Clinical proteomics: A need to define the field and to begin to set adequate standards

Technical report: The clinically useful selection of proteins protocol: An approach to identify clinically useful proteins for validation

Clinical proteomics studies aiming to develop markers of clinical outcome or disease typically involve distinct discovery and validation stages, neither of which focus on the clinical applicability of the candidate markers studied. Our clinically useful selection of proteins (CUSP) protocol proposes a rational approach, with statistical and non-statistical components, to identify proteins for the validation phase of studies that could be most effective markers of disease or clinical outcome. Additionally, this protocol considers commercially available analysis methods for each selected protein to ensure that use of this prospective marker is easily translated into clinical practice. SIGNIFICANCE: When developing proteomic markers of clinical outcomes, there is currently no consideration at the validation stage of how to implement such markers into a clinical setting. This has been identified by several studies as a limitation to the progression of research findings from proteomics studies. When integrated into a proteomic workflow, the CUSP protocol allows for a strategically designed validation study that improves researchers' abilities to translate research findings from discovery-based proteomics into clinical practice.

Biomarkers for assessing pain and pain relief in the neonatal intensive care unit

Newborns admitted to the neonatal intensive care unit (NICU) regularly undergo painful procedures and may face various painful conditions such as postoperative pain. Optimal management of pain in these vulnerable preterm and term born neonates is crucial to ensure their comfort and prevent negative consequences of neonatal pain. This entails accurate and timely identification of pain, non-pharmacological pain treatment and if needed administration of analgesic therapy, evaluation of treatment effectiveness, and monitoring of adverse effects. Despite the widely recognized importance of pain management, pain assessment in neonates has thus far proven to be a challenge. As self-report, the gold standard for pain assessment, is not possible in neonates, other methods are needed. Several observational pain scales have been developed, but these often rely on snapshot and largely subjective observations and may fail to capture pain in certain conditions. Incorporation of biomarkers alongside observational pain scores holds promise in enhancing pain assessment and, by extension, optimizing pain treatment and neonatal outcomes. This review explores the possibilities of integrating biomarkers in pain assessment in the NICU.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Machine Learning-Based Urine Peptidome Analysis to Predict and Understand Mechanisms of Progression to Kidney Failure

Introduction

The identification of patients with chronic kidney disease (CKD) at risk of progressing to kidney failure (KF) is important for clinical decision-making. In this study we assesed whether urinary peptidome (UP) analysis may help classify patients with CKD and improve KF risk prediction.

Methods

The UP was analyzed using capillary electrophoresis coupled to mass spectrometry in a case-cohort sample of 1000 patients with CKD stage G3 to G5 from the French CKD-Renal Epidemiology and Information Network (REIN) cohort. We used unsupervised and supervised machine learning to classify patients into homogenous UP clusters and to predict 3-year KF risk with UP, respectively. The predictive performance of UP was compared with the KF risk equation (KFRE), and evaluated in an external cohort of 326 patients.

Results

More than 1000 peptides classified patients into 3 clusters with different CKD severities and etiologies at baseline. Peptides with the highest discriminative power for clustering were fragments of proteins involved in inflammation and fibrosis, highlighting those derived from α-1-antitrypsin, a major acute phase protein with anti-inflammatory and antiapoptotic properties, as the most significant. We then identified a set of 90 urinary peptides that predicted KF with a c-index of 0.83 (95% confidence interval [CI]: 0.81-0.85) in the case-cohort and 0.89 (0.83-0.94) in the external cohort, which were close to that estimated with the KFRE (0.85 [0.83-0.87]). Combination of UP with KFRE variables did not further improve prediction.

Conclusion

This study shows the potential of UP analysis to uncover new pathophysiological CKD progression pathways and to predict KF risk with a performance equal to that of the KFRE.

Label-Free Quantification from Direct Infusion Shotgun Proteome Analysis (DISPA-LFQ) with CsoDIAq Software

Large-scale proteome analysis requires rapid and high-throughput analytical methods. We recently reported a new paradigm in proteome analysis where direct infusion and ion mobility are used instead of liquid chromatography (LC) to achieve rapid and high-throughput proteome analysis. Here, we introduce an improved direct infusion shotgun proteome analysis protocol including label-free quantification (DISPA-LFQ) using CsoDIAq software. With CsoDIAq analysis of DISPA data, we can now identify up to ∼2000 proteins from the HeLa and 293T proteomes, and with DISPA-LFQ, we can quantify ∼1000 proteins from no more than 1 μg of sample within minutes. The identified proteins are involved in numerous valuable pathways including central carbon metabolism, nucleic acid replication and transport, protein synthesis, and endocytosis. Together with a high-throughput sample preparation method in a 96-well plate, we further demonstrate the utility of this technology for performing high-throughput drug analysis in human 293T cells. The total time for data collection from a whole 96-well plate is approximately 8 h. We conclude that the DISPA-LFQ strategy presents a valuable tool for fast identification and quantification of proteins in complex mixtures, which will power a high-throughput proteomic era of drug screening, biomarker discovery, and clinical analysis.

