Mass-spectrometry-based clinical proteomics--a review and prospective

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.

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.

Proteomic Shotgun and Targeted Mass Spectrometric Datasets of Cerebrospinal Fluid (Liquor) Derived from Patients with Vestibular Schwannoma

Vestibular schwannomas are relatively rare intracranial tumors compared to other brain tumors. Data on the molecular features, especially on schwannoma proteome, are scarce. The 41 cerebrospinal fluid (liquor) samples were obtained during the surgical removal of vestibular schwannoma. Obtained peptide samples were analyzed by shotgun LC-MS/MS high-resolution mass spectrometry. The same peptide samples were spiked with 148 stable isotopically labeled peptide standards (SIS) followed by alkaline fractionation and scheduled multiple reaction monitoring (MRM) for quantitative analysis. The natural counterparts of SIS peptides were mapped onto 111 proteins that were Food and Drug Administration (FDA)-approved for diagnostic use. As a result, 525 proteins were identified by shotgun LC-MS/MS with high confidence (at least two peptides per protein, FDR < 1%) in liquor samples. Absolute quantitative concentrations were obtained for 54 FDA-approved proteins detected in at least five experimental samples. Since there is lack of data on the molecular landscape of vestibular schwannoma, the obtained datasets are unique and one of the first in its field.

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.

Dealing with missing values in proteomics data

Proteomics data are often plagued with missingness issues. These missing values (MVs) threaten the integrity of subsequent statistical analyses by reduction of statistical power, introduction of bias, and failure to represent the true sample. Over the years, several categories of missing value imputation (MVI) methods have been developed and adapted for proteomics data. These MVI methods perform their tasks based on different prior assumptions (e.g., data is normally or independently distributed) and operating principles (e.g., the algorithm is built to address random missingness only), resulting in varying levels of performance even when dealing with the same dataset. Thus, to achieve a satisfactory outcome, a suitable MVI method must be selected. To guide decision making on suitable MVI method, we provide a decision chart which facilitates strategic considerations on datasets presenting different characteristics. We also bring attention to other issues that can impact proper MVI such as the presence of confounders (e.g., batch effects) which can influence MVI performance. Thus, these too, should be considered during or before MVI.

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides

The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.

Proteomic Analysis of Maternal Urine for the Early Detection of Preeclampsia and Fetal Growth Restriction

Background: To explore the use of maternal urine proteome for the identification of preeclampsia biomarkers. Methods: Maternal urine samples from women with and without preeclampsia were used for protein discovery followed by a validation study. The targeted proteins of interest were then measured in urine samples collected at 20–24 and 30–34 weeks among nine women who developed preeclampsia, one woman with fetal growth restriction, and 20 women with uncomplicated pregnancies from a longitudinal study. Protein identification and quantification was obtained using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Results: Among the 1108 urine proteins quantified in the discovery study, 21 were upregulated in preeclampsia and selected for validation. Nineteen (90%) proteins were confirmed as upregulated in preeclampsia cases. Among them, two proteins, ceruloplasmin and serpin A7, were upregulated at 20–24 weeks and 30–34 weeks of gestation (p < 0.05) in cases of preeclampsia, and could have served to identify 60% of women who subsequently developed preeclampsia and/or fetal growth restriction at 20–24 weeks of gestation, and 78% at 30–34 weeks, for a false-positive rate of 10%. Conclusions: Proteomic profiling of maternal urine can differentiate women with and without preeclampsia. Several proteins including ceruloplasmin and serpin A7 are upregulated in maternal urine before the diagnosis of preeclampsia and potentially fetal growth restriction.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Microheterogeneity and preanalytical stability of protein biomarkers of inflammation and renal function

