set of analytes. High-throughput profiling at a limited depth is becoming an increasingly utilized strategy for tackling large sample sets but the time spent on loading the sample, flushing the column(s), and re-equilibrating the system reduces the ratio of meaningful data acquired to total operation time and IU. The dual-trap single-column configuration (DTSC) presented here maximizes IU in rapid analysis (15 min per sample) of blood and cell lysates by parallelizing trap column cleaning and sample loading and desalting with the analysis of the previous sample. We achieved 90% IU in low microflow (9.5 μL/min) analysis of blood while reproducibly quantifying 300-400 proteins and over 6000 precursor ions. The same IU was achieved for cell lysates and over 4000 proteins (3000 at CV below 20%) and 40,000 precursor ions were quantified at a rate of 15 min/sample. Thus, DTSC enables high-throughput epidemiological blood-based biomarker cohort studies and cell-based perturbation screening.

Camels' biological fluids contained nanobodies: promising avenue in cancer therapy

Cancer is a major health concern and accounts for one of the main causes of death worldwide. Innovative strategies are needed to aid in the diagnosis and treatment of different types of cancers. Recently, there has been an evolving interest in utilizing nanobodies of camel origin as therapeutic tools against cancer. Nanotechnology uses nanobodies an emerging attractive field that provides promises to researchers in advancing different scientific sectors including medicine and oncology. Nanobodies are characteristically small-sized biologics featured with the ability for deep tissue penetration and dissemination and harbour high stability at high pH and temperatures. The current review highlights the potential use of nanobodies that are naturally secreted in camels’ biological fluids, both milk and urine, in the development of nanotechnology-based therapy for treating different typesQuery of cancers and other diseases. Moreover, the role of nano proteomics in the invention of novel therapeutic agents specifically used for cancer intervention is also illustrated.

Leukotriene A4 Hydrolase and Hepatocyte Growth Factor Are Risk Factors of Sudden Cardiac Death Due to First-Ever Myocardial Infarction

Patients at a high risk for sudden cardiac death (SCD) without previous history of cardiovascular disease remain a challenge to identify. Atherosclerosis and prothrombotic states involve inflammation and non-cardiac tissue damage that may play active roles in SCD development. Therefore, we hypothesized that circulating proteins implicated in inflammation and tissue damage are linked to the future risk of SCD. We conducted a prospective nested case–control study of SCD cases with verified myocardial infarction (N = 224) and matched controls without myocardial infarction (N = 224), aged 60 ± 10 years time and median time to event was 8 years. Protein concentrations (N = 122) were measured using a proximity extension immunoassay. The analyses revealed 14 proteins significantly associated with an increased risk of SCD, from which two remained significant after adjusting for smoking status, systolic blood pressure, BMI, cholesterol, and glucose levels. We identified leukotriene A4 hydrolase (LTA4H, odds ratio 1.80, corrected confidence interval (CIcorr) 1.02–3.17) and hepatocyte growth factor (HGF; odds ratio 1.81, CIcorr 1.06–3.11) as independent risk markers of SCD. Elevated LTA4H may reflect increased systemic and pulmonary neutrophilic inflammatory processes that can contribute to atherosclerotic plaque instability. Increased HGF levels are linked to obesity-related metabolic disturbances that are more prevalent in SCD cases than the controls.

Differential Protein Content between Fresh and Freeze-Dried Plasma Rich in Growth Factors Eye Drops

The purpose of this study was to analyze the proteomic composition of plasma rich in growth factors eye drops (PRGF) in comparison to lyophilized PRGF eye drops (PRGF lyo). The differential protein expression of keratocyte (HK) cells after PRGF or PRGF lyo treatment was also determined. Blood from different donors was collected and processed to obtain PRGF and PRGF lyo eye drops. Then, HK cells were treated with both formulations. A proteomic analysis was performed to evaluate the differential proteomic profile between PRGF and PRGF lyo, and the differential protein expression by HK cells after treatment with both blood-derived products. About 280 proteins were detected between both blood-derived formulation, with only 8 of them reaching significant differences. Furthermore, 101 out of 3213 proteins showed statistically significant deregulation in HK cells after treatment with PRGF or PRGF lyo. Gene Ontology analysis showed that these significant deregulated proteins were involved in 30 functional processes. However, the Ingenuity Pathway Analysis showed that no significant differences were found in any of the identified processes. In summary, the present study show that no significant differences were found in the proteomic profile or in the signaling pathways activation in HK cells between PRGF and PRGF lyo.