Optimization of a Protocol for Protein Extraction from Calcified Aortic Valves for Proteomics Applications: Development of a Standard Operating Procedure

The comprehension of the pathophysiological mechanisms, the identification of druggable targets, and putative biomarkers for aortic valve stenosis can be pursued through holistic approaches such as proteomics. However, tissue homogenization and protein extraction are made difficult by tissue calcification. The reproducibility of proteome studies is key in clinical translation of the findings. Thus, we aimed to optimize a protocol for aortic valve homogenization and protein extraction and to develop a standard operating procedure (SOP), which researchers can use to maximize protein yield while reducing inter-laboratory variability. We have compared the protein yield between conventional tissue grinding in nitrogen followed by homogenization with a Potter apparatus with a more advanced bead-beating system. Once we confirmed the superiority of the latter, we further optimized it by testing the effect of beads size, the number of homogenization cycles, tube capacity, lysis buffer/tissue mass ratio, and two different lysis buffers. Optimal protein extraction was achieved with 2.8 mm zirconium dioxide beads, in two homogenization cycles, in the presence of 20 µL RIPA buffer/mg tissue, using 2 mL O-ring cryotubes. As a proof of concept of the usefulness of this SOP for proteomics, the AV proteome of men and women with aortic stenosis was characterized, resulting in the quantification of proteins across six orders of magnitude and uncovering some putative proteins dysregulated by sex.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.

Quantification of FAM20A in human milk and identification of calcium metabolism proteins

BACKGROUND

FAM20A, a recently discovered protein, is thought to have a fundamental role in inhibiting ectopic calcification. Several studies have demonstrated that variants of FAM20A are causative for the rare autosomal recessive disorder, enamel-renal syndrome (ERS). ERS is characterized by defective mineralization of dental enamel and nephrocalcinosis suggesting that FAM20A is an extracellular matrix protein, dysfunction of which causes calcification of the secretory epithelial tissues. FAM20A is a low-abundant protein that is difficult to detect in biofluids such as blood, saliva, and urine. Thus, we speculated the abundance of FAM20A to be high in human milk, since the secretory epithelium of lactating mammary tissue is involved in the secretion of highly concentrated calcium. Therefore, the primary aim of this research is to describe the processes/methodology taken to quantify FAM20A in human milk and identify other proteins involved in calcium metabolism.

METHOD

This study used mass spectrometry-driven quantitative proteomics: (1) to quantify FAM20A in human milk of three women and (2) to identify proteins associated with calcium regulation by bioinformatic analyses on whole and milk fat globule membrane fractions.

RESULTS

Shotgun MS/MS driven proteomics identified FAM20A in whole milk, and subsequent analysis using targeted proteomics also successfully quantified FAM20A in all samples. Combination of sample preparation, fractionation, and LC-MS/MS proteomics analysis generated 136 proteins previously undiscovered in human milk; 21 of these appear to be associated with calcium metabolism.

CONCLUSION

Using mass spectrometry-driven proteomics, we successfully quantified FAM20A from transitional to mature milk and obtained a list of proteins involved in calcium metabolism. Furthermore, we show the value of using a combination of both shotgun and targeted driven proteomics for the identification of this low abundant protein in human milk.

Extracellular vesicles in kidney transplantation: a state-of-the-art review

Kidney transplantation is the optimal treatment for patients with kidney failure; however, early detection and timely treatment of graft injury remain a challenge. Precise and noninvasive techniques of graft assessment and innovative therapeutics are required to improve kidney transplantation outcomes. Extracellular vesicles (EVs) are lipid bilayer-delimited particles with unique biosignatures and immunomodulatory potential, functioning as intermediaries of cell signalling. Promising evidence exists for the potential of EVs to develop precision diagnostics of graft dysfunction, and prognostic biomarkers for clinician decision making. The inherent targeting characteristics of EVs and their low immunogenic and toxicity profiles combined with their potential as vehicles for drug delivery make them ideal targets for development of therapeutics to improve kidney transplant outcomes. In this review, we summarize the current evidence for EVs in kidney transplantation, discuss common methodological principles of EV isolation and characterization, explore upcoming innovative approaches in EV research, and discuss challenges and opportunities to enable translation of research findings into clinical practice.

