Computer-aided gradient optimization of hydrophilic interaction liquid chromatographic separations of intact proteins and protein glycoforms

A review of intact glycopeptide enrichment and glycan separation through hydrophilic interaction liquid chromatography stationary phase materials

Protein glycosylation, one of the most important biologically relevant post-translational modifications for biomarker discovery, faces analytical challenges due to heterogeneous glycosite, diverse glycans, and mass spectrometry limitations. Glycopeptide enrichment by removing abundant hydrophobic peptides helps overcome some of these obstacles. Hydrophilic interaction liquid chromatography (HILIC), known for its selectivity, glycan separations, intact glycopeptide enrichment, and compatibility with mass spectrometry, has seen recent advancements in stationary phases like Amide-80, glycoHILIC, amino acids or peptides for improved HILIC-based glycopeptide analysis. Utilization of these materials can improve glycopeptide enrichment through solid-phase extraction and separation via high-performance liquid chromatography. Additionally, using glycopeptides themselves to modify HILIC stationary phases holds promise for improving selectivity and sensitivity in glycosylation analysis. Additionally, HILIC has capability to assess the information about glycosites and structural information of glycans. This review summarizes recent breakthroughs in HILIC stationary materials, highlighting their impact on glycopeptide analysis. Ongoing research on advanced materials continues to refine HILIC's performance, solidifying its value as a tool for exploring protein glycosylation.

Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography

Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed. A workflow was designed to operate within a closed-loop that allowed direct interaction between the LC × LC-MS system and a data-processing computer which ran in an unsupervised and automated fashion. Obtaining accurate retention models in LC × LC is difficult due to the challenges associated with the exact determination of retention times, curve fitting because of the use of gradient elution, and gradient deformation. Thus, retention models were compared in terms of repeatability of determination. Additionally, the design of shifting gradients in the second dimension and the prediction of peak widths were investigated. The algorithm was tested on separations of a tryptic digest of a monoclonal antibody using an objective function that included the sum of resolutions and analysis time as quality descriptors. The algorithm was able to improve the separation relative to a generic starting method using these complex gradient profiles after only four method-development iterations (i.e., sets of chromatographic conditions). Further iterations improved retention time and peak width predictions and thus the accuracy in the separations predicted by the algorithm.

Influence of ion-pairing reagents on the separation of intact glycoproteins using hydrophilic-interaction liquid chromatography - high-resolution mass spectrometry

Hydrophilic-interaction liquid chromatography (HILIC) of intact proteins offers high-resolution separations of glycoforms of glycoproteins differing in the number of (neutral) glycans. However, to obtain efficient separations it is essential that the positively charged sites of the proteins are shielded by acidic (negative) ion-pair reagents (IPRs), so as to enhance the contribution of the hydroxyl groups of the (neutral) sugars in the glycoprotein. Here, we studied the influence of various IPRs that differ in physico-chemical properties, such as hydrophobicity and acidity, on the capillary-scale HILIC separation of intact (glyco)proteins. We evaluated the use of fluoroacetic acid (MFA), difluoroacetic acid (DFA), trifluoroacetic acid (TFA), and heptafluorobutyric acid (HFBA) as diluents for sample preparation, as solvents for sample loading on a reversed-phase trap prior to the HILIC separation, and as mobile-phase components for HILIC and HILIC-MS. To reduce the contribution of ion-exchange interaction with the (silica-based) stationary phase, we used an acrylamide-based monolithic column. We studied the influence of the different IPRs on each step of the separation of a mixture of proteins of different size and hydrophilicity and on the separation of the five glycoforms of ribonuclease B. The content of IPR in the sample was shown not to affect the separation and the MS detection. However, a low content of TFA and DFA in the mobile phase is favourable, as it reduces adduct formation and leads to higher signal intensity. The optimized HILIC conditions successfully resolved nine major glycoforms groups of a ∼40 kDa glycoprotein horseradish peroxidase (HRP), as an example of a complex glycoprotein.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Hydrophilic Interaction Liquid Chromatography (HILIC): Latest Applications in the Pharmaceutical Researches

Background:

Significant advances have been occurred in analytical research since the 1970s by Liquid Chromatography (LC) as the separation method. Reverse Phase Liquid Chromatography (RPLC) method, using hydrophobic stationary phases and polar mobile phases, is the most commonly used chromatographic method. However, it is difficult to analyze some polar compounds with this method. Another separation method is the Normal Phase Liquid Chromatography (NPLC), which involves polar stationary phases with organic eluents. NPLC presents low-efficiency separations and asymmetric chromatographic peak shapes when analyzing polar compounds. Hydrophilic Interaction Liquid Chromatography (HILIC) is an interesting and promising alternative method for the analysis of polar compounds. HILIC is defined as a separation method that combines stationary phases used in the NPLC method and mobile phases used in the RPLC method. HILIC can be successfully applied to all types of liquid chromatographic separations such as pharmaceutical compounds, small molecules, metabolites, drugs of abuse, carbohydrates, toxins, oligosaccharides, peptides, amino acids and proteins.

Objective:

This paper provides a general overview of the recent application of HILIC in the pharmaceutical research in the different sample matrices such as pharmaceutical dosage form, plasma, serum, environmental samples, animal origin samples, plant origin samples, etc. Also, this review focuses on the most recent and selected papers in the drug research from 2009 to the submission date in 2020, dealing with the analysis of different components using HILIC.

Results and Conclusion:

The literature survey showed that HILIC applications are increasing every year in pharmaceutical research. It was found that HILIC allows simultaneous analysis of many compounds using different detectors.

Measuring and using scanning-gradient data for use in method optimization for liquid chromatography

The use of scanning gradients can significantly reduce method-development time in reversed-phase liquid chromatography. However, there is no consensus on how they can best be used. In the present work we set out to systematically investigate various factors and to formulate guidelines. Scanning gradients are used to establish retention models for individual analytes. Different retention models were compared by computing the Akaike information criterion and the prediction accuracy. The measurement uncertainty was found to influence the optimum choice of model. The use of a third parameter to account for non-linear relationships was consistently found not to be statistically significant. The duration (slope) of the scanning gradients was not found to influence the accuracy of prediction. The prediction error may be reduced by repeating scanning experiments or - preferably - by reducing the measurement uncertainty. It is commonly assumed that the gradient-slope factor, i.e. the ratio between slopes of the fastest and the slowest scanning gradients, should be at least three. However, in the present work we found this factor less important than the proximity of the slope of the predicted gradient to that of the scanning gradients. Also, interpolation to a slope between that of the fastest and the slowest scanning gradient is preferable to extrapolation. For comprehensive two-dimensional liquid chromatography (LC × LC) our results suggest that data obtained from fast second-dimension gradients cannot be used to predict retention in much slower first-dimension gradients.

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.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Mass spectrometric evidence for the mechanism of free-radical depolymerization of various types of glycosaminoglycans

Glycosaminoglycans (GAGs) are large, complex carbohydrate molecules that interact with a wide range of proteins involved in physiological and pathological processes. Several naturally derived GAGs have emerged as potentially useful therapeutics in clinical applications. Natural polysaccharides, however, generally have high molecular weights with a degree of polydispersity, making it difficult to investigate their structural properties. In this study, we establish a free-radical-mediated micro-reaction system and use hydrophilic interaction chromatography (HILIC)-Fourier transform mass spectrometry (FTMS) to profile the degraded products of various types of GAGs, heparin, chondroitin sulfate A, NS-heparosan, and oversulfated chondroitin sulfate (OSCS), to reveal the free-radical degradation mechanism of GAGs. The results show that the bulk fragments of GAGs generated by free-radical degradation can maintain their basic structural units and sulfate substituents. In addition, an abundance of oligomers modified with oxidation at their reducing ends or by dehydration also appeared. We discovered that these modifications were related in terms of the degree of sulfation and the α- or β-linkage of HexNY (Y = SO₃^- or Ac), and especially that the different linkage of the disaccharide unit is the main factor in modification. In addition, the method based on micro-free-radical reaction and HILIC-FTMS is both effective and sensitive, thus suggesting its broad practical value for the structural characterization and in the biological structure-function studies of GAGs.