Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma

Applicability of selected reaction monitoring for precise screening tests

INTRODUCTION

The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences.

AREAS COVERED

Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations.

EXPERT OPINION

The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.

Establishing Personalized Blood Protein Reference Ranges Using Noninvasive Microsampling and Targeted Proteomics: Implications for Antidoping Strategies

To prevent doping practices in sports, the World Anti-Doping Agency implemented the Athlete Biological Passport (ABP) program, monitoring biological variables over time to indirectly reveal the effects of doping rather than detect the doping substance or the method itself. In the context of this program, a highly multiplexed mass spectrometry-based proteomics assay for 319 peptides corresponding to 250 proteins was developed, including proteins associated with blood-doping practices. "Baseline" expression profiles of these potential biomarkers in capillary blood (dried blood spots (DBS)) were established using multiple reaction monitoring (MRM). Combining DBS microsampling with highly multiplexed MRM assays is the best-suited technology to enhance the effectiveness of the ABP program, as it represents a cost-effective and robust alternative analytical method with high specificity and selectivity of targets in the attomole range. DBS data were collected from 10 healthy athlete volunteers over a period of 140 days (28 time points per participant). These comprehensive findings provide a personalized targeted blood proteome "fingerprint" showcasing that the targeted proteome is unique to an individual and likely comparable to a DNA fingerprint. The results can serve as a baseline for future studies investigating doping-related perturbations.

The Impact of Gut Microbiota Changes on Methotrexate-Induced Neurotoxicity in Developing Young Rats

Methotrexate (MTX) is an essential part of therapy in the treatment of acute lymphoblastic leukemia (ALL) in children, and inferior intellectual outcomes have been reported in children who are leukemia survivors. Although several studies have demonstrated that the interaction between gut microbiota changes and the brain plays a vital role in the pathogenesis of chemotherapy-induced brain injury, preexisting studies on the effect of MTX on gut microbiota changes focused on gastrointestinal toxicity only. Based on our previous studies, which revealed that MTX treatment resulted in inferior neurocognitive function in developing young rats, we built a young rat model mimicking MTX treatment in a child ALL protocol, trying to investigate the interactions between the gut and brain in response to MTX treatment. We found an association between gut microbiota changes and neurogenesis/repair processes in response to MTX treatment, which suggest that MTX treatment results in gut dysbiosis, which is considered to be related to MTX neurotoxicity through an alteration in gut-brain axis communication.

Advances in D-dimer testing: progress in harmonization of clinical assays and innovative detection methods

The D-dimer is a sensitive indicator of coagulation and fibrinolysis activation, especially valuable as a biomarker of intravascular thrombosis. Measurement of plasma D-dimer levels plays a crucial role in the diagnosis and monitoring of conditions such as deep vein thrombosis, pulmonary embolism, and disseminated intravascular coagulation. A variety of immunoassays, including enzyme-linked immunosorbent assays, latex-enhanced immunoturbidimetric assays, whole-blood aggregation analysis, and immunochromatography assays, are widely used in clinical settings to determine D-dimer levels. However, the results obtained from different D-dimer assays vary significantly. These assays exhibit intra-method coefficients of variation ranging from 6.4% to 17.7%, and the measurement discrepancies among different assays can be as high as 20-fold. The accuracy and reliability of D-dimer testing cannot be guaranteed due to the lack of an internationally endorsed reference measurement system (including reference materials and reference measurement procedures), which may lead to misdiagnosis and underdiagnosis, limiting its full clinical application. In this review, we present an in-depth analysis of clinical D-dimer testing, summarizing the existing challenges, the current state of metrology, and progress towards harmonization. We also review the latest advancements in D-dimer detection techniques, which include mass spectrometry and electrochemical and optical immunoassays. By comparing the basic principles, the definition of the measurand, and analytical performance of these methods, we provide an outlook on the potential improvements in D-dimer clinical testing.

Proteomic analysis of plasma proteins from patients with cardiac rupture after acute myocardial infarction using TMT-based quantitative proteomics approach

BACKGROUND

Cardiac rupture (CR) is a rare but catastrophic mechanical complication of acute myocardial infarction (AMI) that seriously threatens human health. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of CR has yet to be elucidated.

METHODS

In the present study, a quantitative approach with tandem mass tag (TMT) labeling and liquid chromatography-tandem mass spectrometry was used to characterize the differential protein expression profiles of patients with CR. Plasma samples were collected from patients with CR (n = 37), patients with AMI (n = 47), and healthy controls (n = 47). Candidate proteins were selected for validation by multiple reaction monitoring (MRM) and enzyme-linked immunosorbent assay (ELISA).

RESULTS

In total, 1208 proteins were quantified and 958 differentially expressed proteins (DEPs) were identified. The difference in the expression levels of the DEPs was more noticeable between the CR and Con groups than between the AMI and Con groups. Bioinformatics analysis showed most of the DEPs to be involved in numerous crucial biological processes and signaling pathways, such as RNA transport, ribosome, proteasome, and protein processing in the endoplasmic reticulum, as well as necroptosis and leukocyte transendothelial migration, which might play essential roles in the complex pathological processes associated with CR. MRM analysis confirmed the accuracy of the proteomic analysis results. Four proteins i.e., C-reactive protein (CRP), heat shock protein beta-1 (HSPB1), vinculin (VINC) and growth/differentiation factor 15 (GDF15), were further validated via ELISA. By receiver operating characteristic (ROC) analysis, combinations of these four proteins distinguished CR patients from AMI patients with a high area under the curve (AUC) value (0.895, 95% CI, 0.802-0.988, p < 0.001).