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial.

Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.

New Progress in Early Diagnosis of Atherosclerosis

Coronary atherosclerosis is a potentially chronic circulatory condition that endangers human health. The biological cause underpinning cardiovascular disease is coronary atherosclerosis, and acute cardiovascular events can develop due to thrombosis, platelet aggregation, and unstable atherosclerotic plaque rupture. Coronary atherosclerosis is progressive, and three specific changes appear, with fat spots and stripes, atherosclerosis and thin-walled fiber atherosclerosis, and then complex changes in arteries. The progression and severity of cardiovascular disease are correlated with various levels of calcium accumulation in the coronary artery. The therapy and diagnosis of coronary atherosclerosis benefit from the initial assessment of the size and degree of calcification. This article will discuss the new progress in the early diagnosis of coronary atherosclerosis in terms of three aspects: imaging, gene and protein markers, and trace elements. This study intends to present the latest methods for diagnosing patients with early atherosclerosis through a literature review.

Calorimetric Markers for Detection and Monitoring of Multiple Myeloma

Simple Summary

This review highlights the potential of differential scanning calorimetry for multiple myeloma diagnosis and monitoring of the treatment outcome. The thermodynamic signatures of blood sera from patients with multiple myeloma are strongly dependent on the concentration and isotype of the secreted monoclonal immunoglobulins. Mathematical methods developed to analyze the biocalorimetry data and distinguish “diseased” from “healthy” thermogram to stratify plasma calorimetric profiles and determine specific interrelations between calorimetric and biochemical/clinical data are discussed.

Abstract

This review summarizes data obtained thus far on the application of differential scanning calorimetry (DSC) for the analysis of blood sera from patients diagnosed with multiple myeloma (MM) with the secretion of the most common isotypes of monoclonal proteins (M-proteins), free light chains (FLC) and non-secretory MM, as well as Waldenström macroglobulinemia and the premalignant state monoclonal gammopathy of undetermined significance. The heterogeneous nature of MM is reflected in the thermal stability profiles of the blood serum proteome of MM patients found to depend on both the level and the isotype of the secreted M-proteins or FLC. Common calorimetric markers feature the vast majority of the different myeloma types, i.e., stabilization of the major serum proteins and decrease in the albumin/globulin heat capacity ratio. A unique calorimetric fingerprint of FLC molecules forming amorphous aggregates is the low-temperature transition centered at 57 °C for a calorimetric set of FLC MM and at 46–47 °C for a single FLC MM case for which larger aggregates were formed. The calorimetric assay proved particularly advantageous for non-secretory MM and is thus a suitable tool for monitoring such patients during treatment courses. Thus, DSC provides a promising blood-based approach as a complementary tool for MM detection and monitoring.

Selective isolation of extracellular vesicles from minimally processed human plasma as a translational strategy for liquid biopsies

Background

Intercellular communication is mediated by extracellular vesicles (EVs), as they enclose selectively packaged biomolecules that can be horizontally transferred from donor to recipient cells. Because all cells constantly generate and recycle EVs, they provide accurate timed snapshots of individual pathophysiological status. Since blood plasma circulates through the whole body, it is often the biofluid of choice for biomarker detection in EVs. Blood collection is easy and minimally invasive, yet reproducible procedures to obtain pure EV samples from circulating biofluids are still lacking. Here, we addressed central aspects of EV immunoaffinity isolation from simple and complex matrices, such as plasma.

Methods

Cell-generated EV spike-in models were isolated and purified by size-exclusion chromatography, stained with cellular dyes and characterized by nano flow cytometry. Fluorescently-labelled spike-in EVs emerged as reliable, high-throughput and easily measurable readouts, which were employed to optimize our EV immunoprecipitation strategy and evaluate its performance. Plasma-derived EVs were captured and detected using this straightforward protocol, sequentially combining isolation and staining of specific surface markers, such as CD9 or CD41. Multiplexed digital transcript detection data was generated using the Nanostring nCounter platform and evaluated through a dedicated bioinformatics pipeline.

