Discovery Of Urinary Biomarkers Research Articles

Proteomic approaches in the discovery of potential urinary biomarkers of mucopolysaccharidosis type II

Mucopolysaccharindosis type II (MPS II) is a rare lysosomal storage disorder caused by deficient or absent activity of the iduronate-2-sulfatase (IDS) enzyme, which leads to pathological accumulation of the glycosaminoglycans(GAGs). The absence of early diagnosis can result in irreversible developmental, neurological, and physiological damage. The lack of clear understanding of the etiology of physiological dysfunction in MPS II has been a major obstacle to the development of new treatment. Therefore, a reliable biomarker for early diagnosis and exploration of pathogenic mechanism are of great importance. Proteomics provides powerful tool for protein expression alterations and study of complicated pathological process. This study was performed to identify the differential protein profile in urine of MPS II patients using two-dimensional gel electrophoresis(2D-PAGE)combining with MALDI-TOF/TOF and a total of 15 differentially expressed proteins were identified. Content of alpha1-antitrypsin, Gm2 activator and lipocalin-type prostaglandin D synthase was measured by ELISA method. The value of urinary α1-AT/Cr in MPS II group was 0.79 ± 0.10 mg/mmol, significantly higher than 0.42 ± 0.05 mg/mmol in healthy control group; whereas the value of GM2A/Cr and L-PGDS/Cr in MPS II group was 1.30 ± 0.12 μg/mmol and 9.86 ± 1.16 ng/mmol respectively, which was significantly lower than 2.19 ± 0.19 μg/mmol and 13.98 ± 1.48 ng/mmol in healthy control group. The proteins can be considered as accessory diagnostic biomarkers for MPS II. This approach helped to discover early diagnostic markers and provided a better understanding of the pathogenic mechanism of MPS II.

Comprehensive Analysis of Individual Variation in the Urinary Proteome Revealed Significant Gender Differences

Disease biomarkers are the measurable changes associated with a pathophysiological process. Without homeostatic control, urine accumulates systematic changes in the body. Thus, urine is an attractive biological material for the discovery of disease biomarkers. One of the major bottlenecks in urinary biomarker discovery is that the concentration and composition of urinary proteins are influenced by many physiological factors. To elucidate the individual variation and related factors influencing the urinary proteome, we comprehensively analyzed the urine samples from healthy adult donors (aged 20-69 years). Co-expression network analysis revealed protein clusters representing the metabolic status, gender-related differences and age-related differences in urinary proteins. In particular, we demonstrated that gender is a crucial factor contributing to individual variation. Proteins that were increased in the male urine samples include prostate-secreted proteins and TIMP1, a protein whose abundance alters under various cancers and renal diseases; however, the proteins that were increased in the female urine samples have known functions in the immune system. Nine gender-related proteins were validated on 85 independent samples by multiple reaction monitoring. Five of these proteins were further used to build a model that could accurately distinguish male and female urine samples with an area under curve value of 0.94. Based on the above results, we strongly suggest that future biomarker investigations should consider gender as a crucial factor in experimental design and data analysis. Finally, reference intervals of each urinary protein were estimated, providing a baseline for the discovery of abnormalities.

A urinary extracellular vesicle microRNA biomarker discovery pipeline; from automated extracellular vesicle enrichment by acoustic trapping to microRNA sequencing.

Development of a robust automated platform for enrichment of extracellular vesicles from low sample volume that matches the needs for next-generation sequencing could remove major hurdles for genomic biomarker discovery. Here, we document a protocol for urinary EVs enrichment by utilizing an automated microfluidic system, termed acoustic trap, followed by next-generation sequencing of microRNAs (miRNAs) for biomarker discovery. Specifically, we compared the sequencing output from two small RNA library preparations, NEXTFlex and CATS, using only 130 pg of input total RNA. The samples prepared using NEXTflex was found to contain larger number of unique miRNAs that was the predominant RNA species whereas rRNA was the dominant RNA species in CATS prepared samples. A strong correlation was found between the miRNA expressions of the acoustic trap technical replicate in the NEXTFlex prepared samples, as well as between the acoustic trap and ultracentrifugation enrichment methods. Together, these results demonstrate a robust and automated strategy for biomarker discovery from small volumes of urine.