CONCLUSIONS

Our study highlights the value of comprehensive proteomic characterization for identifying plasma proteome changes in patients with CR. This pilot study could serve as a valid foundation and initiation point for elucidation of the mechanisms of CR, which might aid in identifying effective diagnostic biomarkers in the future.

Quality control of allergen products with mass spectrometry part I: Positioning within the EU regulatory framework

Mass spectrometry (MS) has advanced greatly and many of its applications are ready for utilization within regulatory procedures and could significantly contribute to overcome challenges in standardization of allergen products. It seems sensible to discuss MS within the regulatory framework, before addressing technical questions. While the application to purified proteins is well established from product development to manufacturer's release analytics, its application to complex products such as allergen products is still under development. It needs to be determined where it can complement or replace established methods or where MS offers limited improvement. Despite its technical appeal and versatility, currently MS is mentioned in regulatory guidelines only as one possible measurement method. For example, no specific MS method is given in the European Pharmacopoeia. We discuss applications of MS within the EU regulatory framework. This includes their advantages and disadvantages and their positioning between research, characterization, manufacturer's release analytics and official batch testing. We discuss the qualitative detection of single and multiple allergens as proof of identity, qualitative to semi-quantitative protein profiles for batch to batch consistency testing, and quantification of allergens to state mass units of allergens. MS may also facilitate standardization of allergen products, reference products and reference standards.

Targeted Glycoproteomics Analysis Using MRM/PRM Approaches

MS-target analyses are frequently utilized to analyze and validate structural changes of biomolecules across diverse fields of study such as proteomics, glycoproteomics, glycomics, lipidomics, and metabolomics. Targeted studies are commonly conducted using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) techniques. A reliable glycoproteomics analysis in intricate biological matrices is possible with these techniques, which streamline the analytical workflow, lower background interference, and enhance selectivity and specificity.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Quantitative mass spectrometric analysis of hepatocellular carcinoma biomarker alpha-fetoprotein

Serum alpha-fetoprotein (AFP) has been used as a marker for the diagnosis of hepatocellular carcinoma (HCC) and its core fucosylation is associated with the early stage of HCC. However, current methods for the detection of AFP with core fucose are not highly accurate for early diagnosis. In this study, we established an enzyme-assisted mass spectrometric method for the quantitative analysis of AFP/core fucose with high specificity and sensitivity. We employed endoglycosidase treatment of AFP to improve the biomarker analysis. The accuracy and precision are within the US FDA-suggested value, and a good linearity (r2 = 0.9930) and a detection limit of 15.6 ng mL-1 can be achieved.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Monitoring Both Extended and Tryptic Forms of Stable Isotope-Labeled Standard Peptides Provides an Internal Quality Control of Proteolytic Digestion in Targeted Mass Spectrometry-Based Assays

Targeted mass spectrometry (MS)-based proteomic assays, such as multiplexed multiple reaction monitoring (MRM)-MS assays, enable sensitive and specific quantification of proteotypic peptides as stoichiometric surrogates for proteins. Efforts are underway to expand the use of MRM-MS assays in clinical environments, which requires a reliable strategy to monitor proteolytic digestion efficiency within individual samples. Towards this goal, extended stable isotope-labeled standard (SIS) peptides (hE), which incorporate native proteolytic cleavage sites, can be spiked into protein lysates prior to proteolytic (trypsin) digestion, and release of the tryptic SIS peptide (hT) can be monitored. However, hT measurements alone cannot monitor the extent of digestion and may be confounded by matrix effects specific to individual patient samples; therefore, they are not sufficient to monitor sample-to-sample digestion variability. We hypothesized that measuring undigested hE, along with its paired hT, would improve detection of digestion issues compared to only measuring hT. We tested the ratio of the SIS pair measurements, or hE/hT, as a quality control (QC) metric of trypsin digestion for two MRM assays: a direct-MRM (398 targets) and an immuno-MRM (126 targets requiring immunoaffinity peptide enrichment) assay, with extended SIS peptides observable for 54% (216) and 62% (78) of the targets, respectively. We evaluated the quantitative bias for each target in a series of experiments that adversely affected proteolytic digestion (e.g., variable digestion times, pH, and temperature). We identified a subset of SIS pairs (36 for the direct-MRM, 7 for the immuno-MRM assay) for which the hE/hT ratio reliably detected inefficient digestion that resulted in decreased assay sensitivity and unreliable endogenous quantification. The hE/hT ratio was more responsive to a decrease in digestion efficiency than a metric based on hT measurements alone. For clinical-grade MRM-MS assays, this study describes a ready-to-use QC panel and also provides a road map for designing custom QC panels.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Multisite assessment of reproducibility in high-content cell migration imaging data

High-content image-based cell phenotyping provides fundamental insights into a broad variety of life science disciplines. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, with particular relevance for high-quality open-access data sharing and meta-analysis. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy, have not been systematically investigated. Here, using high-content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells, and time points. Significant technical variability occurred between laboratories and, to lesser extent, between persons, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image analysis of perturbation effects and meta-analysis depend on standardized procedures combined with batch correction.