Personalized Proteomics for Precision Diagnostics in Hearing Loss: Disease-Specific Analysis of Human Perilymph by Mass Spectrometry

Despite a vast amount of data generated by proteomic analysis on cochlear fluid, novel clinically applicable biomarkers of inner ear diseases have not been identified hitherto. The aim of the present study was to analyze the proteome of human perilymph from cochlear implant patients, thereby identifying putative changes of the composition of the cochlear fluid perilymph due to specific diseases. Sampling of human perilymph was performed during cochlear implantation from patients with clinically or radiologically defined inner ear diseases like enlarged vestibular aqueduct (EVA; n = 14), otosclerosis (n = 10), and Ménière's disease (n = 12). Individual proteins were identified by a shotgun proteomics approach and data-dependent acquisition, thereby revealing 895 different proteins in all samples. Based on quantification values, a disease-specific protein distribution in the perilymph was demonstrated. The proteins short-chain dehydrogenase/reductase family 9C member 7 and esterase D were detected in nearly all samples of Ménière's disease patients, but not in samples of patients suffering from EVA and otosclerosis. The presence of both proteins in the inner ear tissue of adult mice and neonatal rats was validated by immunohistochemistry. Whether these proteins have the potential for a biomarker in the perilymph of Ménière's disease patients remains to be elucidated.

Preexisting hypertension and pregnancy-induced hypertension reveal molecular differences in placental proteome in rodents

Preexisting or new onset of hypertension affects pregnancy and is one of the leading causes of maternal and fetal morbidity and mortality. In certain cases, it also leads to long-term maternal cardiovascular complications. The placenta is a key player in the pathogenesis of complicated hypertensive pregnancies, however the pathomechanisms leading to an abnormal placenta are poorly understood. In this study, we compared the placental proteome of two pregnant hypertensive models with their corresponding normotensive controls: a preexisting hypertension pregnancy model (stroke-prone spontaneously hypertensive rats; SHRSP) versus Wistar-Kyoto and the transgenic RAS activated gestational hypertension model (transgenic for human angiotensinogen Sprague-Dawley rats; SD-PE) versus Sprague-Dawley rats, respectively. Label-free proteomics using nano LC-MS/MS was performed for identification and quantification of proteins. Between the two models, we found widespread differences in the expression of placental proteins including those related to hypertension, inflammation, and trophoblast invasion, whereas pathways such as regulation of serine endopeptidase activity, tissue injury response, coagulation, and complement activation were enriched in both models. We present for the first time the placental proteome of SHRSP and SD-PE and provide insight into the molecular make-up of models of hypertensive pregnancy. Our study informs future research into specific preeclampsia and chronic hypertension pregnancy mechanisms and translation of rodent data to the clinic.

What Room for Two-Dimensional Gel-Based Proteomics in a Shotgun Proteomics World?

Two-dimensional gel electrophoresis was instrumental in the birth of proteomics in the late 1980s. However, it is now often considered as an outdated technique for proteomics—a thing of the past. Although this opinion may be true for some biological questions, e.g., when analysis depth is of critical importance, for many others, two-dimensional gel electrophoresis-based proteomics still has a lot to offer. This is because of its robustness, its ability to separate proteoforms, and its easy interface with many powerful biochemistry techniques (including western blotting). This paper reviews where and why two-dimensional gel electrophoresis-based proteomics can still be profitably used. It emerges that, rather than being a thing of the past, two-dimensional gel electrophoresis-based proteomics is still highly valuable for many studies. Thus, its use cannot be dismissed on simple fashion arguments and, as usual, in science, the tree is to be judged by the fruit.

Proteomics for hematopoietic stem cell transplantation

Introduction: After the genomic era, the analysis of the proteome has gained increasing importance. Peptides and/or proteins present in tissue or body fluids can depict health and are prone to change during disease, not only in configuration but also in abundance. Early on, high throughput proteome analysis was implemented in the diagnostic of therapy-linked or induced complications arising after allogeneic hematopoietic stem cell transplantation (HSCT). Several proteomic approaches are currently used in the prediction or diagnosis of acute and/or chronic graft-versus-host disease (GvHD).Areas covered: This review will report on two high throughput proteomics technologies used in the clinical setting to date, namely enzyme-linked-immunosorbent assays (ELISA) for key proteins involved in the pathogenesis of acute GvHD and on capillary electrophoresis coupled on-line to mass spectrometry (CE-MS). Here, we summarize the current data and discuss the strength as well as the limitations of each method and compare the usefulness and practicability in the post-HSCT setting for prediction and diagnosis of acute GvHD.Expert commentary: Both technologies are applied in the clinic and have been tested on several hundred patients after HSCT. The data from both technologies may complement each other in diagnosis of GvHD.

