Mass spectrometry-based biomarker discovery: toward a global proteome index of individuality

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

BACKGROUND

The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes.

METHODS

For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles. For in-depth analysis, we used immunogenomic sequencing to study the B cell repertoire of a subgroup of patients and an in vitro assay using primary human muscle cells to interrogate serum-induced complement formation.

FINDINGS

This strategy identified four distinct patient phenotypes based on their proteomic patterns in their serum. Notably, one patient phenotype, here named PS3, was characterised by high disease severity and complement activation as defining features. Assessing a subgroup of patients, hyperexpanded antibody clones were present in the B cell repertoire of the PS3 group and effectively activated complement as compared to other patients. In line with their disease phenotype, PS3 patients were more likely to benefit from complement-inhibiting therapies. These findings were validated in a prospective cohort of 18 patients using a cell-based assay.

INTERPRETATION

Collectively, this study suggests proteomics-based clustering as a gateway to assign patients to a biological signature likely to benefit from complement inhibition and provides a stratification strategy for clinical practice.

FUNDING

CN and CBS were supported by the Forschungskommission of the Medical Faculty of the Heinrich Heine University Düsseldorf. CN was supported by the Else Kröner-Fresenius-Stiftung (EKEA.38). CBS was supported by the Deutsche Forschungsgemeinschaft (DFG-German Research Foundation) with a Walter Benjamin fellowship (project 539363086). The project was supported by the Ministry of Culture and Science of North Rhine-Westphalia (MODS, "Profilbildung 2020" [grant no. PROFILNRW-2020-107-A]).

Paradigm shift in biomarker translation: a pipeline to generate clinical grade biomarker candidates from DIA-MS discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package. This innovative tool uses the discovery cohort analysis to select precursors, peptides, and proteins that adhere to established targeted assay criteria. TEAQ was applied to Data-Independent Acquisition MS data from plasma samples acquired on an Orbitrap™ Astral™ MS. Identified precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation from 11-point loading curves under three throughputs, to develop a resource for clinical-grade targeted assays. From a clinical cohort of individuals with inflammatory bowel disease (n=492), TEAQ successfully identified 1116 signature peptides for 327 quantifiable proteins from 1180 identified proteins. Embedding stringent selection criteria adaptable to targeted assay development into the analysis of discovery data will streamline the transition to validation and clinical studies.

Diatomite-Based, Flexible SERS Immunosensor Platform for Rapid, Specific, and Sensitive Detection of Circulating Cancer-Specific Protein Biomarkers in Serum Using Raman Probes

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented. In this approach, diatomite/AgNPs strips with maximum SERS activity prepared using the layer-by-layer (LbL) technique were modified with specific antibodies, and specific antigens (HER2, CA15-3, PSA, and MUC4) were captured and detected. By using Raman probes specific to the captured antigens in serum, a SERS limit of detection (LOD) of 0.1 ng/mL was measured (calculated LOD < 0.1 ng/mL). This value is lower than the cutoff amount of cancer antigens in the person's blood. The specificity for the antigens of each antibody was calculated to be higher than 95%. As a result, an immunosensor for rapid detection of cancer biomarkers in serum with good specificity, high sensitivity, good reproducibility, and low cost has been demonstrated. Overall, we show that the prepared diatomite-based SERS substrate with a high surface-to-volume ratio is a useable platform for immunoassay tests.

A pendulum-type electrochemical aptamer-based sensor for continuous, real-time and stable detection of proteins

Continuous detection of proteins is crucial for health management and biomedical research. Electrochemical aptamer-based (E-AB) sensor that relies on binding affinity between a recognition oligonucleotide and its specific target is a versatile platform to fulfill this purpose. Yet, the vast majority of E-AB sensors are characterized by voltammetric methods, which suffer from signal drifts and low-frequency data acquisition during continuous operations. To overcome these limitations, we developed a novel E-AB sensor empowered by Gold nanoparticle-DNA Pendulum (GDP). Using chronoamperometric interrogation, the developed sensor enabled drift-resistant, high-frequency, and real-time monitoring of vascular endothelial growth factor (VEGF), a vital signaling protein that regulates angiogenesis, endothelial cell proliferation and vasculogenesis. We assembled VEGF aptamer-anchored GDP probes to a reduced graphene modified electrode, where a fast chronoamperometric current transient occurs as the GDP rapidly transport to the electrode surface. In the presence of target molecules, longer and concentration-dependent time decays were observed because of slower motion of the GDP in its bound state. After optimizing several decisive parameters, including composition ratios of GDP, probe density, and incubation time, the GDP empowered E-AB sensor achieves continuous, selective, and reversible monitoring of VEGF in both phosphate buffered saline (PBS) solutions and artificial urine with a wide detection range from 13 fM to 130 nM. Moreover, the developed sensor acquires signals on a millisecond timescale, and remains resistant to signal degradation during operation. This study offers a new approach to designing E-AB architectures for continuous biomolecular monitoring.

Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies

Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Microgel-based etalon immunoassay for IgG detection

We developed an immunoassay for mouse immunoglobulin (IgG) quantitation using poly(N-isopropylacrylamide-co-acrylic acid) (pNIPAm-co-AAc) microgel-based etalon devices. To achieve this, a biotinylated primary antibody specific to mouse IgG was immobilized on the top Au layer of an etalon device via its interaction with a streptavidin-modified etalon surface. Mouse IgG captured on the etalon surface from the solution was quantified using an HRP-conjugated secondary antibody. HRP catalyzed the oxidation of 4-chloro-1-naphthol (4CN) to form insoluble 4-chloro-1-naphthon (4CNP), resulting in a concentration change of 4CN in solution. The etalon was able to detect the 4CN concentration change by monitoring the extent of the etalon's reflectance peak shift, which allows the quantitation of mouse IgG. The etalon-based assay can detect mouse IgG down to 0.018 nM with a linear range of 0.02-5 nM.

Layer-by-layer coating of natural diatomite with silver nanoparticles for identification of circulating cancer protein biomarkers using SERS

Surface-enhanced Raman scattering (SERS) is an emerging spectroscopy technique for detecting and characterizing chemical or biological structures in the vicinity of plasmonic nanostructures. Colloidal, solid, and flexible nanostructures are widely used in SERS experiments to enhance the Raman intensity. The nanostructure used in SERS is one of the main influencing parameters and a growing research area. Fabrication of simple and cheap SERS substrates with a high enhancement factor is desired. In this study, we fabricated a reproducible, cheap, and flexible SERS active strip by coating natural diatomite (biosilica) with silver nanoparticles (AgNPs) using the layer-by-layer assembly method and the fabricated strip is used for the label-free identification of circulating cancer protein biomarkers. SERS active strips were fabricated having different numbers of AgNP layers on natural diatomite and comprehensive characterization techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV/vis absorption spectrophotometry were used. SERS activities of the strips depending on the number of layers were evaluated using 4-aminothiophenol (4-ATP) and rhodamine 6G (Rh6G) molecules. We found that the SERS intensity is strongly dependent on the number of AgNP layers, with the maximum SERS intensity obtained from the strip with 5 layers of AgNPs, having a 2.0 × 105 enhancement factor. The strip with the highest SERS activity was used for the label-free identification of circulating cancer protein biomarkers (HER2, CA15-3, PSA, MUC4, and CA27-29). The results demonstrate that the fabricated strip can help in the effective label-free identification of circulating protein biomarkers and open new directions for SERS-based label-free biosensing applications.

Serum proteomics profiling identifies a preliminary signature for the diagnosis of early-stage lung cancer

PURPOSE

Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival.

EXPERIMENTAL DESIGN

The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach.

RESULTS

We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.

Current Challenges and Developments in Perovskite-Based Electrochemical Biosensors for Effective Theragnostics of Neurological Disorders

Early-stage diagnosis of neurological disease and effective therapeutics play a significant role in improving the chances of saving lives through suitable and personalized courses of treatment. Biomolecules are potential indicators of any kind of disorder in a biological system, and they are recognized as a critical quantitative parameter in disease diagnosis and therapeutics, collectively known as theragnostics. The effective diagnosis of neurological disorders solely depends on the detection of the imbalance in the concentration of neurological biomarkers such as nucleic acids, proteins, and small metabolites in bodily fluids such as blood serum, plasma, urine, etc. This process of neurological biomarker detection can lead to an effective prognosis with a prediction of the treatment efficiency and recurrence. While review papers on electrochemical, spectral, and electronic biosensors for the detection of a wide variety of biomarkers related to neurological disorders are available in the literature, the prevailing challenges and developments in perovskite-based biosensors for effective theragnostics of neurological disorders have received scant attention. In this Mini-Review, we discuss the topical advancements in design strategies of perovskite-based electrochemical biosensors with detailed insight into the detection of neurological disease or disorder-specific biomarkers and their trace-level detection in biological fluids with high specificity and sensitivity. The tables in this Review give the performance analysis of recently developed perovskite-based electrochemical biosensors for effective theragnostics of neurological disorders. To conclude, the current challenges in biosensing technology for early diagnosis and therapeutics of neurological disorders are discussed along with a forecast of their anticipated developments.

Mass Spectrometry Based Protein Biomarkers and Drug Target Discovery and Clinical Diagnosis in Age-Related Progressing Neurodegenerative Diseases

Neurodegenerative diseases correspond to overly complex health disorders that are driven by intersecting pathophysiology that are often trapped in vicious cycles of degeneration and cognitive decline. The usual diagnostic route of these diseases is based on postmortem examination that involves identifying pathology that is specific to the disease in the brain. However, in such cases, accurate diagnosis of the specific disease is limited because clinical disease presentations are often complex that do not easily allow to discriminate patient's cognitive, behavioral, and functional impairment profiles. Additionally, an early identification and therapeutic intervention of these diseases is pivotal to slow the progression of neurodegeneration and extend healthy life span. Mass spectrometry-based techniques have proven to be hugely promising in biological sample analysis and discovery of biomarkers including protein and peptide biomarkers for potential drug target discovery. Recent studies on these biomarkers have demonstrated their potential for applications in early diagnostics and identifying therapeutic targets to battle against neurodegenerative diseases. In this review, we have presented principles of mass spectrometry (MS) and the associated workflows in analyzing and imaging biological samples for discovery of biomarkers. We have especially focused on age- related progressing neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal dementia (FTD) and the related MS-based biomarkers developments for these diseases. Finally, we present a future perspective discussing the potential research directions ahead.

