A newborn screening approach to diagnose 3-hydroxy-3-methylglutaryl-CoA lyase deficiency.

3-Hydroxy-3-methylglutaryl-coenzyme A lyase deficiency (HMGCLD) is a rare autosomal recessively inherited metabolic disorder. Patients suffer from avoidable neurologically devastating metabolic decompensations and thus would benefit from newborn screening (NBS). The diagnosis is currently made by measuring dry blood spot acylcarnitines (C5OH and C6DC) followed by urinary organic acid profiling for the differential diagnosis from several other disorders. Using untargeted metabolomics (reversed-phase UHPLC coupled to an Orbitrap Elite hybrid mass spectrometer) of plasma samples from 5 HMGCLD patients and 19 age-matched controls, we found 3-methylglutaconic acid and 3-hydroxy-3-methylglutaric acid, together with 3-hydroxyisovalerylcarnitine as the most discriminating metabolites between the groups. In order to evaluate the NBS potential of these metabolites we quantified the most discriminating metabolites from untargeted metabolomics in 23 blood spots from 4 HMGCLD patients and 55 controls by UHPLC tandem mass spectrometry. The results provide a tool for expanded NBS of HMGCLD using tandem mass spectrometry. Selected reaction monitoring transition 262/85 could be used in a first-tier NBS analysis to screen for elevated 3-hydroxyisovalerylcarnitine. In a positive case, a second-tier analysis of 3-hydroxy-3-methylglutaric acid and 3-methylglutaconic acid in a dry blood spot using UHPLC tandem mass spectrometry instruments confirms the diagnosis. In conclusion, we describe the identification of new diagnostic biomarkers for HMGCLD and their application in NBS in dry blood spots. By using second-tier testing, all patients with HMGCLD were unequivocally and correctly diagnosed.

Urease-immobilized magnetic microparticles in urine sample preparation for metabolomic analysis by gas chromatography-mass spectrometry.

Urea, as an end product of protein metabolism and an abundant polar compound, significantly complicates the metabolomic analysis of urine by GC-MS. We developed a sample preparation method removing urea from urine samples prior the GC-MS analysis. The method based on urease immobilized on magnetic microparticles was compared with the others that are conventionally used (liquid-liquid extraction, free urease protocol), and samples without any treatment. To study the impact of sample preparation approaches on the quality of analytical data, we employed comprehensive metabolomic analysis (using both GC-MS and LC-MS/MS platforms) of standard material based on human urine. Multivariate statistical analysis has shown that immobilized urease treatment provides similar results to a free urease approach. However, significant alterations in the profiles of metabolites were observed in the samples without any treatment and after the extraction. Compared to other approaches that were tested, the immobilization of urease on microparticles reduces both the number of artifacts and the variability of the metabolites (average CV of extraction 19.7%, no treatment 11.4%, free urease 5.0%, and immobilized urease 2.5%). The method that was developed was applied in a GC-MS metabolomic experiment of glutaric aciduria type I, where both known diagnostically important biomarkers and unknowns, as the most discriminating compounds, were found.

Impact of sample dimensionality on orthogonality metrics in comprehensive two-dimensional separations.

Orthogonality is a key parameter in the evaluation of the performance of a 2D chromatography-based separation system. Two different perspectives on orthogonality are determined: the extent of the separation space utilized (global orthogonality) and the uniformity of the coverage of the separation space (local orthogonality). This work aims to elucidate the impact of sample dimensionality (the number of separation processes involved) on orthogonality evaluation through the use of descriptors from seven different algorithms utilizing mutually different properties of a chromatogram: Pearson correlation, conditional entropy, asterisk equations, convex hull, arithmetic mean (AN) and harmonic mean of the nearest neighbor, and geometric surface coverage (SC). Artificial chromatograms generated in silico and real GC × GC separations of diesel, plasma, and urine were used for the evaluation of orthogonality. The sample dimensionality has a deep effect on the orthogonality results of all approaches. The SC algorithm emerged as the best descriptor of local orthogonality samples of both low and high dimensionality, the AN algorithm on the global orthogonality of low-dimensionality samples. However, in the case of samples of high dimensionality, AN consistently indicated just the exploitation of the whole separation space; therefore, only local orthogonality is optimized by means of SC. Since no approach was able to monitor both global and local orthogonality as a single value, a new descriptor, ASCA, was developed. It combines the best global (AN) and local (SC) orthogonality algorithms by averaging, giving the same importance to data spread and crowding. ASCA thus provides the best estimation of orthogonality.

Sample-independent approach to normalize two-dimensional data for orthogonality evaluation using whole separation space scaling.

Orthogonality is a key parameter that is used to evaluate the separation power of chromatography-based two-dimensional systems. It is necessary to scale the separation data before the assessment of the orthogonality. Current scaling approaches are sample-dependent, and the extent of the retention space that is converted into a normalized retention space is set according to the retention times of the first and last analytes contained in a unique sample to elute. The presence or absence of a highly retained analyte in a sample can thus significantly influence the amount of information (in terms of the total amount of separation space) contained in the normalized retention space considered for the calculation of the orthogonality. We propose a Whole Separation Space Scaling (WOSEL) approach that accounts for the whole separation space delineated by the analytical method, and not the sample. This approach enables an orthogonality-based evaluation of the efficiency of the analytical system that is independent of the sample selected. The WOSEL method was compared to two currently used orthogonality approaches through the evaluation of in silico-generated chromatograms and real separations of human biofluids and petroleum samples. WOSEL exhibits sample-to-sample stability values of 3.8% on real samples, compared to 7.0% and 10.1% for the two other methods, respectively. Using real analyses, we also demonstrate that some previously developed approaches can provide misleading conclusions on the overall orthogonality of a two-dimensional chromatographic system.