The Human Ovary and Future of Fertility Assessment in the Post-Genome Era

Proteomics has opened up new avenues in the field of gynecology in the post-genome era, making it possible to meet patient needs more effectively and improve their care. This mini-review aims to reveal the scope of proteomic applications through an overview of the technique and its applications in assisted procreation. Some of the latest technologies in this field are described in order to better understand the perspectives of its clinical applications. Proteomics seems destined for a promising future in gynecology, more particularly in relation to the ovary. Nevertheless, we know that reproductive biology proteomics is still in its infancy and major technical and ethical challenges must first be overcome.

Urinary peptidome analyses for the diagnosis of chronic kidney disease in dogs

Chronic kidney disease (CKD) is clinically important in canine medicine. Current diagnostic tools lack sensitivity for detection of subclinical CKD. The aim of the present study was to evaluate urinary peptidome analysis for diagnosis of CKD in dogs. Capillary electrophoresis coupled to mass spectrometry analysis demonstrated presence of approximately 5400 peptides in dog urine. Comparison of urinary peptide abundance of dogs with and without CKD led to the identification of 133 differentially excreted peptides (adjusted P for each peptide <0.05). Sequence information was obtained for 35 of these peptides. This 35 peptide subset and the total group of 133 peptides were used to construct two predictive models of CKD which were subsequently validated by researchers masked to results in an independent cohort of 20 dogs. Both models diagnosed CKD with an area under the receiver operating characteristic (ROC) curve of 0.88 (95% confidence intervals [CI], 0.72-1.0). Most differentially excreted peptides represented fragments of collagen I, indicating possible association with fibrotic processes in CKD (similar to the equivalent human urinary peptide CKD model, CKD273). This first study of the urinary peptidome in dogs identified peptides that were associated with presence of CKD. Future studies are needed to validate the utility of this model for diagnosis and prediction of progression of canine CKD in a clinical setting.

Clinical Usefulness of Proteomics in Inflammatory Bowel Disease: A Comprehensive Review

The protein domain is probably the most ubiquitously affected in disease, response and recovery, and therefore proteomics holds special promise for biomarker discovery in general, and particularly in inflammatory bowel disease [IBD], i.e. ulcerative colitis and Crohn's disease. Tremendous progress has been made over the past decade in the development and refinement of proteomics technologies. These advances provide opportunities for a long-anticipated personalized medicine approach to the treatment of IBD. The present review examines the current state of IBD proteomics research and its usefulness in clinical practice. We performed a systematic bibliographic search to identify studies investigating the use of proteomics in patients with IBD, and we then summarized the current 'state of the art' in the applications of proteomic technologies in the study of IBD. In particular, in the present review we provide: [1] a brief introduction to proteomics in health and disease; [2] a review of the different stages from biomarker discovery to clinical application; and [3] a comprehensive review of the clinical usefulness and application of proteomics in IBD, including: [a] screening to differentiate IBD from healthy controls; [b] differentiating Crohn's disease from ulcerative colitis; [c] prediction of the behaviour or the IBD course; [d] prediction of IBD response to biological treatment; and [e] monitoring response to treatment. We also review the importance of the type of sample-blood vs intestinal tissue-for the study of proteomics in IBD patients. Finally, we emphasize the current limitations of proteomic studies in IBD.

Urinary proteome signature of Renal Cysts and Diabetes syndrome in children

Renal Cysts and Diabetes Syndrome (RCAD) is an autosomal dominant disorder caused by mutations in the HNF1B gene encoding for the transcriptional factor hepatocyte nuclear factor-1B. RCAD is characterized as a multi-organ disease, with a broad spectrum of symptoms including kidney abnormalities (renal cysts, renal hypodysplasia, single kidney, horseshoe kidneys, hydronephrosis), early-onset diabetes mellitus, abnormal liver function, pancreatic hypoplasia and genital tract malformations. In the present study, using capillary electrophoresis coupled to mass spectrometry (CE-MS), we investigated the urinary proteome of a pediatric cohort of RCAD patients and different controls to identify peptide biomarkers and obtain further insights into the pathophysiology of this disorder. As a result, 146 peptides were found to be associated with RCAD in 22 pediatric patients when compared to 22 healthy age-matched controls. A classifier based on these peptides was generated and further tested on an independent cohort, clearly discriminating RCAD patients from different groups of controls. This study demonstrates that the urinary proteome of pediatric RCAD patients differs from autosomal dominant polycystic kidney disease (PKD1, PKD2), congenital nephrotic syndrome (NPHS1, NPHS2, NPHS4, NPHS9) as well as from chronic kidney disease conditions, suggesting differences between the pathophysiology behind these disorders.

