UPLC-TOF-MS Method for Simultaneous Quantification of Steroid Hormones in Tissue Homogenates of Zebrafish with Solid-Phase Extraction.

The quantification of steroid hormones of individual zebrafish (Danio rerio) provides perspective to understand endogenous hormone function. A UPLC-TOF-MS method was developed to provide a reproducible, sensitive, and efficient assay to determine the concentration of steroid hormones, including cortisol, testosterone, androstenedione, 11-deoxycortisol, 11-deoxycorticosterone, and 17-hydroxyprogesterone in whole-body homogenates of each zebrafish. Solid-phase extraction was used to sample matrix clean-up and acquired a recovery from 89.7% to 107.9%. The analytes were separated on an Aquity BEH C18 column using gradient elution. Mass spectrometric analysis was performed by single reaction monitoring (SRM) using positive electrospray ionization mode. The total running time was 6 min, which was greatly shortened compared with a previously reported method. The developed method exhibited excellent linearity for all the analytes, with regression coefficients higher than 0.99. The limit of detection varied between 0.1 and 0.5 ng/L and the limit of quantification was 0.5-1.7 ng/L for all analytes. The precision of the method was assessed on replicate measurements and was found to be in the ranges of 1.9 % to 6.6% and 4.3% to 8.6%, for intra- and inter-day analysis, respectively. This method was validated according to FDA guidance and applied to determine steroid hormone levels in the tissue homogenate of zebrafish acutely treated with caffeine and ethanol.

Re-Examining the Impact of Minimal Scans in Liquid Chromatography-Mass Spectrometry Analysis.

Liquid chromatography-mass spectrometry (LC-MS) is one of the most widely used analytical tools. High analysis volumes and sample complexity often demand more informative LC-MS acquisition schemes to improve efficiency and throughput without compromising data quality, and such a demand has been always hindered by the prerequisite that a minimum of 13-20 MS scans (data points) across an analyte peak are required for accurate quantitation. The current study systematically re-evaluated and compared the impact of different scan numbers on quantitation analysis using both triple quadrupoles mass spectrometry (TQMS) and high-resolution mass spectrometry (HRMS). Contrary to the 13-20 minimal scan prerequisite, the data obtained from a group of eight commercial drugs in the absence and presence of biological matrices suggest that 6 scans per analyte peak are sufficient to achieve highly comparable quantitation results compared to that obtained using 10 and 20 scans, respectively. The fewer minimal scan prerequisite is presumably attributed to an improved LC system and advanced column technology, better MS detector, and more intelligent peak detection and integration algorithms leading to a more symmetric peak shape and smaller peak standard deviation. As a result, more informative acquisition schemes can be broadly set up for higher throughput and more data-rich LC-MS/MS analysis as demonstrated in a hepatocyte clearance assay in which fewer MS scans executed on HRMS led to broader metabolite coverage without compromising data quality in hepatic clearance assessment. The demonstrated acquisition scheme would substantially increase the throughput, robustness, and richness of the nonregulatory analysis, which can be broadly applied in diverse fields including pharmaceutical, environmental, forensic, toxicological, and biotechnological.

Toward Comprehensive Per- and Polyfluoroalkyl Substances Annotation Using FluoroMatch Software and Intelligent High-Resolution Tandem Mass Spectrometry Acquisition.

Thousands of per- and polyfluoroalkyl substances (PFAS) exist in the environment and pose a potential health hazard. Suspect and nontarget screening with liquid chromatography (LC)-high-resolution tandem mass spectrometry (HRMS/MS) can be used for comprehensive characterization of PFAS. To date, no automated open source PFAS data analysis software exists to mine these extensive data sets. We introduce FluoroMatch, which automates file conversion, chromatographic peak picking, blank feature filtering, PFAS annotation based on precursor and fragment masses, and annotation ranking. The software library currently contains ∼7 000 PFAS fragmentation patterns based on rules derived from standards and literature, and the software automates a process for users to add additional compounds. The use of intelligent data-acquisition methods (iterative exclusion) nearly doubled the number of annotations. The software application is demonstrated by characterizing PFAS in landfill leachate as well as in leachate foam generated to concentrate the compounds for remediation purposes. FluoroMatch had wide coverage, returning 27 PFAS annotations for landfill leachate samples, explaining 71% of the all-ion fragmentation (CF2)n related fragments. By improving the throughput and coverage of PFAS annotation, FluoroMatch will accelerate the discovery of PFAS posing significant human risk.

Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms.

Rapid advancements in metabolomics technologies have allowed for application of liquid chromatography mass spectrometry (LCMS)-based metabolomics to investigate a wide range of biological questions. In addition to an important role in studies of cellular biochemistry and biomarker discovery, an exciting application of metabolomics is the elucidation of mechanisms of drug action (Creek et al., Antimicrob Agents Chemother 60:6650-6663, 2016; Allman et al., Antimicrob Agents Chemother 60:6635-6649, 2016). Although it is a very useful technique, challenges in raw data processing, extracting useful information out of large noisy datasets, and identifying metabolites with confidence, have meant that metabolomics is still perceived as a highly specialized technology. As a result, metabolomics has not yet achieved the anticipated extent of uptake in laboratories around the world as genomics or transcriptomics. With a view to bring metabolomics within reach of a nonspecialist scientist, here we describe a routine workflow with IDEOM, which is a graphical user interface within Microsoft Excel, which almost all researchers are familiar with. IDEOM consists of custom built algorithms that allow LCMS data processing, automatic noise filtering and identification of metabolite features (Creek et al., Bioinformatics 28:1048-1049, 2012). Its automated interface incorporates advanced LCMS data processing tools, mzMatch and XCMS, and requires R for complete functionality. IDEOM is freely available for all researchers and this chapter will focus on describing the IDEOM workflow for the nonspecialist researcher in the context of studies designed to elucidate mechanisms of drug action.

Sex-specific Estrogen Levels and Reference Intervals from Infancy to Late Adulthood Determined by LC-MS/MS.

CONTEXT: The lack of sensitive and robust analytical methods has hindered the reliable quantification of estrogen metabolites in subjects with low concentrations. OBJECTIVE: To establish sex-specific reference ranges for estrone (E1) and estradiol (E2) throughout life and to evaluate sex-differences using the state-of-the-art liquid chromatography tandem mass spectrometry (LC-MS/MS) method for quantification of E1, E2, and estriol (E3). DESIGN: LC-MS/MS method development and construction of estrogen reference ranges. SETTINGS: Population-based cross-sectional cohorts from the greater Copenhagen and Aarhus areas. PARTICIPANTS: Healthy participants aged 3 months to 61 years (n = 1838). RESULTS: An isotope diluted LC-MS/MS method was developed and validated for measurements of serum E1, E2, and E3. Limits of detections (LODs) were 3 pmol/L (E1), 4 pmol/L (E2), and 12 pmol/L (E3), respectively. This sensitive method made it possible to differentiate between male and female concentration levels of E1 and E2 in children. In girls, E2 levels ranged from

Enhancing Metabolome Coverage in Data-Dependent LC-MS/MS Analysis through an Integrated Feature Extraction Strategy.