Development and Evaluation of Europium-Based Quantitative Lateral Flow Immunoassay for the Chronic Kidney Disease Marker Cystatin-C

This study aimed to establish a Europium label time-resolved fluorescence immunoassay (TRFIA) to detect the chronic kidney disease (CKD) biomarker Cystatin-C. An Europium based Time resolved fluorescence immunoassay was developed to detect the concentration of Cystatin-C in a urine sample to increase the sensitivity with captured anti-Cystatin-C antibodies immobilized on nitrocellulose membrane and then bonded with detection anti-Cystatin-C labelled with CM-EU, followed by fluorescence measurement using time-resolved fluorometry in 15 min. The performance of this TRFIA was evaluated using the clinical urine serum and compared with the ELISA assays. The linear calibration range was 0.015-32 µg/ml, and the limit of detection (LOD) quantified was 0.0001 µg/ml. This current work has improved the LOD of our previous work from 0.013 µg/ml to 0.001 µg/ml. These results indicated that the CM-EU nanoparticle-based LFIA is rapid, more sensitive, reliable, and reproducible for point-of-care testing of Cys-C concentrations in urine.

Development and Evaluation of a Quantitative Fluorescent Lateral Flow Immunoassay for Cystatin-C, a Renal Dysfunction Biomarker

The diagnosis, prognosis, and control of chronic kidney disease rely on an understanding of the glomerular filtration rate (GFR). The renal clearance of the cystatin-C is closely associated with the GFR. Cystatin-C is a more suitable GFR marker than the commonly used creatinine. General techniques for cystatin-C calculation, such as particle-enhanced turbidimetric and nephelometric assay, are time-consuming and tedious. Here, we propose a rapid, quantitative immunoassay for the detection of cystatin-C. A fluorescence-based lateral-flow kit was developed in a sandwich format by using a monoclonal antibody. A Linear calibration was obtained over the clinical diagnostic range of 0.023-32 µg/mL and the limit of detection (LOD) was 0.023 µg/mL and the limit of quantification (LOQ) was 0.029 µg/mL. Average recoveries from spiked urine samples ranged from 96-100% and the coefficient of variation was less than 4% for both intra and inter-day assays with excellent repeatability. With the comparison with an ELISA kit, the developed kit is highly sensitive, performs well over the detection range, provides repeatable results in a short time, and can easily be used at point-of-care (POC), making it an ideal candidate for rapid testing in early detection, community screening for renal function disorders.

Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring.

Insights and Perspectives Regarding Nanostructured Fluorescent Materials toward Tackling COVID-19 and Future Pandemics

The COVID-19 outbreak has exposed the world's preparation to fight against unknown/unexplored infectious and life-threatening pathogens. The unavailability of vaccines, slow or sometimes unreliable real-time virus/bacteria detection techniques, insufficient personal protective equipment (PPE), and a shortage of ventilators and many other transportation equipments have further raised serious concerns. Material research has been playing a pivotal role in developing antimicrobial agents for water treatment and photodynamic therapy, fast and ultrasensitive biosensors for virus/biomarkers detection, as well as for relevant biomedical and environmental applications. It has been noticed that these research efforts nowadays primarily focus on the nanomaterials-based platforms owing to their simplicity, reliability, and feasibility. In particular, nanostructured fluorescent materials have shown key potential due to their fascinating optical and unique properties at the nanoscale to combat against a COVID-19 kind of pandemic. Keeping these points in mind, this review attempts to give a perspective on the four key fluorescent materials of different families, including carbon dots, metal nanoclusters, aggregation-induced-emission luminogens, and MXenes, which possess great potential for the development of ultrasensitive biosensors and infective antimicrobial agents to fight against various infections/diseases. Particular emphasis has been given to the biomedical and environmental applications that are linked directly or indirectly to the efforts in combating COVID-19 pandemics. This review also aims to raise the awareness of researchers and scientists across the world to utilize such powerful materials in tackling similar pandemics in future.

Flexible and wearable electrochemical biosensors based on two-dimensional materials: Recent developments

The research interest in wearable sensors has tremendously increased in recent years. Amid the different biosensors, electrochemical biosensors are unparalleled and ideal for the design and manufacture of such flexible and wearable sensors because of their various benefits, including convenient operation, quick response, portability, and inherent miniaturization. A number of studies on flexible and wearable electrochemical biosensors have been reported in recent years for invasive/non-invasive and real-time monitoring of biologically relevant molecules such as glucose, lactate, dopamine, cortisol, and antigens. To attain this, novel two-dimensional nanomaterials and their hybrids, various substrates, and detection methods have been explored to fabricate flexible conductive platforms that can be used to develop flexible electrochemical biosensors. In particular, there are many advantages associated with the advent of two-dimensional materials, such as light weight, high stretchability, high performance, and excellent biocompatibility, which offer new opportunities to improve the performance of wearable electrochemical sensors. Therefore, it is urgently required to study wearable/flexible electrochemical biosensors based on two-dimensional nanomaterials for health care monitoring and clinical analysis. In this review, we described recently reported flexible electrochemical biosensors based on two-dimensional nanomaterials. We classified them into specific groups, including enzymatic/non-enzymatic biosensors and affinity biosensors (immunosensors), recent developments in flexible electrochemical immunosensors based on polymer and plastic substrates to monitor biologically relevant molecules. This review will discuss perspectives on flexible electrochemical biosensors based on two-dimensional materials for the clinical analysis and wearable biosensing devices, as well as the limitations and prospects of the these electrochemical flexible/wearable biosensors.Graphical abstract.