Identification of Marker Peptides for the Whey Protein Quantification in Edam-Type Cheese

Several technologies are available for incorporating whey proteins into a cheese matrix. However, there is no valid analytical method available to determine the whey protein content in matured cheese, to date. Consequently, the aim of the present study was to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the quantification of individual whey proteins based on specific marker peptides ('bottom-up' proteomic approach). Therefore, the whey protein-enriched model of the Edam-type cheese was produced in a pilot plant and on an industrial scale. Tryptic hydrolysis experiments were performed to evaluate the suitability of identified potential marker peptides (PMPs) for α-lactalbumin (α-LA) and β-lactoglobulin (β-LG). Based on the findings, α-LA and β-LG appeared to be resistant to proteolytic degradation during six weeks of ripening and no influence on the PMP was observed. Good levels of linearity (R² > 0.9714), repeatability (CVs < 5%), and recovery rate (80% to 120%) were determined for most PMPs. However, absolute quantification with external peptide and protein standards revealed differences in model cheese depending on the PMP, e.g., 0.50% ± 0.02% to 5.31% ± 0.25% for β-LG. As protein spiking prior to hydrolysis revealed differing digestion behavior of whey proteins, further studies are required to enable valid quantification in various cheese types.

Detection and analysis of chiral molecules as disease biomarkers

The chirality of small metabolic molecules is important in controlling physiological processes and indicating the health status of humans. Abnormal enantiomeric ratios of chiral molecules in biofluids and tissues occur in many diseases, including cancers and kidney and brain diseases. Thus, chiral small molecules are promising biomarkers for disease diagnosis, prognosis, adverse drug-effect monitoring, pharmacodynamic studies and personalized medicine. However, it remains difficult to achieve cost-effective and reliable analysis of small chiral molecules in clinical procedures, in part owing to their large variety and low concentration. In this Review, we describe current and emerging techniques that detect and quantify small-molecule enantiomers and their biological importance.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Analysis of 2′-hydroxyflavanone (2HF) in mouse whole blood by HPLC–MS/MS for the determination of pharmacokinetic parameters

Given the lack of investments, structure, and difficulty of metabolite isolation, promising natural product studies do not progress to preclinical studies, such as pharmacokinetics. 2′-Hydroxyflavanone (2HF) is a flavonoid that has shown promising results in different types of cancer and leishmaniasis. For accurate quantification of 2HF in BALB/c mouse blood, a validated HPLC-MS/MS method was developed. Chromatographic analysis was performed using C18 (5μm, 150 mm × 4.6 mm). The mobile phase consisted of water containing 0.1% formic acid, acetonitrile, and methanol (35/52/13 v/v/v) at a flow rate and total running time of 0.8 mL/min and 5.50 min, respectively, with an injection volume of 20 µL. 2HF was detected by electrospray ionization in negative mode (ESI-) using multiple reaction monitoring (MRM). The validated bioanalytical method showed satisfactory selectivity without significant interference for the 2HF and IS. In addition, the concentration range between 1 and 250 ng/mL showed good linearity (r = 0.9969). The method showed satisfactory results for the matrix effect. Precision and accuracy intervals varied between 1.89% and 6.76% and 95.27% and 100.77%, respectively, fitting the criteria. No degradation of 2HF in the biological matrix was observed since stability under freezing and thawing conditions, short duration, postprocessing, and long duration showed deviations less than 15%. Once validated, the method was successfully applied in a 2HF oral pharmacokinetic study with mouse blood, and the pharmacokinetic parameters were determined. 2HF demonstrated a Cmax of 185.86 ng/mL, a Tmax of 5 min, and a half-life (T1/2) of 97.52 min.

Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study

The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.

Reduction of wrinkles: From a computational hypothesis to a clinical, instrumental, and biological proof

BACKGROUND

Facial wrinkles are clear markers of the aging process, being chronological, photo-induced, or reflecting repetitive facial expressions. The aim of this study is to provide new insights into the biophysical and biological mechanisms involved in the formation, prevention, or elimination of the expression wrinkles.

MATERIALS AND METHODS

We use a computational model to get a better understanding of the wrinkle mechanical behavior and evolution after skin softening and suggesting a possible antiaging mechanism. Then, we provide a clinical demonstration of the anti-wrinkle effect of a long-term application of a 20% glycerol in a moisturizer formula (GBM) versus its vehicle on crow's feet. Skin hydration, elasticity, and wrinkles visibility were evaluated by a combination of clinical and instrumental in vivo data, inverse finite element analysis, and proteomic data.

RESULTS

The computational model shows a predominantly compressive stress beneath the wrinkle and its significant decrease by the softening of stratum corneum. The associated clinical study confirmed a significant increase of skin hydration and elasticity as well as a decrease of wrinkle visibility after 2 and 4 months as application for both formulas; this effect being stronger for GBM. A softening effect on stratum corneum and dermis was also observed for the GBM. Furthermore, proteomic data revealed an effect of upregulation of four proteins associated with desquamation, cell-glycan extracellular interactions, and protein glycation/oxidation, functions related to the tissue mechanics and adhesion.

