Comparative Urine Analysis by Liquid Chromatography−Mass Spectrometry and Multivariate Statistics: Method Development, Evaluation, and Application to Proteinuria

Interaction of Colloidal Gold Nanoparticles with Urine and Saliva Biofluids: An Exploratory Study

The use of gold nanoparticles for drug delivery, photothermal or photodynamic therapy, and biosensing enhances the demand for knowledge about the protein corona formed on the surface of nanoparticles. In this study, gold nanospheres (AuNSs), gold nanorods (AuNRs), and gold nanoflowers (AuNFs) were incubated with saliva or urine. After the interaction, the surface of gold nanoparticles was investigated using UV-VIS spectroscopy, zeta potential, and dynamic light scattering. The shifting of the localized surface plasmon resonance (LSPR) band, the increase in hydrodynamic diameter, and the changes in the surface charge of nanoparticles indicated the presence of biomolecules on the surface of AuNSs, AuNRs, and AuNFs. The incubation of AuNFs with saliva led to nanoparticle aggregation and minimal protein adsorption. AuNSs and AuNRs incubated in saliva were analyzed through liquid chromatography with tandem mass spectrometry (LC-MS/MS) to identify the 96 proteins adsorbed on the surface of the gold nanoparticles. Among the 20 most abundant proteins identified, 14 proteins were common in both AuNSs and AuNRs. We hypothesize that the adsorption of these proteins was due to their high sulfur content, allowing for their interaction with gold nanoparticles via the Au-S bond. The presence of distinct proteins on the surface of AuNSs or AuNRs was also investigated and possibly related to the competition between proteins present on the external layers of corona and gold nanoparticle morphology.

Toilet-based continuous health monitoring using urine

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people's daily lives and identify a range of biomarkers for health and disease are desirable. However, urine - a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content - is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

Relationship between amino acid ratios and decline in estimated glomerular filtration rate in diabetic and non-diabetic patients in South Africa

Background

Diabetic kidney disease is a major complication resulting from type 1 and type 2 diabetes. Currently, the microalbuminuria test is used to monitor renal function; however, it does not detect albumin until progressive loss of renal function has occurred.

Objective

This study analysed the relationship between changes in amino acid ratios and estimated glomerular filtration rate (eGFR) decline in diabetic and non-diabetic patients.

Methods

Urine samples were collected from participants between February 2019 to April 2019 and analysed from November 2020 to January 2021. Diabetic (glycated haemoglobin > 6.4%) and non-diabetic patients (glycated haemoglobin ≤ 6.4%) from Chris Hani Baragwanath Hospital, South Africa, were further categorised based on the degree of renal function predicted by the eGFRs. Amino acids were quantified using tandem mass spectrometry to determine the concentrations and ratios of tyrosine/phenylalanine, ornithine/arginine, arginine/citrulline and citrulline/ornithine at different stages of the chronic kidney disease.

Results

Among diabetic patients, the tyrosine/phenylalanine ratio showed a statistically significant increase (p = 0.04) as the eGFR declined from stage 1 to stage 4; the ornithine/arginine ratio showed a strong negative correlation with eGFR. The citrulline/ornithine ratio differed between the diabetic and non-diabetic patients in stage 1 of chronic kidney disease.

Conclusion

Amino acid ratios (ornithine/arginine and tyrosine/phenylalanine) are affected by the progression of diabetes and can be correlated to renal function. The citrulline/ornithine ratios differ between the studied groups in stage 1 of the disease and may be utilised to predict the onset of chronic kidney disease.

Gold nanoparticles-based assays for biodetection in urine

Urine is a biofluid easy to collect through a non-invasive technique that allows collecting a large volume of sample. The use of urine for disease diagnosis is not yet well explored. However, it has gained attention over the last three years. It has been applied in the diagnosis of several illnesses such as kidney disease, bladder cancer, prostate cancer and cardiovascular diseases. In the last decade, gold nanoparticles (Au NPs) have attracted attention in biosensors' development for the diagnosis of diseases due to their electrical and optical properties, ability to conjugate with biomolecules, high sensitivity, and selectivity. Therefore, this article aims to present a comprehensive view of state of the art on the advances made in the quantification of analytes in urinary samples using AuNPs based assays, with a focus on protein analysis. The type of diagnosis methods, the Au NPs synthesis approaches and the strategies for surface modification aiming at selectivity towards the different targets are highlighted.