Peptide variability and signatures associated with disease progression in CSF collected longitudinally from ALS patients

We employ shotgun proteomics and data-independent acquisition (DIA) mass spectrometry to analyze cerebrospinal fluid longitudinally collected from 14 amyotrophic lateral sclerosis (ALS) patients (8 males and 6 females). We perform three main analyses of these data: (1) examine the intra- and inter-patient protein variability in CSF; (2) explore the association of inflammation with rate of disease progression; and (3) develop a mixed-effects model to best explain the decrease in ALS-Functional Rating Scale (ALS-FRS) score. Overall, the CSF protein abundances are tightly regulated with the intra-individual variability contributing just 4% to the overall variance. In four patients, a moderately significant correlation (p < 0.1) was observed between inflammation and rate of disease progression. Using a least absolute shrinkage and selection operator (LASSO) variable selection, we selected 55 viable peptides for mathematical modeling via a linear mixed-effects regression. We then employed forward selection to generate a final model by minimizing Akaike's information criterion (AIC). The final model utilized changes in abundance from 28 peptides as fixed effects to model progression of the disease in these patients. These peptides were from proteins involved in stress response and innate immunity. Graphical abstract.

Enhanced sensitivity of VEGF detection using catalase-mediated chemiluminescence immunoassay based on CdTe QD/H2O2 system

Background

Since vascular endothelial growth factor (VEGF) is a significant regulator of cancer angiogenesis, it is essential to develop a technology for its sensitive detection. Herein, we sensitized a chemiluminescence (CL) immunoassay through the combination of H₂O₂-sensitive TGA-CdTe quantum dot (QD) as signal transduction, dextran as a cross-linker to prepare enzyme-labeled antigen and the ultrahigh bioactivity of catalase (CAT) as reporter enzyme.

Results

Under the optimized experimental conditions, the chemiluminescence enzyme-linked immunosorbent assay (CL-ELISA) method can detect VEGF in the excellent linear range of 2–35,000 pg mL⁻¹, with a detection limit (S/N = 3) of 0.5 pg mL⁻¹ which was approximately ten times lower than the commercial colorimetric immunoassay. This proposed method has been successfully applied to the clinical determination of VEGF in the human serum samples, and the results illustrated an excellent correlation with the conventional ELISA method (R² = 0.997). The suitable recovery rate of the method in the serum ranged from 97 to 107%, with a relative standard deviation of 1.2% to 13.4%.

Conclusions

The novel immunoassay proposes a highly sensitive, specific, and stable method for very low levels detection of VEGF that can be used in the primary diagnosis of tumors. With the well-designed sensing platform, this approach has a broad potential to be applied for quantitative analysis of numerous disease-related protein biomarkers for which antibodies are available.

Making Sharper Peaks for Reverse-Phase Liquid Chromatography of Proteins

Protein separations have gained increasing interest over the past two decades owing to the dramatic growth of proteins as therapeutics and the completion of the Human Genome Project. About every decade, the field of protein high-performance liquid chromatography (HPLC) seems to mature, having reached what appears to be a theoretical limit. But then scientists well versed in the basic principles of HPLC invented a way around the limit, generating another decade of exciting progress. There is still the need for higher resolution and better compatibility with mass spectrometry because it is an essential tool for identification of proteins and their modifications. To make advances, the fundamental principles need to be understood. This review covers recent advances and current needs in the context of the principles that underlie the many contributions to peak broadening.

THE PRESENT AND FUTURE OF THE MASS SPECTROMETRY-BASED INVESTIGATION OF THE EXOSOME LANDSCAPE

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post-genome era, exosome-oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS-based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS-based proteomics and several new detection strategies for MS-based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Identification of significant protein markers by mass spectrometry after co-treatment of cells with different drugs: An in vitro survey platform

RATIONALE

Understanding drug-drug interactions and predicting the side effects induced by polypharmacy are difficult because there are few suitable platforms that can predict drug-drug interactions and possible side effects. Hence, developing a platform to identify significant protein markers of drug-drug interactions and their associated side effects is necessary to avoid adverse effects.

METHODS

Human liver cells were treated with ethosuximide in combination with cimetidine, ketotifen, metformin, metronidazole, or phenytoin. After sample preparation and extraction, mitochondrial proteins from liver cells were isolated and digested with trypsin. Then, peptide solutions were detected using a nano ultra-performance liquid chromatographic system combined with tandem mass spectrometry. The Ingenuity Pathway Analysis tool was used to simulate drug-drug interactions and identify protein markers associated with drug-induced adverse effects.

RESULTS

Several protein markers were identified by the proposed method after liver cells were co-treated with ethosuximide and other drugs. Several of these protein markers have previously been reported in the literature, indicating that the proposed platform is workable.

CONCLUSIONS

Using the proposed in vitro platform, significant protein markers of drug-drug interactions could be identified by mass spectrometry. This workflow can then help predict indicators of drug-drug interactions and associated adverse effects for increased safety in clinical prescriptions.

