Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles

Capacitively coupled contactless conductivity detection to account for system-induced gradient deformation in liquid chromatography

The time required for method development in gradient-elution liquid chromatography (LC) may be reduced by using an empirical modelling approach to describe and predict analyte retention and peak width. However, prediction accuracy is impaired by system-induced gradient deformation, which can be especially prominent for steep gradients. As the deformation is unique to each LC instrument, it needs to be corrected for if retention modelling for optimization and method transfer is to become generally applicable. Such a correction requires knowledge of the actual gradient profile. The latter has been measured using capacitively coupled "contactless" conductivity detection (C⁴D), featuring a low detection volume (approximately 0.05 μL) and compatibility with very high pressures (80 MPa or more). Several different solvent gradients, from water to acetonitrile, water to methanol, and acetonitrile to tetrahydrofuran, could be measured directly without the addition of a tracer component to the mobile phase, exemplifying the universal nature of the approach. Gradient profiles were found to be unique for each solvent combination, flowrate, and gradient duration. The profiles could be described by convoluting the programmed gradient with a weighted sum of two distribution functions. Knowledge of the exact profiles was used to improve the inter-system transferability of retention models for toluene, anthracene, phenol, emodin, sudan-I and several polystyrene standards.

Dextran as internal calibrant for N-glycan analysis by liquid chromatography coupled to ion mobility-mass spectrometry

LC-MS is one of the most important tools for the comprehensive characterization of N-glycans. Despite many efforts to speed up glycan analysis via optimized sample preparation (e.g., faster enzyme digestion in combination with instant or rapid labeling dyes), a major bottleneck remains the rather long measurement times of HILIC chromatography. Further complication arises from the necessity to concomitantly calibrate with an external standard to allow for accurate retention times and the conversion into more robust GU values. Here we demonstrate the use of an internal calibration strategy for HILIC chromatography to speed up glycan analysis. By reducing the number of utilized dextran oligosaccharides, the calibrant can be spiked directly into the sample such that external calibration runs are no longer required. The minimized dextran ladder shows accurate GU calibration with a minor deviation of well below 1% and can be applied without modifications in sample preparation or data processing. We further demonstrate the simultaneous use of the minimized dextran ladder as calibrant for the estimation of CCS values in traveling wave ion mobility spectrometry. In both cases, the minimized dextran ladder enables the measurement of calibrant and sample in a single HPLC run without losing information or accuracy.

Validation of a rapid, comprehensive and clinically relevant amino acid profile by underivatised liquid chromatography tandem mass spectrometry

Background Quantification of plasma amino acids is key to the diagnosis of inherited defects of amino acid synthesis, catabolism and transport, many of which present as clinical emergencies. The utility of this test is limited by the long analysis time and subsequent inability of laboratories to provide results in real-time. Traditionally, analysis has been performed by ion exchange chromatography (IEC) but recently there has been a move towards liquid chromatography tandem mass spectrometry (LC-MS/MS) which provides the potential for faster analysis. However, the necessity to derivatise the sample and/or utilise an ion-pair reagent, combined with lack of commercially available stable isotope internal standards (IS) has prevented laboratories fully exploiting the benefits of this methodology. We describe an underivatised LC-MS/MS method enabling patient results to be reported with an improved turnaround time (<1 h). Methods Methanolic IS was added to plasma (10 μL) to precipitate protein. Following centrifugation amino acids were analysed by LC-MS/MS using selected reaction monitoring (SRM) for each analyte and corresponding IS. Results Patient samples (n = 57) and external quality assessment (EQA) material (n = 11) were analysed and results compared with IEC. Comparable accuracy and precision were obtained with 15-min analysis time. Conclusions This method enables the analysis of a clinically comprehensive amino acid profile without the need for derivatisation/ion-pair reagents and benefitting from improved analytical quantitation through multipoint calibration and use of stable isotope IS. The analysis time is fast in comparison to IEC, improves efficiency of laboratory workflow and enables stat analysis of clinically urgent samples.

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modelling

Rapid optimization of gradient liquid chromatographic (LC) separations often utilizes analyte retention modelling to predict retention times as function of eluent composition. However, due to the dwell volume and technical imperfections, the actual gradient may deviate from the set gradient in a fashion unique to the employed instrument. This makes accurate retention modelling for gradient LC challenging, in particular when very fast separations are pursued. Although gradient deformation has been addressed in method-transfer situations, it is rarely taken into account when reporting analyte retention parameters obtained from gradient LC data, hampering the comparison of data from various sources. In this study, a response-function-based algorithm was developed to determine analyte retention parameters corrected for geometry-induced deformations by specific LC instruments. Out of a number of mathematical distributions investigated as response-functions, the so-called "stable function" was found to describe the formed gradient most accurately. The four parameters describing the model resemble the statistical moments of the distribution and are related to chromatographic parameters, such as dwell volume and flow rate. The instrument-specific response function can then be used to predict the actual shape of any other gradient programmed on that instrument. To incorporate the predicted gradient in the retention modelling of the analytes, the model was extended to facilitate an unlimited number of linear gradient steps to solve the equations numerically. The significance and impact of distinct gradient deformation for fast gradients was demonstrated using three different LC instruments. As a proof of principle, the algorithm and retention parameters obtained on a specific instrument were used to predict the retention times on different instruments. The relative error in the predicted retention times went down from an average of 9.8% and 12.2% on the two other instruments when using only a dwell-volume correction to 2.1% and 6.5%, respectively, when using the proposed algorithm. The corrected retention parameters are less dependent on geometry-induced instrument effects.

Recent applications of retention modelling in liquid chromatography

Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.