In untargeted metabolomics, conventional data preprocessing software (e.g., XCMS, MZmine 2, MS-DIAL) are used extensively due to their high efficiency in metabolic feature extraction. However, these programs present limitations in recognizing low-abundance metabolic features, thus hindering complete metabolome coverage from the analysis. In this work, we explored the possibility of enhancing the metabolome coverage of data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) results by rescuing metabolic features that are missed by conventional software. To achieve this goal, we first categorized the metabolic features into four confidence levels based on their chromatographic peak shapes and the presence of corresponding MS/MS spectra. We then assessed the false positives and quantitative accuracy of the metabolic features that contain MS/MS spectra but are not recognized by conventional software. Our results indicate that these missed features contain valid and important metabolic information and should be integrated into the conventional metabolomics results. Thus, we developed a data-preprocessing pipeline to extract low-abundance metabolic features and integrate them with the results from conventional programs. This integrated feature extraction strategy was tested on a set of fecal metabolomic data retrieved from mice who have undergone normal diet vs high-fat diet treatments. In our test data set, the integrated feature extraction approach increased the number of significant features being extracted by 24.4% and identified five additional metabolites bearing critical biological meanings. Our results show that this integrated feature extraction strategy remarkably improves the metabolome coverage beyond that of conventional data preprocessing, therefore facilitating the confirmation of metabolites of interest and accomplishment of a higher success rate in de novo metabolite identification.

Implementation of Chemometric Tools To Improve Data Mining and Prioritization in LC-HRMS for Nontarget Screening of Organic Micropollutants in Complex Water Matrixes.

One of the most critical steps in nontarget screening of organic micropollutants (OMP) in complex environmental samples is handling of massive data obtained from liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS). Multivariate chemometric methods have brought about great progress in processing big data obtained from high-dimensional chromatographic systems. This work aimed at a comprehensive evaluation of two LC-Q-Orbitrap mass spectrometry full-scan data sets for target and nontarget screening of OMPs in drinking and wastewater samples, respectively. For each data set, following segmentation in the chromatographic dimension, at first multivariate curve resolution alternating least-squares (MCR-ALS) was employed for simultaneous resolution of global matrices. The chromatographic peaks and the corresponding mass spectra of OMP were fully resolved in the presence of highly co-eluting irrelevant and interfering peaks. Then partial least-squares-discriminant analysis was conducted to investigate the behavior of MCR-ALS components in different water classes and selection of most relevant components. Further prioritization of features in wastewater before and after ozonation and their reduction to 24 micropollutants were then obtained by univariate statistics. Two-way information retrieved from MCR-ALS of LC-MS1 data was also used to choose common precursor ions between recovered and measured data through data-dependent acquisition. MS1 and MS2 spectral features were used for tentative identification of prioritized OMPs. This study indicates that the described strategy can be used as a promising tool to facilitate both feature selection through a reliable classification and interference-free identification of micropollutants in nontargeted and class-wise environmental studies.

LC-MS/MS Software for Screening Unknown Erectile Dysfunction Drugs and Analogues: Artificial Neural Network Classification, Peak-Count Scoring, Simple Similarity Search, and Hybrid Similarity Search Algorithms.

Screening and identifying unknown erectile dysfunction (ED) drugs and analogues, which are often illicitly added to health supplements, is a challenging analytical task. The analytical technique most commonly used for this purpose, liquid chromatography-tandem mass spectrometry (LC-MS/MS), is based on the strategy of searching the LC-MS/MS spectra of target compounds against database spectra. However, such a strategy cannot be applied to unknown ED drugs and analogues. To overcome this dilemma, we have constructed a standalone software named AI-SIDA (artificial intelligence screener of illicit drugs and analogues). AI-SIDA consists of three layers: LC-MS/MS viewer, AI classifier, and Identifier. In the second AI classifier layer, an artificial neural network (ANN) classification model, which was constructed by training 149 LC-MS/MS spectra (including 27 sildenafil-type, 6 vardenafil-type, 11 tadalafil-type ED drugs/analogues and other 105 compounds), is included to classify the LC-MS/MS spectra of the query compound into four categories: i.e., sildenafil, vardenafil, and tadalafil families and non-ED compounds. This ANN model was found to show 100% classification accuracy for the 187 LC-MS/MS modeling and test data sets. In the third Identifier layer, three search algorithms (pick-count scoring, simple similarity search, and hybrid similarity search) are implemented. In particular, the hybrid similarity search was found to be very powerful in identifying unknown ED drugs/analogues with a single modification from the library ED drugs/analogues. Altogether, the AI-SIDA software provides a very useful and powerful platform for screening unknown ED drugs and analogues.