Laser Ablation Electrospray Ionization Time-of-Flight Mass Spectrometry for Direct Analysis of Biological Tissue

Direct analysis and identification of biological tissue is significant for clinical applications. In this study, porcine liver and kidney have been analyzed using laser ablation electrospray ionization time-of-flight mass spectrometry (LAESI-TOFMS). This method showed good reproducibility for the same types of tissue and is capable of distinguishing different tissue species. The margin assessment was also performed using porcine renal tissue, and the response time was less than 6 s. Furthermore, human hepatocarcinoma tissue and normal tissue were identified using this method. Our results indicate that LAESI-TOFMS is a feasible approach for direct identification of tumor tissue and potential for assessment of the resection margin.

Metandem: An online software tool for mass spectrometry-based isobaric labeling metabolomics

Mass spectrometry-based stable isotope labeling provides the advantages of multiplexing capability and accurate quantification but requires tailored bioinformatics tools for data analysis. Despite the rapid advancements in analytical methodology, it is often challenging to analyze stable isotope labeling-based metabolomics data, particularly for isobaric labeling using MS/MS reporter ions for quantification. We report Metandem, a novel online software tool for isobaric labeling-based metabolomics, freely available at http://metandem.com/web/. Metandem provides a comprehensive data analysis pipeline integrating feature extraction, metabolite quantification, metabolite identification, batch processing of multiple data files, online parameter optimization for custom datasets, data normalization, and statistical analysis. Systematic evaluation of the Metandem tool was demonstrated on UPLC-MS/MS, nanoLC-MS/MS, CE-MS/MS and MALDI-MS platforms, via duplex, 4-plex, 10-plex, and 12-plex isobaric labeling experiments and the application to various biological samples.

ProteomicsDB

ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC–MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein–protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.

Urinary Metabolomic and Proteomic Analyses in a Mouse Model of Prostatic Inflammation

Lower urinary tract symptoms (LUTS) are common among aging men. Since prostatic inflammation is one of its etiologies, it is plausible that urinary metabolite and protein biomarkers could be identified and used to diagnose inflammation-induced LUTS. We characterized the urine metabolome and proteome in a mouse model of bacterial-induced prostatic inflammation. Mass Spectrometry (MS)-based multi-omics analysis was employed to discover urinary protein and metabolite-based biomarkers. The investigation of isobaric dimethylated leucine (DiLeu) labeling on metabolites allowed metabolomics and proteomics analysis on the same liquid chromatography (LC)-MS platform. In total, 143 amine-containing metabolites and 1058 urinary proteins were identified and quantified (data are available via ProteomeXchange with identifier PXD018023); among them, 14 metabolites and 168 proteins were significantly changed by prostatic inflammation. Five metabolic pathways and four inflammation-related biological processes were potentially disrupted. By comparing our findings with urinary biomarkers identified in a mouse model of genetic-induced prostate inflammation and with those previously found to be associated with LUTS in older men, we identified creatine, haptoglobin, immunoglobulin kappa constant and polymeric Ig receptor as conserved biomarkers for prostatic inflammation associated with LUTS. These data suggest that these putative biomarkers could be used to identify men in which prostate inflammation is present and contributing to LUTS.

Postmortem proteomics to discover biomarkers for forensic PMI estimation

The assessment of postmortem degradation of skeletal muscle proteins has emerged as a novel approach to estimate the time since death in the early to mid-postmortem phase (approximately 24 h postmortem (hpm) to 120 hpm). Current protein-based methods are limited to a small number of skeletal muscle proteins, shown to undergo proteolysis after death. In this study, we investigated the usability of a target-based and unbiased system-wide protein analysis to gain further insights into systemic postmortem protein alterations and to identify additional markers for postmortem interval (PMI) delimitation. We performed proteomic profiling to globally analyze postmortem alterations of the rat and mouse skeletal muscle proteome at defined time points (0, 24, 48, 72, and 96 hpm), harnessing a mass spectrometry-based quantitative proteomics approach. Hierarchical clustering analysis for a total of 579 (rat) and 896 (mouse) quantified proteins revealed differentially expressed proteins during the investigated postmortem period. We further focused on two selected proteins (eEF1A2 and GAPDH), which were shown to consistently degrade postmortem in both rat and mouse, suggesting conserved intra- and interspecies degradation behavior, and thus preserved association with the PMI and possible transferability to humans. In turn, we validated the usefulness of these new markers by classical Western blot experiments in a rat model and in human autopsy cases. Our results demonstrate the feasibility of mass spectrometry-based analysis to discover novel protein markers for PMI estimation and show that the proteins eEF1A2 and GAPDH appear to be valuable markers for PMI estimation in humans.