Results

Beads with covalently-conjugated antibodies on their surface outperformed streptavidin-conjugated beads, coated with biotinylated antibodies, in EV immunoprecipitation. Fluorescent EV spike recovery evidenced that target EV subpopulations can be efficiently retrieved from plasma, and that their enrichment is dependent not only on complex matrix composition, but also on the EV surface phenotype. Finally, mRNA profiling experiments proved that distinct EV subpopulations can be captured by directly targeting different surface markers. Furthermore, EVs isolated with anti-CD61 beads enclosed mRNA expression patterns that might be associated to early-stage lung cancer, in contrast with EVs captured through CD9, CD63 or CD81. The differential clinical value carried within each distinct EV subset highlights the advantages of selective isolation.

Conclusions

This EV isolation protocol facilitated the extraction of clinically useful information from plasma. Compatible with common downstream analytics, it is a readily implementable research tool, tailored to provide a truly translational solution in routine clinical workflows, fostering the inclusion of EVs in novel liquid biopsy settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-022-00404-1.

Integrated Analysis of Cortex Single-Cell Transcriptome and Serum Proteome Reveals the Novel Biomarkers in Alzheimer's Disease

Blood-based proteomic analysis is a routine practice for detecting the biomarkers of human disease. The results obtained from blood alone cannot fully reflect the alterations of nerve cells, including neurons and glia cells, in Alzheimer's disease (AD) brains. Therefore, the present study aimed to investigate novel potential AD biomarker candidates, through an integrated multi-omics approach in AD. We propose a comprehensive strategy to identify high-confidence candidate biomarkers by integrating multi-omics data from AD, including single-nuclei RNA sequencing (snRNA-seq) datasets of the prefrontal and entorhinal cortices, as wells as serum proteomic datasets. We first quantified a total of 124,658 nuclei, 8 cell types, and 3701 differentially expressed genes (DEGs) from snRNA-seq dataset of 30 human cortices, as well as 1291 differentially expressed proteins (DEPs) from serum proteomic dataset of 11 individuals. Then, ten DEGs/DEPs (NEBL, CHSY3, STMN2, MARCKS, VIM, FGD4, EPB41L2, PLEKHG1, PTPRZ1, and PPP1R14A) were identified by integration analysis of snRNA-seq and proteomics data. Finally, four novel candidate biomarkers (NEBL, EPB41L2, FGD4, and MARCKS) for AD further stood out, according to bioinformatics analysis, and they were verified by enzyme-linked immunosorbent assay (ELISA) verification. These candidate biomarkers are related to the regulation process of the actin cytoskeleton, which is involved in the regulation of synaptic loss in the AD brain tissue. Collectively, this study identified novel cell type-related biomarkers for AD by integrating multi-omics datasets from brains and serum. Our findings provided new targets for the clinical treatment and prognosis of AD.

Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.

Development of an artificial synovial fluid useful for studying Staphylococcus epidermidis joint infections

Staphylococcus epidermidis is a major causative agent of prosthetic joint infections (PJI). The ability to form biofilms supports this highly selective pathogenic potential. In vitro studies essentially relying on phenotypic assays and genetic approaches have provided a detailed picture of the molecular events contributing to biofilm assembly. A major limitation in these studies is the use of synthetic growth media, which significantly differs from the environmental conditions S. epidermidis encounters during host invasion. Building on evidence showing that growth in serum substantially affects S. epidermidis gene expression profiles and phenotypes, the major aim of this study was to develop and characterize a growth medium mimicking synovial fluid, thereby facilitating research addressing specific aspects related to PJI. Using fresh human plasma, a protocol was established allowing for the large-scale production of a medium that by biochemical analysis matches key characteristics of synovial fluid and therefore is referred to as artificial synovial fluid (ASF). By analysis of biofilm-positive, polysaccharide intercellular adhesion (PIA)-producing S. epidermidis 1457 and its isogenic, PIA- and biofilm-negative mutant 1457-M10, evidence is provided that the presence of ASF induces cluster formation in S. epidermidis 1457 and mutant 1457-M10. Consistent with the aggregative properties, both strains formed multilayered biofilms when analyzed by confocal laser scanning microscopy. In parallel to the phenotypic findings, expression analysis after growth in ASF found upregulation of genes encoding for intercellular adhesins (icaA, aap, and embp) as well as atlE, encoding for the major cell wall autolysin being responsible for eDNA release. In contrast, growth in ASF was associated with reduced expression of the master regulator agr. Collectively, these results indicate that ASF induces expression profiles that are able to support intercellular adhesion in both PIA-positive and PIA-negative S. epidermidis. Given the observation that ASF overall induced biofilm formation in a collection of S. epidermidis isolates from PJI, the results strongly support the idea of using growth media mimicking host environments. ASF may play an important role in future studies related to the pathogenesis of S. epidermidis PJI.