Urine Metabolomics in Hypertension Research

Functional genomics requires an understanding of the complete network of changes within an organism by extensive measurements of moieties from mRNA, proteins, and metabolites. Metabolomics utilizes analytic chemistry tools to profile the complete spectrum of metabolites found in a tissue, cells, or biofluids using a wide range of tools from infrared spectroscopy, fluorescence spectroscopy, NMR spectroscopy, and mass spectrometry. In this protocol, we outline a procedure for performing metabolomic analysis of urine samples using liquid chromatography-mass spectrometry (LC-MS). We outline the advantages of using this approach and summarize some of the early promising studies in cardiovascular diseases using this approach.

Sample normalization methods in quantitative metabolomics

To reveal metabolomic changes caused by a biological event in quantitative metabolomics, it is critical to use an analytical tool that can perform accurate and precise quantification to examine the true concentration differences of individual metabolites found in different samples. A number of steps are involved in metabolomic analysis including pre-analytical work (e.g., sample collection and storage), analytical work (e.g., sample analysis) and data analysis (e.g., feature extraction and quantification). Each one of them can influence the quantitative results significantly and thus should be performed with great care. Among them, the total sample amount or concentration of metabolites can be significantly different from one sample to another. Thus, it is critical to reduce or eliminate the effect of total sample amount variation on quantification of individual metabolites. In this review, we describe the importance of sample normalization in the analytical workflow with a focus on mass spectrometry (MS)-based platforms, discuss a number of methods recently reported in the literature and comment on their applicability in real world metabolomics applications. Sample normalization has been sometimes ignored in metabolomics, partially due to the lack of a convenient means of performing sample normalization. We show that several methods are now available and sample normalization should be performed in quantitative metabolomics where the analyzed samples have significant variations in total sample amounts.

Sandwich enzyme-linked immunosorbent assay for the quantification of human serum albumin fragment 408-423 in bodily fluids

Urinary levels of human serum albumin (hSA) fragment 408-423 have been proposed to represent an early marker for graft-versus-host disease (GvHD) and chronic kidney diseases. Here, we developed an enzyme-linked immunosorbent assay (ELISA) for the quantification of hSA(408-423). The sandwich ELISA has a detection limit of 0.5ng/ml and is highly specific for hSA(408-423) because it does not cross-react with other albumin fragments or the full-length precursor. This ELISA allows rapid and convenient quantification of hSA(408-423) in bodily fluids, further clarifying the prognostic and diagnostic value of this peptide in GvHD, kidney disease, and other disorders.

Validation of a two-step quality control approach for a large-scale human urine metabolomic study conducted in seven experimental batches with LC/QTOF-MS

After his study of food science at the Rheinische Friedrich-Wilhelms University of Bonn, Tobias J Demetrowitsch obtained his doctoral degree in the research field of metabolomics at the Christian-Albrechts-University of Kiel. The present paper is part of his doctoral thesis and describes an extended strategy to evaluate and verify complex or large-scale experiments and data sets.

Large-scale studies result in high sample numbers, requiring the analysis of samples in different batches. So far, the verification of such LC–MS-based metabolomics studies is difficult. Common approaches have not provided a reliable validation procedure to date. This article shows a novel verification process for a large-scale human urine study (analyzed by a LC/QToF-MS system) using a two-step validation procedure. The first step comprises a targeted approach that aims to examine and exclude statistical outliers. The second step consists of a principle component analysis, with the aim of a tight cluster of all quality controls and a second for all volunteer samples. The applied study design provides a reliable two-step validation procedure for large-scale studies and additionally contains an inhouse verification procedure.

Identification of glyceollin metabolites derived from conjugation with glutathione and glucuronic acid in male ZDSD rats by online liquid chromatography-electrospray ionization tandem mass spectrometry