Generation of GLA-Knockout Human Embryonic Stem Cell Lines to Model Autophagic Dysfunction and Exosome Secretion in Fabry Disease-Associated Hypertrophic Cardiomyopathy

Fabry disease (FD) is a rare inherited disorder characterized by a wide range of systemic symptoms; it is particularly associated with cardiovascular and renal problems. Enzyme replacement therapy and pharmacological chaperone migalastat are the only approved and effective treatment strategies for FD patients. It is well documented that alpha-galactosidase A (GLA) enzyme activity deficiency causes globotriaosylceramide (Gb3) accumulation, which plays a crucial role in the etiology of FD. However, the detailed mechanisms remain unclear, and the lack of a reliable and powerful disease model is an obstacle. In this study, we created such a model by using CRISPR/Cas9-mediated editing of GLA gene to knockout its expression in human embryonic stem cells (hESCs). The cardiomyocytes differentiated from these hESCs (GLA-null CMs) were characterized by the accumulation of Gb3 and significant increases of cell surface area, the landmarks of FD-associated cardiomyopathy. Furthermore, we used mass spectrometry to compare the proteomes of GLA-null CMs and parental wild type CMs and found that the Rab GTPases involved in exocytotic vesicle release were significantly downregulated. This caused impairment of autophagic flux and protein turnover, resulting in an increase of reactive oxygen species and apoptosis. To summarize, we established a FD model which can be used as a promising tool to study human hypertrophic cardiomyopathy in a physiologically and pathologically relevant manner and to develop new therapies by targeting Rab GTPases signaling-related exosomal vesicles transportation.

Disposable electrochemical immunosensor for analysis of cystatin C, a CKD biomarker

Specific monitoring of cystatin C (CysC) levels in biological fluids is critical for diagnosis, treatment and mechanistic understanding of a spectrum of diseases, particularly chronic kidney disease (CKD). Despite evidences that CysC correlates with the high risk and/or progression of CKD, its use in clinical practice is still scarce. In this context, we report the development of a simple and sensitive immunosensor for the detection of CysC. The biosensor combines the technology of cost-effective screen-printed electrodes with the high specificity of a sandwich immunoassay. Optimized conditions showed that the sensor operates in a linear range between 10 and 100 ng mL^-1, with a detection limit and a sensitivity of 6.0 ng mL^-1 and 6.4 ± 0.3 μA ng mL^-1 cm^-2, respectively. Moreover, the sensor provided precise results (RSD ≤ 6.2%) and the quantification of CysC in CKD serum samples revealed to be in agreement with the values obtained by a particle-enhanced nephelometric immunoassay. In this light, the proposed immunosensor qualifies for clinical application, constituting a step forward in the development of fast, sensitive and cost-effective diagnostic tools that can improve the current medical care settings of CKD patients.

Regional protein expression in human Alzheimer's brain correlates with disease severity

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions display varying and region-specific changes in protein expression. These changes provide insights into the progression of disease, novel AD-related pathways, the presence of a gradient of protein expression change from less to more affected regions and a possibly protective protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and open new avenues to enhance molecular understanding of AD pathophysiology, provide new targets for intervention and broaden the conceptual frameworks for future AD research.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS

We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano-flow liquid chromatography high resolution tandem mass spectrometry. Label free quantitation was used to identify differential proteins between individuals with ALS (n = 33) and healthy controls (n = 30) in both fluids. In CSF, 118 (p-value < 0.05) and 27 proteins (q-value < 0.05) were identified as significantly altered between ALS and controls. In plasma, 20 (p-value < 0.05) and 0 (q-value < 0.05) proteins were identified as significantly altered between ALS and controls. Proteins involved in complement activation, acute phase response and retinoid signaling pathways were significantly enriched in the CSF from ALS patients. Subsequently various machine learning methods were evaluated for disease classification using a repeated Monte Carlo cross-validation approach. A linear discriminant analysis model achieved a median area under the receiver operating characteristic curve of 0.94 with an interquartile range of 0.88–1.0. Three proteins composed a prognostic model (p = 5e-4) that explained 49% of the variation in the ALS-FRS scores. Finally we investigated the specificity of two promising proteins from our discovery data set, chitinase-3 like 1 protein and alpha-1-antichymotrypsin, using targeted proteomics in a separate set of CSF samples derived from individuals diagnosed with ALS (n = 11) and other neurological diseases (n = 15). These results demonstrate the potential of a panel of targeted proteins for objective measurements of clinical value in ALS.

Clinical biomarkers for cancer recognition and prevention: A novel approach with optical measurements

Cancer is the most important cause of death worldwide, and early cancer detection is the most fundamental factor for efficacy of treatment, prognosis, and increasing survival rate. Over the years great effort has been devoted to discovering and testing new biomarkers that can improve its diagnosis, especially at an early stage. Here we report the potential usefulness of new, easily applicable, non-invasive and relatively low-cost clinical biomarkers, based on abnormalities of oral mucosa spectral reflectance and fractal geometry of the vascular networks in several different tissues, for identification of hereditary non-polyposis colorectal cancer carriers as well for detection of other tumors, even at an early stage. In the near future the methodology/technology of these procedures should be improved, thus making possible their applicability worldwide as screening tools for early recognition and prevention of cancer.

Urinary proteomics analysis based on mass spectrometry and identification of therapeutic targets of Shenkangling interventions in rats with adriamycin nephropathy using iTRAQ

OBJECTIVE

The isobaric tags for relative and absolute quantification (iTRAQ) technique for proteomic analysis was employed to identify diagnostic markers and therapeutic targets of Shenkangling intervention or prednisone tablets in rats with adriamycin nephropathy (AN).