Measurement uncertainty and risk of false conformity decision in the performance evaluation of liquid chromatography analytical procedures.

Liquid chromatography is one of the main techniques used in pharmaceutical quality control analytical procedures. However, there will always be a measurement uncertainty (MU) associated with them, that can lead to the approval of an out of specification lot (consumer risk) or rejection of a lot within specification (producer risk). Thus, the aim of this study was to evaluate the performance of liquid chromatography analytical procedures based on their measurement uncertainty and to estimate the risk of false conformity decisions. The uncertainties of the analytical procedures were estimated based on the results of validation (trueness and precision). Then, the ratio between overall uncertainty and specification range (U/T%) was calculated. It was noted that in most cases (73%), random errors (precision) contributes more significantly to the overall uncertainty when compared to systematic errors (trueness). Monte Carlo method was used, generating different manufacturing processes scenarios, and analytical results based on the MU of each analytical procedure. Then, consumer's and producer's risks were estimated from the simulated values. Pharmaceutical dosage forms that require more steps in sample preparation had higher measurement uncertainties, often above the recommended target uncertainty. As most of the analytical procedures showed U/T% values above recommended, the majority presented high estimated risk values and did not fit for purpose. Therefore, it is important to considerate the measurement uncertainty as part of analytical procedures validation, since trueness and precision values affect directly the measurement uncertainty and the risk of false conformity decisions.

Optimization and Comparison of Information-Dependent Acquisition (IDA) to Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH) for High-Resolution Mass Spectrometry in Clinical Toxicology.

BACKGROUND: Untargeted data acquisition on high-resolution mass spectrometers (HRMSs) has been used in clinical toxicology for screening and identifying unknown compounds in patient samples. A common modality for untargeted HRMS data acquisition is information-dependent acquisition (IDA), which analyzes the most abundant small molecules within an acquisition cycle. This process can potentially lead to false negatives of clinically relevant compounds at low concentrations. Sequential window acquisition of all theoretical fragment ion spectra (SWATH) has emerged as a method of unbiased, untargeted HRMS data acquisition in which no spectral data are lost. SWATH has yet to be optimized and assessed for use in clinical toxicology. METHOD: We developed a variable-window SWATH method (vSWATH) and compared it to IDA by limit of detection studies in drug-supplemented urine (81 compounds) and against a retrospective cohort of 50 clinical urine samples characterized by LC-MS/MS. RESULTS: vSWATH had a lower limit of detection than IDA for 33 (41%) drugs and metabolites added into urine samples. Both IDA and vSWATH were equivalent in discovering compounds from clinical urine samples and confirmed 26 additional compounds not previously discovered by targeted LC-MS/MS. Lastly, the unbiased acquisition of spectra in vSWATH allowed for identification of 5 low-abundance compounds missed by IDA. CONCLUSIONS: This vSWATH method for clinical toxicology demonstrated equivalent analytical sensitivity and specificity for untargeted drug screening and identification in urine samples. vSWATH provided the additional benefit of collecting all tandem mass spectrometry spectra in a sample, which could be useful in discovering low-abundance compounds not discovered by IDA.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Extended diagnosis of purine and pyrimidine disorders from urine: LC MS/MS assay development and clinical validation.