Glyceollin-related metabolites produced in rats following oral glyceollin administration were screened in plasma, feces, and urine, and these metabolites were identified by precursor and product ion scanning using liquid chromatography coupled online with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). Precursor ion scanning in the negative ion (NI) mode was used to identify all glyceollin metabolites based on production of a diagnostic radical product ion (m/z 148) upon decomposition. Using this approach, precursor peaks of interest were found at m/z 474 and 531. Tandem mass spectra of these two peaks allowed us to characterize them as byproducts of glutathione conjugation. The peak at m/z 474 was identified as the deprotonated cysteinyl conjugate of glyceollins with an addition of an oxygen atom, whereas m/z 531 was identified as the deprotonated cysteinylglyceine glyceollin conjugate plus an oxygen. These results were confirmed by positive ion (PI) mode analyses. Mercapturic acid conjugates of glyceollins were also identified in NI mode. In addition, glucuronidation of glyceollins was observed, giving a peak at m/z 513 corresponding to the deprotonated conjugate. Production of glucuronic acid conjugates of glyceollins was confirmed in vitro in rat liver microsomes. Neither glutathione conjugation byproducts nor glucuronic acid conjugates of glyceollins have been previously reported.

Microextraction by Packed Sorbent (MEPS) and Solid-Phase Microextraction (SPME) as Sample Preparation Procedures for the Metabolomic Profiling of Urine

For a long time, sample preparation was unrecognized as a critical issue in the analytical methodology, thus limiting the performance that could be achieved. However, the improvement of microextraction techniques, particularly microextraction by packed sorbent (MEPS) and solid-phase microextraction (SPME), completely modified this scenario by introducing unprecedented control over this process. Urine is a biological fluid that is very interesting for metabolomics studies, allowing human health and disease characterization in a minimally invasive form. In this manuscript, we will critically review the most relevant and promising works in this field, highlighting how the metabolomic profiling of urine can be an extremely valuable tool for the early diagnosis of highly prevalent diseases, such as cardiovascular, oncologic and neurodegenerative ones.

A multi-class bioanalytical methodology for the determination of bisphenol A diglycidyl ethers, p-hydroxybenzoic acid esters, benzophenone-type ultraviolet filters, triclosan, and triclocarban in human urine by liquid chromatography-tandem mass spectrometry

A liquid-liquid extraction (LLE; ethyl acetate) protocol, followed by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) methodology, was developed for the determination of 19 compounds, including bisphenol A diglycidyl ethers (BADGEs; industrial ethers), benzophenone-type UV filters (BP-UV filters; precursors and metabolites), p-hydroxybenzoic acid esters (parabens; preservatives), triclosan (TCS) and triclocarban (TCC) in human urine. Urine specimens were enzymatically deconjugated with β-glucuronidase (from Helix pomatia) and extracted by a LLE procedure for the measurement of total concentrations (i.e., free+conjugated forms) of target analytes. Absolute recoveries of BADGEs, BP-UV filters, parabens, TCS and TCC ranged 25-135%, 84-125%, 52-126%, 75-118% and 90-124%, respectively. Method precision (absolute values; N=5 replicate analyses at the fortification level of 10 ng, k=5 days) ranged from 5.8 (ethyl paraben) to 24.0% (TCS). The limits of quantification (LOQs) varied depending on the target compound and generally ranged from 0.2 to 2.0 ng/mL. The matrix effects ranged from +11 (2,3,4-trihydroxybenzophenone) to -86% (2,4-dihydroxybenzophenone). A total of 30 urine specimens collected from Athens, Greece, were analyzed for the 19 target compounds to demonstrate the applicability of the developed method. The concentrations of target chemicals in urine were presented on volume-, specific gravity (SG)-, and creatinine-normalization bases. MeP, EtP, PrP, OH-EtP, BADGE·2H2O, BP-1 and TCS were found frequently in urine at concentrations in the range of 2.7-436 ng/mL, <0.5-25.4 ng/mL, <0.5-575 ng/mL, <2-18.4 ng/mL, <0.5-13.8 ng/mL, <1-14.6 ng/mL and <0.5-95.3 ng/mL, respectively.

Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)

Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0291-6) contains supplementary material, which is available to authorized users.

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF)