CONCLUSIONS

We provide an in vivo demonstration of the anti-ageing benefit of glycerol at high dose (20%) reflected by a cumulative skin surface softening effect. The use of high moisturizing potent formulations should bring additional performance to other conventional moisturizing formulations.

Absolute Quantitative Targeted Proteomics Assays for Plasma Proteins

Preclinical and clinical trials require rapid, precise, and multiplexed analytical methods to characterize the complex samples and to allow high-throughput biomarker monitoring with low consumption of sample material. Targeted proteomics has been used to address these challenges when quantifying protein abundances in complex biological matrices. In many of these studies, blood plasma is collected either as the main research or diagnostic sample or in combination with other specimens. Mass spectrometry (MS)-based targeted proteomics using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) with stable isotope-labeled internal standard (SIS) peptides allows robust characterization of blood plasma protein via absolute quantification. Compared to other commonly used technologies like enzyme-linked immunosorbent assay (ELISA), targeted proteomics is faster, more sensitive, and more cost-effective. Here we describe a protocol for the quantification of proteins in blood plasma using targeted MRM proteomics with heavy-labeled internal standards. The 270-protein panel allows rapid and robust absolute quantitative proteomic characterization of blood plasma in a 1 h gradient. The method we describe here works for non-depleted plasma, which makes it simple and easy to implement. Moreover, the protocol works with the two most commonly used blood plasma collection methods used in practice, namely, either K₂EDTA or sodium citrate as anticoagulants.

Bioinformatics Tools and Knowledgebases to Assist Generating Targeted Assays for Plasma Proteomics

In targeted proteomics experiments, selecting the appropriate proteotypic peptides as surrogate for the target protein is a crucial pre-acquisition step. This step is largely a bioinformatics exercise that involves integrating information on the peptides and proteins and using various software tools and knowledgebases. We present here a few resources that automate and simplify the selection process to a great degree. These tools and knowledgebases were developed primarily to streamline targeted proteomics assay development and include PeptidePicker, PeptidePickerDB, MRMAssayDB, MouseQuaPro, and PeptideTracker. We have used these tools to develop and document thousands of targeted proteomics assays, many of them for plasma proteins with focus on human and mouse. An important aspect in all these resources is the integrative approach on which they are based. Using these tools in the first steps of designing a singleplexed or multiplexed targeted proteomic experiment can reduce the necessary experimental steps tremendously. All the tools and knowledgebases we describe here are Web-based and freely accessible so scientists can query the information conveniently from the browser. This chapter provides an overview of these software tools and knowledgebases, their content, and how to use them for targeted plasma proteomics. We further demonstrate how to use them with the results of the HUPO Human Plasma Proteome Project to produce a new database of 3.8 k targeted assays for known human plasma proteins. Upon experimental validation, these assays should help in the further quantitative characterizing of the plasma proteome.

Progress in Targeted Mass Spectrometry (Parallel Accumulation-Serial Fragmentation) and Its Application in Plasma/Serum Proteomics

Targeted mass spectrometry using multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) has been commonly used for protein biomarker validation in plasma, serum, or other clinically relevant specimens due to its high specificity, selectivity, and multiplexing capability compared with immunoassays. As the emerging mode termed parallel accumulation-serial fragmentation (prmPASEF) significantly improved analyte throughput (100-1000), sensitivity (attomole level), and acquisition speed, it promises to broaden the application of targeted mass spectrometry to simultaneous biomarker discovery and validation with high accuracy. Here, we summarize the general approach of the MRM and PRM techniques used for serum/plasma proteomics and describe a detailed step-by-step procedure for the development of MRM/PRM assays for secreted proteins.

Smartphone-based microplate reader for high-throughput quantitation of disease markers in serum

Herein, a smartphone-based portable reader with integrated optics for standard microtiter plates (96 wells) has been designed and demonstrated for high-throughput quantitation of validated biomarkers in serum. The customized optical attachment was simply constructed with a convex lens and a light source, by which the transmitted light through a 96-well microtiter plate was converged for imaging with a smartphone, so that accurate and wide-range reading of the plate can be achieved. More importantly, relying on the digitized colorimetric analysis of the obtained images, concentrations of various biomarkers can be determined directly using the customized mobile app. A set of validated biomarkers for inflammation and infection, C-reactive protein (CRP), serum amyloid A (SAA), and procalcitonin (PCT) have been quantitated with this new system; both the response ranges and limits of detection meet the requirement of clinical tests. The consistency with the results obtained using a commercial microplate reader proves its reliability and precision, augments its potential as a point-of-care diagnostic device for on-site testing or resource-limited settings.

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)-also known as multiple reaction monitoring (MRM)-and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

Developing quantitative assays for six urinary glycoproteins using parallel reaction monitoring, data-independent acquisition, and TMT-based data-dependent acquisition

Quantitative approaches encompassing parallel reaction monitoring (PRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) are commonly used to investigate protein expression profiles. However, analytical performances of assays developed using PRM, DIA, and Tandem Mass Tag (TMT)-based DDA for quantitative proteomics have yet not been investigated. Here, we developed assays for glycopeptides identified from six glycoproteins, including Leucine-rich alpha-2-glycoprotein (LRG1), Prostaglandin-H2 D-isomerase (PTGDS), Aminopeptidase N (ANPEP), CD63 antigen (CD63), Clusterin (CLU), and Prostatic acid phosphatase (ACPP), using PRM, DDA, and DIA and evaluated the analytical performances of each assay using the different acquisition modes. We also compared assays in each acquisition mode on three different orbitrap instruments: Thermo Fisher Q Exactive, Exploris 480, and Lumos. We found that DIA showed the largest linear range, highest sensitivity, and most reproducibility. We then applied our developed DIA assays to urine samples from non-aggressive (n = 48) and aggressive (n = 35) prostate cancer patients. In conclusion, we developed assays for the six glycoproteins, evaluated the analytical performances of each assay in DIA, PRM, and PRM acquisition modes on three types of mass spectrometry instruments, and chose the DIA assays for the quantitative analysis of urine samples from patients with aggressive and non-aggressive prostate cancer.