Ability of a urine gene expression classifier to reduce the number of follow-up cystoscopies in bladder cancer patients

This study aimed to improve our previous urine gene expression classifiers focusing on the detection of non-high-risk non-muscle-invasive bladder cancer (NMIBC), and develop a new classifier able to decrease the frequency of cystoscopies during bladder cancer (BC) patients' surveillance. A total of 597 urines from BC patients, controls and patients in follow-up for BC (PFBC) were included. The study has 3 phases. In the urinary biomarker discovery phase, 84 urines from BC and control patients were retrospectively included and analyzed by Ribonucleic Acid (RNA) sequencing. In the classifier development phase, a total of 132 selected genes from previous phase were evaluated by nCounter in 214 prospectively collected urines from PFBC (98 with tumor). A diagnostic classifier was generated by logistic regression. Finally, in the classifier validation phase, a multicentric and international cohort of 248 urines (134 BC and 114 nonrecurrent PFBC) was used to validate classifier performance. A total of 521 genes were found differentially expressed between non-high-risk NMIBC samples and all other groups (P < 0.05). An 8-gene diagnostic classifier with an area under curve (AUC) of 0.893 was developed. Validation of this classifier in a cohort of PFBC achieved an overall sensitivity (SN) and a negative predictive value (NPV) of 96% and 97%, respectively (AUC = 0.823). Notably, this accuracy was maintained in non-high-risk NMIBC group (SN = 94%; NPV = 98%). In conclusion, this 8-gene expression classifier has high SN and NPV in a real clinical scenario. The use of this classifier can reduce the number of follow-up cystoscopies in PFBC, although assessing its final place in clinical setting is necessary.

Discovery and Qualification of Candidate Urinary Biomarkers of Disease Activity in Lupus Nephritis.

Lupus nephritis (LN) is a severe clinical manifestation of systemic lupus erythematosus (SLE) associated with significant morbidity and mortality. Assessment of severity and activity of renal involvement in SLE requires a kidney biopsy, an invasive procedure with limited prognostic value. Noninvasive biomarkers are needed to inform treatment decisions and to monitor disease activity. Proteinuria is associated with disease progression in LN; however, the composition of the LN urinary proteome remains incompletely characterized. To address this, we profiled LN urine samples using complementary mass spectrometry-based methods: proteingel fractionation, chemical labeling using tandem mass tags, and data-independent acquisition. Combining results from these approaches yielded quantitative information on 2573 unique proteins in urine from LN patients. A multiple-reaction monitoring (MRM) method was established to confirm eight proteins in an independent cohort of LN patients, and seven proteins (transferrin, α-2-macroglobulin, haptoglobin, afamin, α-1-antitrypsin, vimentin, and ceruloplasmin) were confirmed to be elevated in LN urine compared to healthy controls. In this study, we demonstrate that deep mass spectrometry profiling of a small number of patient samples can identify high-quality biomarkers that replicate in an independent LN disease cohort. These biomarkers are being used to inform clinical biomarker strategies to support longitudinal and interventional studies focused on evaluating disease progression and treatment efficacy of novel LN therapeutics.

Fast quantitative urinary proteomic profiling workflow for biomarker discovery in kidney cancer

BackgroundUrine has evolved as a promising body fluids in clinical proteomics because it can be easily and noninvasively obtained and can reflect physiological and pathological status of the human body. Many efforts have been made to characterize more urinary proteins in recent years, but few have focused on the analysis throughput and detection reproducibility. Increasing the urine proteomic profiling throughput and reproducibility is urgently needed for discovering potential biomarker in large cohorts.MethodsIn this study, we developed a fast and robust workflow for streamlined urinary proteome analysis. The workflow integrate highly efficient sample preparation technique and urinary specific data-independent acquisition (DIA) approach. The performance of the workflow was systematically evaluated and the workflow was subsequently applied in a proof-of-concept urine proteome study of 21 kidney cancer (KC) patients and 22 healthy controls.ResultsWith this workflow, the entire sample preparation process takes less than 3 h and allows multiplexing on standard centrifuges. Without pre-fractionation, our newly developed DIA method allows quantitative analysis of ~ 1000 proteins within 80 min of MS time (~ 15 samples/day). The quantitation accuracy of the whole workflow was excellent with median CV of 9.1%. The preliminary study on KC identified 125 significantly changed proteins.ConclusionsThe result suggested the feasibility of applying the high throughput workflow in extensive urinary proteome profiling and clinical relevant biomarker discovery.

Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological Viewpoints

Background:IgA nephropathy (IgAN) is the most common primary glomerulonephritis diagnosed based on renal biopsy. Mesangial IgA deposits along with the proliferation of mesangial cells are the histologic hallmark of IgAN. Non-invasive diagnostic tools may help to prompt diagnosis and therapy. The discovery of potential and reliable urinary biomarkers for diagnosis of IgAN depends on applying robust and suitable models. Applying two multivariate modeling methods on a urine proteomic dataset were obtained from IgAN patients, and comparison of the results of these methods were the purpose of this study.Methods:Two models were constructed for urinary protein profiles of 13 patients and 8 healthy individuals, based on sparse linear discriminant analysis (SLDA) and elastic net (EN) regression methods. A panel of selected biomarkers with the best coefficients were proposed and further analyzed for biological relevance using functional annotation and pathway analysis.Results:Transferrin, α1-antitrypsin, and albumin fragments were the most important up-regulated biomarkers, while fibulin-5, YIP1 family member 3, prasoposin, and osteopontin were the most important down-regulated biomarkers. Pathway analysis revealed that complement and coagulation cascades and extracellular matrix-receptor interaction pathways impaired in the pathogenesis of IgAN.Conclusion:SLDA and EN had an equal importance for diagnosis of IgAN and were useful methods for exploring and processing proteomic data. In addition, the suggested biomarkers are reliable candidates for further validation to non-invasive diagnose of IgAN based on urine examination.

Discovery of urinary biomarkers for early detection of diabetic- induced chronic kidney diseases

Discovery of urinary biomarkers for early detection of diabetic- induced chronic kidney diseases

Development of a metabolic pathway-based pseudo-targeted metabolomics method using liquid chromatography coupled with mass spectrometry

The pseudo-targeted metabolomics approach was developed recently which combined the advantages of untargeted and targeted analysis. However, the current pseudo-targeted analysis method has limitations due to the technical characteristics. In this study, a novel metabolic pathway-based pseudo-targeted approach was proposed for urine metabolomics analysis using an ultra-high-performance liquid chromatography (UPLC)-MS/MS system operated in the multiple reaction monitoring (MRM) mode. MRM ion pairs were acquired from urine samples through untargeted analysis using UPLC-HRMS, as well as by searching for metabolites in related pathways in relevant databases and from previous relevant research, including amino acids, fatty acids, nucleosides, carnitines, glycolysis metabolites, and steroids. This improved pseudo-targeted method exhibited good repeatability and precision, and no complicated peak alignment was required. As a proof of concept, the developed novel method was applied to the discovery of urine biomarkers for patients with esophageal squamous cell carcinoma (ESCC). The results showed that ESCC patients had altered acylcarnitines, amino acids, nucleosides, and steroid derivative levels et al. compared to those of healthy controls. The novelty of this study lies in the fact that it provides an approach for acquiring MRM ion pairs not only from untargeted MS analysis but also from targeted searching for metabolites in related metabolic pathways. By improving the detection limit of low-abundance metabolites, it enlarges the range for the discovery of potential biomarkers. Our work provides a foundation for achieving pseudo-targeted metabolomics analysis on the widely used LC-MS/MS MRM platform.

Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer.

Prostate cancer (PCa) continues to be one of the most common cancers in men worldwide. Prostate specific antigen (PSA) measured in blood has been used for decades as an aid for physicians to detect the presence of prostate cancer. However, the PSA test has limited sensitivity and specificity, leading to unnecessary biopsies, overdiagnosis and overtreatment of patients. For these reasons, there is an urgent need for more accurate PCa biomarkers that can detect PCa with high sensitivity and specificity. Urine is a unique source of potential protein biomarkers that can be measured in a non-invasive way. This review comprehensively summarizes state of the art approaches used in the discovery and validation of urinary biomarkers for PCa. Numerous strategies are currently being used in the discovery of urinary biomarkers for prostate cancer including gel-based separation techniques, mass spectrometry, activity-based proteomic assays and software approaches. Antibody-based approaches remain preferred method for validation of candidate biomarkers with rapidly advancing multiplex immunoassays and MS-based targeted approaches. In the last decade, there has been a dramatic acceleration in the development of new techniques and approaches in the discovery of protein biomarkers for prostate cancer including computational, statistical and data mining methods. Many urinary-based protein biomarkers have been identified and have shown significant promise in initial studies. Examples of these potential biomarkers and the methods utilized in their discovery are also discussed in this review.

Urinary biomarkers of prostate cancer.