METHODS

Fifty healthy, clean-grade Sprague-Dawley rats were selected, with 10 rats in the normal group and the remaining 40 rats receiving a tail vein injection of 5.5 mg/kg of adriamycin (ADR) to induce AN. Treatment began 1 week later. The normal group received gastric administration of normal saline. Forty rats with induced AN were further randomly divided into the AN modeling group (n = 10), AN modeling + prednisone treatment group (n = 10), AN modeling + Shenkangling intervention group (n = 10), and AN modeling + prednisone + Shenkangling intervention group (n = 10). iTRAQ was employed in combination with mass spectrometry to analyze the differentially expressed proteins in the urine after 3 weeks of treatment (in the fourth week of the experiment).

RESULTS

Compared with normal rats, AN rats had 6 down-regulated proteins and 1 upregulated protein. Compared with AN rats, prednisone rats had 2 down-regulated and 6 upregulated proteins. Compared with AN rats, combined treatment rats had 2 down-regulated and 8 upregulated proteins. Compared with the AN model group, the Shenkangling treatment group had 3 down-regulated and 9 upregulated proteins. Gro, Afamin, Cystatin-related protein 2, Afamin, and isoform CRA_a were considered diagnostic markers of primary nephrotic syndrome (PNS). Telomerase was considered the therapeutic target of prednisone. Urinary protein 2, Apolipoprotein A-II, 45 kDa calcium-binding protein, Vitronectin, and Osteopontin were the therapeutic targets of the Shenkangling intervention. Afamin, isoform CRA_a, Apolipoprotein A-IV, Coagulation factor XII, Prolactin-induced protein, and Coagulation factor XII were the therapeutic targets of the Shenkangling intervention combined with prednisone.

CONCLUSION

The feasibility of urinary proteomics analysis in rats using a large number of proteins with finite molecular weights is controversial. The markers screened in this study may be of clinical value for the diagnosis and treatment of nephropathy. However, these findings should be confirmed in future cohort studies.

Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease

Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer's disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

Fabrication of an immunosensor for early and ultrasensitive determination of human tissue plasminogen activator (tPA) in myocardial infraction and breast cancer patients

Sensitive detection of biomarkers will mean accurate and early diagnosis of diseases. A tissue plasminogen activator (tPA) has a crucial role in many cardiovascular diseases and it is related to many processes such as angiogenesis in cancer cells. Therefore, sensitive determination of tPA is important in diagnosis and clinical research. tPA monoclonal antibody was covalently attached onto single-wall carbon nanotubes (SWCNTs) using diimide-activated imidation coupling. Functionalized SWCNTs were immobilized onto a glassy carbon electrode and the modification process was investigated by sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE), SEM, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS). Cyclic voltammograms (CVs) in a scan rate of 100 mVs^-1 was studied and comparisons were made between the modified glassy carbon electrodes (immobilized with antibodies) as a working electrode before and after the formation of tPA-antibody complex. Results of the SDS-PAGE demonstrated that the antibody was covalently and site directly attached to the SWCNTs. The fabricated biosensor provided a good linear response range from 0.1 to 1.0 ng mL^-1 with a low detection limit of 0.026 ng mL^-1. The immunosensor showed selectivity, reproducibility, good sensitivity, and acceptable stability. Satisfactory results were observed for early and sensitive determination of tPA in human serum samples. For the first time, such specific biosensor is currently being fabricated for tPA in our laboratories and successfully could determine tPA in myocardial infraction and breast cancer patients. Graphical abstract Fabricated biosensor for determination of tPA.

Variability Assessment of 90 Salivary Proteins in Intraday and Interday Samples from Healthy Donors by Multiple Reaction Monitoring-Mass Spectrometry

PURPOSE

Saliva is an attractive sample source for the biomarker-based testing of several diseases, especially oral cancer. Here, we sought to apply multiplexed LC-MRM-MS to precisely quantify 90 disease-related proteins and assess their intra- and interindividual variability in saliva samples from healthy donors.

EXPERIMENTAL DESIGN

We developed two multiplexed LC-MRM-MS assays for 122 surrogate peptides representing a set of disease-related proteins. Saliva samples were collected from 10 healthy volunteers at three different time points (Day 1 morning and afternoon, and Day 2 morning). Each sample was spiked with a constant amount of a ¹⁵ N-labeled protein and analyzed by MRM-MS in triplicate. Quantitative results from LC-MRM-MS were calculated by single-point quantification with reference to a known amount of internal standard (heavy peptide).

RESULTS

The CVs for assay reproducibility and technical variation were 13 and 11%, respectively. The average concentrations of the 99 successfully quantified proteins ranged from 0.28 ± 0.58 ng mL^-1 for profilin-2 (PFN2) to 8.55 ±8.96 μg mL^-1 for calprotectin (S100A8). For the 90 proteins detectable in >50% of samples, the average CVs for intraday, interday, intraindividual, and interindividual samples were 38%, 43%, 45%, and 69%, respectively. The fluctuations of most target proteins in individual subjects were found to be within ± twofold.

CONCLUSIONS AND CLINICAL RELEVANCE

Our study elucidated the intra- and interindividual variability of 90 disease-related proteins in saliva samples from healthy donors. The findings may facilitate the further development of salivary biomarkers for oral and systemic diseases.

Assessing biological and technological variability in protein levels measured in pre-diagnostic plasma samples of women with breast cancer

Background

Quantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike ‘omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms.