Using matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling in order to predict clinical outcomes of patients with heart failure

BACKGROUND

Current risk prediction models in heart failure (HF) including clinical characteristics and biomarkers only have moderate predictive value. The aim of this study was to use matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) profiling to determine if a combination of peptides identified with MALDI-MS will better predict clinical outcomes of patients with HF.

METHODS

A cohort of 100 patients with HF were recruited in the biomarker discovery phase (50 patients who died or had a HF hospital admission vs. 50 patients who did not have an event). The peptide extraction from plasma samples was performed using reversed phase C18. Then samples were analysed using MALDI-MS. A multiple peptide biomarker model was discovered that was able to predict clinical outcomes for patients with HF. Finally, this model was validated in an independent cohort with 100 patients with HF.

RESULTS

After normalisation and alignment of all the processed spectra, a total of 11,389 peptides (m/z) were detected using MALDI-MS. A multiple biomarker model was developed from 14 plasma peptides that was able to predict clinical outcomes in HF patients with an area under the receiver operating characteristic curve (AUC) of 1.000 (p = 0.0005). This model was validated in an independent cohort with 100 HF patients that yielded an AUC of 0.817 (p = 0.0005) in the biomarker validation phase. Addition of this model to the BIOSTAT risk prediction model increased the predictive probability for clinical outcomes of HF from an AUC value of 0.643 to an AUC of 0.823 (p = 0.0021). Moreover, using the prediction model of fourteen peptides and the composite model of the multiple biomarker of fourteen peptides with the BIOSTAT risk prediction model achieved a better predictive probability of time-to-event in prediction of clinical events in patients with HF (p = 0.0005).

CONCLUSIONS

The results obtained in this study suggest that a cluster of plasma peptides using MALDI-MS can reliably predict clinical outcomes in HF that may help enable precision medicine in HF.

Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those because of collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality because of the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality because of both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments.

Clinical Proteomics: Closing the Gap from Discovery to Implementation

Clinical proteomics, the application of proteome analysis to serve a clinical purpose, represents a major field in the area of proteome research. Over 1000 manuscripts on this topic are published each year, with numbers continuously increasing. However, the anticipated outcome, the transformation of the reported findings into improvements in patient management, is not immediately evident. In this article, the value and validity of selected clinical proteomics findings are investigated, and it is assessed how far implementation has progressed. A main conclusion from this assessment is that to achieve implementation, well-powered clinical studies are required in the appropriate population, addressing a specific clinical need and with a clear context-of-use. Efforts toward implementation, to be feasible, must be supported by the key players in science: publishers and funders. The authors propose a change on objectives, from additional discovery studies toward studies aiming at validation of the plethora of potential biomarkers that have been described, to demonstrate practical value of clinical proteomics. All elements required, potential biomarkers, technologies, and bio-banked samples are available (based on today's literature), hence a change in focus from discovery toward validation and application is not only urgently necessary, but also possible based on resources available today.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

Background

In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease.

Methods

We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results

For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology.

Conclusions

Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

Combination of the fetal urinary metabolome and peptidome for the prediction of postnatal renal outcome in fetuses with PUV

Most of biomarker panels, extracted from single omics traits, still need improvement since they display a gray zone where prediction is uncertain. Here we verified whether a combination of omics traits, fetal urinary metabolites and peptides analyzed in the same sample, improved prediction of postnatal renal function in fetuses with posterior urethral valves (PUV) compared to individual omics traits. Using CE-MS, we explored the urinary metabolome of 13 PUV fetuses with end stage renal disease (ESRD) and 12 PUV fetuses without postnatal ESRD at 2 years postnatally. This allowed the selection of 24 differentially abundant metabolite features which were modelled into predictive classifiers, alone or in combination with 12 peptides previously identified as predictive of ESRD. Validation in 35 new fetuses showed that the combination of peptides and metabolites significantly outperformed the 24 metabolite features with increased AUC (0.987 vs 0.905), net reclassification improvement (36%) and better sensitivity accuracy (86% vs 60%). In addition, the two trait combination tended to improve, but without reaching statistical significance, the already high performances of the 12 peptide biomarkers (AUC 0.967, accuracy 80%). In conclusion, this study demonstrates the potential of cumulating different omics traits in biomarker research where single omics traits fall short.

SIGNIFICANCE

Although increasingly proposed in disease-diagnosis and -prognosis because of their improved efficacy over single markers, panels of body fluid biomarkers based on single omics analysis still fail to display perfect accuracy, probably due to biological variability. Here, we hypothesized that combination of different omics traits allowed to better capture this biological variability. As proof of concept, we studied the added value of fetal urine metabolites and peptides using CE-MS, starting from the same urine sample, to predict postnatal renal outcome in fetuses with posterior urethral valves. We observed that the prognostic power of combined metabolite and peptide markers was clearly higher than that of metabolites alone and slightly, but non-significantly, improved compared to the peptides alone. To our knowledge, this report is the first to demonstrate that combining multiomics traits extracted from (fetal) urine samples displays clear promise for kidney disease stratification.

IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts

Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.

The Impact of Pre-Analytical Conditions on Human Serum Peptidome Profiling

The successful use of proteomic technology for the discovery of clinically relevant, new candidate biomarkers, especially in the low molecular weight range (peptidome), calls for a careful consideration of standardized operating procedures (SOP) for pre-analytical variables, including samples handling and storage. The current lack of standardization, widely considered a relevant source of random and systematic errors, underlies the uncertainty of analytical results and poor comparability, especially in multi-centric or inter-laboratory studies. In their recent study, Tsuchida et al. used the MALDI-TOF/MS technique to investigate the effect of long-term storage at -20 °C, -80 °C, and in liquid nitrogen on serum samples obtained for peptidomic analyses. The authors have also evaluated the effects of different sample thawing modalities. By including results from the same series as that reported on in a previous publication, they have effectively defined some important requirements for the peptidomic analysis of serum samples (e.g., maximum time intervals between venepuncture and serum separation [1 h], minimum temperature for long-term sera storage temperature [-80 °C], ideal conditions for sample thawing).

Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

PURPOSE OF REVIEW

Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years.

RECENT FINDINGS

Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, α-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression.

SUMMARY

Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making.

Proteomic effects of wet cupping (Al-hijamah)

Wet cupping (Al-hijamah) is a therapeutic technique practiced worldwide as a part of the Unani system of medicine. It involves bloodletting from acupoints on a patient’s skin to produce a therapeutic outcome. A thorough review of research articles on wet cupping with relevance to proteomics field that are indexed by Google Scholar, PubMed, and/or Science Direct databases was performed. Eight original research articles were summarized in this paper. Overall, wet cupping did not have a significant effect on C-reactive protein, Hsp-27, sister chromatid exchanges, and cell replication index. In contrast, wet cupping was found to produce higher oxygen saturation, eliminate lactate from subcutaneous tissues, remove blood containing higher levels of malondialdehyde and nitric oxide, and produce higher activity of myeloperoxidase. The proteomic effects of wet cupping therapy have not been adequately investigated. Thus, future studies on wet cupping that use systemic and sound protocols to avoid bias should be conducted.

Quantification of Cardiovascular Disease Biomarkers in Human Platelets by Targeted Mass Spectrometry

Platelets are known to be key players in thrombosis and hemostasis, contributing to the genesis and progression of cardiovascular diseases. Due to their pivotal role in human physiology and pathology, platelet function is regulated tightly by numerous factors which have either stimulatory or inhibitory effects. A variety of factors, e.g., collagen, fibrinogen, ADP, vWF, thrombin, and thromboxane promote platelet adhesion and aggregation by utilizing multiple intracellular signal cascades. To quantify platelet proteins for this work, a targeted proteomics workflow was applied. In detail, platelets are isolated and lyzed, followed by a tryptic protein digest. Subsequently, a mix of stable isotope-labeled peptides of interesting biomarker proteins in concentrations ranging from 0.1 to 100 fmol is added to 3 μg digest. These peptides are used as an internal calibration curve to accurately quantify endogenous peptides and corresponding proteins in a pooled platelet reference sample by nanoLC-MS/MS with parallel reaction monitoring. In order to assure a valid quantification, limit of detection (LOD) and limit of quantification (LOQ), as well as linear range, were determined. This quantification of platelet activation and proteins by targeted mass spectrometry may enable novel diagnostic strategies in the detection and prevention of cardiovascular diseases.

Identification of Differentially Expressed Splice Variants by the Proteogenomic Pipeline Splicify

Proteogenomics, i.e. comprehensive integration of genomics and proteomics data, is a powerful approach identifying novel protein biomarkers. This is especially the case for proteins that differ structurally between disease and control conditions. As tumor development is associated with aberrant splicing, we focus on this rich source of cancer specific biomarkers. To this end, we developed a proteogenomic pipeline, Splicify, which can detect differentially expressed protein isoforms. Splicify is based on integrating RNA massive parallel sequencing data and tandem mass spectrometry proteomics data to identify protein isoforms resulting from differential splicing between two conditions. Proof of concept was obtained by applying Splicify to RNA sequencing and mass spectrometry data obtained from colorectal cancer cell line SW480, before and after siRNA-mediated downmodulation of the splicing factors SF3B1 and SRSF1. These analyses revealed 2172 and 149 differentially expressed isoforms, respectively, with peptide confirmation upon knock-down of SF3B1 and SRSF1 compared with their controls. Splice variants identified included RAC1, OSBPL3, MKI67, and SYK. One additional sample was analyzed by PacBio Iso-Seq full-length transcript sequencing after SF3B1 downmodulation. This analysis verified the alternative splicing identified by Splicify and in addition identified novel splicing events that were not represented in the human reference genome annotation. Therefore, Splicify offers a validated proteogenomic data analysis pipeline for identification of disease specific protein biomarkers resulting from mRNA alternative splicing. Splicify is publicly available on GitHub (https://github.com/NKI-TGO/SPLICIFY) and suitable to address basic research questions using pre-clinical model systems as well as translational research questions using patient-derived samples, e.g. allowing to identify clinically relevant biomarkers.

Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease

Capillary electrophoresis coupled with mass spectrometry (CE-MS) has been used as a platform for discovery and validation of urinary peptides associated with chronic kidney disease (CKD). CKD affects ∼ 10% of the population, with high associated costs for treatments. A urinary proteome-based classifier (CKD273) has been discovered and validated in cross-sectional and longitudinal studies to assess and predict the progression of CKD. It has been implemented in studies employing cohorts of > 1000 patients. CKD273 is commercially available as an in vitro diagnostic test for early detection of CKD and is currently being used for patient stratification in a multicentre randomized clinical trial (PRIORITY). The validity of the CKD273 classifier has recently been evaluated applying the Oxford Evidence-Based Medicine and Southampton Oxford Retrieval Team guidelines and a letter of support for CKD273 was issued by the US Food and Drug Administration. In this article we review the current evidence published on CKD273 and the challenges associated with implementation. Definition of a possible surrogate early endpoint combined with CKD273 as a biomarker for patient stratification currently appears as the most promising strategy to enable the development of effective drugs to be used at an early time point when intervention can still be effective.

A capillary electrophoresis coupled to mass spectrometry pipeline for long term comparable assessment of the urinary metabolome

Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.

Personalized Proteomics: The Future of Precision Medicine

Medical diagnostics and treatment has advanced from a one size fits all science to treatment of the patient as a unique individual. Currently, this is limited solely to genetic analysis. However, epigenetic, transcriptional, proteomic, posttranslational modifications, metabolic, and environmental factors influence a patient’s response to disease and treatment. As more analytical and diagnostic techniques are incorporated into medical practice, the personalized medicine initiative transitions to precision medicine giving a holistic view of the patient’s condition. The high accuracy and sensitivity of mass spectrometric analysis of proteomes is well suited for the incorporation of proteomics into precision medicine. This review begins with an overview of the advance to precision medicine and the current state of the art in technology and instrumentation for mass spectrometry analysis. Thereafter, it focuses on the benefits and potential uses for personalized proteomic analysis in the diagnostic and treatment of individual patients. In conclusion, it calls for a synthesis between basic science and clinical researchers with practicing clinicians to design proteomic studies to generate meaningful and applicable translational medicine. As clinical proteomics is just beginning to come out of its infancy, this overview is provided for the new initiate.

A method for identifying discriminative isoform-specific peptides for clinical proteomics application

Background

Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer.

However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids.

Results

Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis.

Conclusions

Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2907-8) contains supplementary material, which is available to authorized users.

Urine proteome analysis as a discovery tool in patients with deep vein thrombosis and pulmonary embolism

PURPOSE

Early and accurate detection of deep vein thrombosis (DVT) is an important clinical need. Based on the hypothesis that urinary peptides may hold information on DVT in conjunction with pulmonary embolism (PE), the study was aimed at identifying such peptide biomarkers using capillary electrophoresis coupled mass spectrometry.

EXPERIMENTAL DESIGN

Patients with symptoms of unprovoked/idiopathic DVT and/or PE were examined by doppler-sonography or angio-computed tomography. Urinary proteome analysis allowed for identification of respective peptide biomarkers. To confirm their biological relevance, we induced PE in mice and assessed human ex vivo thrombi.

RESULTS

We identified 62 urinary peptides as DVT-specific biomarkers, i.e. fragments of collagen type I and a fragment of fibrinogen β-chain. The presence of fibrinogen α/β in the acute thrombus, and collagen type I and osteopontin in the older, organized thrombus was demonstrated. The classifier DVT62 established through support vector machine (SVM) modeling based on the 62 identified peptides was validated in an independent cohort of 47 subjects (six cases and 41 controls) with a sensitivity of 100% and specificity of 83%.

CONCLUSIONS AND CLINICAL RELEVANCE

Urine proteome analysis enabled the detection of DVT-specific peptides, which were validated in human and mouse tissue. Furthermore, it allowed for the establishment of an urinary-proteome based classifier that is relatively specific for DVT. The data provide the basis for assessment of these biomarkers in a prospective clinical study.