The development of more specific biomarkers for prostate cancer and/or high-risk prostate cancer is necessary, because the prostate-specific antigen test lacks specificity for the detection of prostate cancer and can lead to unnecessary prostate biopsies. Urine is a promising source for the development of new biomarkers of prostate cancer. Biomarkers derived from prostate cancer cells are released into prostatic fluids and then into urine. Urine after manipulation of the prostate is enriched with prostate cancer biomarkers, which include prostate cancer cells, DNAs, RNAs, proteins and other small molecules. The urinary prostate cancer antigen3 test is the first Food and Drug Administration-approved RNA-based urinary marker, and it helps in the detection of prostate cancer on repeat biopsy. The SelectMDx test is based on messenger RNA detection of DLX1 and HOXC6 in urine after prostate massage, and helps in the detection of high-risk prostate cancer on prostate biopsy. Exosomes are extracellular vesicles with a diameter of 30-200nm that are secreted from various types of cells. Urinary prostate cancer-derived exosomes also contain RNAs and proteins specific for prostate cancer (e.g. PCA3 and TMPRSS2-ERG), and could be promising sources of novel biomarker discovery. The ExoDx Prostate test is a commercially available test based on the detection of three genes (PCA3, ERG and SPDEF) in urinary exosomes. Advancement of comprehensive analysis (microarray, mass spectrometry and next-generation sequencing) has resulted in the discovery of several urinary biomarkers. Non-invasive urinary markers can help in the decision to carry out prostate biopsy or in the design of a therapeutic strategy.

Pre-operative level of FGF23 predicts severe acute kidney injury after heart surgery in children.

Early detection of acute kidney injury (AKI) after cardiac surgery has improved recently with the discovery and validation of novel urinary biomarkers. However, objective tools to predict the risk of AKI before the insult are still missing. We tested the hypothesis that pre-operative serum fibroblast growth factor 23 (FGF23) concentrations would be elevated in children who develop AKI after heart surgery with cardiopulmonary bypass (CPB). We also compared post-operative FGF23 concentrations to other biomarkers for early detection of AKI. Blood and urine samples were collected in a prospective observational study from 83 children with congenital heart disease. Severe AKI (sAKI) development (KDIGO stages II-III) in the first seven days after surgery was the primary outcome. Thirty of 76 (39.5%) and 11/76 (14.5%) of patients developed AKI and sAKI, respectively. Pre-operative serum creatinine, cystatin C, and urine biomarker concentrations did not differ between sAKI patients and controls. Pre-operative serum FGF23 levels were higher in patients who developed sAKI (median [IQR] value of 819RU/ml [397.7, 1196.8] vs. 324.3RU/ml [124.6, 679.8] (p = 0.02). FGF23 12-24h after the termination of CPB was also associated with sAKI in the first week after surgery (498RU/ml [226, 928] vs. 1435RU/ml [831, 12,996]). Pre- and post-operative FGF23 levels are higher in children who develop sAKI after cardiac surgery. We suggest FGF23 may be able to detect sub-clinical kidney injury and can be used with demographic AKI risk factors to enhance post-operative sAKI risk prediction.

Early urinary candidate biomarker discovery in a rat thioacetamide-induced liver fibrosis model.

Biomarker is the change associated with the disease. Blood is relatively stable because of the homeostatic mechanisms of the body. However, urine accumulates changes of the body, which makes it a better early biomarker source. Liver fibrosis is a reversible pathological condition, whereas cirrhosis, the end-stage of liver fibrosis, is irreversible. Consequently, noninvasive early biomarkers for fibrosis are desperately needed. In this study, differential urinary proteins were identified in the thioacetamide liver fibrosis rat model using tandem mass tagging and two-dimensional liquid chromatography tandem mass spectrometry. A total of 766 urinary proteins were identified, 143 and 118 of which were significantly changed in the TAA 1-week and 3-week groups, respectively. Multiple reaction monitoring (MRM)-targeted proteomics was used to further validate the abundant differentially expressed proteins. A total of 40 urinary proteins were statistically significant, 15 of which had been previously reported as biomarkers of liver fibrosis, cirrhosis or other related diseases and 10 of which had been reported to be associated with the pathology and mechanism of liver fibrosis. These differential proteins were detected in urine before the alanine aminotransferase and aspartate transaminase changes in the serum and before fibrosis was observed upon hematoxylin and eosin (HE) and Masson's staining.

Discovery of urinary biomarkers to discriminate between exogenous and semi-endogenous thiouracil in cattle: A parallel-like randomized design.