The proteomic and particle composition of human platelet lysate for cell therapy products

Following health agencies warning, the use of animal origin supplements should be avoided in biological products proposed as therapy in humans. Platelet lysate and several other growth factors sources are alternatives to replace fetal calf serum, the current gold standard in clinical-grade cell culture. However, the platelet supplement's content lacks data due to different production methods. The principle behind these products relays on the lysis of platelets that release several proteins, some of which are contained in heterogeneous granules and coordinate biological functions. This study aims to analyze the composition and reproducibility of a platelet lysate produced with a standardized method, by describing several batches' protein and particle content using proteomics and dynamic light scattering. Proteomics data revealed a diversified protein content, with some related to essential cellular processes such as proliferation, morphogenesis, differentiation, biosynthesis, adhesion, and metabolism. It also detected proteins responsible for activation and binding of transforming growth factor beta, hepatocyte growth factor, and insulin-like growth factor. Total protein, biochemical, and growth factors quantitative data showed consistent and reproducible values across batches. Novel data on two major particle populations is presented, with high dispersion level at 231 ± 96 d.nm and at 30 ± 8 d.nm, possibly being an important way of protein trafficking through the cellular microenvironment. This experimental and descriptive analysis aims to support the content definition and quality criteria of a cell supplement for clinical applications.

The circulating proteomic signature of alcohol-associated liver disease

Despite being a leading cause of advanced liver disease, alcohol-associated liver disease (ALD) has no effective medical therapies. The circulating proteome, which comprises proteins secreted by different cells and tissues in the context of normal physiological function or in the setting of disease and illness, represents an attractive target for uncovering novel biology related to the pathogenesis of ALD. In this work, we used the aptamer-based SomaScan proteomics platform to quantify the relative concentration of over 1300 proteins in a well-characterized cohort of patients with the spectrum of ALD. We found a distinct circulating proteomic signature that correlated with ALD severity, including over 600 proteins that differed significantly between ALD stages, many of which have not previously been associated with ALD to our knowledge. Notably, certain proteins that were markedly dysregulated in patients with alcohol-associated hepatitis were also altered, to a lesser degree, in patients with subclinical ALD and may represent early biomarkers for disease progression. Taken together, our work highlights the vast and distinct changes in the circulating proteome across the wide spectrum of ALD, identifies potentially novel biomarkers and therapeutic targets, and provides a proteomic resource atlas for ALD researchers and clinicians.

Selective reaction monitoring approach using structure-defined synthetic glycopeptides for validating glycopeptide biomarkers pre-determined by bottom-up glycoproteomics

Clusterin is a heavily glycosylated protein that is upregulated in various cancer and neurological diseases. The findings by the Hancock and Iliopoulos group that levels of the tryptic glycopeptide derived from plasma clusterin, 372Leu-Ala-Asn-Leu-Thr-Gln-Gly-Glu-Asp-Gln-Tyr-Tyr-Leu-Arg385 with a biantennary disialyl N-glycan (A2G2S2 or FA2G2S2) at Asn374 differed significantly prior to and after curative nephrectomy for clear cell renal cell carcinoma (RCC) patients motivated us to verify the feasibility of this glycopeptide as a novel biomarker of RCC. To determine the precise N-glycan structure attached to Asn374, whether A2G2S2 is composed of the Neu5Acα2,3Gal or/and the Neu5Acα2,6Gal moiety, we synthesized key glycopeptides having one of the two putative isomers. Selective reaction monitoring assay using synthetic glycopeptides as calibration standards allowed "top-down glycopeptidomics" for the absolute quantitation of targeted label-free glycopeptides in a range from 313.3 to 697.5 nM in the complex tryptic digests derived from serum samples of RCC patients and healthy controls. Our results provided evidence that the Asn374 residue of human clusterin is modified dominantly with the Neu5Acα2,6Gal structure and the levels of clusterin bearing an A2G2S2 with homo Neu5Acα2,6Gal terminals at Asn374 decrease significantly in RCC patients as compared with healthy controls. The present study elicits that a new strategy integrating the bottom-up glycoproteomics with top-down glycopeptidomics using structure-defined synthetic glycopeptides enables the confident identification and quantitation of the glycopeptide targets pre-determined by the existing methods for intact glycopeptide profiling.