Methods

We obtained ten blood plasma “case” samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes.

Results

Although we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly (p < 1E-01) altered expression levels between cases and controls, with no proteins retaining statistical significance after false discovery correction.

Conclusions

Technical, practical, and study design considerations are essential to maximize information obtained in limited pre-diagnostic samples of cancer patients. This study provides a framework that estimates biological effect sizes critical for consideration in designing studies for pre-diagnostic blood-based biomarker detection.

Electronic supplementary material

The online version of this article (10.1186/s40364-017-0110-y) contains supplementary material, which is available to authorized users.

Binding induced strand displacement amplification for homogeneous protein assay

An ultrasensitive and homogenous strategy for protein assay was established based upon binding-induced strand displacement amplification (BI-SDA). Binding-Induced DNA strand-displacement occurred between Apt-T•signal DNA and Apt-C, and release of signal DNA upon addition of platelet-derived growth factor (PDGF BB). The released signal DNA further hybridized with multifunctional hairpin DNA probe and induced the strand-displacement amplification in the presence of Klenow Fragment (exo^-) and dNTPs. The BI-SDA product contain G-quaruplex DNA, which could be recognized and reported by the fluorescence of fluorochrome N-methyl porphyrin propionic acid IX (NMM). The fluorescence intensity was proportional to the concentration of PDGF-BB over the range of 1.0×10^-11mol/L -2.0×10^-9mol/L, with a detection limit of 3.6pmol/L. This proposed strategy showed good selectivity and practicality, and might be applied to other proteins in the future.

Multiplex Immunosensor Arrays for Electrochemical Detection of Cancer Biomarker Proteins

Measuring panels of protein biomarkers offer a new personalized approach to early cancer detection, disease monitoring and patients' response to therapy. Multiplex electrochemical methods are uniquely positioned to provide faster, more sensitive, point of care (POC) devices to detect protein biomarkers for clinical diagnosis. Nanomaterials-based electrochemical methods offer sensitivity needed for early cancer detection. This review discusses recent advances in multiplex electrochemical immunosensors for cancer diagnostics and disease monitoring. Different electrochemical strategies including enzyme-based immunoarrays, nanoparticle-based immunoarrays and electrochemiluminescence methods are discussed. Many of these methods have been integrated into microfluidic systems, but measurement of more than 2-4 protein markers in a small single serum sample is still a challenge. For POC applications, a simple, low cost method is required. Major challenges in multiplexed microfluidic immunoassays are reagent additions and washing steps that require creative engineering solutions. 3-D printed microfluidics and paper-based microfluidic devices are also explored.

Direct injection of HILIC fractions on the reversed-phase trap column improves protein identification rates for salivary proteins

Online combination of hydrophilic interaction chromatography (HILIC) and RP chromatography for separation of tryptic peptides is a challenging approach due to the incompatibility of direct loading HILIC fractions on the RP trapping column. High amounts of organic modifiers in loading solvents decrease the binding efficiency of tryptic peptides on C18 phases and lower the number of identifications. A 500 μL loop upfront of the trapping column filled with aqueous mobile phase was employed as a mixing chamber and enabled direct injections and improved saliva protein identification rates of HILIC fractions.

In-Depth Characterization and Validation of Human Urine Metabolomes Reveal Novel Metabolic Signatures of Lower Urinary Tract Symptoms

Lower urinary tract symptoms (LUTS) are a range of irritative or obstructive symptoms that commonly afflict aging population. The diagnosis is mostly based on patient-reported symptoms, and current medication often fails to completely eliminate these symptoms. There is a pressing need for objective non-invasive approaches to measure symptoms and understand disease mechanisms. We developed an in-depth workflow combining urine metabolomics analysis and machine learning bioinformatics to characterize metabolic alterations and support objective diagnosis of LUTS. Machine learning feature selection and statistical tests were combined to identify candidate biomarkers, which were statistically validated with leave-one-patient-out cross-validation and absolutely quantified by selected reaction monitoring assay. Receiver operating characteristic analysis showed highly-accurate prediction power of candidate biomarkers to stratify patients into disease or non-diseased categories. The key metabolites and pathways may be possibly correlated with smooth muscle tone changes, increased collagen content, and inflammation, which have been identified as potential contributors to urinary dysfunction in humans and rodents. Periurethral tissue staining revealed a significant increase in collagen content and tissue stiffness in men with LUTS. Together, our study provides the first characterization and validation of LUTS urinary metabolites and pathways to support the future development of a urine-based diagnostic test for LUTS.

Kidney Disease and the Nexus of Chronic Kidney Disease and Acute Kidney Injury: The Role of Novel Biomarkers as Early and Accurate Diagnostics

Chronic kidney disease (CKD) and acute kidney injury (AKI) are interconnected and the presence of one is a risk for the other. CKD is an important predictor of AKI after exposure to nephrotoxic drugs or major surgery, whereas persistent or repetitive injury could result in the progression of CKD. This brings new perspectives to the diagnosis and monitoring of kidney diseases highlighting the need for a panel of kidney-specific biomarkers that reflect functional as well as structural damage and recovery, predict potential risk and provide prognosis. This article discusses the kidney-specific biomarkers, symmetric dimethylarginine (SDMA), clusterin, cystatin B, and inosine.