Extracellular Vesicle Membrane Protein Profiling and Targeted Mass Spectrometry Unveil CD59 and Tetraspanin 9 as Novel Plasma Biomarkers for Detection of Colorectal Cancer

Extracellular vesicles (EVs) are valuable sources for the discovery of useful cancer biomarkers. This study explores the potential usefulness of tumor cell-derived EV membrane proteins as plasma biomarkers for early detection of colorectal cancer (CRC). EVs were isolated from the culture supernatants of four CRC cell lines by ultracentrifugation, and their protein profiles were analyzed by LC-MS/MS. Bioinformatics analysis of identified proteins revealed 518 EV membrane proteins in common among at least three CRC cell lines. We next used accurate inclusion mass screening (AIMS) in parallel with iTRAQ-based quantitative proteomic analysis to highlight candidate proteins and validated their presence in pooled plasma-generated EVs from 30 healthy controls and 30 CRC patients. From these, we chose 14 potential EV-derived targets for further quantification by targeted MS assay in a separate individual cohort comprising of 73 CRC and 80 healthy subjects. Quantitative analyses revealed significant increases in ADAM10, CD59 and TSPAN9 levels (2.19- to 5.26-fold, p < 0.0001) in plasma EVs from CRC patients, with AUC values of 0.83, 0.95 and 0.87, respectively. Higher EV CD59 levels were significantly correlated with distant metastasis (p = 0.0475), and higher EV TSPAN9 levels were significantly associated with lymph node metastasis (p = 0.0011), distant metastasis at diagnosis (p = 0.0104) and higher TNM stage (p = 0.0065). A two-marker panel consisting of CD59 and TSPAN9 outperformed the conventional marker CEA in discriminating CRC and stage I/II CRC patients from healthy controls, with AUC values of 0.98 and 0.99, respectively. Our results identify EV membrane proteins in common among CRC cell lines and altered plasma EV protein profiles in CRC patients and suggest plasma EV CD59 and TSPAN9 as a novel biomarker panel for detecting early-stage CRC.

Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum

Multiple reaction monitoring (MRM) mass spectrometry is emerging as a relevant tool for measuring customized molecular markers in freshwater sentinel species. While this technique is typically used for the validation of protein molecular markers preselected from shotgun experiments, recent gains of MRM multiplexing capacity offer new possibilities to conduct large-scale screening of animal proteomes. By combining the strength of active biomonitoring strategies and MRM technologies, this study aims to propose a new strategy for the discovery of candidate proteins that respond to environmental variability. For this purpose, 249 peptides derived from 147 proteins were monitored by MRM in 273 male gammarids caged in 56 environmental sites, representative of the diversity of French water bodies. A methodology is here proposed to identify a set of customized housekeeping peptides (HKPs) used to correct analytical batch effects and allow proper comparison of peptide levels in gammarids. A comparative analysis performed on HKPs-normalized data resulted in the identification of peptides highly modulated in the environment and derived from proteins likely involved in the environmental stress response. Overall, this study proposes a breakthrough approach to screen and identify potential proteins responding to relevant environmental conditions in sentinel species.

An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies

PURPOSE

Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction, remain largely unexplored. Data suggest the DNA damage response (DDR) as a central signaling network that intersects with pathways associated with deregulated addicting oncoproteins with kinase activity in cancer cells.

EXPERIMENTAL

DESIGN: We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models. An NSCLC tissue pipeline combining patient-derived xenografts (PDXs) and ex vivo patient organotypic cultures has been established for treatment responsiveness assessment.

RESULTS

We identified an 'oncogene addiction phosphorylation signature' (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments.

CONCLUSIONS

We propose a score derived from OAPS as a quantitative measure to evaluate oncogene addiction of cancer cell samples. This work underlines the importance of protein phosphorylation assessment for patient stratification in precision oncology and corresponding identification of tumor subtypes sensitive to inhibition of a particular oncogene.

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.

The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges

Simple Summary

In this review we seek to outline the role of new technologies in biomarker discovery, particularly within the veterinary field and with an emphasis on ‘omics’, as well as to examine why many biomarkers-despite much excitement-have not yet made it to clinical practice. Further we emphasise the critical need for close collaboration between clinicians, researchers and funding bodies and the need to set clear goals for biomarker requirements and realistic application in the clinical setting, ensuring that biomarker type, method of detection and clinical utility are compatible, and adequate funding, time and sample size are available for all phases of development.