BACKGROUND AND AIMS: Inborn errors of purine and pyrimidine metabolism are a diverse group of disorders with possible serious or life-threatening symptoms. They may be associated with neurological symptoms, renal stone disease or immunodeficiency. However, the clinical presentation can be nonspecific and mild so that a number of cases may be missed. Previously published assays lacked detection of certain diagnostically important biomarkers, including SAICAr, AICAr, beta-ureidoisobutyric acid, 2,8-dihydroxyadenine and orotidine, necessitating the use of separate assays for their detection. Moreover, the limited sensitivity for some analytes in earlier assays may have hampered the reliable detection of mild cases. Therefore, we aimed to develop a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay that allows the simultaneous and sensitive detection of an extended range of purine and pyrimidine biomarkers in urine. METHODS: The assay was developed and validated using LC-MS/MS and clinically tested by analyzing ERNDIM Diagnostic Proficiency Testing (DPT) samples and further specimens from patients with various purine and pyrimidine disorders. RESULTS: Reliable determination of 27 analytes including SAICAr, AICAr, beta-ureidoisobutyric acid, 2,8-dihydroxyadenine and orotidine was achieved in urine following a simple sample preparation. The method clearly distinguished pathological and normal samples and differentiated between purine and pyrimidine defects in all clinical specimens. CONCLUSIONS: A LC-MS/MS assay allowing the simultaneous, sensitive and reliable diagnosis of an extended range of purine and pyrimidine disorders has been developed. The validated method has successfully been tested using ERNDIM Diagnostic Proficiency Testing (DPT) samples and further clinical specimens from patients with various purine and pyrimidine disorders. Sample preparation is simple and assay duration is short, facilitating an easier inclusion of the assay into the diagnostic procedures.

Targeted Metabolite Profiling-Based Identification of Antifungal 5-n-Alkylresorcinols Occurring in Different Cereals against Fusarium oxysporum.

A rapid and convenient biochemometrics-based analysis of several cereal-derived extracts was used to identify n-alkyl(enyl)resorcinols (AR) as antifungals against Fusarium oxysporum. Total AR content and liquid chromatography/mass spectrometry (LC-MS)-based profiles were recorded for each extract, in addition to their antifungal activity, to help integrate these chemical and biological datasets by orthogonal partial least squares regression. In this study, we developed and used a micro-scale amended medium (MSAM) assay to evaluate the in vitro mycelial growth inhibition at low amounts of extracts. Triticale husk-derived extracts had the highest AR content (662.1 µg olivetol equivalent/g dry extract), exhibiting >79% inhibition at the highest doses (10.0⁻1.0 µg/µL). Correlation of the chemical and antifungal datasets using supervised metabolite profiling revealed that 5-n-nonadecanylresorcinol, 5-n-heneicosylresorcinol, and 5-n-tricosyl-resorcinol were the most active ARs occurring in cereal products from Colombia. Hence, we propose the biochemometrics-based approach as a useful tool for identifying AR-like antifungals against F. oxysporum.

A Comprehensive LC-QTOF-MS Metabolic Phenotyping Strategy: Application to Alkaptonuria.

BACKGROUND: Identification of unknown chemical entities is a major challenge in metabolomics. To address this challenge, we developed a comprehensive targeted profiling strategy, combining 3 complementary liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) techniques and in-house accurate mass retention time (AMRT) databases established from commercial standards. This strategy was used to evaluate the effect of nitisinone on the urinary metabolome of patients and mice with alkaptonuria (AKU). Because hypertyrosinemia is a known consequence of nitisinone therapy, we investigated the wider metabolic consequences beyond hypertyrosinemia. METHODS: A total of 619 standards (molecular weight, 45-1354 Da) covering a range of primary metabolic pathways were analyzed using 3 liquid chromatography methods-2 reversed phase and 1 normal phase-coupled to QTOF-MS. Separate AMRT databases were generated for the 3 methods, comprising chemical name, formula, theoretical accurate mass, and measured retention time. Databases were used to identify chemical entities acquired from nontargeted analysis of AKU urine: match window theoretical accurate mass ±10 ppm and retention time ±0.3 min. RESULTS: Application of the AMRT databases to data acquired from analysis of urine from 25 patients with AKU (pretreatment and after 3, 12, and 24 months on nitisinone) and 18 HGD -/- mice (pretreatment and after 1 week on nitisinone) revealed 31 previously unreported statistically significant changes in metabolite patterns and abundance, indicating alterations to tyrosine, tryptophan, and purine metabolism after nitisinone administration. CONCLUSIONS: The comprehensive targeted profiling strategy described here has the potential of enabling discovery of novel pathways associated with pathogenesis and management of AKU.

pmartR: Quality Control and Statistics for Mass Spectrometry-Based Biological Data.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography-MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Multiple analytical approaches for the organic and inorganic characterization of Origanum vulgare L. samples.