In the European Union, the use of thyreostats for animal fattening purposes has been banned and monitoring plans have been established to detect potential abuse. However, this is not always straightforward as thyreostats such as thiouracil may also have a semi-endogenous origin. Therefore, this study aimed at defining urinary metabolites, which may aid in defining the origin of detected thiouracil. Hereto, a parallel-like randomized in vivo study was conducted in which calves (n = 8) and cows (n = 8) were subjected to either a control treatment, rapeseed-enriched diet to induce semi-endogenous formation, or thiouracil treatment. Urine samples (n = 330) were assessed through metabolic fingerprinting, employing liquid-chromatography and Q-ExactiveTM Orbitrap mass spectrometry. Urinary fingerprints comprised up to 40,000 features whereby multivariate discriminant analysis was able to point out significant metabolome differences between treatments (Q2(Y) ≥ 0.873). Using the validated models, a total of twelve metabolites (including thiouracil) were assigned marker potential. Combining these markers into age-dependent biomarker panels rendered a tool by which sample classification could be improved in comparison with thiouracil-based thresholds, and this during on-going thiouracil treatment (specificities ≥ 95.2% and sensitivities ≥ 85.7%), post-treatment (sensitivities ≥ 80% for ≥ 24 h after last administration), and simulated low-dose thiouracil treatment (exogenous thiouracil below 30 ng μL-1). Moreover, the metabolic relevance of revealed markers was supported by the suggested identities, for which a structural link with thiouracil could be determined in most cases. The proposed biomarker panels may contribute to a more justified decision-making in monitoring thiouracil abuse.

Urinary extracellular vesicle biomarkers in urological cancers: From discovery towards clinical implementation.

Urine contains cellular elements, biochemicals, and proteins derived from glomerular filtration of plasma, renal tubule excretion, and urogenital tract secretions that reflect an individual's metabolic and pathophysiologic state. Despite intensive research into the discovery of urinary biomarkers to facilitate early diagnosis, accurate prognosis and prediction of therapy response in urological cancers, none of these markers has reached widespread use. Their implementation into daily clinical practice is hampered by a substantial degree of heterogeneity in performance characteristics and uncertainty about reliability, clinical utility and cost-effectiveness, in addition to several technical limitations. Extracellular vesicles (EV) have raised interest as a potential source of biomarker discovery because of their role in intercellular communication and the resemblance of their molecular content to that of the releasing cells. We review currently used urinary biomarkers in the clinic and attempts that have been made to identify EV-derived biomarkers for urological cancers. In addition, we discuss technical and methodological considerations towards their clinical implementation.

Candidate urine biomarker discovery from only five pairs of samples before and after tumor resection in glioma patients

Without control of the homeostatic mechanisms, urine accumulates systemic changes in the body to be an excellent biomarker source. But urine is affected by many factors other than the disease. Even though many candidate biomarkers are discovered in animal models, it may still require large number of clinical samples for successful candidate biomarkers discovery. Self‐control study may effectively reduce individual differences among patients. Gliomas are the most common primary malignant brain tumors with a very poor prognosis. Early diagnosis of gliomas and monitoring of tumor recurrence is crucial to improve the outcome of glioma patients. However, whether it is possible to find several promising biomarkers in relatively small samples of glioma patients remained unknown. In this study, urine samples of five glioma patients were collected at the time of tumor diagnosis and after total surgical removal of the brain tumor. A comparative proteomic analysis of urine samples before and after tumor resection were performed using liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS). Using label‐free proteome quantification, a total of 1422 urinary proteins were identified. It was found that 30 urine proteins changed significantly after tumor resection and 12 differential proteins had been reported to be associated with gliomas in previous studies, such as CEACAM1, ANXA7, CRYAB, pIgR, cathepsin D, beta‐glucuronidase, acid ceramidase and CD276 antigen. After functional annotation of these differential proteins, it was found that regulation of tissue remodeling, autophagy, negative regulation of gene expression, positive regulation of natural killer cell mediated cytotoxicity and angiogenesis were significantly enriched. In this study, candidate urinary biomarkers were identified in glioma patients that have the potential to detect the disease and monitor tumor recurrence after treatment. Our results suggested that self‐control before and after tumor resection is an effective way for finding differential proteins related to the tumor even in small number of urine samples. Our findings will provide clues for future biomarker studies involving brain cancers.Support or Funding InformationThis work was supported by the National Key Research and Development Program of China (2016YFC1306300), Beijing Natural Science Foundation (7173264, 7172076), and funds from Beijing Normal University (11100704, 10300‐310421102).This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

A novel non-targeted screening method for urinary exposure biomarker discovery of phthalates using liquid chromatography-mass spectrometry

Phthalates are a group of compounds widely used in consumer products and they are ubiquitous in the environment due to their widespread commercial use.