Protein biomarker microheterogeneity has attracted increasing attention in epidemiological and clinical research studies. Knowledge concerning the preanalytical stability of proteins is paramount to assess the biological significance of their proteoforms. We investigated the stability of the inflammatory markers C-reactive protein (CRP), serum amyloid A (SAA), and calprotectin (S100A8/9), and the renal function marker, cystatin C (CnC). In total 16 proteoforms were quantified by immuno-MALDI-TOF MS in EDTA plasma and serum samples from 15 healthy volunteers. Prior to analysis blood samples were stored at either room temperature from 1 h up to 8 days, or underwent up to 9 consecutive freeze/thaw cycles. Pearson's correlation coefficient and t-test, intra-class correlation coefficient (ICC), and Autoregressive Integrated Moving-Average (ARIMA) models were used to investigate the stability of proteoform concentrations and distributions in blood. Plasma and serum concentrations of CRP and SAA proteoforms were highly stable during room temperature exposure and repeated freeze/thaw cycles, demonstrating excellent reproducibility (ICC > 0.75), no serial dependency in ARIMA models, and stable distribution of proteoforms. Stability analyses for proteoforms of S100A8/9 and CnC identified only minor preanalytical changes in concentrations and distributions, and none of the proteoforms were produced during prolonged exposure to room temperature or repeated freezing/thawing. The four proteins and their proteoforms are stable during sub-optimal sample handling, and represent robust biomarker candidates for future biobank studies aimed at investigating the microheterogeneity of SAA, S100A8/9, and CnC in relation to inflammation, renal dysfunction and various clinical outcomes.

A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.

Concentration Determination of >200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation

The use of protein biomarkers as surrogates for clinical endpoints requires extensive multilevel validation including development of robust and sensitive assays for precise measurement of protein concentration. Multiple reaction monitoring (MRM) is a well-established mass-spectrometric method that can be used for reproducible protein-concentration measurements in biological specimens collected via microsampling. The dried blood spot (DBS) microsampling technique can be performed non-invasively without the expertise of a phlebotomist, and can enhance analyte stability which facilitate the application of this technique in retrospective studies while providing lower storage and shipping costs, because cold-chain logistics can be eliminated. Thus, precise, sensitive, and multiplexed methods for measuring protein concentrations in DBSs can be used for de novo biomarker discovery and for biomarker quantification or verification experiments. To achieve this goal, MRM assays were developed for multiplexed concentration measurement of proteins in DBSs.The lower limit of quantification (LLOQ) was found to have a median total coefficient of variation (CV) of 18% for 245 proteins, whereas the median LLOQ was 5 fmol of peptide injected on column, and the median inter-day CV over 4 days for measuring endogenous protein concentration was 8%. The majority (88%) of the assays displayed parallelism, whereas the peptide standards remained stable throughout the assay workflow and after exposure to multiple freeze-thaw cycles. For 190 proteins, the measured protein concentrations remained stable in DBS stored at ambient laboratory temperature for up to 2 months. Finally, the developed assays were used to measure the concentration ranges for 200 proteins in twenty same sex, same race and age matched individuals.

Considerations for mass spectrometry-based multi-omic analysis of clinical samples

Introduction: The role of mass spectrometry in biomolecule analysis has become paramount over the last several decades ranging in the analysis across model systems and human specimens. Accordingly, the presence of mass spectrometers in clinical laboratories has also expanded alongside the number of researchers investigating the protein, lipid, and metabolite composition of an array of biospecimens. With this increase in the number of omic investigations, it is important to consider the entire experimental strategy from sample collection and storage, data collection and analysis.Areas covered: In this short review, we outline considerations for working with clinical (e.g. human) specimens including blood, urine, and cerebrospinal fluid, with emphasis on sample handling, profiling composition, targeted measurements and relevance to disease. Discussions of integrated genomic or transcriptomic datasets are not included. A brief commentary is also provided regarding new technologies with clinical relevance.Expert opinion: The role of mass spectrometry to investigate clinically related specimens is on the rise and the ability to integrate multiple omics datasets from mass spectrometry measurements will be crucial to further understanding human health and disease.