Origanum vulgare L. samples, marketed in different geographic locations, were characterized by their organic and inorganic chemical composition. A total of 35 commercial samples were collected from various sites and analyzed to determine the qualitative and quantitative profile of essential oils, phenolic compounds and some inorganic elements. The variation in the content and composition of the essential oil was assessed by GC and GC-MS analyses, the phenolic fraction was investigated by UPLC®/PDA, and the inorganic elements were determined by ICP-MS. The Principal Component Analysis (PCA) was applied with the aim to sort out the Origanum vulgare L. samples with different composition according to the different belonging origins. The results showed appreciable qualitative and quantitative differences among samples from different geographic origin.

When results matter: reliable creatinine concentrations in hyperbilirubinemia patients.

Background Failure to report a creatinine concentration, especially in icteric patients who are eligible for a liver transplant, can result in a life-threatening situation. We assessed the influence of bilirubin interference on several creatinine assays and investigated ways to circumvent icteric interference without interfering with our normal automated sample logistics. Methods Using icteric patient sera (total bilirubin >255 µmol/L) we determined creatinine concentrations using an enzymatic and Jaffé assay (Roche Diagnostics) in both normal (i.e. undiluted) and decreased mode (i.e. diluted) as well as an enzyme-coupled amperometric assay on a Radiometer ABL837 FLEX analyzer. Creatinine concentrations from the five methods were compared with an in-house developed LC-MS/MS method. Passing and Bablok (proportional and constant bias) as well as difference plot parameters (bias and 95% limits of agreement [LoA]) were calculated. Interferograph-based regression analysis of the enzymatic and Jaffé results was used to investigate if such an approach could be used to report corrected creatinine concentrations in icteric patient sera. Results In icteric patient sera the enzyme-coupled amperometric assay was hardly influenced by icteric interference as shown by a difference plot bias of -1.5% (95% LoA -11.6 to +8.5%). The undiluted Jaffé method had a bias of -1.4% with a very broad 95% LoA (-35.1 to +32.2%) emphasizing the poor specificity of this method. The undiluted enzymatic method had the largest bias (-13.4%, 95% LoA -35.8 to +9.0%). Diluting sera in the enzymatic method did not improve the bias (-10.5%, 95% LoA -25.4 to 4.4%), while diluting the Jaffé method resulted in a bias increase (+11.4%, 95% LoA -14.7 to 37.5%). Using interferograph-based regression analysis we were able to reliably correct enzymatic creatinine concentrations in 97 out of 100 icteric patient sera. Conclusions Analytically, quantifying creatinine in icteric sera using the Radiometer ABL837 FLEX analyzer is the method of choice within our laboratory. However, not all laboratories are equipped with this method and even if available, the limited number of highly icteric patient sera makes this method costly. For those laboratories using the Roche enzymatic method, mathematically correcting an icteric creatinine concentration using an interferograph based on an LC-MS/MS reference method is a suitable alternative to report reliable creatinine results in icteric patients.

Improved application of RNAModMapper - An RNA modification mapping software tool - For analysis of liquid chromatography tandem mass spectrometry (LC-MS/MS) data.

Research into post-transcriptional processing and modification of RNA continues to speed forward, as their ever-emerging role in the regulation of gene expression in biological systems continues to unravel. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has proven for over two decades to be a powerful ally in the elucidation of RNA modification identity and location, but the technique has not proceeded without its own unique technical challenges. The throughput of LC-MS/MS modification mapping experiments continues to be impeded by tedious and time-consuming spectral interpretation, particularly during for the analysis of complex RNA samples. RNAModMapper was recently developed as a tool to improve the interpretation and annotation of LC-MS/MS data sets from samples containing post-transcriptionally modified RNAs. Here, we delve deeper into the methodology and practice of RNAModMapper to provide greater insight into its utility, and remaining hurdles, in current RNA modification mapping experiments.