Abstract

New biomarkers promise to transform veterinary practice through rapid diagnosis of diseases, effective monitoring of animal health and improved welfare and production efficiency. However, the road from biomarker discovery to translation is not always straightforward. This review focuses on molecular biomarkers under development in the veterinary field, introduces the emerging technological approaches transforming this space and the role of ‘omics platforms in novel biomarker discovery. The vast majority of veterinary biomarkers are at preliminary stages of development and not yet ready to be deployed into clinical translation. Hence, we examine the major challenges encountered in the process of biomarker development from discovery, through validation and translation to clinical practice, including the hurdles specific to veterinary practice and to each of the ‘omics platforms–transcriptomics, proteomics, lipidomics and metabolomics. Finally, recommendations are made for the planning and execution of biomarker studies with a view to assisting the success of novel biomarkers in reaching their full potential.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hampers their use in research and clinical application. Mass spectrometry measures of fifty N-glycans, on seven serum proteins in liver disease, were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized FDA-supported BioCompute Object. Using the biomarker data model allows capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers, can integrate N-glycan biomarker data with multi-source biomedical data, and can foster discovery and insight within a unified data framework for glycan biomarker representation thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

Early Prediction of COVID-19 Patient Survival by Targeted Plasma Multi-Omics and Machine Learning

The recent surge of coronavirus disease 2019 (COVID-19) hospitalizations severely challenges healthcare systems around the globe and has increased the demand for reliable tests predictive of disease severity and mortality. Using multiplexed targeted mass spectrometry assays on a robust triple quadrupole MS setup which is available in many clinical laboratories, we determined the precise concentrations of hundreds of proteins and metabolites in plasma from hospitalized COVID-19 patients. We observed a clear distinction between COVID-19 patients and controls and, strikingly, a significant difference between survivors and nonsurvivors. With increasing length of hospitalization, the survivors' samples showed a trend toward normal concentrations, indicating a potential sensitive readout of treatment success. Building a machine learning multi-omic model that considers the concentrations of 10 proteins and five metabolites, we could predict patient survival with 92% accuracy (area under the receiver operating characteristic curve: 0.97) on the day of hospitalization. Hence, our standardized assays represent a unique opportunity for the early stratification of hospitalized COVID-19 patients.

Reduction of multiple reaction monitoring protein target list using correlation analysis

High mass resolution mass spectrometry provides hundreds to thousands of protein identifications per sample, and quantification is typically performed using label-free quantification. However, the gold standard of quantitative proteomics is multiple reaction monitoring (MRM) using triple quadrupole mass spectrometers and stable isotope reference peptides. This raises the question how to reduce a large data set to a small one without losing essential information. Here we present the reduction of such a data set using correlation analysis of bovine dairy ingredients and derived products. We were able to explain the variance in the proteomics data set using only 9 proteins across all major dairy protein classes: caseins, whey, and milk fat globule membrane proteins. We term this method Trinity-MRM. The reproducibility of the protein extraction and Trinity-MRM methods was shown to be below 5% in independent experiments (multi-day single-user and single-day multi-user) using double cream. Further application of this reductionist approach might include screening of large sample cohorts for biologically interesting samples before analysis by high-resolution mass spectrometry or other omics methodologies.

Prognosis of Alzheimer’s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

Using a peptide-based mass spectrometry approach to quantitate proteolysis of an intact heterogeneous procollagen substrate by BMP1 for antagonistic antibody screening

Proteases are critical proteins involved in cleaving substrates that may impact biological pathways, cellular processes, or disease progression. In the biopharmaceutical industry, modulating the levels of protease activity is an important strategy for mitigating many types of diseases. While a variety of analytical tools exist for characterizing substrate cleavages, in vitro functional screening for antibody inhibitors of protease activity using physiologically relevant intact protein substrates remains challenging. In addition, detecting such large protein substrates with high heterogeneity using high-throughput mass spectrometry screening has rarely been reported in the literature with concerns for assay robustness and sensitivity. In this study, we established a peptide-based in vitro functional screening assay for antibody inhibitors of mouse bone morphogenic protein 1 (mBMP1) metalloprotease using a heterogeneous recombinant 66-kDa mouse Procollagen I alpha 1 chain (mProcollagen) substrate. We compared several analytical tools including capillary gel electrophoresis Western blot (CE-Western blot), as well as both intact protein and peptide-based mass spectrometry (MS) to quantitate the mBMP1 proteolytic activity and its inhibition by antibodies using this heterogeneous mProcollagen substrate. We concluded that the peptide-based mass spectrometry screening assay was the most suitable approach in terms of throughput, sensitivity, and assay robustness. We then optimized our mBMP1 proteolysis reaction after characterizing the enzyme kinetics using the peptide-based MS assay. This assay resulted in Z' values ranging from 0.6 to 0.8 from the screening campaign. Among over 1200 antibodies screened, IC₅₀ characterization was performed on the top candidate hits, which showed partial or complete inhibitory activities against mBMP1.

Plasma proteomic analysis to identify potential biomarkers of histologic chorioamnionitis in women with preterm premature rupture of membranes

Introduction

To identify potential biomarkers in the plasma that could predict histologic chorioamnionitis (HCA) in women with preterm premature rupture of membranes (PPROM), using shotgun and targeted proteomic analyses.