Determination of the concentration range for 267 proteins from 21 lots of commercial human plasma using highly multiplexed multiple reaction monitoring mass spectrometry

Multiple reaction monitoring (MRM) is a key tool for biomarker validation and the translation of potential biomarkers into the clinic.

Blood platelet enrichment in mass-producible surface acoustic wave (SAW) driven microfluidic chips

The ability to separate specific biological components from cell suspensions is indispensable for liquid biopsies, and for personalized diagnostics and therapy. This paper describes an advanced surface acoustic wave (SAW) based device designed for the enrichment of platelets (PLTs) from a dispersion of PLTs and red blood cells (RBCs) at whole blood concentrations, opening new possibilities for diverse applications involving cell manipulation with high throughput. The device is made of patterned SU-8 photoresist that is lithographically defined on the wafer scale with a new proposed methodology. The blood cells are initially focused and subsequently separated by an acoustic radiation force (ARF) applied through standing SAWs (SSAWs). By means of flow cytometric analysis, the PLT concentration factor was found to be 7.7, and it was proven that the PLTs maintain their initial state. A substantially higher cell throughput and considerably lower applied powers than comparable devices from literature were achieved. In addition, fully coupled 3D numerical simulations based on SAW wave field measurements were carried out to anticipate the coupling of the wave field into the fluid, and to obtain the resulting pressure field. A comparison to the acoustically simpler case of PDMS channel walls is given. The simulated results show an ideal match to the experimental observations and offer the first insights into the acoustic behavior of SU-8 as channel wall material. The proposed device is compatible with current (Lab-on-a-Chip) microfabrication techniques allowing for mass-scale, reproducible chip manufacturing which is crucial to push the technology from lab-based to real-world applications.

Targeted Proteomics

Targeted proteomics detects proteins of interest with high sensitivity, quantitative accuracy, and reproducibility. In a targeted proteomics assay, surrogate peptides are generated by proteolytic digestion of target proteins and selected reaction monitoring (SRM) assays are developed to quantify these peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this report, we describe the details of quantitative analysis of target protein in cells and tissue samples.

An SDS-PAGE based proteomic approach for N-terminome profiling

Schematic diagram of the SDS-PAGE based N-termini enrichment (GelNrich) workflow.

Identification of differentially expressed proteins in rats with spinal cord injury during the transitional phase using an iTRAQ-based quantitative analysis

BACKGROUND

Spinal cord injury (SCI) is a disease associated with high disability and mortality rates. The transitional phase from subacute phase to intermediate phase may play a major role in the process of secondary injury. Changes in protein expression levels have been shown to play key roles in many central nervous system (CNS) diseases. Nevertheless, the roles of proteins in the transitional phase of SCI are not clear.

METHODS

We examined protein expression in a rat model 2 weeks after SCI and identified differentially expressed proteins (DEPs) using isobaric tagging for relative and absolute protein quantification (iTRAQ). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEPs were performed. Furthermore, we constructed a protein-protein interaction (PPI) network, and the top 10 high-degree core nodes were identified. Meanwhile, we validated protein level changes of five high-degree core regulated proteins using Western blots.

RESULTS

A total of 162 DEPs were identified between the injury group and the control, of which 101 (62.35%) were up-regulated and 61 (37.65%) were down-regulated in the transitional phase of SCI. Key molecular function, cellular components, biological process terms and pathways were identified and may be important mechanisms in the transitional phase of SCI. Alb, Calm1, Vim, Apoe, Syp, P4hb, Cd68, Eef1a2, Rab3a and Lgals3 were the top 10 high-degree core nodes. Western blot analysis performed on five of these proteins showed the same trend as iTRAQ results.

CONCLUSION

The current study may provide novel insights into how proteins regulate the pathogenesis of the transitional phase after SCI.

Immune response and differentially expressed proteins in the lung tissue of BALB/c mice challenged by aerosolized Brucella melitensis 5

Objective

This study was performed to develop a murine aerosol infection model of brucellosis to investigate the pathogenicity and immune reactions induced by aerosolized Brucella and to identify key proteins associated with Brucella infection in lung tissue.