Body fluid peptide and protein signatures in diabetic kidney diseases

Body fluid protein-based biomarkers carry the hope of improving patient management in diabetes by enabling more accurate and earlier detection of diabetic kidney disease (DKD), but also of defining the most suitable therapeutic targets. We present the data on some of the best studied individual protein markers in body fluids and conclude that their potential in clinical application for assessing DKD is moderate. Proteome-based approaches aiming at the identification of panels of body fluid biomarkers might be a valid alternative. We discuss the past (first) clinical proteomics studies in DKD, stressing their drawbacks but also the lessons that could be learned from them, as well as the more recent studies that have a potential for actual clinical implementation. We also highlight relevant issues and current problems associated with clinical proteomics from discovery towards application, and give suggestions for solutions that may help guiding proteomic studies, thereby removing some of the current hurdles for implementation of potentially beneficial results.

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.

Current application of proteomics in biomarker discovery for inflammatory bowel disease

Recently, the field of proteomics has rapidly expanded in its application towards clinical research with objectives ranging from elucidating disease pathogenesis to discovering clinical biomarkers. As proteins govern and/or reflect underlying cellular processes, the study of proteomics provides an attractive avenue for research as it allows for the rapid identification of protein profiles in a biological sample. Inflammatory bowel disease (IBD) encompasses several heterogeneous and chronic conditions of the gastrointestinal tract. Proteomic technology provides a powerful means of addressing major challenges in IBD today, especially for identifying biomarkers to improve its diagnosis and management. This review will examine the current state of IBD proteomics research and its use in biomarker research. Furthermore, we also discuss the challenges of translating proteomic research into clinically relevant tools. The potential application of this growing field is enormous and is likely to provide significant insights towards improving our future understanding and management of IBD.

Seroprofiling at the Candida albicans protein species level unveils an accurate molecular discriminator for candidemia

Serum antibodies to specific Candida proteins have been reported as potential diagnostic biomarkers for candidemia. However, their diagnostic usefulness at the protein species level has hardly been examined. Using serological proteome analysis, we explored the IgG-antibody responses to Candida albicans protein species in candidemia and control patients. We found that 87 discrete protein species derived from 34 unique proteins were IgG-targets, although only 43 of them were differentially recognized by candidemia and control sera. An increase in the speciation of the immunome, connectivity and modularity of antigenic species co-recognition networks, and heterogeneity of antigenic species recognition patterns was associated with candidemia. IgG antibodies to certain discrete protein species were better predictors of candidemia than those to their corresponding proteins. A molecular discriminator delineated from the combined fingerprints of IgG antibodies to two distinct species of phosphoglycerate kinase and enolase accurately classified candidemia and control patients. These results provide new insight into the anti-Candida IgG-antibody response development in candidemia, and demonstrate that an immunoproteomic signature at the molecular level may be useful for its diagnosis. Our study further highlights the importance of defining pathogen-specific antigens at the chemical and molecular level for their potential application as immunodiagnostic reagents or even vaccine candidates.

Pretransplant urinary proteome analysis does not predict development of chronic kidney disease after liver transplantation

BACKGROUND & AIMS

Chronic kidney disease (CKD) is a common complication after liver transplantation. Kidney biopsies cannot be easily performed before liver transplantation to predict patients at high risk for CKD. The aim of our study was to determine whether pre-, peri- and post-transplant factors, as well as peptides present in preliver transplant urine samples were associated with loss in kidney function at 6 months post-transplantation using proteome analysis.

METHODS

Eighty patients who underwent a liver transplantation and that had pretransplant glomerular filtration rate (GFR) value of ≥60 mL/min/1.73 m² (MDRD) were included in the study.

RESULTS

GFR decreased significantly after transplantation. At month 6 post-transplantation, 40 patients displayed a CKD, i.e. eGFR of <60 mL/min/1.73 m², while the other 40 patients did not. Although thousands of peptides were identified, none was significantly associated with the development of CKD at 6 months after liver transplantation. Moreover, using a urinary peptidome classifier to detect preexisting CKD, no difference was found in CKD scores between the 2 groups. After analysis of a large number of pre-, peri- and post-transplant parameters, viral hepatitis as a cause for liver transplantation was the sole independent predictive factor for CKD. No difference in peptides with differential urinary abundance between patients who received a graft for virus related liver disease vs. all other causes of liver disease was observed.

CONCLUSION

Urinary peptidome analysis before liver transplantation failed to identify a peptide pattern associated with the development of CKD at 6 months after liver transplantation.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Can proteomics-based diagnostics aid clinical psychiatry?

Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.

Identifying novel biomarkers through data mining-a realistic scenario?

In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.