Two metrics for measuring orthogonality for two-dimensional chromatography.

Orthogonality can be used as a selection parameter for two-dimensional chromatography column selection (e.g. in GC × GC or LC × LC) or for method optimization purposes, both aiming for maximal orthogonality for a particular analytical application. In order to improve the concurrence of two-dimensional chromatography expert's orthogonality grading, two orthogonality metrics, %FIT and %BIN, were developed, evaluated and compared with the Asterisks orthogonality metric. The %BIN is a bin counting approach where the number of bins is fixed at 25 and deviations from the expected average number of peaks per bin is used as the basis for the orthogonality calculation. The %FIT is based on fitting polynomials of degree two, through the xy and the yx data and calculating the average minimal distance and standard deviation of all data points above and below the fitted polynomials. The orthogonality metrics were evaluated by using 14 different types of computer generated xy datasets and two measured LC × LC datasets. Both %FIT and %BIN, were shown to have a larger discriminative power than the Asterisks equations, and are in good agreement with the orthogonality scores for 2D-chromatograms provided by nine experts.

Improved method for diagnosis of Nerium oleander poisoning in necropsy tissues / Método aprimorado para diagnóstico da intoxicação por Nerium oleander em tecidos de necropsia

Nerium oleander is an ornamental cardiotoxic plant found in tropical and subtropical areas of the World. Its toxicity is related to the content of cardioactive glycosides, mainly oleandrin, found throughout the plant. The present study aimed to describe a new and improved method for oleandrin detection in tissue samples. The determination of oleandrin was made after extraction with a modified QuEChERS technique and measurement by UFLC-MS/MS. A total of 36 guinea pigs (Cavia porcellus) were distributed into 3 groups (n=12): control group that received only water orally (CON), and two treated groups that received hydroalcoholic oleander extract at doses of 150mg.kg-1 (OLE 150) and 300mg.kg-1 (OLE 300) in single oral dose. After three hours, fragments of heart, kidneys, liver and brain were collected for determination of oleandrin levels. The extraction and chromatographic procedures were effective for oleandrin detection and quantification in tissues, with retention time of 1.2 min and detection limit of 0.001μg g-1. The chromatographic analysis of treated guinea pigs indicated that oleandrin is distributed equally among the analyzed tissues. The developed methodology is a reliable, effective and rapid form of diagnosis of N. oleander poisoning based on necropsy tissue samples.(AU)

Nerium oleander é uma planta cardiotóxica ornamental encontrada em áreas tropicais e subtropicais do mundo. Sua toxicidade é relacionada á presença de glicosídeos cardioativos, principalmente a oleandrina, encontrada em toda a planta. O presente estudo objetiva descrever um novo e aprimorado método para detecção da oleandrina em amostras de tecido. A determinação da oleandrina foi feita após extração utilizando técnica modificada de QuEChERS e mensuração por UFLC-MS/MS. Um total de 36 cobaios (Cavia porcellus) foi distribuído em três grupos (n=12): grupo controle que recebeu apenas água por via oral (CON), e dois grupos tratados que receberam extrato hidroalcóolico de oleander nas doses de 150mg.kg-1 (OLE 150) e 300mg.kg-1 (OLE 300) em uma única dose oral. Após três horas, fragmentos do coração, rins, fígado e cérebro foram coletados para determinação dos níveis de oleandrina. A extração e procedimentos cromatográficos foram eficientes na detecção e quantificação da oleandrina nos tecidos, com tempo de retenção de 1,2min e limite de detecção de 0,001μg g-1. A análise cromatográfica dos animais tratados indicou que a oleandrina é distribuída de forma equalizada pelos tecidos analisados. A metodologia desenvolvida representa uma forma de diagnóstica segura, efetiva e rápida da intoxicação por N. oleander a partir de amostras de tecidos de necropsia.(AU)