Methods

This retrospective cohort study included 78 singleton pregnant women with PPROM (24–34 gestational weeks) who delivered within 96 h of blood sampling. Maternal plasma samples were analyzed by label-free liquid chromatography-tandem mass spectrometry for proteome profiling in a nested case-control study design (HCA cases vs. non-HCA controls [n = 9 each]). Differential expression of 12 candidate proteins was assessed by multiple reaction monitoring-mass spectrometry (MRM-MS) analysis in individual plasma samples from cases and controls matched by gestational age at sampling (n = 40, cohort 1). A validation study was further performed in an independent study group (n = 38, cohort 2) using ELISA and turbidimetric immunoassay for three differentially expressed proteins.

Results

Shotgun proteomics analyses yielded 18 proteins that were differentially expressed (P < 0.05) between HCA cases and non-HCA controls. MRM-MS analysis of 12 differentially expressed proteins further revealed that the CRP, C4A, and SAA4 levels were significantly increased in women with HCA. A multi-marker panel comprising plasma SAA4 and C4A showed enhanced potential for differentiating HCA from non-HCA women (area under the curve = 0.899). Additional validation of these findings by ELISA assays revealed that the CRP levels were significantly higher in women with HCA than in those without HCA, whereas the plasma levels of C4A and SAA4 did not significantly differ between the two groups.

Conclusions

Plasma C4A, SAA4, and CRP were identified as potential biomarkers for detecting HCA in women with PPROM, based on targeted and shotgun proteomic analyses, showing good accuracy when used as a combined dual-biomarker panel (C4A and SAA4). Nevertheless, ELISA validation of these proteins, except for CRP, may not yield clinically useful markers for predicting HCA.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Proteomics in thrombosis research

A State of the Art lecture titled “Proteomics in Thrombosis Research” was presented at the ISTH Congress in 2021. In clinical practice, there is a need for improved plasma biomarker‐based tools for diagnosis and risk prediction of venous thromboembolism (VTE). Analysis of blood, to identify plasma proteins with potential utility for such tools, could enable an individualized approach to treatment and prevention. Technological advances to study the plasma proteome on a large scale allows broad screening for the identification of novel plasma biomarkers, both by targeted and nontargeted proteomics methods. However, assay limitations need to be considered when interpreting results, with orthogonal validation required before conclusions are drawn. Here, we review and provide perspectives on the application of affinity‐ and mass spectrometry‐based methods for the identification and analysis of plasma protein biomarkers, with potential application in the field of VTE. We also provide a future perspective on discovery strategies and emerging technologies for targeted proteomics in thrombosis research. Finally, we summarize relevant new data on this topic, presented during the 2021 ISTH Congress.

Systematic optimization of targeted and multiplexed MS-based screening workflows for protein biomarkers

Background: The capability of targeted MS-based methods to simultaneously measure multiple analytes with high selectivity and sensitivity greatly facilitates the discovery and quantitation of novel biomarkers. However, the complexity of biological samples is a major bottleneck that requires extensive sample preparation. Results: This paper reports a generic workflow to optimize surrogate peptide-based protein biomarker screening for seven human proteins in a multiplexed manner without the need for any specific affinity reagents. Each step of the sample processing and LC-MS methods is systematically assessed and optimized for better analytical performance. Conclusion: The established method is used for the screening of multiple myeloma patient samples to determine which proteins could be robustly measured and serve as potential biomarkers of the disease.

UPLC-MS/MS-Based Analysis of Trastuzumab in Plasma Samples: Application in Breast Cancer Patients Sample Monitoring

Trastuzumab is a target-based recombinant humanized IgG1 monoclonal antibody (mAbs), extensively employed for treatment of metastatic breast cancer with human epidermal growth receptor 2 (HER2) overexpression. Studies around the world have reported that mAbs have substantial inter-patient unpredictable absorption, distribution, metabolism, and excretion (ADME-pharmacokinetics) because of multiple elements manipulating the concentration of mAbs in plasma. Herein, we have established a bioanalytical technique using UPLC-MS/MS with an easy sample workup method and in-solution digestion protocol to assay the trastuzumab plasma samples from breast cancer patients in clinical studies. Surrogated proteolytic peptides were used for accurate quantification of trastuzumab (CanMab) with a trastuzumab signature peptide with [13C6, 15N4]-arginine and [13C6, 15N2]-lysine stable isotope-labeled (SIL) peptide. Experiments to validate the method were accurately carried out according to the guidelines mentioned in the bioanalytical method validation protocol. The evaluation established excellent linearity over a wide range of 5–500 µg/mL. The experimental procedure was efficaciously performed in a pilot study of five breast cancer patients and residual concentrations of drugs from responding and non-responding subjects were compared. The receiver operating characteristic (ROC) examination displayed that 52.25 µg/mL was the Cmin threshold predictive response with a satisfactory sensitivity of 88.58% and specificity of 79.25%.

Quantitative Detection of Protein Splice Variants by Selected Reaction Monitoring (SRM) Mass Spectrometry

Molecular diversification of the cellular proteome through alternative splicing has emerged as an important biological principle. However, the lack of tools to specifically detect and quantify proteoforms (Smith et al., Nat Methods 10:186-187, 2013) is a major impediment to functional studies. Recently, biological mass spectrometry (MS) has undergone impressive advances (Mann, Nat Rev Mol Cell Biol 17:678, 2016), including the generation of a highly diverse set of biological applications (Aebersold and Mann, Nature 537:347-355, 2016), and has demonstrated to be an essential tool to address many biological questions (Savitski et al., Science 346:1255784, 2014; Rinner et al., Nat Methods 5:315-318, 2008). In particular, targeted LC-MS, with its high selectivity and specificity, is ideally suited for the precise and sensitive quantification of specific proteins and their proteoforms (Picotti and Aebersold, Nat Methods 9:555-566, 2012). We describe in detail the application of this workflow applied to dissect the molecular diversity of the synaptic adhesion proteins and their splicing-derived proteoforms (Schreiner et al., Elife 4:e07794, 2015).