Determination of the Time since Deposition of Blood Traces Utilizing a Liquid Chromatography-Mass Spectrometry-Based Proteomics Approach

Knowledge about when a bloodstain was deposited at a crime scene can be of critical value in forensic investigation. A donor of a genetically identified bloodstain could be linked to a suspected time frame and the crime scene itself. Determination of the time since deposition (TsD) has been extensively studied before but has yet to reach maturity. We therefore conducted a proof-of-principle study to study time- and storage-dependent changes of the proteomes of dried blood stains. A bottom-up proteomics approach was employed, and high-resolution liquid-chromatography-mass-spectrometry (HR-LC-MS) and data-independent acquisition (DIA) were used to analyze samples aged over a 2 month period and two different storage conditions. In multivariate analysis, samples showed distinct clustering according to their TsD in both principal component analysis (PCA) and in partial least square discriminant analysis (PLS DA). The storage condition alters sample aging and yields different separation-driving peptides in hierarchical clustering and in TsD marker peptide selection. Certain peptides and amino acid modifications were identified and further assessed for their applicability in assessing passed TsD. A prediction model based on data resampling (Jackknife) was applied, and prediction values for selected peptide ratios were created. Depending on storage conditions and actual sample age, mean prediction performances ranges in between 70 and 130% for the majority of peptides and time points. This places this study as a first in investigating LC-MS based bottom-up proteomics approaches for TsD determination.

Internal Standard Triggered-Parallel Reaction Monitoring Mass Spectrometry Enables Multiplexed Quantification of Candidate Biomarkers in Plasma

Despite advances in proteomic technologies, clinical translation of plasma biomarkers remains low, partly due to a major bottleneck between the discovery of candidate biomarkers and costly clinical validation studies. Due to a dearth of multiplexable assays, generally only a few candidate biomarkers are tested, and the validation success rate is accordingly low. Previously, mass spectrometry-based approaches have been used to fill this gap but feature poor quantitative performance and were generally limited to hundreds of proteins. Here, we demonstrate the capability of an internal standard triggered-parallel reaction monitoring (IS-PRM) assay to greatly expand the numbers of candidates that can be tested with improved quantitative performance. The assay couples immunodepletion and fractionation with IS-PRM and was developed and implemented in human plasma to quantify 5176 peptides representing 1314 breast cancer biomarker candidates. Characterization of the IS-PRM assay demonstrated the precision (median % CV of 7.7%), linearity (median R² > 0.999 over 4 orders of magnitude), and sensitivity (median LLOQ < 1 fmol, approximately) to enable rank-ordering of candidate biomarkers for validation studies. Using three plasma pools from breast cancer patients and three control pools, 893 proteins were quantified, of which 162 candidate biomarkers were verified in at least one of the cancer pools and 22 were verified in all three cancer pools. The assay greatly expands capabilities for quantification of large numbers of proteins and is well suited for prioritization of viable candidate biomarkers.

Advances in glycopeptide enrichment methods for the analysis of protein glycosylation over the past decade

Advances in bioanalytical technology have accelerated the analysis of complex protein glycosylation, which is beneficial to understanding glycosylation in drug discovery and disease diagnosis. Due to its biological uniqueness in the course of disease occurrence and development, disease-specific glycosylation requires quantitative characterization of protein glycosylation. We provide a comprehensive review of recent advances in glycosylation analysis, including workflows for glycoprotein digestion, glycopeptide separation and enrichment, and mass-spectrometry sequencing. We specifically focus on different strategies for glycopeptide enrichment through physical interaction, chemical oxidation, or metabolic labeling of intact glycopeptides. The recent advances and challenges of O-glycosylation analysis are presented, and the development of improved enrichment methods combining different proteases to analyze O-glycosylation is also proposed. This article is protected by copyright. All rights reserved.