Methods

BALB/c mice were exposed to aerosolized Brucella melitensis 5 (M5) for 30 minutes and killed at 1, 3, 7, and 15 days post-exposure. Clinical observation, pathological analysis of lung tissue, and cytokine expression detection were then performed. Proteomic analysis based on two-dimensional electrophoresis and mass spectrometry was used to identify proteins exhibiting significant changes in expression in lung tissues during Brucella infection.

Results

Pathological analysis revealed alveolar wall thickening, telangiectasia with hyperemia, inflammatory cell infiltration, large areas of congestion and bleeding, and areas of focal necrosis. The T-helper 1 type immune response played an important role during aerosol infection, and 12 differentially expressed proteins were involved in the infectious process in lung tissue.

Conclusion

These results contribute to our understanding of the pathogenic process of Brucella in the lung tissue of BALB/c mice challenged with aerosolized Brucella. Some of the identified proteins may be potential targets in future therapeutic strategies.

Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT

Background

Protein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.

Design and methods

The study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.

Results

The methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.

Conclusions

The limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.

Trial registration

Current Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.

Funding

This project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.

ExSTA: External Standard Addition Method for Accurate High-Throughput Quantitation in Targeted Proteomics Experiments

PURPOSE

Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time.

EXPERIMENTAL DESIGN

To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated.

RESULTS

The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values.

CONCLUSIONS AND CLINICAL RELEVANCE

While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.

Single Molecule Nanopore Spectrometry for Peptide Detection

Sensing and characterization of water-soluble peptides is of critical importance in a wide variety of bioapplications. Single molecule nanopore spectrometry (SMNS) is based on the idea that one can use biological protein nanopores to resolve different sized molecules down to limits set by the blockade duration and noise. Previous work has shown that this enables discrimination between polyethylene glycol (PEG) molecules that differ by a single monomer unit. This paper describes efforts to extend SMNS to a variety of biologically relevant, water-soluble peptides. We describe the use of Au25(SG)18 clusters, previously shown to improve PEG detection, to increase the on- and off-rate of peptides to the pore. In addition, we study the role that fluctuations play in the single molecule nanopore spectrometry (SMNS) methodology and show that modifying solution conditions to increase peptide flexibility (via pH or chaotropic salt) leads to a nearly 2-fold reduction in the current blockade fluctuations and a corresponding narrowing of the peaks in the blockade distributions. Finally, a model is presented that connects the current blockade depths to the mass of the peptides, which shows that our enhanced SMNS detection improves the mass resolution of the nanopore sensor more than 2-fold for the largest cationic peptides studied.

Absolute quantitation of disease protein biomarkers in a single LC-MS acquisition using apolipoprotein F as an example

LC-MS and immunoassay can detect protein biomarkers. Immunoassays are more commonly used but can potentially be outperformed by LC-MS. These techniques have limitations including the necessity to generate separate calibration curves for each biomarker. We present a rapid mass spectrometry-based assay utilising a universal calibration curve. For the first time we analyse clinical samples using the HeavyPeptide IGNIS kit which establishes a 6-point calibration curve and determines the biomarker concentration in a single LC-MS acquisition. IGNIS was tested using apolipoprotein F (APO-F), a potential biomarker for non-alcoholic fatty liver disease (NAFLD). Human serum and IGNIS prime peptides were digested and the IGNIS assay was used to quantify APO-F in clinical samples. Digestion of IGNIS prime peptides was optimised using trypsin and SMART Digest™. IGNIS was 9 times faster than the conventional LC-MS method for determining the concentration of APO-F in serum. APO-F decreased across NAFLD stages. Inter/intra-day variation and stability post sample preparation for one of the peptides was ≤13% coefficient of variation (CV). SMART Digest™ enabled complete digestion in 30 minutes compared to 24 hours using in-solution trypsin digestion. We have optimised the IGNIS kit to quantify APO-F as a NAFLD biomarker in serum using a single LC-MS acquisition.

Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)

Mass spectrometry (MS) is one of the most commonly used technologies for quantifying proteins in complex samples, with excellent assay specificity as a result of the direct detection of the mass-to-charge ratio of each target molecule. However, MS-based proteomics, like most other analytical techniques, has a bias towards measuring high-abundance analytes, so it is challenging to achieve detection limits of low ng/mL or pg/mL in complex samples, and this is the concentration range for many disease-relevant proteins in biofluids such as human plasma. To assist in the detection of low-abundance analytes, immuno-enrichment has been integrated into the assay to concentrate and purify the analyte before MS measurement, significantly improving assay sensitivity. In this work, the immuno- Matrix-Assisted Laser Desorption/Ionization (iMALDI) technology is presented for the quantification of proteins and peptides in biofluids, based on immuno-enrichment on beads, followed by MALDI-MS measurement without prior elution. The anti-peptide antibodies are functionalized on magnetic beads, and incubated with samples. After washing, the beads are directly transferred onto a MALDI target plate, and the signals are measured by a MALDI-Time of Flight (MALDI-TOF) instrument after the matrix solution has been applied to the beads. The sample preparation procedure is simplified compared to other immuno-MS assays, and the MALDI measurement is fast. The whole sample preparation is automated with a liquid handling system, with improved assay reproducibility and higher throughput. In this article, the iMALDI assay is used for determining the peptide angiotensin I (Ang I) concentration in plasma, which is used clinically as readout of plasma renin activity for the screening of primary aldosteronism (PA).

The impact of growth hormone on proteomic profiles: a review of mouse and adult human studies

Growth hormone (GH) is a protein that is known to stimulate postnatal growth, counter regulate insulin's action and induce expression of insulin-like growth factor-1. GH exerts anabolic or catabolic effects depending upon on the targeted tissue. For instance, GH increases skeletal muscle and decreases adipose tissue mass. Our laboratory has spent the past two decades studying these effects, including the effects of GH excess and depletion, on the proteome of several mouse and human tissues. This review first discusses proteomic techniques that are commonly used for these types of studies. We then examine the proteomic differences found in mice with excess circulating GH (bGH mice) or mice with disruption of the GH receptor gene (GHR^-/-). We also describe the effects of increased and decreased GH action on the proteome of adult patients with either acromegaly, GH deficiency or patients after short-term GH treatment. Finally, we explain how these proteomic studies resulted in the discovery of potential biomarkers for GH action, particularly those related with the effects of GH on aging, glucose metabolism and body composition.

Current trends in quantitative proteomics - an update

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.

Filter Plate-Based Screening of MIP SPE Materials for Capture of the Biomarker Pro-Gastrin-Releasing Peptide

Affinity-based solid-phase extraction (SPE) is an attractive low-cost sample preparation strategy for biomarker analysis. Molecularly imprinted polymers (MIPs) as affinity sorbents offer unique opportunities for affinity SPE, due to their low manufacturing cost and high robustness. A limitation is the prediction of their affinity; therefore, screening of analyte recovery and specificity within a large range of SPE conditions is important in order to ensure high-sensitivity detection and assay reproducibility. Here, a µ-SPE method for screening of the MIP-SPE materials using a commercial 384-well filter plate is presented. The method allows for rapid and automated screening using 10-30 µL of packed SPE sorbent per well and sample volumes in the range of 10-70 µL. This enables screening of many different SPE sorbents while simultaneously identifying optimal SPE conditions. In addition, the 384-well format also facilitates detection with a multitude of analytical platforms. Performance of the µ-MIP-SPE method was investigated using a series of MIPs designed to capture pro-gastrin-releasing peptide (ProGRP). Fractions coming from sample load, cartridge wash, and elution were collected and analyzed using mass spectrometry (MS). The top-performing MIPs were identified, together with proper SPE conditions.

Quantification of prospective type 2 diabetes mellitus biomarkers by stable isotope dilution with bi-labeled standard glycated peptides

A new analytical approach is proposed for quantification of multiple glycated peptides in human plasma tryptic digests.