Urinary biomarkers in prostate cancer detection and monitoring progression.

Prostate cancer (CaP) is the most common cancer in men and the second leading cause of cancer deaths in males in Australia. Although serum prostate-specific antigen (PSA) has been the most widely used biomarker in CaP detection for decades, PSA screening has limitations such as low specificity and potential association with over-diagnosis. Current biomarkers used in the clinic are not useful for the early detection of CaP, or monitoring its progression, and have limited value in predicting response to treatment. Urine is an ideal body fluid for the detection of protein markers of CaP and is emerging as a potential source for biomarker discovery. Gene-based biomarkers in urine such as prostate cancer antigen-3 (PCA3), and genes for transmembrane protease serine-2 (TMPRSS2), and glutathione S-transferase P (GSTP1) have been developed and evaluated in the past decades. Among these biomarkers, urinary PCA3 is the only one approved by the FDA in the USA for clinical use. The study of urine microRNAs (miRNAs) is another burgeoning area for investigating biomarkers to achieve a pre-biopsy prediction of CaP to contribute to early detection. The development of mass spectrometry (MS)-based proteomic techniques has sparked new searches for novel protein markers for many diseases including CaP. Urinary biomarkers for CaP represent a promising alternative or an addition to traditional biomarkers. Future success in biomarker discovery will rely on collaboration between clinics and laboratories. In addition, research efforts need to be moved from biomarker discovery to validation in a large cohort or separate population of patients and translation of these findings to clinical practice. In this review, we discuss urine as a potential source for CaP biomarker discovery, summarise important genetic urine biomarkers in CaP and focus on MS-based proteomic approaches as well as other recent developments in quantitative techniques for CaP urine biomarker discovery.

Discovery and validation of potential urinary biomarkers for bladder cancer diagnosis using a pseudotargeted GC-MS metabolomics method.

Bladder cancer (BC) is the second most prevalent malignancy in the urinary system and is associated with significant mortality; thus, there is an urgent need for novel noninvasive diagnostic biomarkers. A urinary pseudotargeted method based on gas chromatography–mass spectrometry was developed and validated for a BC metabolomics study. The method exhibited good repeatability, intraday and interday precision, linearity and metabolome coverage. A total of 76 differential metabolites were defined in the discovery sample set, 58 of which were verified using an independent validation urine set. The verified differential metabolites revealed that energy metabolism, anabolic metabolism and cell redox states were disordered in BC. Based on a binary logistic regression analysis, a four-biomarker panel was defined for the diagnosis of BC. The area under the receiving operator characteristic curve was 0.885 with 88.0% sensitivity and 85.7% specificity in the discovery set and 0.804 with 78.0% sensitivity and 70.3% specificity in the validation set. The combinatorial biomarker panel was also useful for the early diagnosis of BC. This approach can be used to discriminate non-muscle invasive and low-grade BCs from healthy controls with satisfactory sensitivity and specificity. The results show that the developed urinary metabolomics method can be employed to effectively screen noninvasive biomarkers.

A novel method for the isolation of extracellular vesicles and RNA from urine.

The discovery of urinary extracellular biomarkers has been impeded by the lack of efficient methods for the isolation of extracellular vesicles (EVs: exosomes and microvesicles) and extracellular nucleic acid (RNA and DNA) from urine. Ultracentrifugation (UC), considered the gold standard for vesicle isolation from many biofluids, is efficacious but laborious, and, like most commercially available methods, is unable to isolate enough material from small volumes for protein or RNA-based biomarker discovery. We have developed a novel precipitation method for the isolation of EVs and nucleic acids from urine. The method, which is now commercially available, takes less than 30 min and does not require polyethylene glycol. Transmission electron microscopy and Nanosight particle analysis confirm that the method isolates intact vesicles with a similar size, shape, and number to UC. Immunoblot analysis of preparations made from a variety of urine samples demonstrates that the method isolates multiple vesicle protein markers more efficiently than other commercial kits, especially from more diluted samples. Bioanalyzer, quantitative reverse transcription polymerase chain reaction, and array analysis show that the method is extremely efficient at the isolation of extracellular miRNAs. The Ymir Genomics EV and Extracellular RNA Isolation Kits offer an efficient and rapid alternative to UC and other commercial kits.