Proteomic Studies of Blood and Vascular Wall in Atherosclerosis

The review is devoted to the analysis of literature data related to the role of proteomic studies in the study of atherosclerotic cardiovascular diseases. Diagnosis of patients with atherosclerotic plaques before clinical manifestations is an arduous task. The review presents the results of research on the new proteomic potential biomarkers of coronary heart disease, coronary atherosclerosis, acute coronary syndrome, myocardial infarction, carotid artery atherosclerosis. Also, the analysis of literature data on proteomic studies of the vascular wall was carried out. To assess the involvement of proteins in the pathological process of atherosclerosis, it is important to investigate the specific relationships between proteins in the arteries, expression and concentration of proteins. The development of proteomic technologies has made it possible to analyse the number of proteins associated with the development of the disease. Analysis of the proteomic profile of the vascular wall in atherosclerosis can help to detect possible diagnostically significant protein structures or potential biomarkers of the disease and develop novel approaches to the diagnosis of atherosclerosis and its complications.

Proteomic analysis of a plastid gene encoding RPS4 mutant in Chinese cabbage (Brassica campestris L. ssp. pekinensis)

Plastids are important plant cell organelles containing a genome and bacterial-type 70S ribosomes-primarily composed of plastid ribosomal proteins and ribosomal RNAs. In this study, a chlorophyll-deficient mutant (cdm) obtained from double-haploid Chinese cabbage 'FT' was identified as a plastome mutant with an A-to-C base substitution in the plastid gene encoding the ribosomal protein RPS4. To further elucidate the function and regulatory mechanisms of RPS4, a comparative proteomic analysis was conducted between cdm and its wild-type 'FT' plants by isobaric tags and a relative and absolute quantitation (iTRAQ)-based strategy. A total of 6,245 proteins were identified, 540 of which were differentially abundant proteins (DAPs) in the leaves of cdm as compared to those of 'FT'-including 233 upregulated and 307 downregulated proteins. Upregulated DAPs were mainly involved in translation, organonitrogen compound biosynthetic process, ribosomes, and spliceosomes. Meanwhile, downregulated DAPs were mainly involved in photosynthesis, photosynthetic reaction centres, photosynthetic light harvesting, carbon fixation, and chlorophyll binding. These results indicated an important role of RPS4 in the regulation of growth and development of Chinese cabbage, possibly by regulating plastid translation activity by affecting the expression of specific photosynthesis- and cold stress-related proteins. Moreover, a multiple reaction monitoring (MRM) test and quantitative real-time polymerase chain reaction analysis confirmed our iTRAQ results. Quantitative proteomic analysis allowed us to confirm diverse changes in the metabolic pathways between cdm and 'FT' plants. This work provides new insights into the regulation of chlorophyll biosynthesis and photosynthesis in Chinese cabbage.

Proteomics Analysis of Tears and Saliva From Sjogren's Syndrome Patients

Sjogren's syndrome (SS) is characterized by dysfunctional mucous membranes and dysregulated moisture-secreting glands resulting in various symptoms, including dry mouth and dry eyes. Here, we wanted to profile and compare the tear and saliva proteomes of SS patients to healthy controls. Tear and saliva samples were collected and subjected to an isotopic dimethylation labeling shotgun proteomics workflow to identify alterations in protein levels. In tear samples, we identified 83 upregulated and 112 downregulated proteins. Pathway enrichment analysis of the changing proteins by Metascape identified leukocyte transendothelial migration, neutrophil degranulation, and post-translation protein phosphorylation in tears of SS patients. In healthy controls' tears, an enrichment for proteins related to glycolysis, amino acid metabolism and apoptotic signaling pathway were identified. In saliva, we identified 108 upregulated and 45 downregulated proteins. Altered pathways in SS patients' saliva included cornification, sensory perception to taste and neutrophil degranulation. In healthy controls' saliva, an enrichment for proteins related to JAK-STAT signaling after interleukin-12 stimulation, phagocytosis and glycolysis in senescence were identified. Dysregulated protease activity is implicated in the initiation of inflammation and immune cell recruitment in SS. We identified 20 proteases and protease inhibitors in tears and 18 in saliva which are differentially expressed between SS patients and healthy controls. Next, we quantified endogenous proteoglycan 4 (PRG4), a mucin-like glycoprotein, in tear wash and saliva samples via a bead-based immune assay. We identified decreased levels of PRG4 in SS patients' tear wash compared to normal samples. Conversely, in saliva, we found elevated levels of PRG4 concentration and visualized PRG4 expression in human parotid gland via immunohistological staining. These findings will improve our mechanistic understanding of the disease and changes in SS patients' protein expression will help identify new potential drug targets. PRG4 is among the promising targets, which we identified here, in saliva, for the first time.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.