Multiplexed longitudinal measurement of protein biomarkers in DBS using an automated SISCAPA workflow

BACKGROUND

The use of DBS for quantitative protein biomarker measurement has been hindered by issues associated with blood hematocrit variations and lack of detection sensitivity, particularly when multiple biomarkers are measured.

MATERIALS & METHODS

An automated, multiplexed SISCAPA analysis was used to normalize blood volume variations in DBS and quantify proteins of varying abundance in longitudinal specimens.

CONCLUSION

The results showed that after normalizing the spot-to-spot hematocrit variations, peptide surrogates of protein biomarkers could be accurately quantitated in DBS. This allowed the establishment of baselines for a variety of biomarkers in multiple individuals and enabled detection of changes over time, thus offering an effective solution for longitudinal personal monitoring of biomarkers relevant in health and disease.

Mass Spectrometry-Based Metabolomic and Proteomic Strategies in Organic Acidemias

Organic acidemias (OAs) are inherited metabolic disorders caused by deficiency of enzymatic activities in the catabolism of amino acids, carbohydrates, or lipids. These disorders result in the accumulation of mono-, di-, or tricarboxylic acids, generally referred to as organic acids. The OA outcomes can involve different organs and/or systems. Some OA disorders are easily managed if promptly diagnosed and treated, whereas, in others cases, such as propionate metabolism-related OAs (propionic acidemia, PA; methylmalonic acidemia, MMA), neither diet, vitamin therapy, nor liver transplantation appears to prevent multiorgan impairment. Here, we review the recent developments in dissecting molecular bases of OAs by using integration of mass spectrometry- (MS-) based metabolomic and proteomic strategies. MS-based techniques have facilitated the rapid and economical evaluation of a broad spectrum of metabolites in various body fluids, also collected in small samples, like dried blood spots. This approach has enabled the timely diagnosis of OAs, thereby facilitating early therapeutic intervention. Besides providing an overview of MS-based approaches most frequently used to study the molecular mechanisms underlying OA pathophysiology, we discuss the principal challenges of metabolomic and proteomic applications to OAs.

A simple dual online ultra-high pressure liquid chromatography system (sDO-UHPLC) for high throughput proteome analysis

We report a new and simple design of a fully automated dual-online ultra-high pressure liquid chromatography system. The system employs only two nano-volume switching valves (a two-position four port valve and a two-position ten port valve) that direct solvent flows from two binary nano-pumps for parallel operation of two analytical columns and two solid phase extraction (SPE) columns. Despite the simple design, the sDO-UHPLC offers many advantageous features that include high duty cycle, back flushing sample injection for fast and narrow zone sample injection, online desalting, high separation resolution and high intra/inter-column reproducibility. This system was applied to analyze proteome samples not only in high throughput deep proteome profiling experiments but also in high throughput MRM experiments.

Qualitative identification of growth hormone-releasing hormones in human plasma by means of immunoaffinity purification and LC-HRMS/MS

The use of growth hormone-releasing hormones (GHRHs) is prohibited in sports according to the regulations of the World Anti-Doping Agency (WADA). The aim of the present study was to develop a method for the simultaneous detection of four different GHRHs and respective metabolites from human plasma by means of immunoaffinity purification and subsequent nano-ultrahigh performance liquid chromatography-high resolution/high accuracy (tandem) mass spectrometry. The target analytes included Geref (Sermorelin), CJC-1293, CJC-1295, and Egrifta (Tesamorelin) as well as two metabolites of Geref and CJC-1293, which were captured from plasma samples using a polyclonal GHRH antibody in concert with protein A/G monolithic MSIA™ D.A.R.T.'S® (Disposable Automation Research Tips) prior to separation and detection. The method was fully validated and found to be fit for purpose considering the parameters specificity, linearity, recovery (19-37%), lower limit of detection (<50 pg/mL), imprecision (<20%), and ion suppression/enhancement effects. The analytes' stability and metabolism were elucidated using in vitro and in vivo approaches. EDTA blood samples were collected from rats 2, 4, and 8 h after intravenous administration of GHRH (one compound per test animal). All intact substances were detected for at least 4 h but no anticipated metabolite was confirmed in laboratory rodents' samples; conversely, a Geref metabolite (GHRH3-29) was found in a human plasma sample collected after subcutaneous injection of the drug to a healthy male volunteer. The obtained results demonstrate that GHRHs are successfully detected in plasma using an immunoaffinity-mass spectrometry-based method, which can be applied to sports drug testing samples. Further studies are however required and warranted to account for potential species-related differences in metabolism and elimination of the target analytes.