To standardize the use of cerebrospinal fluid (CSF) for biomarker research, a set of stability studies have been performed on porcine samples to investigate the influence of common sample handling procedures on proteins, peptides, metabolites and free amino acids. This study focuses at the effect on proteins and peptides, analyzed by applying label-free quantitation using microfluidics nanoscale liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (chipLC-MS) as well as matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FT-ICR-MS) and Orbitrap LC-MS/MS to trypsin-digested CSF samples. The factors assessed were a 30 or 120 min time delay at room temperature before storage at -80 degrees C after the collection of CSF in order to mimic potential delays in the clinic (delayed storage), storage at 4 degrees C after trypsin digestion to mimic the time that samples remain in the cooled autosampler of the analyzer, and repeated freeze-thaw cycles to mimic storage and handling procedures in the laboratory. The delayed storage factor was also analyzed by gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) for changes of metabolites and free amino acids, respectively. Our results show that repeated freeze/thawing introduced changes in transthyretin peptide levels. The trypsin digested samples left at 4 degrees C in the autosampler showed a time-dependent decrease of peak areas for peptides from prostaglandin D-synthase and serotransferrin. Delayed storage of CSF led to changes in prostaglandin D-synthase derived peptides as well as to increased levels of certain amino acids and metabolites. The changes of metabolites, amino acids and proteins in the delayed storage study appear to be related to remaining white blood cells. Our recommendations are to centrifuge CSF samples immediately after collection to remove white blood cells, aliquot, and then snap-freeze the supernatant in liquid nitrogen for storage at -80 degrees C. Preferably samples should not be left in the autosampler for more than 24 h and freeze/thaw cycles should be avoided if at all possible.

Current technological challenges in biomarker discovery and validation

In this review we will give an overview of the issues related to biomarker discovery studies with a focus on liquid chromatography-mass spectrometry (LC-MS) methods. Biomarker discovery is based on a close collaboration between clinicians, analytical scientists and chemometritians/statisticians. It is critical to define the final purpose of a biomarker or biomarker pattern at the onset of the study and to select case and control samples accordingly. This is followed by designing the experiment, starting with the sampling strategy, sample collection, storage and separation protocols, choice and validation of the quantitative profiling platform followed by data processing, statistical analysis and validation workflows. Biomarker candidates that result after statistical validation should be submitted for further validation and, ideally, be connected to the disease mechanism after their identification. Since most discovery studies work with a relatively small number of samples, it is necessary to assess the specificity and sensitivity of a given biomarker-based assay in a larger set of independent samples, preferably analyzed at another clinical center. Targeted analytical methods of higher throughput than the original discovery method are needed at this point and LC-tandem mass spectrometry is gaining acceptance in this field. Throughout this review, we will focus on possible sources of variance and how they can be assessed and reduced in order to avoid false positives and to reduce the number of false negatives in biomarker discovery research.

Current issues in measurement and reporting of urinary albumin excretion

BACKGROUND

Urinary excretion of albumin indicates kidney damage and is recognized as a risk factor for progression of kidney disease and cardiovascular disease. The role of urinary albumin measurements has focused attention on the clinical need for accurate and clearly reported results. The National Kidney Disease Education Program and the IFCC convened a conference to assess the current state of preanalytical, analytical, and postanalytical issues affecting urine albumin measurements and to identify areas needing improvement.

CONTENT

The chemistry of albumin in urine is incompletely understood. Current guidelines recommend the use of the albumin/creatinine ratio (ACR) as a surrogate for the error-prone collection of timed urine samples. Although ACR results are affected by patient preparation and time of day of sample collection, neither is standardized. Considerable intermethod differences have been reported for both albumin and creatinine measurement, but trueness is unknown because there are no reference measurement procedures for albumin and no reference materials for either analyte in urine. The recommended reference intervals for the ACR do not take into account the large intergroup differences in creatinine excretion (e.g., related to differences in age, sex, and ethnicity) nor the continuous increase in risk related to albumin excretion.

DISCUSSION

Clinical needs have been identified for standardization of (a) urine collection methods, (b) urine albumin and creatinine measurements based on a complete reference system, (c) reporting of test results, and (d) reference intervals for the ACR.

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.

Label-free proteomics of serum

In this chapter we describe a method to analyze human serum with the goal of discovering disease-related changes in the serum proteome. The methodology is based on the removal of the six most abundant serum proteins by immunoaffinity chromatography. This step is followed by trypsin digestion and reversed-phase high-performance liquid chromatography (HPLC) coupled on-line to mass spectrometry (MS) using either a capillary HPLC or a microfluidics chip HPLC system. The obtained, highly complex data sets are processed and statistically analyzed to discover significant differences between groups of samples. The complete analytical procedure will be described with serum samples, to which a given amount of horse heart cytochrome c has been added as well as with serum samples from early stage cervical cancer patients prior to and after therapy. The use of reversed-phase HPLC to separate serum proteins at 80 degrees C with subsequent analysis by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in order to lower the concentration sensitivity will also be briefly described.