Serum amyloid P component and pro-platelet basic protein in extracellular vesicles or serum are novel markers of liver fibrosis in chronic hepatitis C patients

Extracellular vesicles (EVs) contain proteins, mRNAs, and microRNAs, and their cargos have emerged as novel diagnostic markers in various diseases. We aimed to discover novel and noninvasive biomarkers of liver fibrosis by proteomic analysis using serum EVs in patients with chronic hepatitis C. We performed shotgun proteomics using serum EVs isolated from 54 patients with histologically assessed liver fibrosis. Shotgun proteomics identified a total of 974 proteins, and 445 proteins were detected in more than half of the patients. Among them, a total of 9 proteins were identified as proteins that tended to increase or decrease with liver fibrosis with a significance of p<0.005 and that were different between F1-2 patients and F3-4 patients with a significance of p<0.01. Among the 9 proteins, targeted proteomics using serum EVs isolated from the sera of another 80 patients with histologically assessed liver fibrosis verified that serum amyloid P component (SAP) and pro-platelet basic protein (PPBP) levels in EVs significantly decreased with the progression of liver fibrosis and were significantly lower in F3-4 patients than in F1-2 patients. The diagnostic accuracies of SAP and PPBP in EVs for the liver fibrosis stage were comparable to those of type IV collagen 7S, hyaluronic acid, and the fibrosis-4 index (FIB-4 index). Moreover, serum SAP and PPBP levels correlated with the levels in EVs, and the ability of serum SAP and PPBP to diagnose liver fibrosis stage was also comparable to the abilities of type IV collagen 7S, hyaluronic acid, and the FIB-4 index. In conclusion, proteomic analysis of serum EVs identified SAP and PPBP as candidate biomarkers for predicting liver fibrosis in patients with chronic hepatitis C. In addition, SAP and PPBP levels in serum are strongly correlated with those in EVs and could represent markers of liver fibrosis.

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro

For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological sample's complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptide's monoisotopic mass, which is critical for the peptide's identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instrument's detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706.

Effects of Kinetic Chain Length on the Degradation of Poly(β-amino ester)-Based Networks and Use in 3D Printing by Projection Microstereolithography

Poly(β-amino ester)-diacrylates (PBAE-dAs) are promising resins for three-dimensional (3D) printing. This study investigated the degradation of two PBAEs with different chemistries and kinetic chain lengths. PBAE-dA monomers were synthesized from benzhydrazide and poly(ethylene glycol) (A6) or butanediol (B6) diacrylate and then photopolymerized with pentaerythritol tetrakis(3-mercaptopropionate), which formed thiol-polyacrylate kinetic chains. This tetrathiol acts as a cross-linker and chain-transfer agent that controls the polyacrylate kinetic chain length. A6 networks exhibited bulk degradation, while B6 networks exhibited surface degradation, which transitioned to a combined surface and bulk degradation. Increasing the tetrathiol concentration shortened the polyacrylate kinetic chain and time-to-reverse gelation but degradation mode was unaffected. Hydrolysis occurred primarily through the β-amino ester. As network hydrophilicity increased, the slower degrading ester in the thiol-polyacrylate chains contributed to degradation. Overall, this work demonstrates control over network degradation rate, mode of degradation, and time-to-reverse gelation in PBAE networks and their application in 3D printing.

The omics strategy: the use of systems vaccinology to characterise immune responses to childhood immunisation

INTRODUCTION

Vaccines have had a transformative impact on child health. Despite this impact the immunological processes involved in protective responses are not entirely understood and vaccine development has been largely empirical. Recent technological advances offer the opportunity to reveal the immunology underlying vaccine response at an unprecedented resolution. These data could revolutionise the way vaccines are developed and tested and further augment their role in securing the health of children around the world.

AREAS COVERED

Systems level information and the tools are now being deployed by vaccinologists at all stages of the vaccine development pathway; however, this review will specifically describe some of the key findings that have be gleaned from multi-omics datasets collected in the context of childhood immunisation.

EXPERT OPINION

Despite the success of vaccines there remains hard-to-target pathogens, refractory to current vaccination strategies. Moreover, zoonotic diseases with pandemic potential are a threat to global health, as recently illustrated by COVID-19. Systems vaccinology holds a great deal of promise in revealing a greater understanding of vaccine responses and consequently modernising vaccinology. However, there is a need for future studies -particularly in vulnerable populations that are targets for vaccination programmes - if this potential is to be fulfilled.