Accuracy and Reproducibility in Quantification of Plasma Protein Concentrations by Mass Spectrometry without the Use of Isotopic Standards

Background

Quantitative proteomic analysis with mass spectrometry holds great promise for simultaneously quantifying proteins in various biosamples, such as human plasma. Thus far, studies addressing the reproducible measurement of endogenous protein concentrations in human plasma have focussed on targeted analyses employing isotopically labelled standards. Non-targeted proteomics, on the other hand, has been less employed to this end, even though it has been instrumental in discovery proteomics, generating large datasets in multiple fields of research.

Results

Using a non-targeted mass spectrometric assay (LCMS^E), we quantified abundant plasma proteins (43 mg/mL—40 ug/mL range) in human blood plasma specimens from 30 healthy volunteers and one blood serum sample (ProteomeXchange: PXD000347). Quantitative results were obtained by label-free mass spectrometry using a single internal standard to estimate protein concentrations. This approach resulted in quantitative results for 59 proteins (cut off ≥11 samples quantified) of which 41 proteins were quantified in all 31 samples and 23 of these with an inter-assay variability of ≤ 20%. Results for 7 apolipoproteins were compared with those obtained using isotope-labelled standards, while 12 proteins were compared to routine immunoassays. Comparison of quantitative data obtained by LCMS^E and immunoassays showed good to excellent correlations in relative protein abundance (r = 0.72–0.96) and comparable median concentrations for 8 out of 12 proteins tested. Plasma concentrations of 56 proteins determined by LCMS^E were of similar accuracy as those reported by targeted studies and 7 apolipoproteins quantified by isotope-labelled standards, when compared to reference concentrations from literature.

Conclusions

This study shows that LCMS^E offers good quantification of relative abundance as well as reasonable estimations of concentrations of abundant plasma proteins.

Urine biomarkers for necrotizing enterocolitis

Necrotizing enterocolitis (NEC) remains a significant cause of morbidity and mortality in premature neonates. Despite decades of investigation, treating clinicians are still not able to determine which premature infants are at greatest risk of developing NEC and which of the affected infants will develop severe NEC requiring operation. A biomarker is a specific molecular indicator that can be used to identify or measure the progress of a disease. Many potential biomarkers have been studied for their potential relevance to NEC. Those showing promise include C-reactive protein, intestinal fatty acid-binding protein, platelet-activating factor and many others. None to date have achieved sufficient predictive value to be clinically useful. Distinguishing between the specific changes in NEC and the non-specific inflammation of sepsis has proven challenging. Urine is a particularly attractive site for potential biomarkers. It can be collected readily and non-invasively, and it is a rich source of both proteins and peptides. Preliminary work has revealed some promising biomarkers of NEC in urine. Combined with clinical data, they have been shown to be highly predictive in small series of patients. Advances in high-throughput molecular analysis have opened the door to finding biomarkers that may meaningfully improve the outcome of infants at risk for NEC.

Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics

In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Biomarker Characterization by MALDI-TOF/MS

Mass spectrometric techniques frequently used in clinical diagnosis, such as gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, ambient ionization mass spectrometry, and matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF/MS), are discussed. Due to its ability to rapidly detect large biomolecules in trace amounts, MALDI-TOF/MS is an ideal tool for characterizing disease biomarkers in biologic samples. Clinical applications of MS for the identification and characterization of microorganisms, DNA fragments, tissues, and biofluids are introduced. Approaches for using MALDI-TOF/MS to detect various disease biomarkers including peptides, proteins, and lipids in biological fluids are further discussed. Finally, various sample pretreatment methods which improve the detection efficiency of disease biomarkers are introduced.

Current trends in magnetic particle enrichment for mass spectrometry-based analysis of cardiovascular protein biomarkers

Magnetic particles have traditionally been utilized to isolate and enrich various cardiovascular protein biomarkers for mass spectrometry-based proteomic analysis. The application of functionalized magnetic particles for immunocapture is attractive due to their easy manipulation, large surface area-to-volume ratios for maximal antibody binding, good recovery and high magnetic saturation. Magnetic particle enrichment coupled with mass spectrometry can act as a complementary tool for clinical sandwich-immunoassay development since it can provide improved target specificity and true metrological traceability. The purpose of this review is to summarize current separation methods and technologies that use magnetic particles to enrich protein biomarkers from complex matrices, specifically focusing on cardiovascular disease-related proteins and the advantages of magnetic particles over existing techniques.

Bioanalytical chemistry of cytokines--a review

Cytokines are bioactive proteins produced by many different cells of the immune system. Due to their role in different inflammatory disease states and maintaining homeostasis, there is enormous clinical interest in the quantitation of cytokines. The typical standard methods for quantitation of cytokines are immunoassay-based techniques including enzyme-linked immusorbent assays (ELISA) and bead-based immunoassays read by either standard or modified flow cytometers. A review of recent developments in analytical methods for measurements of cytokine proteins is provided. This review briefly covers cytokine biology and the analysis challenges associated with measurement of these biomarker proteins for understanding both health and disease. New techniques applied to immunoassay-based assays are presented along with the uses of aptamers, electrochemistry, mass spectrometry, optical resonator-based methods. Methods used for elucidating the release of cytokines from single cells as well as in vivo collection methods are described.

A new sample preparation method for the absolute quantitation of a target proteome using 18O labeling combined with multiple reaction monitoring mass spectrometry

A new sample preparation method for target proteome absolute quantitation using ¹⁸O labeling-MRM MS.

Targeted discovery and validation of plasma biomarkers of Parkinson's disease

Despite extensive research, an unmet need remains for protein biomarkers of Parkinson's disease (PD) in peripheral body fluids, especially blood, which is easily accessible clinically. The discovery of such biomarkers is challenging, however, due to the enormous complexity and huge dynamic range of human blood proteins, which are derived from nearly all organ systems, with those originating specifically from the central nervous system (CNS) being exceptionally low in abundance. In this investigation of a relatively large cohort (∼300 subjects), selected reaction monitoring (SRM) assays (a targeted approach) were used to probe plasma peptides derived from glycoproteins previously found to be altered in the CNS based on PD diagnosis or severity. Next, the detected peptides were interrogated for their diagnostic sensitivity and specificity as well as the correlation with PD severity, as determined by the Unified Parkinson's Disease Rating Scale (UPDRS). The results revealed that 12 of the 50 candidate glycopeptides were reliably and consistently identified in plasma samples, with three of them displaying significant differences among diagnostic groups. A combination of four peptides (derived from PRNP, HSPG2, MEGF8, and NCAM1) provided an overall area under curve (AUC) of 0.753 (sensitivity: 90.4%; specificity: 50.0%). Additionally, combining two peptides (derived from MEGF8 and ICAM1) yielded significant correlation with PD severity, that is, UPDRS (r = 0.293, p = 0.004). The significance of these results is at least two-fold: (1) it is possible to use a targeted approach to identify otherwise very difficult to detect CNS related biomarkers in peripheral blood and (2) the novel biomarkers, if validated in independent cohorts, can be employed to assist with clinical diagnosis of PD as well as monitoring disease progression.